23 Workflow Design for MDCT of the Thorax
R. Loose, S. Schaller, M. Oldendorf
R. Loose, MD, PhD
Institut für diagnostische und interventionelle Radiologie, Klinikum Nürnberg-Nord, Prof.-Ernst-Nathan-Strasse 1, 90419 Nürnberg, Germany
S. Schaller, PhD
Siemens Medical Systems, Siemensstrasse 1, 91301 Forchheim, Germany
M. Oldendorf, MD
Institut für diagnostische und interventionelle Radiologie, Klinikum Nürnberg-Nord, Prof.-Ernst-Nathan-Strasse 1, 90419 Nürnberg, Germany
CONTENTS
23.1 Introduction 341
23.2 Scan Protocols and Data Volumes 341 23.3 Postprocessing 342
23.4 Network Communication and Storage 343 23.5 Workfl ow 343
23.5.1 General Developments in Routine Workfl ow 343 23.5.2 Workfl ow Automation 345
References 346
23.1 Introduction
Since the introduction of the powerful tool of CT into clinical radiology approximately 30 years ago (Hounsfi eld 1973), technical improvements have been made with steps of 20–50% with regard to the speed of scanning or image reconstruction (Nagel 2000; Kalender et al. 1990). Two major leaps were the introduction of the slip-ring technique with continuous rotation of X-ray tube and detector and the spiral technique (Kalender et al. 1990). The fi rst two-detector spiral scanner was introduced by Elscint (Haifa, Israel) in 1994. The introduction of the fi rst 4-row multi-detector CT systems (MDCT) in 1998 with 0.5-s rotation time increased both, scan- ning speed and scan volume, up to 800% in compari- son with standard single-detector scanners with 1-s rotation time and by 600% in comparison with the fastest 0.75-s scanners. Within 2 years several manu-
facturers developed 16-row scanners with rotation times down to 0.4 s. Presently, MDCT scanners with 2, 4, 6, 8, 10, and 16 rows are available with rotation times of approximately 0.4 s. Whereas the advantages of increased scanning speed, fewer motion artifacts, and more thinner slices are obvious, it is less clear how to deal with the massive amounts of data gen- erated by MDCT. Major unresolved issues relate to data storage, access and transfer across networks, remote access, viewing of images, and workfl ow.
Besides general considerations, personal results are based on 3 years of experience with a 4-row MDCT system (Somatom VolumeZoom, Siemens, Erlangen, Germany) and some weeks of experience with a new 6-row MDCT system (Emotion 6, Siemens) at the Hospital Nuremberg-North with an annual frequency of 11,000 examinations at both CT scan- ners. The following considerations focus on scan protocols, postprocessing, network communication, storage, and workfl ow.
23.2 Scan Protocols and Data Volumes
If MDCT is used in clinical routine and not only in scientifi c research projects, most of the thorax exami- nations can be performed with only two different col- limations. Table 23.1 shows as an example different combinations of beam collimation and readout of adaptive detector arrays for 16-row scanners which are provided by General Electric (Milwaukee, Wis.), Philips (Eindhoven, The Netherlands), Siemens (Erlangen, Germany), and Toshiba (Tokyo, Japan).
Spiral data sets can be acquired in a range from 16×0.5 to 16×2 mm with these systems. In over 90%
of all examinations we use the wider of both avail- able collimations if only axial slices have to be gener- ated. If any 3D postprocessing, such as multiplanar reconstruction (MPR), maximum intensity projec- tion (MIP), or Volume Rendering Technique (VRT), is planned, the thinner collimation is recommended
as long as the scan time does not exceed the patient’s maximal breath-hold time. Furthermore, aspects of data volume, X-ray tube load, and patient dose have to be considered when scan protocols with thin slices are used. The smaller detectors in the center of an adaptive array have a slightly lower geometric effi - ciency than the detectors at the margin of the array which results in higher patient dose for constant signal-to-noise ratio. Data volume and X-ray tube load both increase with decreasing slice width.
As an example, the raw and slice data volume of a 16-row MDCT thorax examination is estimated (scan length 300 mm, collimation 16×0.75 mm, pitch 1.5, reconstruction increment 0.5 mm). The number of reconstructed slices will be 300/0.5=600 if no addi- tional postprocessing is done. In case of reconstruc- tions with different kernels or generation of MPR or MIP images, the volume of image data may easily exceed 1000 slices or 500 Megabytes. The MDCT scan- ner Sensation 16 (Siemens) uses 672 channels with 1160 samples per rotation at 0.5-s rotation speed and 2320 samples at 0.75 s (fl ying focal spot). After 17 tube rotations, the whole scan volume is covered with a raw data volume of 1160 samples 672 channels 16 rows 2 bytes/pixel 17 rotations = 424 Megabytes at 0.5 s or 848 Megabytes at 0.75 s. With an assumed frequency of 40 patients, the daily data volume of one MDCT scanner is in the range of 40 Gigabyte.
23.3 Postprocessing
After an incremental or spiral scan with a single- or multidetector CT, the raw data set holds the highest degree of information and is used for reconstruc- tion of slices with individual parameters such as slice thickness, reconstruction increment, and fi lter (kernel). After the raw data have been deleted, any further postprocessing has to be done with the recon- structed images with fewer capabilities. The need for images with good spatial resolution (bone, HR lung) and good contrast detectability (soft tissue) is the reason why raw data sets have to be processed several
times with different fi lters. In addition, MDCT scan- ners allow the reconstruction of images with a wide range of the slice thickness. As MDCT scanners are not only used to improve the scanning speed, but also to acquire more and thinner slices, the data volume of the same examination is two to four times higher with MDCTs. Table 23.2 gives a survey of the typical number of slices for different examinations and post- processings. Normally, CT images are reconstructed with reconstruction increments, which are in the range of the slice thickness with gaps, contiguous or overlapping slices. If one tries to achieve coronal, sagittal, or oblique slices – like MRI – with highest image quality, the reconstruction increments have to be small (50–70%) in comparison with the slice thickness. These widely overlapping slices are used as temporary data for further reconstructions in any orientation and, in fact, such a complex examination can easily exceed a total number of 1000 images (Deichen et al. 2000; Oldendorf et al. 2000; Loose et al. 2000).
The CT examinations of skull base, temporal bone, or facial sinuses often require two scans, one axial and one coronal, if single-slice scanners are used.
As CT examinations of the thorax can be performed only with axial scans, all MPR or MIP postprocess- ings with coronal, sagittal, or oblique orientations need a large data set of thin axial slices.
Another postprocessing technique, which is well known from MR angiography, is the MIP. Without application of a threshold, the contiguous images of a volume data set are superimposed. This technique is used, for example, for a better detectability of pulmo-
Table 23.1. Specifi cation of 16-row MDCT scanners
Manu- Scanner Minimum ro- Rows Detection Collimations facturer tation time (s) length (mm) (mm) GE LightSpeed-16 0.5 16 20 16 0.63, 16 1.25 Philips Mx8000 Infi nite 0.4 16 24 16 0.75, 16 1.5 Siemens Sensation-16 0.4 16 24 16 0.75, 16 1.5 Toshiba Aquilion-16 0.4 16 32 16 0 .5, 16 1, 16 2
Table 23.2. Typical number of slices for different MDCT scan protocols of the thorax and optional postprocessing.
MIP maximum intensity projection, VRT Volume Rendering Technique
Reconstruction increment 1 mm,
scan length 300 mm 300 slices
Reconstruction increment 0.5 mm,
scan length 300 mm 600 slices
Additional coronal and
sagittal reconstructions 200–600 slices Oblique reformations, MIPs, or VRT 200–400 slices
nary nodules in lung cancer screening. In an animal osteosarcoma model, the detection rate of pulmonary nodules with MIP technique was twice as high as with MPR technique (Coakley et al. 1998). To avoid a mis- registration of nodules at borderlines of MIP slabs, the slabs should be reconstructed with a 10–20% overlap.
Volume Rendering Technique (VRT) enables a very good pseudorealistic visualization of anatomi- cal structures, but the creation of these images was time-consuming. Certain colors or gray levels are mapped to certain ranges of Hounsfi eld units with the additional option of cutting off unnecessary anatomical details. In clinical routine the number of patients where we use VRT is below 1% of all exami- nations.
23.4 Network Communication and Storage
In clinical networks MDCT scanners produce a very high network load. The large number of slices requires networks with a high bandwidth and archives with high storage capacity, as one MDCT scanner can easily produce several Terabyte of data per year. As mentioned previously, data volumes of 500 Megabyte per examination or more have to be managed. The transmission of such data packages in a 10-Megabit Ethernet would produce an unaccept- able long continuous burst of data over 8 min with 100% network load. Furthermore, such high network loads over several minutes may cause timeouts or a crash of other software applications in the same seg- ment of the network. Hence, it is highly recommended to operate MDCT scanners in “intelligent” networks with appropriate segmentation (min. switched Eth- ernet with 100 Mbit/s) and fast backbones (min.
1 Gigabit/s). The network load of imaging modalities should be kept out of all segments where images are not needed. Even under real conditions in a switched 100-Mbit/s network, we observed transfer rates of approximately 10 CT slices per second which is 50%
of the maximum speed. The transmission time of a 500-image data set to a workstation is in the range of 1 min. As the transmission speed over external net- works (wireless LAN, DSL, ISDN) for teleradiology is approximately two magnitudes slower, only selected and reduced data sets should be transferred.
When CT data sets are acquired with non-isotro- pic voxels (wide collimation and large slice thick- ness), only the raw data sets of these CT examina- tions provide all capabilities of slice reconstructions.
As additional clinical questions may arise 1 or 2 days after the examination, the temporary storage of the raw data is extremely important. The CT raw data are not DICOM-compatible, and hence, storage in PACS archives is not possible. With normal confi guration and patient throughput the internal hard disk of an MDCT holds the raw data for less than 1 day. In fact, we have doubled the raw data storage space in our MDCT.
A solution to reduce the storage volume is image compression. “Lossless” algorithms allow a reduc- tion of storage volumes by a factor of 2–3, whereas
“lossy” algorithms, such as JPEG, JPEG2000, or Wave- let, enable a reduction factor of 10 or more without visible reduction of image quality. Whereas there are many studies about image compression, no clear guidelines about compression algorithm and com- pression level exist for the daily routine.
In a clinical network with a PACS archive, laser imagers, reporting workstations, postprocessing workstations, workstations for clinical conferences, and the ward image viewers, different data sets have to be sent to these destinations. All the very thin slices with small reconstruction increments could be used as temporary data only for further reconstruc- tions and hence need not be archived. Nevertheless, PACS archives are overloaded with all these images as there is no defi nition or recommendation as to which images are for temporary and which are for permanent use. All steps of image reconstruction, postprocessing, storage, and network communica- tion have been done manually in the past and were very time-consuming. Current developments are combined protocols for data acquisition, postpro- cessing, and network distribution, which can be confi gured individually with regard to the workfl ow, the anatomical structures, and the questions of the referring physicians (Loose et al. 2000).
23.5 Workflow
23.5.1 General Developments in Routine Workflow
Historically, in CT we distinguish between inplane (or x–y) resolution and longitudinal (or z-) resolu- tion. In the days of single-slice CT, the z-resolution was determined before the data acquisition by select- ing an appropriate collimation. The x–y resolution could still be changed retrospectively during the
image reconstruction by selecting the desired kernel (Kalender 1995).
Starting with the introduction of multi-slice CT systems in 1998, the workfl ow changed towards rou- tine thin-slice acquisition. This led to a change in paradigm, because the desired slice width could now be selected after the data had been acquired by choos- ing it appropriately in the spiral image reconstruc- tion (Klingenbeck-Regn et al. 1999; Hu et al. 2000;
Ohnesorge et al. 1999; Flohr et al. 2000a, 2000b;
Schaller et al., in press). As an example, the SureView image reconstruction concept allowed reconstruction of a variety of slice thicknesses even after a data set had been acquired at a given collimation. Yet, the x–y resolution and the z-resolution were still treated dif- ferently: one was selected via slice-thickness, the other via the kernel. This is also refl ected in specifi cations, where in-plane resolution is characterized by a reso- lution measured in terms of line pairs per centimeter visible (lp/cm) and longitudinal resolution is given by specifying the full width at half maximum (FWHM) of the slice profi le in the z-direction.
Presently, many users routinely perform multiple reconstructions with several different kernels and slice widths on a single data set. As an example, con- sider a thorax CT scan. The choice of reconstruction settings always is a tradeoff between image noise and resolution in x–y and z. For high-resolution lung images, it is desirable to reconstruct narrow slices using a fairly sharp kernel. These images are com- monly viewed using wide window settings. Therefore, noise is not a major concern. However, when looking at the mediastinum, narrower window settings are used; hence, lower noise levels are desired. This is typically achieved by using larger slice thicknesses and smoother kernels. Furthermore, sometimes one set of thin-slice images is reconstructed for use on a postprocessing workstation, whereas a few thicker images are used for fi lming or archiving.
It has been shown that all of these different results can also be generated from one suitably reconstructed high-resolution set of volume data. If one stack of thin-slice images is reconstructed as an isotropic set of quasi-raw data, then thicker images can be gen- erated by calculating thick MPRs, i.e., by averaging multiple images. Similarly, images corresponding to smoother kernels can be generated by using image fi lters on the high-resolution images; therefore, in the future, we anticipate a workfl ow where one standard reconstruction is performed, resulting in a high- resolution isotropic data set. All other manipulations will be performed on this volume data. All three spa- tial dimensions will be treated the same way.
23.5.2 Workflow Automation
Modern multi-slice CT scanners allow reconstruc- tion of high-resolution reformatted images in the coronal or sagittal or oblique orientation. To this end, users must reconstruct several hundred thin- slice images, load these images into a 3D card, and produce the MPRs there. New software platforms have effi ciently simplifi ed these operations for rou- tine use: images of a certain study type can be auto- loaded into the 3D task after image reconstruction.
There, predefi ned protocols are automatically called upon for further processing of the data; thus, for example, a thorax study can trigger reconstruction of many hundreds of images, these are autoloaded into 3D, and there automatic generation of few thick sagittal slices is performed. These sagittal slices are then archived. Figure 23.1 shows the automation task cards supplied by the Syngo software platform.
To further simplify this routine procedure, a recent software version released on the Siemens Somatom Sensation 16 completely changes the paradigm and incorporates selection of the slice orientation into the acquisition platform. With this new software inter- face, the user directly selects an arbitrarily oriented slice as the primary output of the scan. Intermediate axial reconstruction results are hidden from the user for the sake of simplifi ed usage and to avoid data congestion related to the need to reconstruct many hundreds of slices as an intermediate result.
One of the applications where this is of particular interest is cardiac CT imaging, where frequently short- axis images of the heart are desired. If reconstructions are to be performed in several different heart phases, the old paradigm meant reconstructing several sets of thin-slice images for all desired heart phases, loading each of those sets into a 3D application and perform- ing the reformation, ideally all at the same orientation.
The new paradigm makes the workfl ow a lot more effi cient. One can now select the reconstruction range with arbitrary orientation of the primary images and then start reconstruction of all desired heart phases.
Figure 23.2 shows the user interface.
Fig. 23.2. Scan user interface for selection of arbitrarily oriented primary slices Fig. 23.1. User interface used to defi ne workfl ow automation steps
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