Characterization of a Time-of-Flight PET Scanner based on Lanthanum Bromide

Transcription

1 2005 IEEE Nuclear Science Symposium Conference Record M04-8 Characterization of a Time-of-Flight PET Scanner based on Lanthanum Bromide J. S. Karp, Senior Member, IEEE, A. Kuhn, Member, IEEE, A. E. Perkins, Member, IEEE, S. Surti, Member, IEEE, M. E. Werner, M. E. Daube-Witherspoon, Senior Member, IEEE, L. Popescu, Member, IEEE, S. Vandenberghe, G. Muehllehner, Fellow, IEEE Abstract A proto-type time-of-flight (TOF) 3D PET scanner based on lanthanum bromide detectors has been developed. The LaBr 3 (5%Ce) Anger-logic detectors in this new scanner use 4x4x30 mm 3 pixels and continuous light-guide coupled to a hexagonal array of 50-mm PMTs. The scanner consists of 24 modules with a 93-cm detector diameter and 25-cm axial fieldof-view. Initial characterization of scanner performance has been performed, including energy and timing performance. We currently measure an overall system energy resolution of 7.5% and a system timing resolution is 460 ps, although we expect these results to improve eventually when the electronics are fully optimized. Since there are not yet standard tests to quantify the benefit of TOF, we designed two phantoms with hot and cold spheres in 27-cm and 35-cm diameter vessels to evaluate the TOF performance as a function of body size. The data from this scanner are reconstructed with a fully 3D listmode iterative TOF algorithm with all data corrections incorporated into the system model. We find that TOF reconstruction reduces the noise and background variability, especially for the larger phantom representing a large patient. In addition, TOF improves detail and contrast of the spheres (lesions), especially the smallest 10-mm sphere. The TOF reconstruction reaches convergence faster than the non-tof reconstruction, and the rate of convergence is seen to be more insensitive to object size. These results indicate that TOF will help improve image quality and potentially reduce scan time with clinical patients. I. INTRODUCTION In this paper we describe the development of a time-offlight (TOF) scanner based on lanthanum bromide. The fundamental improvement with TOF has been shown to result from a variance reduction in the reconstruction of the data by incorporating the TOF information in the forward- and backprojection steps. The variance reduction translates to an effective sensitivity gain which has in the past been described as a ratio of D/Δx [1], where D is the object diameter and Δx = c Δt/2. It is intuitive that the TOF gain is related to this Manuscript received November 11, This work was supported in part by the National Institutes of Health under Grant No. R33-EB001684, and sponsored research agreements with Saint-Gobain and Philips Medical Systems. J. S. Karp is with the Department of Radiology, University of Pennsylvania, Philadelphia, PA USA (tel: , joelkarp@mail.med.upenn.edu). A. Kuhn, S. Surti, M. Werner, M. E. Daube-Witherspoon, L. Popescu, and G. Muehllehner, are with the Department of Radiology, University of Pennsylvania, Philadelphia, PA USA. A. E. Perkins is with Philips Research, Briarcliff Manor, NY USA. S. Vandenberghe was with Philips Research USA and is now with the Medisip/Elis University Ghent, Belgium. ratio, but not necessarily equal to it. In fact, it is too simplistic to characterize the TOF gain as a single value, since it will certainly be task dependent and must also depend on the method of data correction and image reconstruction. For example, Tomitani et al [2] argued that once reconstruction effects are included, the variance reduction (or sensitivity gain) should be D/1.6Δx, thus reducing the impact of TOF compared to the gain proposed in the paper of Budinger et al [1]. We have recently performed a lesion detectability study based on non-pre-whitening match filter analysis and simulated data with 10-mm lesions [3], and find the gain in (SNR-NPW) 2 is comparable to the reduction in variance predicted by [2]. For example, for a 27-cm diameter phantom the improvement is approximately 3.1, 1.9, and 1.4 for assumptions of 300 ps, 600 ps, and 1000 ps (fwhm) timing resolution. Using the formula in [2] we calculate gains of 3.8, 1.9, and 1.1 for the same assumptions, whereas using the formula in [1] we calculate gains of 6.0, 3.0, and 1.8. These differences suggest to us that we must proceed carefully in trying to quantify the TOF gain with measured data, but certainly it will be important to strive for the best possible timing resolution in order to extract the maximum benefit from TOF reconstruction. We have developed the lanthanum bromide PET scanner since we have achieved very good timing resolution and energy resolution with pixelated LaBr 3 detectors. LaBr 3 with 0.5% cerium concentration was first reported by the research group at Delft University, van Loef et al [4] and this work was followed by the group at Radiation Monitoring Devices, Shah et al [5] who found that the rise time, thus timing resolution improves as the cerium concentration increases. In [5] the timing resolution of LaBr 3 vs. BaF 2 is reported to 390 ps fwhm for 0.5% cerium, 260 ps for 5% cerium, and 170 ps for 30% cerium. The light output remains fairly constant, close to 60,000 photons per MeV, and the energy resolution of these small samples is better than 4% fwhm for 511 kev. Compared to BaF 2 the light output is 30x higher, thus with LaBr 3 we can overcome a major limitation of the TOF scanners in the 1980 s that had poor spatial resolution. We have developed arrays of LaBr 3 with 5% cerium concentration by working closely with Saint-Gobain in order to demonstrate that the excellent performance of this scintillator could be translated into a PET detector, which requires good sensitivity and spatial resolution, in addition to energy and timing resolution. In previous work, Kuhn et al [6] is has been shown that an Anger-logic LaBr 3 (5%Ce) /05/$ IEEE 1919

2 detector with 4x4x30 mm 3 pixels can achieve an average timing resolution of 313 ps fwhm and an average energy resolution of 5.1% fwhm at 511 kev. The crystal array is coupled to a continuous light-guide and hexagonal arrangement of 50-mm diameter PMTs. We chose Photonis XP20D0 PMTs for their combination of timing performance and reasonable cost. The choice of 50-mm PMTs is also one of practicality, since larger PMTs lead to a fewer number for the whole system. Since the light output of LaBr 3 is very high the crystal identification is excellent with large PMTs, and since the decay time is short the probability of pulse pile-up is relatively low. Large detectors were constructed with crystal arrays 27 pixels wide by 60 crystals high. A complete scanner consists of 24 detector modules with 432 PMTs; 6 PMT rows in the axial direction and 72 PMT columns around. Each detector module is coupled to four PMT columns with the two edge columns shared with adjacent detector modules on either side in order to identify pixels at the very edge of the module. The performance of the large detector, in terms if energy and timing resolution is nearly identical to the small proto-type arrays mentioned above and described in more detail in [7]. the rotating line source, each detector pixel in the PET scanner is in coincidence with a reasonably large number of different pixels on the opposite side (see figure 2). The assumption of independence greatly reduces the number of counts that we need to collect for a good estimate. We have considered other source configurations, including fixed sources permanently outside the transverse FOV, but have not yet tested these configurations. Given the location of the source, the true TOF difference between the arrival times of each gamma is computed and compared to the measured TOF difference to generate the TOF difference error. The peak (relative bias) of each pixel s TOF difference error histogram is located by curve fitting. The location of the peak is subtracted from the a priori bias estimate (zero without prior information) and the process is iterated until the crystal timing histograms are all centered about zero. The output is a timing offset correction (final bias) for each crystal that is used to correct the measured timing differences between crystals as they are recorded. This correction is written as a look-up table that can be used either as real-time or off-line timing correction for the list-mode data before reconstruction. Fig. 1. LaBr 3 detector module with 1620 pixels each 4x4x30 mm 3 coupled through light-guide to hexagonal arrangement of 50-mm diameter PMTs. Each detector module is coupled to four PMT columns with the two edge columns shared with adjacent detector modules. With these detectors we have designed a PET scanner with a detector diameter of 93 cm and axial FOV of 25 cm. The predicted sensitivity for this fully 3D system is about 6 cps/kbq, which is very competitive with current PET scanners from GE, Siemens, and Philips Medical Systems using denser scintillators (BGO, LSO, or GSO), but generally these scanners have shorter crystals and shorter axial FOV. Our system is also expected to have a high noise-equivalent countrate due to the fast timing which limits random coincidences, and the energy resolution which reduces scatter and randoms. II. TIMING CALIBRATION METHOD In the calibration technique, the data are written in listmode format with the source location known or encoded in the data for the case of a rotating source. For these initial measurements we used a rotating 18-F filled line source at a 20-cm radius. We assume that the absolute time biases for each pixel on the detector are independent, and so the TOFdifference bias for a given pixel pair is simply the difference in the absolute time biases of each pixel. Therefore, it is not a requirement that each pixel on the detector be measured in coincidence with each other pixel on the detector. By using Fig. 2. Each crystal is in coincidence with a large number of opposing crystals in the transverse direction as the line source rotates (left) which aids in averaging out any timing biases from different trigger regions or detectors. Each crystal is also in coincidence with opposing crystals along the entire axial FOV (right). A 2-D map of this timing offset estimate table for four detectors is shown in Fig. 3. The intensity of the map represents the timing offset for each pixel. The PMT gains have been adjusted to minimize energy variations and optimize the energy resolution, but have not been fully optimized. Only small fluctuations are observed from crystal to crystal and the larger variation arises from the spatiallydependent light collection that is a function of the PMT array pattern. The regions between the PMTs need the largest off-set correction. The entire range (minimum to maximum) of offset factors for the entire scanner is about +/- 1ns. The timing offset calibration was applied to a list-mode collection of a Ge-68 point source positioned at the center of the scanner. The timing resolution of the summed histogram over all crystal pairs is 460ps fwhm. The difference between this measured FWHM value and 313 ps previously reported in [6] are due to losses in the electronics that have not yet been fully optimized in the TOF scanner. 1920

3 Fig D map of the timing offset for 4 detectors. The intensity of the map represents the timing offset for each pixel. Only small fluctuations are observed from crystal to crystal and the larger variation arises from the spatially-dependent light collection that is due to the PMT array pattern. III. SCATTER FRACTION The energy calibration method is similar to that previously developed for non-tof scanners. Using a point source in the center of the FOV, we measure the energy off-sets for each pixel and generate correction table that is applied on-line before writing the list-data to disk. With correction, the energy spectrum for 511 kev is shown in Fig. 4 and demonstrates an overall energy resolution of 7.5% fwhm. Again, the differences between this result and the bench-top measurements with these detectors reported in [6] are due to losses in the electronics that have been adapted for the TOF scanner. The cause of these losses is under investigation. Fig. 5. Scatter fraction vs. lower level energy threshold for the NEMA NU- 2 scatter phantom, 20-cm x 70-cm, as well as two similar phantoms but with 27-cm and 35-cm diameter. The scatter fraction was calculated using the prescribed NEMA NU-2 analysis. Since the scanner operates as a fully 3D scanner and has a large axial FOV of 25 cm, the scatter fraction would be considerably higher if not for the excellent energy resolution, as seen in Fig. 4. This also has impact on the accuracy of the scatter correction. We use a model-based scatter correction method [8] which is based on a single-scatter simulation, and the accuracy of this assumption improves as the lower level energy threshold is raised. At 475 kev the relative number of multiple-scatter events is sufficiently small that they may be scaled from a single-scatter estimate with reasonable accuracy. Fig 6. shows the scatter estimate (SSS) using this model for two water-filled phantoms, 27-cm and 35-cm diameter. 2.9 cm Fig. 4. Energy spectrum of point source in air, representing sum of all pixels in scanner after correction energy offsets. An ELLD of 475 kev corresponds to the edge of the photo-peak. Fig. 4 shows that we can raise the lower level energy discriminator (ELLD) to at least 475 kev. Thus, we performed the NEMA NU-2 scatter measurement [8] with the line source within the 20-cm x 70-cm phantom using this energy threshold. In addition, we performed two similar measurements using annular sleeves surrounding the 20-cm diameter phantom to first increase the diameter to 27-cm and then to 35-cm diameter. This was done to estimate the scatter fraction for larger patients, since the 20-cm diameter phantom is considered to be representative of a thin patient, whereas the 27-cm diameter phantom represents an average patient and the 35-cm diameter phantom represents a large patient. These results are shown in Fig. 5. At 475keV the scatter fraction is 23%, 30%, and 36% for the 20-cm, 27-cm, and 35-cm phantoms, respectively. Fig. 6. Radial profile of water-filled phantom distributions, (left) 27-cm and (right) 35-cm diameter, with calculated scatter estimate (SSS). IV. LESION PHANTOM MEASUREMENTS We performed measurements with two water-filled cylindrical phantoms of diameters 27-cm (vol. = 24 l) and 35- cm (vol. = 53 l) representing an average and heavy patient respectively. A ring of six spheres with diameters 37, 28, 22, 17, 13, and 10-mm was placed at radial distance of 7-cm in the two phantoms and centered axially in scanner for data acquisition. The two large spheres were cold in all measurements. Two data sets were collected with each phantom for activity concentrations of 8:1 and 4:1 in the four small spheres with respect to the background. Additional uniform cylinders were placed next to the lesion phantoms (axially) during measurement with the same activity concentration as that in the lesion phantom in order to simulate activity outside the field-of-view. This configuration is very similar to the IEC image quality phantom prescribed by NEMA NU-2, except for the size of the phantom itself. Data were collected in list-mode with an ELLD = 475 kev applied before image reconstruction. Image reconstruction was 1921

4 performed for different count levels using a fully 3D list-mode TOF reconstruction with blob basis functions [9, 10]. Fig. 7 shows reconstructed images for the two phantoms with 8:1 activity concentration ratio for a clinical equivalent wholebody scan time of 3 mins/frame, which was determined by scaling the activity concentration in the phantom to a corresponding patient dose of 10 mci. Visually these images show that the TOF images converge faster to high contrast values (about 5 iterations), while the non-tof images require more iterations (at least 10 iterations) to converge to similar contrast values but at the expense of increased noise. Note that the reconstruction algorithm [9] uses geometrically ordered subsets, so that the number of iterations required is significantly reduced. For the 35-cm phantom, it is very clear the 10-mm diameter hot sphere has very low contrast and is at the limit of detectability even after 10 iterations in the non- TOF image, while TOF image shows a good contrast and detectability. In fact, the TOF reconstruction images of the 35- cm are similar in quality to the non-tof images of the 27-cm phantom, indicating that with TOF we can achieve results with heavy patients that would not be achievable in a non- TOF scanner. In Fig. 8 we show representative images for data acquired with 4:1 activity concentration ratio for the hot spheres with respect to the background for varying counts represented again as clinical equivalent scan time per frame. In the non- TOF image the 10-mm lesion is barely visible in the 27-cm cylinder but clearly visible in the TOF images. For the 35-cm phantom, the 13-mm lesion is not visible in the non-tof images but clearly visible in the TOF images at least at 3 mins/frame or greater. TOF information clearly leads to better detectability of small lesions, faster convergence, and improved background variability. We can also argue that the TOF allows one to reduce scan time for example, TOF images acquired with 3 mins/frame (or less) are better than the non-tof images acquired with 6 mins/frame. These results are borne out in the quantitative measurement of contrast and background variability measurements performed for the 10-mm sphere using the NEMA NU image quality measurements techniques [8]. A plot of these measurements is shown in Fig. 9. SUMMARY We have developed a proto-type whole-body TOF scanner based on lanthanum bromide that is fully 3D and has an axial FOV of 25 cm. The system is currently operating in a laboratory environment. Thus, it does not yet have gantry covers or a patient bed, so have only performed phantom measurements to date. We will, however, complete the system in the near future so that we can evaluate the TOF benefit with patients. In addition, some aspects of the system are still in development, such as the electronics, so we have not yet achieved the full potential of the TOF performance of these detectors. We have demonstrated that the excellent intrinsic performance of LaBr 3 (5%Ce) can be translated to TOF PET, whereby a favorable combination of sensitivity and spatial resolution are achieved in addition to excellent energy resolution and timing resolution. To date, we have measured an overall system energy resolution of 7.5% fwhm and a system timing resolution of 460 ps fwhm. With the lower level energy threshold set to 475 kev, the scatter fraction is 23% for the NEMA NU-2 phantom which is 20-cm diameter, and have also measured 30% and 36% for 27-cm and 35-cm diameter phantoms, respectively. These additional measurements were acquired to estimate the scatter fraction for average (27-cm) and heavy (35-cm) patients, as well as the thin (20-cm) patient. We have acquired and reconstructed lesion phantoms to begin evaluation of scanner performance with TOF. The data are reconstructed with a list-mode fully 3D iterative algorithm with data corrections included in the system model. We designed the phantoms to be similar to the NEMA NU-2 image quality phantom, but inserted the spheres into a body of 27-cm diameter and 35-cm diameter. As expected we find that TOF reduces the noise and background variability, especially for the larger phantom representing a large patient. In addition, TOF improves detail and contrast of the spheres (lesions), especially the smallest 10-mm sphere. The TOF reconstruction reaches convergence faster than the non-tof reconstruction, and the rate of convergence is seen to be more insensitive to object size. These results indicate that TOF will help improve image quality and potentially reduce scan time with clinical patients. REFERENCES Fig. 9. Plot of contrast vs background variability for the 10-mm hot sphere in the two phantoms with 4:1 activity concentration ratio with respect to background. The points along a line represent increasing number iterations starting with iteration 1 in the bottom left corner (lowest contrast and lowest background variability). [1] T. F. Budinger, "Time-of-Flight positron emission tomography - status relative to conventional PET," J. Nucl. Med., vol. 24, pp , [2] T. Tomitani, "Image-reconstruction and noise evaluation in photon time-of-flight assisted positron emission tomography," IEEE Trans. Nucl. Sci., vol. 28, pp ,

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