Dosimetric and image quality aspects in mammography and breast tomosynthesis

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Università di Pisa

Dipartimento di Fisica E. Fermi

Scuola di Specializzazione in Fisica Medica

Anno accademico 2018/2019

Tesi di Specializzazione

Dosimetric and image quality aspects in

mammography and breast tomosynthesis

Candidato: Relatori:

Patrizio Barca Fantacci Maria Evelina

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Dosimetric and image quality aspects in mammography

and breast tomosynthesis

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Introduction

This thesis work is part of the RADIOMA project (RADiazioni IOnizzanti in MAmmo-grafia - ionising radiations in mammography) which involves the Department of Physics and the Department of Translational Research and New Technologies in Medicine and Surgery of the Pisa University, the Pisa section of the National Institute for Nuclear Phys-ics and the Unit of Health PhysPhys-ics of the “Azienda Ospedaliero-Universitaria Pisana”. The project is founded by Fondazione Pisa - Technological and Scientific Research Sector.

The main objective of the RADIOMA project was to develop a simple method to es-timate the average absorbed dose of patients undergoing mammographic examinations. In order to achieve this aim, a phantom model was proposed for digital mammography (DM) and digital breast tomosynthesis (DBT). A dosimetric index, the “Average Absorbed Breast Dose” (2ABD), was suggested as a simple estimate of the average absorbed dose in a water-equivalent phantom that simulate the breast. Monte Carlo simulations were also employed to support the proposed approach and investigate new perspectives.

A side objective of the RADIOMA project was to study image quality of synthesised mammograms (SMs) reconstructed from DBT acquisitions in comparison to DM image quality. A systematic analysis was carried out in terms of several image quality paramet-ers and quantitative results were obtained.

This thesis is structured as follows.

In Chapter 1 an overview of the RADIOMA project and its objectives is presented. The European Directive 59/2013 Euratom is discussed by highlighting the main aspects which motivate the project. A brief description of the physical and dosimetric properties of DM and DBT is provided.

In Chapter 2 the proposed dosimetric model and the image quality evaluation are described in detail. In the first part of the chapter, a concise summary of the fundamental steps required to obtain the absorbed dose in a water-equivalent phantom starting from air kerma measurements is presented. The experimental approach is then described. Specifically, the equations employed to estimate incident air kerma in DM and DBT are introduced and the model employed to derive the 2ABD formula is explained. A brief analysis of the Monte Carlo approach is also reported. The second part of the chapter is dedicated to the image quality evaluation. Some essential aspects of the analysis are described. The approaches employed to assess different image quality parameters for DM and SM images are treated in detail.

In Chapter 3 the main results of the RADIOMA project are reported.

Finally, in Chapter 4 the main features of our dosimetric model, the image quality assessment and the obtained results are discussed.

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Chapter 1

Overview of the RADIOMA project

1.1 The RADIOMA project

Digital mammography (DM) is currently the most effective screening and diagnostic tool for early detection of breast cancer. Due to its great sensitivity and specificity it permits to explore the breast in an exhaustive way [1, 2, 3]. Recently, digital breast tomosyn-thesis (DBT) has been introduced to reduce the limitations of tissue overlapping of X-ray projection imaging and to increase sensitivity and specificity of DM [4]. Even though breast cancer screening programs have been promoted in many countries, the World Health Organization estimated that 627000 women died from breast cancer in 2018, that is approximately 15% of all cancer deaths among women [5]. Consequently, the re-cent advances in DM and DBT technology are essential to reduce breast cancer mortality [6]. However, the radio-sensitivity of the breast should be carefully considered when potentially healthy people are involved in mammographic examinations as in screening programs and special care is required in the evaluation of patient exposure. For this reason, the International Commission on Radiological Protection (ICRP) has updated the tissue-weighting factor associated to the breast from 0.05 in 1991 [7] to 0.12 in 2007 [8]. Furthermore, according to the European Directive 2013/59 Euratom, detailed information on the radiation exposure resulting from radiological procedures shall be included in the radiological report of the patient [9]. Therefore, a simple method to estimate the absorbed dose in DM and DBT procedures could be of practical interest.

The current dosimetric approach adopted in DM and DBT is to compute the Average Glandular Dose (AGD) using the Dance method [10, 11, 12]. This method is based on the knowledge of the incident air kerma at the breast surface and tabulated conversion and correction factors must be applied to convert air kerma in glandular absorbed dose. For the DM modality, the conversion and correction factors were derived from Monte Carlo simulations and depend on the age of the patients, anode-filter combination, half value layer (HVL) of the beam and breast thickness [10, 11]. The Dance method has then been extended to DBT by considering the angle of each projection and applying a further correction factor [12]. Even though this method is the gold standard and estimates a reliable dosimetric quantity related to the ionising radiation risks (i.e. the average glandular dose), the practical implementation can often be difficult. In fact, the AGD can not be directly computed if the incident air kerma on the breast surface is not measured or estimated; furthermore, the HVL of the beam must be known in order to search for the needed correction factors; additionally, the correction factors are tabulated for a set of parameters (age, HVL, breast thickness), hence, interpolation is almost always required for an accurate estimation.

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6 1 Overview of the RADIOMA project

In this context, the main aim of the RADIOMA project was to identify a practical ap-proach to evaluate the average absorbed dose associated to DM and DBT examinations in order to satisfy the requirements of the European Directive 59/2013 Euratom. Con-sequently, a phantom-based model was developed and a dosimetric index (the Average Absorbed Breast Dose, 2ABD) was introduced. As intermediate step of the proposed approach, the output of different mammographic machines was also accurately char-acterised in terms of air kerma. The 2ABD index should be interpreted as the average absorbed dose in a water-equivalent phantom which approximates the breast in DM and DBT examinations. To compute the 2ABD only few geometric and exposure parameters are required; therefore, this method could be efficiently implemented in any mammo-graphic machine.

To further investigate dosimetric properties of DM and DBT, Monte Carlo simulations were also considered in the RADIOMA project. The interest in simulations was two-fold: first, we aimed to compare the results of our experimental approach with Monte Carlo results; moreover, Monte Carlo simulations were employed to evaluate attenuation and absorption properties of different materials in order to design specific home-made phantoms to better approximate the breast.

Additionally, image quality was taken into account in this project. In fact, to date, only few studies have objectively compared the image quality of synthetic mammograms (SMs) (i.e. two-dimensional images reconstructed from DBT projections) with DM im-ages [13, 14, 15]. Nevertheless, this is a topic of great interest, since the recent intro-duction of SMs could avoid an additional DM which is often required in complement to DBT examinations [16, 17, 18]. Therefore, a comparison between DM and SM images was carried out by analysing several image quality parameters in order to investigate noise, spatial resolution and contrast-detail properties of DM and SM images.

1.2 The European Directive 59/2013 Euratom

The European Directive 59/2013 Euratom establishes safety standards for exposure to ionising radiation, including medical exposures [9]. More in detail, Chapter VII is en-tirely dedicated to the ionising radiations in medical applications. The directive sets out rules on justification of medical procedures, patient information, radiation dose record-ing/reporting and diagnostic reference levels. It also defines the responsibilities of health professionals, focusing the attention on the assessment of the absorbed dose associated with radiological procedures. In this context, the medical physics expert has to “act or give specialist advice, as appropriate, on matters relating to radiation physics...” (Art. 83, 1) and so he “takes responsibility for dosimetry, including physical measurements for evaluation of the dose delivered to the patient and other individuals subject to med-ical exposure, gives advice on medmed-ical radiologmed-ical equipment...” (Art. 83, 2). According to these indications, all radiological procedures shall be under the direct responsibility of the medical physics expert, who will be responsible of the patient-related dosimetric evaluations. Of fundamental importance in the field of breast imaging is the content of Annex II of the directive, i.e. the weight tissues factors wT required for the calculation

of the effective dose. These values have been proposed by the ICRP according to current knowledge in radiobiology [8]. Compared with the previous Directive 29/1996 Euratom, the weight factor for the breast has been modified from 0.05 to 0.12, increasing the con-cern in the assessment of dosimetric quantities related to breast imaging exposures (DM and/or DBT procedures). Therefore, an accurate implementation of the directive

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neces-1.3 Mammography and tomosynthesis: physical and dosimetric aspects 7

sarly requires the identification of an absorbed dose-related index in order to evaluate "the distribution of individual dose estimates from medical exposure for radiodiagnostic and interventional radiology purposes..." (Art. 64).

In addition, the directive promotes the commitment to a comprehensive information to the patient about risks and benefits associated with exposure to ionising radiation. Furthermore, the radiological report of the patient must include dosimetric data referred to the radiation exposure (Art. 58, b). Moreover, it would be desirable to correlate image quality data with dosimetric data, in order to achieve a complete optimisation of the procedure.

The directive also emphasizes attention to quality assurance programs and dose as-sessment for special practices, including health-screening programs (Art. 61). In 2006, the Italian Group for Mammographic Screening (GISMa) suggested that screening pro-grams must be offered to women in the age group of 50-69 years (specifically, a mam-mogram every two years is recommended) and Regions could extend this age range according to their available resources. In this regard, the Italian Ministry of Health has recommended the extension of screening programs in order to monitor a greater number of women. In fact, many studies have shown that the regular application of a mammo-graphic screening protocol can reduce mortality in women and that an active invitation for a screening program ensures fairness [19]. Therefore, in 2016 Tuscany approved a resolution that extends the mammographic screening age range by 10 years [20]. More specifically, the age range will change from 50 - 69 years to 45 - 74 years with a gradual 5 years implementation process.

Given the aforementioned scenario, the RADIOMA project has represented a starting point in order to develop a simple and reproducible method for the mean absorbed dose evaluation in DM and DBT procedures.

1.3 Mammography and tomosynthesis: physical and dosimetric aspects Digital mammography overview

In conventional DM procedures the patient’s breast is held stationary between the com-pression paddle and the breast support. An X-ray detector placed under the breast sup-port provides a planar image of the breast by acquiring an X-ray projection and convert-ing the energy released by the photons that passed through the breast in grey levels (Fig-ure (1.3.1)). Whereas screening mammography attempts to identify breast cancer in the asymptomatic population (i.e. in early stage), diagnostic procedures are performed to evaluate palpable lesions or suspicious findings identified by screening mammography. The diagnostic mammographic examination may include additional x-ray projections, magnification views, spot compression views, ultrasound, magnetic resonance imaging, or mammoscintigraphy [2].

The morphological differences between breast normal tissue and tumor masses and the presence of breast microcalcifications require the employment of x-ray systems de-signed specifically to optimise breast cancer detection. As shown in Figure (1.3.2), the linear attenuation coefficient (µ) differences between fibroglandular and cancer-ous tissues are extremely small. Therefore, in order to detect cancercancer-ous masses, contrast between fibroglandular tissue and tumor tissue must be maximised. The image contrast between the two tissues increases with the difference between µf ibroglandular and µtumor;

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There-8 1 Overview of the RADIOMA project

fore, in order to obtain the best image contrast between tissues of interest, low energy X-ray spectra are employed in mammography [2]. Detection of small calcifications in the breast is also important because microcalcifications could be early markers of breast cancer; this implies that the system should provide a high spatial resolution. Dedicated x-ray equipment, specialised x-x-ray tubes, breast compression devices, antiscatter grids and X-ray detectors are therefore essential to achieve the requirements of mammographic imaging.

Fig. 1.3.1 Example of the imaging process in mammography. Image adapted from https://www.radiologycafe.com/radiology-trainees/frcr-physics-notes/mammography.

Fig. 1.3.2 Example of attenuation coefficients of different breast tissues. Image adapted from [2].

One of the specific features of a mammographic system is the X-ray spectrum produced by the X-ray tube. In order to obtain an optimised spectrum for breast investigations

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1.3 Mammography and tomosynthesis: physical and dosimetric aspects 9

both the anode and the filter material are fundamental. Typical anode materials are represented by Tungsten (W), Molybdenum (Mo) and Rhodium (Rh), while materials such as Mo, Rh, Alluminium (Al) and Silver (Ag) are frequently used to filter the X-ray beam produced from electrons-to-anode interaction within the X-X-ray tube [2, 21]. Figure (1.3.3) shows an example of an X-ray mammographic spectrum for a W/Rh (50 µm) anode-filter combination and a tube voltage of 28 kVp.

Fig. 1.3.3 Example of a simulated X-ray spectrum for W/Rh (50µm) of a generic mammographic system for 28 kVp and normalised to 1 mGy of air kerma at 1 m from the source. The spectrum has been obtained through the Siemens X-ray Spectra Simulator (Siemens Healthcare, https://health.siemens.com/booneweb/index.html). Notice the fall-off at∼23 keV, consequence of the Rh k-edge.

The first half value layer (HVL) of the beam is the quantity employed to specify the beam quality in mammography and, more in general, in medium and low energy X-ray beams. HVLs are usually expressed in mmAl and represent the equivalent thickness of Al required to reduce to one half the air kerma rate of a narrow X-ray beam at a reference point. Each energy-dependent quantity related to mammographic procedures (e.g. in dosimetry, quality assurance, shielding ecc.) is therefore expressed as a function of the HVL.

The image acquisition process in DM is usually performed under automatic exposure control (AEC). The AEC usually employs a radiation detector located underneath the breast support (e.g. a single ionization chamber or an array of semiconductor diodes) [2, 21]. X-rays transmitted through the breast and antiscatter grid (if present) reach the image receptor generating a signal in the detector. The signal is integrated and when it reaches a preset value the exposure is stopped. The preset value corresponds to a specified signal-to-noise ratio (SNR) in DM (i.e. the value of SNR obtained in an im-age acquired under specific calibration conditions). AEC algorithms use several input parameters such as compressed breast thickness, tube voltage, tube anode selection (if available) and tube filter to achieve the desired SNR in the acquired image. Three AEC options are commonly available: (1) fully automatic AEC mode, that sets the optimal

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kVp and filtration from a short pre-exposure of approximately 100 ms (used to evaluate the attenuation of the breast); (2) automatic kVp mode, which sets the kVp on the basis of a short test exposure, with a user-selected anode and filter materials; (3) automatic time mode, which employes manual target, filter and kVp settings, whilst only the tube load (mAs) is under automatic control. For most patient imaging, the fully automatic AEC mode is used, while for quality controls the AEC technology is often disabled.

The image generation process in DM takes place in the image detector that converts the photons energy into a charge/current signal which is finally associated to grey levels in the image matrix [2]. Modern mammographic systems are equipped with flat panel detector (FPD) arrays which consist of an active matrix that includes a thin film tran-sistor (TFT, which acts as an “on-off” switch), a charge collection electrode, and a stor-age capacitor (Figure(1.3.4)) [2, 21, 22]. The active matrix collects the signal (electric charge) generated during the X-ray exposure, absorption, and conversion process. The collected charge is then stored in a capacitor attached to each detector element and the array is actively read immediately afterward to produce the image. In some “fast” FPD designs, readout of the whole array is performed in hundreds of milliseconds, allowing near real-time acquisition of data for applications such as DBT. Two FPD conversion technologies are currently adopted in DM systems: indirect X-ray conversion, in which a structured cesium iodide (CsI) phosphor converts X-rays into light photons which are emitted onto a photodiode in each detector element generating the charge signal; dir-ect X-ray conversion, based on amorphous selenium (a-Se), in which incident X-rays absorbed by the a-Se directly produce electron-hole pairs that are then collected to pro-duce the signal.

Once the signal is collected by the detector system, a raw (“for processing”) image can be obtained. Usually, a post-processing algorithm is then applied to obtain the final (“for presentation”) image, which is analysed by the radiologists [21, 22].

Fig. 1.3.4 Internal structure of an indirect FPD. Image adapted from [22].

Digital breast tomosynthesis overview

The main limitation of DM is its intrinsic two-dimensional (2D) nature, resulting in tissue overlapping which can lead to loss in lesions visibility and to erroneous

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tion of lesions (i.e. normal features that are only vertically separated can appear as the projection of a lesion in the image plane). Hence, in order to reduce these limita-tions and improve mammographic accuracy, DBT has been introduced in clinical routine [4, 23, 24]. Different DBT systems have received approval for clinical use around the world and their employment has increased in recent years [4, 23, 24]. DBT produces pseudo-three dimensional (3D) images by acquiring a limited number of projections of the breast from a narrow angular range [4]. The main components of a DBT system are the same of a DM system: the X-ray tube produces low-energy X-ray spectra (26-48 kVp) and “fast” FPDs permit to acquire a set of projections; the AEC (full mode) is commonly employed in clinical practice. In a typical DBT procedure, the breast is compressed and held stationary between the compression paddle and the detector; the X-ray tube rotates in one plane around the compressed breast, over a limited angular range, and a projec-tion every few degrees is acquired (Figure (1.3.5)) [4]. Depending on the specific DBT device, a Filtered Back-Projection (FBP) or an Iterative Reconstruction (IR) algorithm is applied to the acquired projections and a set of reconstructed image planes is produced [23, 25]. Thus, differently from DM, DBT provides tomographic “slices” of an entire tis-sue volume during a single acquisition, likewise CT scans. In DBT procedures, the total number of reconstructed images depends on the thickness of the compressed breast. Im-ages are usually displayed on a workstation as 2D “slices”, which allow the radiologist to scroll through the individual images.

Fig. 1.3.5 Acquisition process in DBT compared to DM: two lesions vertically separated are superimposed in conven-tional DM images. However, in DBT different projections at different angles are acquired to reconstruct a series of im-age planes of the breast in which the lesions can be separately identified. Imim-age adapted by https://www.siemens-healthineers.com/mammography/tomosynthesis.

The advent of DBT may help to partially overcome the inherent tissue overlapping limitation of 2D breast imaging, providing more information on tissue localization than DM [23, 24, 25]. As a results of the acquisition and reconstruction processes, DBT images (i.e. the reconstructed image planes) exhibit different physical properties with respect to DM images. In fact, compared to DM, more parameters may influence image quality in DBT: the number and the angular distribution of the projections, the X-ray tube angular range of rotation and motion (continuous or step-and-shoot), the (eventual) detector

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motion and the reconstruction algorithm [4, 23, 24, 25]. For example, DBT provides anisotropic spatial resolution in three dimensions (due to the limited number of acquired projection angles) and eventually in the reconstructed image planes (due to the X-ray tube motion, if continuous) [23, 25]. Also, the image texture of reconstructed image planes is modified compared to DM due to filters applied in the reconstruction process. Some recent studies have investigated these and other aspects in great detail [26, 27, 28, 29]. In the same way, DBT imaging includes many parameters which can influence the breast radiation dose. Exposure parameters such as the anode-filter combination, the kVp and mAs values play the same role in DBT than in DM; however, the number of projections, the X-ray tube angular range and the projection angles are specific features of DBT systems.

As previously mentioned, DBT does not provide a comprehensive 3D representation of the breast volume. In addition, some studies have shown that the detection of microcal-cification on DBT images could often be more difficult compared to DM images [30, 31]. Therefore DBT has been firstly employed in conjunction with DM, allowing a better com-parison between new studies and previous DM examinations [32]. In this regard, recent works have shown that DBT plus DM can provide higher diagnostic performance than two-view DM especially in dense breasts [33, 34]. However, the employment of DBT plus DM leads to a relevant increase in the absorbed breast dose and breast compres-sion time [35]. Therefore, to avoid DM acquisitions additional to DBT procedures, some vendors offer the possibility of reconstructing two dimensional (2D) synthetic mam-mograms (SMs) from DBT dataset [16, 17, 18, 30, 36, 37]. SMs are obtained through a vendor-specific algorithm that combines DBT data (i.e. raw projections and image planes) into a single 2D mammography-like image [16, 17, 18]. Thus, the aim of SMs is to provide a useful 2D image complementary to DBT slices, highlighting important fea-tures that could be less evident in DBT images. Some studies have reported encouraging results regarding the employment of DBT as a standalone technique with 2D-synthetic reconstructed images [17, 35] and the employment of DBT with 2D-synthetic instead of DBT plus DM images is, whenever possible, recommended [38]. However, to date, only few works have quantitively compared SMs and DM images in terms of image quality [14, 15, 16].

Dosimetry in DM and DBT

The current reference dosimetric index used to estimate the radiation dose in DM and DBT is the Average Glandular Dose (AGD), that is representative of the absorbed dose by glandular tissue (more radiosensitive than skin and fatty tissue) [1, 10, 11, 12]. The method was developed by Dance for DM procedures in 90s: it requires the incident air kerma at the breast surface as input quantity, it applies tabulated conversion and cor-rection factors to convert air kerma into glandular absorbed dose and to account for the glandularity of the breast and the X-ray spectrum properties [10, 11]. The conver-sion and correction factors were derived from Monte Carlo simulations and depend on patients age, anode-filter combination, half value layer (HVL) of the beam and breast thickness. In DBT the concept of ADG has been extended to take into account each projection at a given angle [1, 12]. Specifically, AGD for DM and DBT systems can be computed as follows [1, 10, 11, 12]:

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1.3 Mammography and tomosynthesis: physical and dosimetric aspects 13

where Ka,iis the incident air kerma in DM procedures, while Ka,iT is the total incident air

kerma for DBT acquisition but measured in the 0° position (i.e. KT

a,imust be measured or

estimated applying all the parameters of the clinical settings except for the tube motion, which must be disabled); the factor “g” is a conversion factor from Ka,i to glandular

dose in a breast composed of 50% of glandular tissue and 50% of adipose tissue (often referred as “standard” breast), this factor is derived from Dance et al. [11, 12] and it is tabulated for a range of HVLs; the “c” factor corrects for the difference in composition of typical breasts from 50% glandularity (Dance et al. [11, 12]); the factor “s” corrects for differences due to the choice of X-ray spectrum (Dance et al. [11, 12]); the factor T is the “tomo” factor which considers the angular motion of the X-ray tube in DBT examinations [12].

The calculation of AGD is one of the most important assessment in quality assurance process of clinical mammographic systems, as suggested by several international proto-cols and guidelines [40, 41, 42, 43, 44].

The AGD is accepted as the appropriate quantity to use for quantifying the ionising radiation risk and allows the effect of differences in breast glandularity to be taken into account in the dose estimate. However, some complications arise in practical im-plementation. First, the AGD can not be directly computed if the incident air kerma is not measured or estimated; additionally, the HVL of the X-ray beam must be known in order to search for the needed correction factors; furthermore, the correction factors are tabulated for a set of parameters (age, HVL, breast thickness), hence, interpolation is required in many situations.

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Chapter 2

Materials and Methods

In this Chapter a brief presentation of the instrumentation employed in the experimental measurements is provided. The dosimetric approach to evaluate the 2ABD index as well as the images acquisition and analysis are described in detail. The general idea of the Monte Carlo methods is presented as complement of the whole dosimetric approach.

2.1 Instrumentation

Mammographic systems

Three mammographic systems of different vendors were involved in this study; the main features of each model are briefly described below.

Selenia Dimensions

The Selenia Dimension (Hologic, USA) system implements both DM and DBT modalities and provides three different anode-filter combinations: W-Rh and W-Ag for DM, W-Al for DBT. The system covers an angular range of 15° in DBT modality, acquiring a total number of 15 projections through a continuous motion of the X-ray tube. The system is equipped with a moving high transmission cellular grid integrated into the detector assembly. The FPD is based on a-Se technology, the size of the image field is 24x29 cm2

and the focus-to-image distance (FID) is 70 cm. The detector pixel size is 70 µm which is also the pixel size of DM images, while the pixel size in DBT reconstructed images or SMs is 110 µm. Exposures can be controlled either manually or automatically via an AEC system. In the AEC mode, selected areas of the digital detector are used to monitor and control the X-ray exposure (i.e. the Selenia does not have a conventional mammographic AEC sensor such as a ionisation transmission chamber). The detection area can be selec-ted manually or automatically. Three user-selectable AEC operating modes are provided as described in the previous section [1.3]. Different DBT acquisition modalities are sup-ported: “Tomo”, “Tomo-HD”, “Combo” and “Combo-HD”. The “Tomo” mode refers to a single DBT acquisition, but no SMs are reconstructed from the acquisition dataset. Con-versely, in “Tomo-HD” both image planes and SMs are reconstructed. The “Combo” and “Combo-HD” mode are employed to acquire a sequence of DBT and DM projections (in the HD mode the SMs are automatically reconstructed as explained previously). The FBP algorithm is applied to obtain the image planes in DBT modality; the image planes are reconstructed at 1mm intervals. Processed and unprocessed DM images are always

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16 2 Materials and Methods

provided by the system. More details about the Selenia Dimensions system can be found in literature [45, 46].

Amulet Innovality

The Amulet Innovality (Fujifilm Medical System USA Inc., USA) model permits to select the DM modality and two different DBT modes: the standard (ST) mode that uses a nar-row angular range of projections (±7.5°) and the high resolution (HR) mode that uses a wide angular range of projections (±20°). The motion of the X-ray tube in DBT ac-quisitions is continuous and 15 total projections are acquired in both ST and HR modes. This model employs the W-Rh anode-filter combination for DM acquisitions and W-Al for DBT acquisitions. A combination exposure can be selected, in which a DM and a DBT exposure are performed within a single compression. The detector is a FPD based on a-Se with a pixels size of 50 µm (this is also the pixel size of DM images, while for DBT reconstructed image planes the pixel size is 100 µm in both ST and HR modes). The image field size is 24x30 cm2 _{and the FID is 65 cm. The AEC system provides the high}

(H), normal (N) and low (L) dose operating modes, which correspond to different tube current (mA) and/or time of exposure values. Processed and unprocessed images can be saved in standard Digital Imaging and Communications in Medicine (DICOM) format. Images in DBT are recontructed using the FBP algorithm. The system generated a SM (‘S-view’) for each ST and HR DBT acquisition. More details on the Amulet Innovality system can be found in literature [47].

Senographe DS

The Senographe DS (General Electric Medical Systems, USA) mammographic system provides Mo and Rh both as anode and filter materials. The detector is an indirect-conversion FPD based on a-Si which is coupled with a scintillator of CsI(Tl). The active detector size is 19.2x23 cm2 _{with a pixel size of 100 µm. The FID is 61.9 cm. Radiation}

exposure parameters can be set in manual mode or automatic mode (Automatic Optim-ization of Parameters) in which the target material, filter, tube voltage and tube load are selected depending on the “dose reduction” priority chosen by the user. Both processed and unprocessed images can be extracted in DICOM format. More details on the Seno-graphe DS system can be found in literature [48].

The following nomenclature will be used in this thesis:

Name Mammographic device model Device A∗ _{Selenia Dimensions - Hologic}

Device B Selenia Dimensions - Hologic Device C Amulet Innovality - Fujifilm Device D Amulet Innovality - Fujifilm Device E Senographe DS - General Electric Table 2.1.1 Mammographic devices used in this work.∗_{Reference device.}

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2.1 Instrumentation 17

Dosimeters and detectors

A flat ionisation chamber (model 20X60E, Radcal Corporation®, USA) was employed for air kerma measurements in this project. The chamber is composed of polycarbon-ate walls with a conductive graphite external coating and active volume of 60 cm3_.

The chamber was coupled to an electrometer (2026C Radcal Corporation®, USA) and the system accuracy in measurements under reference (calibration) conditions is ±4%. The sensitivity and the resolution of the chamber in radiologic units is 0.03 µGy. The chamber energy dependence is within ±5% (Figure (2.1.1)). More specifications on the chamber performances can be found in [49].

Fig. 2.1.1 Energy dependence for 20X60E model ionisation chamber. Image adapted from[49].

A solid state multimeter (Piranha®, RTI- Electronics AB, Sweden) was used to verify tube voltage accuracy and to estimate the first HVL of the beam in different conditions. For mammographic purposes, the accuracy in tube voltage measurements is given as a function of the anode filter combination and varies between ±1.5% and ±2%. The first HVL of the beam is provided with a ±10% of accuracy. More details on measurements capabilities of the Piranha detector can be found in the Reference Manual [50].

Phantoms

Dosimetry phantoms

A homogeneous water-equivalent square phantom was employed to approximate the breast in experimental measurements (Fig. (2.1.2)). The phantom was composed of a variable number of homogeneous slabs and the dimension of each slab was 16x16 cm2_,

while the thickness was 0.5 cm or 1 cm. The measured mean density of the phantom was 1.043±0.005 g/cm3_.

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Fig. 2.1.2 Slabs of the phantom employed to simulate the breast in experimental measurements.

Image quality phantoms

Five different phantoms were employed for image quality analysis. Specifically, a 30x24x4 cm3 _{homogeneous poly-methyl-methacrylate (PMMA) plate was employed to evaluate}

noise spectral properties and signal-to-noise ratio maps of the images.

Two phantoms were adopted to assess contrast-to-noise ratio (CNR) for low-contrast objects: the Mammographic Accreditation phantom (ACR model 18-220, Fluke Biomed-ical, Everett, WA, USA) and the TORMAM phantom (Leeds Test Objects Ltd, North York-shire, UK).

The ACR phantom (Fig. (2.1.3)) consists of a wax box, containing 16 details, placed on an acrylic base and with a plastic cover. All of these parts together approximate a 45 mm compressed breast of average glandular/adipose composition in terms of beam atten-uation. Six different size nylon fibers simulate fibrous structures, five groups of specks simulate calcification clusters, and five different spheres simulate tumor-like masses. Table (2.1.2) reports the main features of the phantom details [43].

The TORMAM phantom (Fig. (2.1.4)) is composed of 2 parts: the left half contains a range of 6 groups of filaments, 6 groups of micro-calcification (300-100 µm of diameter) and 6 groups of 3 low-contrast detail subgroups to mimic clinical features. Table (2.1.3) summarises the characteristics of the inserts included in the phantom [43]. The right half mimics the appearance of breast tissue and contains micro-calcification clusters in addition to fibrous and nodular details.

An home-made PMMA phantom (Fig. (2.1.5)) which incorporates a 12.5 µm tungsten wire was expressly assembled to evaluate the spatial resolution of the images. The wire was fixed with an angle of 3° with respect to the longer side of a rectangular PMMA support. The final thickness of the PMMA support was 2 mm.

Finally, the CDMAM phantom (v. 3.4, Artinis Medical Systems, Elst, The Netherlands) was employed in contrast-detail analysis (Fig. (2.1.6)). This phantom consists of an alu-minum base 0.05 mm thick with gold discs (99.99% pure gold) of different thicknesses and diameters [40, 41, 43]. In particular, a matrix of square cells with golden discs is attached to the base. The discs diameters range from 0.06 to 2.0 mm and the thicknesses range from 0.03 to 2 µm. Therefore, the contrast of each disc in the final image of the phantom is directly related to the disc thickness (i.e. the contrast is expressed in µm of

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2.1 Instrumentation 19

gold). In each cell of the matrix, one dot is always placed at the center of the cell and another one, identical, is positioned in a randomly selected corner within the cells.

Fig. 2.1.3 Representation (left) and DM image (right) of the ACR phantom.

# Fibers diameters (mm) Specks diameters (mm) Masses diameters (mm)

1 1.56 0.54 2 2 1.12 0.40 1 3 0.89 0.30 0.75 4 0.75 0.24 0.50 5 0.54 0.16 0.25 6 0.40 -

-Table 2.1.2 Description of the details properties of the ACR phantom (see Fig. (2.1.3))[43].

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20 2 Materials and Methods # Filaments diameters (mm) Particles size ranges (µm) Circular details nominal contrasts (%)

A 0.40 354-224 4 B 0.35 283-180 3 C 0.30 226-150 2 D 0.25 177-106 1.5 E 0.225 141-90 1 F 0.20 106-63 0.5

Table 2.1.3 Description of the details properties of the ACR phantom (see Fig. (2.1.4)). The diameter of each filament is 10 mm, while the diameter of each circular detail is 3 mm [43].

Fig. 2.1.5 DM image of the home-made phantom which included a 12.5µmtungsten wire.

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2.2 Dosimetry 21

Software packages

Different software packages were employed in images and data analysis. Specifically, Im-ageJ, (Wayne Rasband, National Institute of Health, USA) and Matlab (The MathWorks, Inc., MA, USA) software packages were employed to analyse DM and SM images. In addition, the CDMAM Analyser v.1.5.5 was used for evaluating CDMAM images. Origin v.9.0 (OriginLab Corporation, MA, USA) was employed in data analysis.

2.2 Dosimetry

2.2.1 Background

The reference quantity in low energy X-ray dosimetry is the absorbed dose to water, which can be determined through a calibrated ionisation chamber. For this purpose, two main approaches have been developed: the air kerma based formalism, that considers a reference ionisation chamber calibrated in terms of air kerma under specific conditions [51], and the more recent dose to water formalism, in which the reference chamber is calibrated in terms of dose to water [52]. However, the ionisation chamber used for esperimental measurements in this work has been calibrated in terms of air kerma and therefore the air kerma based formalism was considered to obtain the absorbed dose to water.

In this section, a brief discussion of the main points required to obtain the dose to wa-ter from a ionisation chamber calibrated in wa-terms of air kerma is presented. The formal-ism to determine the absorbed dose to water for low and medium energy X-rays differs from the high energy X-rays formalism since some basic assumptions of the Bragg-Gray (BG) cavity theory are no more satisfied. Therefore, some conditions shall be considered in this context:

• the fluence and energy distributions of secondary electrons at the chamber walls and in the air cavity are not equal as required by the BG theory;

• the production of bremsstrahlung photons in water or air is negligible in the consid-ered energy range. Therefore, the collision kerma is equal to the total kerma;

• the condition of (transient) charge particle equilibrium (CPE) is always established

due to the small range of secondary electrons in water. This implies that the absorbed dose to water (Dw) can be considered equal to the water collision kerma (Kw,c);

• secondary electrons created outside the chamber walls have very low energies and their contribution to the ionisation within the air cavity is negligible (i.e. the chamber could be considered as a “photon” detector).

Under the above conditions, the following relationship can be written [51, 53, 54]:

Dw = Kw,c ' Kw (2.2.1)

in which Kw is the total water kerma. The following steps are then required to obtain

the absorbed dose to water, Dw, from an air kerma calibrated ionisation chamber.

The ionisation chamber positioned in a point P in air will measure the air kerma in air ((Kair)air). The chamber positioned in a point P in a water phantom will measure the

air kerma in the water phantom ((Kair)w). The latter condition represent the set-up of

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water phantom (i.e. without the ionisation chamber, Dw) by applying conversion and

correction factors, which account the changes between air and phantom measurement conditions [51, 53, 54, 55]: Dw = (Kair)w· ka,w· µen ρ w a w (2.2.2) In Eq. (2.2.2) hµen ρ w a i w

represents the ratio of mass energy absorption coefficient of water to air averaged over the spectral energy fluence distribution at the point of measurement (i.e. in water). Therefore, this quantity is a conversion factor from air kerma to dose to water under reference conditions (calibration conditions). The values of µen

ρ

w

a have been published as a function of the first half value layer (HVL) of the

beam and tabulated values can be used for practical purposes [51, 53, 55, 56]. The term ka,w in Eq. (2.2.2) is a correction factor which takes into account all possible differences

between the calibration conditions and the measurement conditions, this factor is a chamber-specific factor and depends upon the calibration and measurement conditions. More in detail, ka,w includes corrections for:

• energy and angular dependence of the response of the ionisation chamber (i.e.

differ-ences in the spectral energy and angular distribution of the photon fluence between the calibration and the measurement conditions);

• the effect of displacement of water in the phantom by an air volume (the outer shape of ionisation chamber);

• differences in photon fluence due to attenuation and scattering from the ionisation chamber stem between the calibration and measurement conditions;

• effect of the waterproof chamber sleeve (if needed) on the chamber response.

Values of ka,w have been published as a function of the HVL and chamber type for

reference measurement conditions and for practical purposes tabulated values can be adopted [53, 54, 55].

If the reference point of measurement is located on the phantom surface a slightly different approach should be considered [51, 53, 54]. The Eq. (2.2.1) is still valid, but the kerma to water can be obtained from measurement of (Kair)air by applying the

conversion factorhµen ρ w a i air

and a backscatter factor Bw:

(Kw)air ' (Kair)air·

µen ρ w a air (2.2.3) Kw|surf = Bw· (Kw)air (2.2.4)

The backscatter factor Bw accounts for the influence of the phantom and includes the

dependences from the field size and source to surface distances (SSD). More details and tabulated factors for medium and low energy X-ray beams can be found in literature [53, 54, 57, 58, 59]. The conversion factor1 hµen

ρ

w

a

i

air

must be evaluated over the photon fluence spectrum in air, without the phantom. To summarise, the absorbed dose

1_{Strictly speaking, the conversion factor to apply to}_(K

air)air should be

h µtr ρ w a i air

, whereµtr is the energy transfer

coefficient. In this case, the = holds instead of'in Eq. (2.2.3). However, the assumption of CPE leads tohµtr

ρ w a i air ' h µen ρ w a i air

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2.2 Dosimetry 23

at the point of reference on the phantom surface could be derived by the following equation:

Dw|surf ' Bw· (Kair)air·

µ_en ρ w a air (2.2.5) To obtain the absorbed dose at other depths in a water phantom from Dw|surf the

knowledge of the dose-depth relationship in the phantom is required.

Notice that if the absorbed dose is required in a specific material (different than water) the presented formalism can still be employed by using the correct conversion and cor-rection factors. In the following sections all measurments must be considered converted into dose to the phantom at the point of interest as described above.

2.2.2 Evaluation of entrance air kerma

The first step involved in the estimation of the absorbed dose through the proposed model is the calculation of the entrance air kerma in the phantom surface ((Kair)w|surf,

i.e. the air kerma on the phantom surface which incorporates the backscatter contri-bution). For a specific anode-filter combination, the quantity (Kair)w|surf depends on

exposure parameters (tube voltage and tube load) and on geometric parameters (focus-to-surface distance and thickness of the phantom). Following the previous mentioned approach (eq. (2.2.5)), (Kair)w|surf can be obtained from the incident air kerma by

ap-plying a backscatter factor Bw:

(Kair)w|surf = Bw· (Kair)air (2.2.6)

in which (Kair)air is the incident air kerma in air obtained at the same distance from

the X-ray source but without the phantom. The (Kair)air at a specific distance d can be

computed in a simple way if the X-ray tube yield (Y (kV, dref), i.e. the ratio between the

air kerma in air measured at a reference distance dref from the source and the tube load)

is known for a specific tube voltage value:

(Kair(kV p, d))air = Y (kV p, dref) · mAs ·

dref

d 2

(2.2.7) in which the inverse square law is assumed to scale up the air kerma to the actual position. Therefore, to employ eq. (2.2.7) for a generic mammographic device with a given anode-filter combination, a model for the tube yield as a function of the kVp is required. As suggested by the NCRP report N.147, the dependence of the primary beam air kerma from the kVp can be assumed polynomial [60]; hence, a quadractic expression was adopted to fit experimental data:

Y (kV p, dref) = c1· kV p2+ c2· kV p + c3 (2.2.8)

in which the ci=1,2,3 were fitting coefficients. The complete expression of (Kair)air

calcu-lated at the focus-to-surface distance can be written in a more convenient notation: (Kair)air = c1· kV p2+ c2· kV p + c3 · mAs ·

F SD F SD − T

2

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where FSD is the focus-to-support distance, which was chosen as reference distance, and T is the phantom thickness. Notice that, once the ci parameters are derived for a

specific anode-filter combination, only the kVp, mAs, FSD and T values must be known to compute the (Kair)air from Eq. (2.2.9).

In order to develop and assess the proposed model, the Selenia Dimensions (device A) was chosen as reference device. Then, a set of air kerma measurements were performed in DM (W/Ag and W/Rh) and DBT (W/Al) modality through the ionisation chamber in different conditions. The detector was centered at 6 cm from the chest wall edge and positioned 20 cm above the breast support to minimise the backscatter contamination. In addition, to better simulate the clinical settings, the incident air kerma (Kair)air was

measured in many exposure conditions, in particular by adopting the closest exposure parameters to the AEC conditions for a given thickness of the water equivalent phantom. Furthermore, the X-ray tube was free to rotate in DBT mode, as in clinical examinations. The collected data were fitted by eq. (2.2.9) and three sets of ci were obtained for the

three anode-filter combinations of the reference device.

The described method does not account for any variations in tube yield when differ-ent mammographic devices with the same anode/filter combination are compared. In a general case, the proposed procedure should be applied for any mammographic device and anode-filter combination. However, this is time consuming and not always feasible. Therefore, a more practical solution could be the employment of a correction factor η which takes into account differences in the X-ray tube yield of different mammographic machines. This factor could be defined as follows:

η|kV pref = Y (F SD) Yref(F SDref) · F SDref F SD 2 (2.2.10) where Y (F SD) represents the yield (mGy/mAs) of the X-ray tube used and Yref(F SDref)

is the reference tube yield. FSD and FSDref are the distances from the X-ray source at

which Y and Yref are evaluated2. Both Y and Yref must be evaluated at the same tube

voltage (kV pref). The final general expression for the computation of the incident air

kerma in air become:

(Kair)air= η|kV pref · c1· kV p

2_{+ c} 2· kV p + c3 · mAs · F SD F SD − T 2 (2.2.11) Notice that the η|kV pref factor is assumed to be independent from the tube voltage; in

other words, the ratio between the tube yields of two mammographic devices (one of which is the reference device) is assumed to be the same at each kVp for a given anode-filter combination. This is an approximation which could be minimised in practice by adopting as kV pref the most used tube voltage for the specific modality (DM or DBT).

In our case, 29 kVp was chosen for DM acquisitions and 32 kVp for DBT acquisitions. Once the cicoefficients have been computed for the reference device, the eq. (2.2.11)

permits to obtain the (Kair)air for a generic mammographic device and a given

anode-filter combination by only making a single measurement of the X-ray tube yield at kV pref; the (Kair)air can then be computed for a given condition by knowing the kVp,

mAs, FSD and T values. This values can be obtained in a simple way from the header of the acquired DICOM images.

2_{For the Selenia Dimensions device FSD}

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2.2 Dosimetry 25

To test the accuracy of this approach, a set of air kerma measurements were performed in other four mammographic devices3 _{both in DM and DBT modalities under different}

conditions. The measurements were then compared to the computed values through eq. (2.2.11).

The entrance air kerma at the phantom surface can be then obtained from the (Kair)air

through eq. (2.2.6). A Bw factor of ∼1.10 is suggested for the X-ray beams used in

mammography [59, 61, 62]. A set of air kerma measurements were also performed with and without the water equivalent phantom positioned under the detector. Different conditions and different phantom thicknesses were considered to verify the Bw value.

2.2.3 Evaluation of the absorbed dose to water equivalent material

In order to estimate the average absorbed dose to the water equivalent phantom which approximates the breast, a model for the dose-depth relationship in the phantom was assumed. More in detail, the average absorbed dose in a phantom of thickness T can be evaluated through the following expression:

D([0, T ], fD) = 1 T · T ˆ 0 fD(x)dx (2.2.12)

where fD(x)describes the dose distribution as a function of the phantom depth x.

There-fore, a set of experimental measurements was performed by positioning the ionisation chamber at different depths in the water equivalent phantom. A range of 22-34 kVp was employed for the DM modality, while a range of 26-48 kVp for DBT. Mo-Mo (Senographe Ds), Mo-Rh (Senographe Ds), Rh-Rh (Senographe Ds), W-Rh (Selenia Dimensions and Amulet innovality), W-Ag (Selenia Dimensions) and W-Al (Selenia Dimensions and Am-ulet innovality) anode-filter combinations were employed. A phantom thickness T=5.5 cm was chosen for this analysis. The obtained data were fitted through an exponential decay and the final expression of the 2ABD index was obtained as:

2ABD = 1 T · T ˆ 0 D(0) · e−α·xdx (2.2.13)

in which D(0) ≡ Dw|surf is the absorbed dose to water on the phantom surface,

which can be directly computed from (Kair)air as presented in the previous section (eq.

(2.2.5)); α = α(kV p) is a fitting coefficient which describes the distribution of the ab-sorbed dose in the water equivalent phantom as a function of the tube voltage for a given anode-filter combination. For each anode-filter combination α was modeled as a function of the tube voltage as follows:

α = a

kV pb (2.2.14)

where a and b are fitting coefficients.

Notice that for calculating the 2ABD only the tube voltage, the tube load, the breast thickness, the value of the η parameter and the FSD are required (for a given anode-filter combination). The overall uncertainties in 2ABD calculations were evaluated by

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considering as independent all uncertainties associated to the parameters involved in equations (2.2.13) and (2.2.11) and applying the uncertainty propagation formula:

σf = v u u t X i " ∂f dxi 2 · σ2 xi # (2.2.15) where σxi is the standard deviation associated to the xi variable and f is a generic

function of the xi variables.

In order to test the 2ABD model, a comparison between the average absorbed dose computed through experimental measurements and the average absorbed dose calcu-lated by eq. (2.2.13) was performed in a number of conditions. The water equivalent phantom was employed for these measurements. The AEC was previously adopted to obtain a set of clinical exposure parameters for data acquisition of different phantom thicknesses. Then, the ionisation chamber was placed at different depths and a set of measurements was collected for each exposure setting. The average absorbed dose was computed from the experimental data and the obtained values were compared to the 2ABD values calculated by applying eq. (2.2.13) (i.e. by employing only the above men-tioned input parameters of the method). Uncertainties in the average absorbed dose were obtained by considering the 4% of accuracy in the ionisation chamber and by propagating uncertainties of all quantities involved in the calculation.

To further investigate DM and DBT dosimetric properties, a comparison between 2ABD obtained in both modalities was carried out in a number of conditions. More in detail, a number of automatic exposures for different phantom thicknesses (2.5-7.5 cm) were executed in both modalities and the corresponding input parameters required for the 2ABD calculation were recorded. The 2ABD values were computed according to eq. (2.2.13). Additionally, the AGD was calculated as the reference dosimetric index by following the Dance approach [1, 10, 11, 12, 40, 41, 42] for the same AEC conditions of the 2ABD calculation by considering a standard breast of the same thickness of the phantom and a 50% of glandular tissue composition.

2.2.4 Monte Carlo approach

A GEANT4-based application was developed by our group to reproduce mammographic investigations for different setups and breast anatomies [63]. The code provides simu-lation of mean glandular dose and incident air kerma at the upper surface of the breast (backscatter photons are excluded from the computation) in the same run. The code was validated according to the prescription of The American Association of Physicists in Medicine (AAPM, Task Group 195 - Case III, for mammography purposes) [64].

More in detail, for air kerma validation the formalism presented by Sarno et al. was followed [65]. Measurements with a water phantom were executed, estimating the at-tenuation of water slabs. Algorithms for reproducing polychromatic X-ray spectra were verified by comparing experimental HVL with simulated ones obtained from algorithms developed by Hernandez et al. in a previous work [66].

Once the MC code was verified, a comparison between the simulated mean absorbed dose and the 2ABD was carried out on the reference device under different clinical settings.

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2.3 Image quality 27

2.3 Image quality

Image quality of SM and DM images was evaluated by computing different paramet-ers related to noise, contrast and spatial resolution properties of the images. Specific-ally, Noise Power Spectrum (NPS), Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR), Modulation Transfer Function (MTF) and Contrast Detail (CD) scores were stud-ied in this project. The comparison between SM and DM was carrstud-ied out by considering processed (“for presentation”) DM images, which are commonly evaluated in clinical practice.

The Selenia Dimensions (reference) device was employed for phantom images acquis-ition. All the acquisitions were performed by employing the automatic exposure para-meters (AE, Auto-Filter mode) set by the device in both modalities. Specifically, for the ACR, TORMAM and the home-made phantom, the exposure parameters were derived from the AE settings of 4 cm thick PMMA plate. To match the 4 cm PMMA attenuation equivalence, the TORMAM phantom was placed on top of 2.5 cm of PMMA [43]. The home-made phantom with a 12.5 µm tungsten wire was placed under 4 cm of PMMA, while the composition of ACR phantom approximates the attenuation of 4 cm thick PMMA plate [43]. For the CDMAM phantom the acquisition parameters were referred to the AEC settings for 5 cm thick PMMA plate, as suggested by quality assurance proto-cols and international guidelines [40, 41, 42, 43]. Table (2.3.1) summarises the image acquisition settings for both DBT and DM modalities.

Phantoms ACR, TORMAM, PMMA, PMMA+tungsten wire CDMAM

Modality: DBT DM DBT DM

Anode/Filter: W/Al W/Rh W/Al W/Rh

Grid: out in out in

Tube voltage (kVp): 30 28 33 31

Tube load (mAs): 35 75 35 80

Table 2.3.1 System settings employed for images acquisitions.

2.3.1 Noise properties

Noise properties were determined in the homogeneous PMMA phantom by measuring the NPS, which characterises both the magnitude (i.e. variance) as well as the texture (i.e. spatial frequency distribution) of the noise. The 2D-NPS was computed through the following relationship [67]: N P S(fx, fy) = 4x · 4y Nx· Ny ·|F F T (ROIn(x, y))|2 (2.3.1) where 4x and 4y are the pixel sizes, Nx and Ny the number of pixels in each direction

within the region of interest (ROI) and ROIn(x, y)is a "noise" ROI. The "noise" image was

obtained by subtracting two images acquired under the same conditions. Consequently, a factor 1/2 was applied to eq. (2.3.1). The Fast Fourier Transform (FFT) was applied to 50 ROIsnof 128x128 pixels and the ensemble average was considered to obtain the

final NPS. Additionally, the 2D-NPS was averaged along the fx(horizontal), fy (vertical)

and radial directions for both SM and DM modalities.

To further investigate the noise properties of SMs, SNR maps were derived from 30 repeated acquisitions of the homogeneous PMMA phantom and compared to those

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obtained in conventional DM. More in detail, 30 images of the homogeneous PMMA phantom were acquired with identical settings in both modalities. For each pixel, the average and the standard deviation (SD) across the 30 images were computed in order to determine average and SD maps. Then, a pixel-by-pixel SNR map was obtained from the ratio between the average map and SD map. A quantitative evaluation of the SNR maps was performed by calculating the following non-uniformity index (NUI) [68]:

N U I = max(P VROIi) − min(P VROIi)

h_{max(P V}

ROIi)+min(P VROIi)

2

i (2.3.2)

where P VROIi is the mean pixel value within the ith-ROI. To compute the NUI using

eq. (2.3.2) a large ROI was selected in the SNR map for both SM and DM modalities. This ROI was obtained by excluding about 1 cm from the edges of the original image, in order to avoid edge effects. A set of sub-ROIs of 100x100 pixels was then adopted and the maximum and minimum P VROIi were extracted. In addition, the NUI was computed

considering a sub region (14 cm x 14 cm) of the original image centered at 8 cm from the chest wall side (i.e. the region of the image which usually contains the main part of the breast).

2.3.2 Contrast-to-noise ratio

The tumor-like masses and the low-contrast inserts included in the ACR and TORMAM phantoms respectively were employed for CNR evaluation. The following definition was adopted [69]:

CN R = P Vinsert− P Vbkg σbkg

(2.3.3) where P Vinsert and P Vbkg are the mean pixel values in a ROI placed within the insert

and in the background region respectively; σbkg is the standard deviation computed in

the background region. Circular ROIs of a diameter of about 2 mm (i.e. 18 and 30 pixels for SMs and DM images respectively) were employed in both phantoms.

2.3.3 Spatial resolution

Spatial resolution was evaluated in the spatial frequency domain by computing the MTF through the Line Spread Function (LSF) method [42, 70]. The home-made phantom with a 12.5µm tungsten wire (Fig. (2.1.5)) was placed under 4 cm of PMMA to match the same conditions of the previous mentioned acquisitions (Tab. 2.3.1). The over-sampled LSF was extracted from the images and fitted using a Gaussian function (G) with an off-set: y(s) = y0+ G(s); the offset y0 was subtracted before applying the Fourier Transform

(FT); the module of the FT was then computed to obtain the MTF which was finally nor-malised to the MTF(0) value. The MTF was calculated along the tube travel direction (i.e. s=x, M T Fx ) and along the orthogonal direction (i.e. s=y, M T Fy) for both SM and

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2.3 Image quality 29

2.3.4 Contrast-detail evaluation with CDMAM phantom

The contrast-detail analysis was carried out by evaluating CDMAM phantom images. The phantom was placed with a 2 cm thickness of PMMA above and 2 cm thickness below [40, 41, 42, 43]. As suggested by the most recent European Guidelines, sixteen different acquisitions of the phantom were performed by slightly moving the phantom each time and for each position both SMs and DM images were acquired [41, 42, 43]. The CDMAM Analyser v. 1.5.5 software was employed to analyse the images; this soft-ware combines data from multiple images to obtain the threshold contrast, providing a CD curve [71, 72]. More specifically, the software fits the CDMAM scores with psycho-metric curves (probability of correct detection as a function of gold thickness for a given disc diameter), as suggested by international guidelines [41]. From these curves, the gold thicknesses corresponding to 62.5% of correct detection probability are extracted as thresholds for each disc diameter. Because the automated analysis is more successful at locating the gold discs than human observers, the obtained thresholds are then scaled up by the software using specific factors for each detail diameter [41, 71, 72].

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Chapter 3

Results

This Chapter presents the main results of the RADIOMA project. Dosimetric results are described in detail in the first part of the Chapter, while the latter part is dedicated to the image quality comparison between SM and DM for the Hologic Selenia Dimensions system.

3.1 Dosimetry

3.1.1 Evaluation of incident air kerma

Table (3.1.2) reports the comparison between calculated air kerma (using Eq. (2.2.11)) and measured air kerma (through the ionisation chamber) in a number of conditions and for different mammographic devices. Values of coefficient used in the calculation can be found in Tab. (3.1.1)). Both DBT and DM mode were employed for this analysis by con-sidering a single view acquisition for each setting. As highlighted in Tab. (3.1.2), a little bias (mean difference value of about -2.7%) is expressed by the calculated air kerma, which results always slightly lower than the measured one. However, the maximum re-lative discrepancy between the two quantities is about -5% which could be considered acceptable for our purposes, due to the uncertainties associated to our method.

Y∗0(65 cm) c1 c2 c3 a b FSD (cm) Device A; W-Al 0.101±0.005 (5.70±0.87)·10−5 _(3.77±0.56)_·10−3 _{(-8.44±0.89)}_·10−2 _{20.32±2.66 1.04±0.10} _67.5 Device A; W-Ag 0.041±0.005 (2.65±1.03)·10−6 _(5.52±0.59)_·10−3 _{(-9.44±0.81)}_·10−2 _{5.87±0.37 0.71±0.02} _67.5 Device A; W-Rh 0.033±0.005 (3.68±0.43)·10−6 _(2.93±0.21)_·10−3 _{(-4.84±0.41)}_·10−2 _{3.73±0.49 0.56±0.04} _67.5 Device C; W-Al 0.126±0.007 (5.01±0.87)·10−6 _(1.05±0.26)_·10−2 _{(-2.19±0.60)}_·10−1 _{17.68±1.57 0.98±0.03} _63.5 Device E; Mo-Mo 0.085±0.008 (2.77±0.87)·10−7 _(9.97±0.08)_·10−3 _{(-1.88±0.11)}_·10−1 _{9.05±1.55 0.68±0.05} ₆₀ Device E; Rh-Rh 0.065±0.003 (1.89±0.17)·10−5 _(2.58±0.42)_·10−3 _{(-4.46±0.64)}_·10−3 _{9.97±0.94 0.75±0.03} ₆₀ Device E; Mo-Rh 0.067±0.008 (6.21±0.05)·10−8 _(8.92±0.29)_·10−3 _{(-1.81±0.55)}_·10−1 _{4.27±0.69 0.53±0.05} ₆₀

Table 3.1.1 Values of the coefficients of Eq.(2.2.11) and Eq. (2.2.14) evaluated for different devices and anode-filter com-binations. These values were employed to calculate the incident air kerma and 2ABD.∗_Y

0was computed at 32 kVp for W-Al

(i.e. for DBT mode) and 28 kVp for the other anode-filter combinations.

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32 3 Results Mode Phantom thickness

(cm)

Tube load (mAs)

Tube voltage (kV p)

Measured (Kair)air

(mGy)

Calculated (Kair)air

(mGy) Rel. difference Device A W − Al DBT 2 37.5 26 2.11±0.08 2.06±0.15 -2.63% Device A W − Al DBT 2.5 40 27 2.61±0.10 2.54±0.19 -2.81% Device A W − Al DBT 3 37.5 28 2.74±0.11 2.71±0.20 -1.26% Device A W − Al DBT 3.5 40 29 3.33±0.13 3.24±0.24 -2.69% Device A W − Al DBT 4 50 29 4.14±0.17 4.12±0.31 -0.37% Device A W − Al DBT 4.5 50 30 4.66±0.19 4.59±0.34 -1.56% Device A W − Al DBT 5 55 31 5.80±0.23 5.60±0.42 -3.56% Device A W − Al DBT 5.5 60 32 7.08±0.28 6.73±0.50 -5.27% Device A W − Al DBT 6 65 33 8.18±0.33 7.99±0.59 -2.43% Device A W − Al DBT 6.5 70 34 9.78±0.39 9.40±0.70 -4.05% Device A W − Al DBT 7 80 35 12.03±0.48 11.69±0.87 -2.93% Device A W − Al DBT 7.5 80 36 13.02±0.52 12.68±0.94 -2.71% Device A W − Al DBT 8.5 80 38 15.19±0.61 14.78±1.10 -2.79% Device A W − Al DBT 8.5 90 42 21.52±0.86 20.55±1.53 -4.72% Device C W − Al DBT 4 60 30 6.88±0.18 6.81±0.50 -1.03% Device C W − Al DBT 5 40 32 5.75±0.16 5.62±0.40 -2.31% Device C W − Al DBT 6 40 33 6.51±0.22 6.33±0.45 -2.86% Device E Rh − Rh DM 3 40 27 2.83±0.18 2.76±0.42 -2.47% Device E Rh − Rh DM 4.5 45 29 3.99±0.21 3.82±0.48 -4.26% Device E Rh − Rh DM 5 50 29 4.80±0.23 4.65±0.52 -3.12% Device E Rh − Rh DM 7 65 34 9.19±0.31 9.07±0.88 -1.31% Table 3.1.2 Comparison between measured and calculated (Eq. (2.2.11)) incident air kerma for different mammographic devices. The exposure parameters were chosen as close as possible to the corresponding AEC settings for a given phantom thickness.

3.1.2 Evaluation of the absorbed to water equivalent material

Figure (3.1.1) shows a set of dose measurements as a function of the water equivalent phantom depth for the reference mammographic device (Device A, Tab. (2.1.1)) in DBT acquisition modality. Experimental data were fitted through an exponential function (y = a · e−α·x_{) as described in Eq. (2.2.13). The exponential decay model seems to be}

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3.1 Dosimetry 33

Fig. 3.1.1 Example of dose-depth curves obtained for the reference mammographic device (Device A) in the water equivalent phantom.

Additionally, in Fig. (3.1.2) the α coefficients of the exponential functions are plotted as a function of the tube voltage. The Eq. (2.2.14) was employed to fit the data in order to obtain a simple relationship between α(kV p) and kV p values. For completeness, the fitting coefficient values are reported in Tab. (3.1.1), even for other devices and anode-filter combinations.

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34 3 Results

Fig. 3.1.2 Example ofαcoefficient (associated to the water equivalent phantom) expressed as a function of the tube voltage for the reference device.

The mean absorbed dose in the water equivalent phantom calculated from measure-ments and the 2ABD computed through Eq. (2.2.13) from the exposure parameters are compared in Tab. (3.1.3). Discrepancies between the two quantities ranged from -7.5% to +8% by considering all devices and anode-filter combinations. Both DBT and DM modalities were investigated. All values are in good agreement within the respective uncertainties. However, a trend with tube voltage is expressed by the device A and a positive little bias arises on the device B (i.e. the 2ABD is always grater than the mean absorbed dose with a mean difference of about +4.5%), while negative bias are exhib-ited on devices C, D and E (-4.6%, -4% and -1.6% respectively). It should be noted that the comparison carried out on the Amulet Innovality systems (devices C and D) takes into account both ST (±7.5° angular range) and HR (±20° angular range) DBT acquis-ition modes. Nevertheless, the higher angular range seems to have negligible influence on 2ABD accuracy.