2.2 Theory of SPECT

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Single Photon Emission Computed Tomography (SPECT)

Howard G. Gemmell and Roger T. Staff

2.1 Introduction

Although the principles of single photon emission computed tomography (SPECT) have been well understood for many years and several centers were using SPECT clinically in the late 1960s and early 1970s, there has been a dramatic increase in the number of SPECT installations in recent years. It is now unusual to purchase a gamma camera without SPECT capability and most new cameras are dual-headed, which can offer additional advantages in SPECT. A state-of-the-art gamma camera that is well maintained should produce high-quality SPECT images consistently, and even older cameras can produce acceptable images if care is taken. SPECT is essential for imaging the brain with either cerebral blood ﬂow agents, such as^99mTc-HMPAO, or brain receptors, such as

123I-FP-CIT, and for imaging myocardial perfusion with either²⁰¹Tl or the technetium-labeled agents MIBI and tetrofosmin. SPECT is also now widely used in some aspects of skeletal imaging and can be helpful in tumor imaging with, for example, ¹²³I-MIBG, ¹¹¹In octreotide, or ^99mTc NeoSPECT.

What is the purpose of SPECT and what is its advantage over planar imaging? Planar imaging portrays a three-dimensional (3-D) distribution of radioactivity as a 2-D image with no depth information and structures at different depths are superimposed. The result is a loss of contrast in the plane of interest due to the presence of activity

in overlying and underlying structures, as shown in Figure 2.1. Multiple planar views are an attempt to overcome this problem but SPECT has been developed to tackle the problem directly. SPECT also involves collecting conventional plane views of the patient from different directions but many more views are necessary, typically 64 or 128, although each view usually has fewer counts than would be acceptable in a conventional static image. From these images a set of sections through the patient can then be reconstructed mathemat- ically. Conventionally SPECT images are viewed in three orthogonal planes – transaxial, sagittal, and coronal – as shown in Figure 2.2. Usually the transaxial images are directly obtained from SPECT data; a particular row of pixels in each image obtained with a rotating gamma camera cor- responds to a particular transaxial section. The other planes are derived from a stack of transaxial sections.

2.2 Theory of SPECT

In this section the emphasis will be on gamma camera SPECT. The fact that these systems can also be used for conventional imaging makes them an attractive option for any nuclear medicine de- partment. Owing to the cost and lack of ﬂexibil- ity of dedicated tomographic devices producing single or multiple sections with higher resolution and better sensitivity, they are likely to remain the

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Figure 2.1. A contrast of 4:1 in the object becomes a contrast of 2:1 in planar imaging.

choice of specialist centers only. Now that gamma camera SPECT is well developed commercially and relatively inexpensive, it seems certain that interest in longitudinal or limited-angle SPECT will continue to decline and so this will not be considered further.

The aim here is to consider the theory of SPECT only in sufﬁcient detail to enable the user to

Figure 2.2. Orientation in SPECT. The three orthogonal planes conventionally used are demonstrated using^99mTc-HMPAO cerebral blood ﬂow images.

Table 2.1. Operator choices in SPECT

Acquisition Size of image matrix Number of angular increments 180^◦or 360^◦rotation Collimator Acquisition time Uniformity correction Center of rotation correction Scatter correction Reconstruction Method

Filter

Attenuation correction or not Attenuation correction method

understand the principles involved and to make any necessary decisions on an informed basis. A list of likely areas for decisions by the user is given in Table 2.1.

2.2.1 Projection, Back-projection

In order to obtain transaxial sections of the distribution of radioactivity within a patient, projections of that distribution must ﬁrst be

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b a

Figure 2.3. a Proﬁles relating to a single transaxial section from a distribution of radioactivity comprising two point sources.

b Back-projection of these proﬁles builds up an image of the point sources by superimposition but at the expense of a high background.

collected at a series of positions around the patient. In Figure 2.3a, 1-D projections or proﬁles of a distribution of radioactivity comprising two point sources are shown for three positions of the detector. These correspond to a single transaxial section and are obtained from the same single row of pixels on each of the gamma camera images.

Each element in each profile therefore represents the sum of the activity along a line perpendicular to the profile, that is perpendicular to the camera face. This profile element is often referred to as the line integral, being the integral along that line of the 2-D function which describes the variation of radioactivity with position.

The simplest and most common method of re- constructing an image of the original distribution is by “back-projecting” each profile at the appropriate angle on to an image array in the computer as shown in Figure 2.3b. In other words, a constant value equal to the profile element is assumed for each point along that line in the image array. This procedure is carried out for each profile in turn and an image is built up numerically. This image is, however, of poor quality; regions of higher activity show up well but the back-projected image is blurred and has a structured background. This background includes the “spoke”

or “star” artifact whose shape and magnitude will depend on the number of projections. These problems are fundamental to the projection/back- projection procedure but can, to some extent, be dealt with by ﬁltering the proﬁles prior to back- projection.

In practice back-projection is not carried out over 360^◦ as shown in Figure 2.3b. Instead, although data are generally acquired over 360^◦, it is usual to average opposite projections and then back-project only over 180^◦. The advantage of this is that it partially compensates both for the drop in spatial resolution with distance from the camera face and for attenuation. Projections can, under certain circumstances, be collected over only 180^◦ rather than the full 360^◦; when this is done the data are back-projected without prior averaging.

2.2.2 Filters

The problems of blurring and the high background in the reconstructed image are tackled by back-projecting negative numbers adjacent to the positive numbers which represent the object in the original profile. This is achieved by operat- ing on the profile with an appropriate filter as demonstrated in Figure 2.4. After a sufficiently large number of profiles have been back-projected, these negative numbers tend to cancel out the positive background in the image.

Using the mathematical technique known as Fourier analysis it is possible to describe images in terms of their spatial frequencies; these have units of 1/distance, that is cm⁻¹. This is known as work- ing in “frequency or Fourier space” as opposed to

“real” space. In the image of a distribution of radioactivity, for example, ﬁne detail is associated with the higher spatial frequencies and coarser structures with the lower spatial frequencies.

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b a

−

Figure 2.4. a The ﬁlter acts on each point in the proﬁle in turn.

The values obtained are added to obtain the filtered profile, which includes negative as well as positive points. b When the negative values associated with the positive values in each profile are back- projected they tend to cancel out the positive background shown in Figure 2.3b.

Using a modern gamma camera, the upper limit for spatial frequencies recorded in planar images without scatter is about 2 cm⁻¹. How the ﬁlters are designed and selected is best appreciated in frequency space, hence its introduction at this point, but it is not necessary to be familiar with the mathematics involved in Fourier analysis. For those wishing to consider this topic in more detail, the text by Brigham [1] offers an ex- cellent introduction to the concepts involved. In Figure 2.5 three important situations are described in both real and frequency space. In an idealized line spread function (LSF) or delta function, all spatial frequencies are present with equal amplitude (upper images), but in a more realistic LSF there is a loss of signal at the higher spatial frequencies (middle images). Also shown (lower images) is a ﬁlter similar to many used in SPECT.

Any process that results in a loss of signal at the higher spatial frequencies is said to smooth the signal. A filter that has this effect is described as a smoothing filter, and so back-projection itself can be said to act as a smoothing filter, as does the filter shown in Figure 2.5. The projection/back- projection procedure filters the image by a fac- tor 1/f where f is the spatial frequency. Hence the higher the spatial frequency, the more the signal is suppressed and so a smoother image is produced.

This effect is overcome by using a ramp filter in which each spatial frequency is amplified in proportion to the value of that frequency up to a maximum frequency, f_max, which is determined by data sampling. This filter will, of course, en- hance the higher spatial frequencies, the aim being to restore higher spatial frequencies lost by the back-projection process. There are two problems with using the unmodified ramp filter. The first is that with real data, especially at the count densities encountered in clinical imaging, the higher spatial frequencies will be mainly noise rather than fine spatial detail. This noise is enhanced by the ramp filter producing a poor quality or “noisy”

image. Secondly, the sharp cut-off at fmax produces “ringing” in the filter in real space; instead of the filter going negative and then tending to zero (Figures 2.4, 2.5) it will oscillate about the axis, going successively negative and positive. This reflects the difficulty of computing a sharp cut- off in frequency space with a finite, preferably fairly small, number of terms. Ringing will produce structured distortion of the reconstructed image.

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a b

−

Figure 2.5. An idealized line spread function (top), a realistic line spread function (middle) and a ﬁlter typical of those used in SPECT (bottom) are shown in a “real” and b “frequency” space.

It is usual, therefore, to use a filter that is ramp- shaped at the lower spatial frequencies but rolls off at the higher spatial frequencies. This filter is produced by multiplying the ramp by a second filter, which causes the rolling off at higher spatial frequencies thus suppressing the noise. This technique is known as windowing. There are several possible options for this second filter and all manufacturers include a choice of filter in their SPECT

software. Popular filters include the Butterworth, Hamming, and Shepp-Logan filters. Within each filter a number of options are available to control the amount and type of smoothness that the filter can apply. All SPECT filters require a cut-off value.

The high-frequency content of an unﬁltered back- projected SPECT image is almost exclusively noise and adds nothing to the quality of the image. The cut-off value is the spatial frequency above which

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Figure 2.6. Normal ¹²³I-FP-CIT SPECT images reconstructed using a Butterworth ﬁlter with cut-off values ranging from 0.2 to 0.7 cycles/cm.

all of the spatial content is removed. Figure 2.6 shows the effect on a normal¹²³I-FP-CIT SPECT image of using different cut off values in a But- terworth ﬁlter. Filters like the Butterworth and Hanning have additional parameters that change the shape of the ﬁlter, promoting and suppressing different frequencies within the image. Note that different computing systems express cut-off frequencies in different ways, cycle per cm, cycles per pixel or fractions of the Nyquist frequency, so it is important to be aware that a cut-off of 0.4 on one system may not give the same image on a different system.

The various filters each have their advocates, but it is difficult to predict simply by imaging resolution phantoms which filter will produce the

“best” clinical images. In most centers the type of ﬁlter and the degree of smoothing incorporated in it will be decided empirically from the clinical images they produce. Usually an optimum ﬁlter can be agreed on amongst the users con- cerned and this will only be altered when the count rates vary either for a particular patient or for

a different study. At one time the Hamming filter was popular despite its fairly high degree of smoothing because the major problem in SPECT was felt to be lack of counts. With the widespread use of multiple-headed gamma cameras this is now less of a problem and a sharper Butterworth filter is the more usual choice. An exception to this is quality assurance (see Section 5.3.4) when the ramp filter itself is used to optimize resolution. This is because higher count rates and longer collection times are possible when imaging test phantoms.

An alternative to introducing smoothing when filtering the profiles is to smooth the original data before carrying out the reconstruction process and then use a less smooth (or sharper) filter on the profiles prior to back-projection. This is known as pre-filtering or pre-smoothing. Several different methods have been proposed but none has really become established clinically. It is now usual is to carry out 2-D filtering of the data after back- projection rather than the 1-D filtering process described in Figure 2.4.

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2.2.3 Data Sampling

In any consideration of data sampling both translational and angular sampling must be taken into account. The translational data sampling frequency is determined by the size of the pixels in the image array; for example, if the pixel side length is 5 mm then there are two samples per centimeter and so the sampling frequency is 2 cm⁻¹. Sam- pling theory states that, if “aliasing” artifacts are to be avoided, only data containing frequencies of less than half this sampling frequency should be transmitted through the system. This upper limit is known as the Nyquist frequency, f_N. The cut- off frequency, fmax, of the ﬁlters described above is often made equal to f_N, thereby setting any fre- quencies greater than fN in the input signal to zero and so ensuring that “aliasing” is avoided.

Although a modern gamma camera can record spatial frequencies up to 2 cm⁻¹, in the presence of scattered radiation the upper limit is often less than 1 cm⁻¹. If we set fNto 1 cm⁻¹the sampling frequency should be at least 2 cm⁻¹ and so the image pixels should have a side length of 5 mm.

For a 40 cm ﬁeld-of-view gamma camera this implies that a 128× 128 matrix should be used for data acquisition rather than a 64× 64 matrix because in the latter case the pixel size is greater than 6 mm. The use of a 128× 128 matrix instead of a 64× 64 matrix for acquisition will, of course, cause the counts per pixel to drop by a factor of 4, thereby increasing noise. There will also be an increase in computing time, although nowadays this should not be a problem.

There is no point in the angular sampling being ﬁner than the translational sampling discussed above, since the characteristics of the overall system, in particular spatial resolution, will be determined by the poorest component. If the number of translational sampling points is N and the num- ber of projections used for reconstruction is M, then

M= π

2 · N. (2.1)

This implies that, if data acquisition is on a 64× 64 matrix, then about 100 projections should be used to reconstruct the data. As was mentioned above, the number of projections used in reconstruction will usually be only half the number acquired since opposite views are averaged. Equation 2.1 therefore implies that, when collecting over 360^◦, 200 angular increments are required to match a

64× 64 acquisition. Since there are still situations when only 64 views are acquired it must be asked whether this represents adequate sampling. It has been shown that the magnitude of the residual background artifact becomes acceptable at around 64 views. Since 64 views are only just adequate, 128 views are probably indicated but, as ever, the ques- tion is whether any improvement in image quality would be maintained at clinical count rates.

An alternative approach is to use the spatial resolution in the final image to determine what con- stitutes adequate sampling. The sampling interval should be one-third of the resolution in the final reconstruction image where the resolution is de- fined as the full width at half maximum (FWHM) of the line spread function (see Section 5.2.1). A translational sampling interval of 6 mm for a 64× 64 acquisition therefore implies a resolution of 18 mm. This is rather coarse, even when the effects of scattered radiation at distances comparable to the typical radius of rotation are taken into account. This again indicates that finer sampling is required. To apply a similar argument to angular sampling it is necessary to know the distance D of the camera face from the axis of rotation. The arc length for each angular sample isπ · D/M, and this should be around 6 mm to match the trans- lational sampling. Since D cannot be much less than 20 cm, this implies that at least 100 projections should be collected, suggesting once more that 64 views are not sufficient.

The preceding discussion has shown that 128 views on a 128× 128 matrix are required to optimize image quality. With the current generation of computers there will be no difﬁculty in handling the increased amount of data but the problem of lack of counts per pixel remains. The time spent collecting data, up to 30 min, is as long as pa- tients can reasonably be expected to remain still, so increasing collecting times to improve counting statistics is not an option. However, most depart- ments are now opting to purchase dual-headed gamma cameras and these can be used to double the counts although sometimes the priority will be to increase throughput. With these devices ac- quiring 128 views on a 128× 128 matrix becomes a realistic option. Each clinical situation should be taken on its merits and many centers, particularly those with single-headed gamma cameras, will continue to acquire 64 views on a 64 × 64 matrix. However, 128 views on a 128 × 128 matrix are required for SPECT quality control (see Section 5.3.4) when lack of counts is not a

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problem. In practice 64 × 64 × 64 is generally used for cardiac SPECT, while 128× 128 × 128 is used for brain SPECT, reﬂecting the lower resolution images required in cardiac SPECT.

2.2.4 Attenuation Correction

The preceding discussion has ignored the effect of the attenuation of photons by overlying tissue.

If no correction for attenuation is made, super- ﬁcial structures are emphasized at the expense of deeper structures and there is a general decrease in count density from the edge to the center of the image. There are, however, some situations when it is quite reasonable to use uncorrected images. In the case of myocardial perfusion imaging there is an ongoing debate as to the necessity for attenuation correction, with some centers arguing that it can introduce artifacts so the disadvantages outweigh any beneﬁts. However, in the case of brain SPECT, most would accept that attenuation correction is necessary.

Quantitation of the uptake of radioactivity in an organ raises different problems. SPECT clearly has the potential to provide true quantitation, in units of MBq g⁻¹, and attenuation correction is a necessary prerequisite for this. Considerable effort has gone into developing algorithms for attenuation correction but their value in clinical practice is, as yet, unproven. In practice, most users will restrict themselves to whatever attenuation correction software is supplied, but it is important that the underlying assumptions and limitations are appreciated, especially if some form of quantitation is to be attempted. Also since no attenuation correction method has yet established itself as the method of choice, and many methods require signiﬁcant additional operator time to draw body outlines, the option of no correction should be seriously considered where SPECT is to be used simply for imaging.

The most easily implemented method of attenuation correction is pre-processing. In this method the attenuation correction is incorporated into the back-projection by multiplying each element in each proﬁle by a factor that will correct it for attenuation. For each element this factor will de- pend on the thickness L at the appropriate angle of the object being imaged (Figure 2.7). For clinical imaging, this means that the patient’s shape must be known. The shape is generally assumed to be el- liptical and so the size of the major and minor axes of the appropriate ellipse must be speciﬁed prior

Figure 2.7. In the pre-processing attenuation correction method the correction factor applied to each element in the proﬁle collected at angle_θwill depend on the patient thickness L at the appropriate angle.

to reconstruction. A further assumption is that the radioactivity is uniformly distributed along L and that the attenuation is constant, in other words that a large extended source is being imaged. Fi- nally, the factor will depend on whether opposite projections are averaged with an arithmetic or ge- ometric mean. In the former case, which is more common, the correction factor is

µL/[1 − exp(−µL)] (2.2)

whereµ is the linear attenuation coefﬁcient. The value ofµ used must take scatter into account, and is usually determined empirically using a known distribution of radioactivity. For^99mTc a value of around 0.12 cm⁻¹is often used. Equation 2.2 also assumes that the ellipse is centered on the axis of rotation and so, if this is not the case, the original data must be adjusted accordingly; this adjustment will usually be included in the attenuation correction software. Not surprisingly, given the underlying assumptions, the pre-processing method is rather ineffective when applied to distributions comprising small discrete sources of radioactivity, but gives satisfactory results for large sources, in- cluding those found in many clinical situations.

This method requires less computing power than any of the alternatives.

Another popular method of attenuation correction is that of Chang [2]. This is a post-processing method in which the transverse section is ﬁrst

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reconstructed by ﬁltered back-projection and then corrected pixel by pixel using a correction matrix.

This correction matrix is obtained by calculating the attenuation of a point source at each point in the matrix and again requires knowledge of the body outline and the attenuation coefficient. It also assumes narrow beam conditions. Usually a uniform attenuation coefficient is assumed butµ can vary with position within the body. This procedure can be regarded as a first-order correction and is often referred to as “first-order Chang”. Being based on the point source response, this method is better than the pre-processing method for small sources, but is less effective for larger sources, which will be over-corrected or under-corrected depending on their position within the image. To compensate for this, a second stage can be included. In this the first-order correction image is re-projected to form a new set of profiles. These are subtracted from the original profiles to form a set of error profiles. Filtered back-projection of this error set produces an error image that must be attenuation corrected as in the first-order Chang.

The corrected error image is then added to the ﬁrst-order image to obtain the ﬁnal image. This second stage is similar to the step repeated in many of the iterative methods of attenuation correction but in the Chang method the single itera- tion converges so quickly that further iterations are unnecessary. A useful approach in situations where the assumption that µ is uniform cannot be made, for example in cardiac imaging, is to use information about the distribution ofµ. This can be obtained from either CT or transmission images. The latter method, often using gadolin- ium (¹⁵³Gd) line sources to produce the transmission image, is available on several multiple-headed gamma cameras and one manufacturer supplies a dual-headed gamma camera with a CT mounted on the same gantry. This latter option is more expensive but has the additional advantage that the CT images can be fused with the SPECT images for reporting.

2.2.5 Iterative Reconstruction

Although ﬁltered back-projection (FBP) remains the reconstruction method most used in routine clinical SPECT, as computing power has increased other options have become available and should be considered. Iterative reconstruction methods, not based on ﬁltered back-projection, such as MLEM (maximum likelihood, expectation maxi-

mization) and particularly its accelerated version OSEM (ordered subsets, expectation maximiza- tion), offer potential advantages over FBP because they can model the emission process [3]. The iter- ation is similar to the process described above for the Chang attenuation correction (Section 2.2.4) and it is possible to include attenuation and other corrections in the iterative process. Iterative reconstruction tends to handle noise better than FBP and will reduce the streak artifacts caused by high count areas. OSEM is now available on most nuclear medicine computers and so is easy to implement, although the user will notice that, unlike FBP, iterative reconstruction does take a little time, typically a minute or two with modern systems. The value of OSEM is probably fairly task dependent, that is better suited to some tasks than to others, and there is no general agree- ment as to the parameters used, for example the number of iterations or the number of subsets.

For these reasons OSEM is still mainly the choice of the specialist rather than the routine SPECT user.

2.2.6 Scatter

At 140 keV most photons will have been scattered rather than absorbed so a true scatter correction would go a long way towards solving the attenuation problem. A number of different approaches have been proposed and tested in recent years [4].

These include dual/multi energy window methods and deconvolution techniques. As yet, there is no agreed method even for planar imaging (see Sec- tion 1.3.8). A fundamental problem is that each method is again rather task dependent. However, most vendors now offer a scatter correction technique and these are worth investigating. Scatter correction is probably essential if attenuation correction is planned.

2.2.7 Noise

Noise is, of course, a fundamental problem in nuclear medicine, but it presents a much greater problem with SPECT than in planar imaging. The main reason is that the reconstruction process will itself amplify the noise, but it should also be borne in mind that, even with a 30 min total collection, the counts per pixel in each projection will be lower than in a conventional static image and so the per- centage uncertainty per pixel will be greater. It has

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been shown that the noise ampliﬁcation factor in tomography can be approximated by

%rms uncertainty per pixel= N⁻¹²· n¹⁴ (2.3) where N is the counts per pixel and n is the num- ber of pixels in each projection [5]. The ﬁrst factor is familiar to anyone who has considered Poisson noise in conventional images, but the second factor shows that the noise increases with the number of pixels. It is clear that the effect of changing data collection from 64 views at 64× 64 to 128 views at 128× 128 will be marked. N will drop by a factor of 8 and n will increase by a factor of 4, so the uncertainty will increase by a factor of 4.

When noise is a problem, it can be worth com- promising between resolution and sensitivity by choice of collimator as described in the next section. It should also be remembered that the choice of filter will have a significant effect on noise and that in SPECT generally it is always difficult to sep- arate out the many factors affecting the quality of the final image.

2.2.8 Collimation

As in planar imaging it is necessary to strike a bal- ance between resolution and sensitivity with the choice of collimator. Because of the improved contrast in SPECT images it is possible to use a high- resolution collimator without loss in image quality more often than one might expect [6]. An option that increases both sensitivity and resolution is to use a fan-beam collimator (see Section 1.3.3), in which the holes are parallel in the direction of the axis of rotation but converging in the other direction, thereby using the crystal more efﬁciently [7]. Because of the change in geometry, however, different software is required to reconstruct data collected with these collimators.

2.3 Instrumentation

2.3.1 Rotating Gamma Cameras

Single-headed rotating gamma camera systems have been in use for routine SPECT throughout the world for many years but are increasingly being replaced by dual-headed systems. All gamma camera manufacturers now offer SPECT systems as part of their model range. Although all these systems do basically the same thing, namely move

the camera head through 360^◦ around the patient, there are variations in system design. These variations mainly reﬂect differences in conventional camera design, in particular how the head is moved and supported. Most SPECT systems were originally modiﬁcations of the manufacturer’s ba- sic models but current models have usually been designed with SPECT in mind.

Dual-headed and even triple-headed rotating camera systems are now commercially available, their attraction being the increase in sensitivity giving the option of improved image quality and/or shorter imaging times. Dual-headed systems have become particularly popular in recent years as they present a cost-effective alternative to a single-headed systems. This is because, although around 50% more expensive in capital costs, they occupy no more space and require no more staff.

They, therefore, have the potential to increase patient throughput and/or image quality at little additional revenue cost. Most systems are now “variable angle” in that the heads can be positioned at various angles relative to each other (Figure 2.8), for example positioning the heads at 90^◦to each other so the beneﬁts of two heads can still be applied to 180^◦acquisition is popular in myocardial perfusion imaging.

A number of modifications to the standard rotating gamma camera have resulted from the recent emphasis on cerebral SPECT imaging. Ideally the radius of rotation should be as small as possible and values of around 15 cm can be achieved when imaging the brain. One way of reducing this further is to remove the safety pad from the camera face, although a risk assessment should be carried out before doing this; it may be an option for some patient groups but not for others. In practice the limiting factor is often the patient’s shoulders, since it is difficult to avoid these while keeping the patient’s head in the field of view, and the radius of rotation can be as large as 25 cm. Since the body outline is closer to an ellipse than a cir- cle, a camera rotating on a circular orbit will be further from the patient than necessary most of the time. Many rotating cameras have the option of a non-circular orbit or of following the patient contour; depending on the camera design, an eas- ier option is often to retain the circular motion of the camera but move the couch during the study to minimize the patient–camera distance. Other systems combine head and couch movement during acquisition. Modifications of this type are probably more relevant to body rather than

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a

b

Figure 2.8. Dual-headed gamma cameras with the heads at a 180^◦for brain imaging and b 90^◦for myocardial perfusion imaging.

head imaging, but have been shown to improve resolution.

2.3.2 Single- or Multiple-section Devices

These devices produce either single or multiple transverse sections using one or more rings of focused detectors respectively. In these systems the detectors are usually moved laterally and then

rotated to produce the proﬁles used for back- projection. The advantage of these devices over rotating gamma cameras is improved spatial resolution and increased sensitivity per section. This permits high-quality sections to be obtained in a few minutes, and so dynamic SPECT studies are possible. Most clinical SPECT is, however, carried out on stable distributions of radioactivity, and so this potential advantage has yet to be realized. A weakness of these systems is that the section or sections being imaged have to be selected either from

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anatomical landmarks or from a rectilinear scan;

this can be a problem and often wastes time. Given that the spatial resolution of the latest SPECT cameras is only slightly poorer than the much more expensive single-section devices, these systems seem likely to be restricted to a few specialist centers.

2.4 Quality Issues

The quality control requirements for gamma cameras to be used for SPECT are much more stringent than for planar use. SPECT quality control will be considered in Chapter 5; it is not intended to re- peat the detailed advice given there, but rather to consider how image quality can be optimized.

2.4.1 Data Correction

Advances in camera design, in particular linear- ity and energy corrections and the use ofµ-metal shields to screen the photomultiplier tubes from magnetic ﬁelds, have improved gamma camera uniformity considerably. As a result the emphasis has changed from data correction to improve SPECT image quality to quality assurance (QA) to maintain SPECT image quality. Despite these improvements, circular artifacts (holes and rings) caused by camera non-uniformity may still be seen on transaxial sections through a uniformity phantom (see Section 5.3.5), although their amplitude should be less than with older cameras. When a correction for a displacement of the center of rotation is available it should always be implemented;

the camera should, of course, be set up to minimize any such displacement (see Section 5.3.3).

The routine QA on the gamma camera must include the regular acquisition and assessment of a total performance phantom such as the Jaszczak phantom.

2.4.2 The Role of the Operator

The emphasis on the technical aspects of SPECT image quality can obscure the important role of the operator. Good technique in setting up the patient and the camera head is vital. In particular, the radius of rotation, that is the distance between the face of the camera and the patient, must be kept to a minimum. As in planar imaging the closer the camera is to the patient the better the spatial resolution will be. Figure 2.9 shows how the

Figure 2.9. The effect of varying the radius of rotation on transaxial images of an FP-CIT brain phantom. On the left the radius of rotation is 135 mm while on the right it is 185 mm.

image quality deteriorates as the radius of rotation increases.

After each SPECT acquisition the operator should check for patient movement. The simplest way to do this is by inspecting a cine loop of the acquired projections. A popular alternative is to display the “sinogram” as the ﬁrst stage of the reconstruction process. This is a single composite image which is generated by stacking the proﬁles from the same row, often the middle row, from each of the acquired projection images.

This should demonstrate the smooth progress of the camera head around the patient: any dis- continuity indicates patient movement. If patient movement has taken place the original projection images can be shifted laterally to reduce its effect.

Users should ensure that their nuclear medicine software includes a user-friendly image shifting routine.

2.4.3 The Couch

One aspect of rotating gamma camera systems that receives less attention than it should is the effect of couch and head-rest design on image quality. The convention for transaxial brain imaging in all modalities is to image parallel to the orbito-meatal line; this is impossible without an adjustable head-rest. The data can be realigned post-reconstruction to compensate but substan- tial realignment will cause additional smoothing of the data. Another problem with couches is their tendency to vibrate because support is at one end only; for cerebral imaging, some centers use an additional couch support. Finally, although a narrow couch is essential for SPECT, patient comfort is also important and it is not obvious that the best compromise has been achieved – an

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uncomfortable patient is less likely to stay still for 30 minutes. Patient comfort can be increased by using some form of support below the knees.

2.4.4 Limited Angle Acquisition

In cardiac SPECT projections are often only collected over 180^◦. There is good justiﬁcation for this when imaging the myocardium with ²⁰¹Tl. This is because the stress images should be obtained within 30 min of exercise because of redistribu- tion, and so the time is best spent with the camera near the heart, data usually being collected from

−60^◦to+120^◦relative to the vertical. This argument is particularly strong for²⁰¹Tl, whose low- energy gamma rays are readily attenuated as they pass through tissue. This is not a signiﬁcant effect when the detector is near the heart but most of the gamma rays recorded in images collected over the other 180^◦ will have been scattered and so these data would detract from the quality of the reconstructed data. However, 180^◦acquisition is also preferred for cardiac SPECT with ^99mTc agents, even though the energy is higher, and there is some experimental evidence to support this approach [8]. In SPECT contrast is improved with 180^◦ acquisition but there is some distortion of the reconstructed data; it is a matter of judgment as to the best compromise. An extension of this approach for centers with dual-headed, variable geometry gamma cameras is to carry out a 90^◦acquisition with the detector heads at 90^◦degrees to each other, thereby giving 180^◦acquisition. Sim- ilarly some workers have successfully used 180^◦ acquisition when carrying out SPECT of the lum- bar spine.

2.5 Conclusion

Having considered the implications of the operator choices listed in Table 2.1, new users should now be able to make the required decisions on an informed basis. Although the requirements of SPECT, in both a technical and procedural sense, are more demanding than for planar nuclear medicine, they are not as formidable as they might seem, especially for those with modern equip- ment.

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