Scott D. Wollenweber

Quiescent period respiratory gating for PET∕CT

PURPOSE To minimize respiratory motion artifacts, this work proposes quiescent period gating (QPG) methods that extract PET data from the end-expiration quiescent period and form a single PET frame with reduced motion and improved signal-to-noise properties. METHODS Two QPG methods are proposed andevaluated. Histogram-based quiescent period gating (H-QPG) extracts a fraction of PET data determined by a window of the respiratory displacement signal histogram. Cycle-based quiescent period gating (C-QPG) extracts data with a respiratory displacement signal below a specified threshold of the maximum amplitude of each individual respiratory cycle. Performances of both QPG methods were compared to ungated and five-bin phase-gated images across 21 FDG-PET/CT patient data sets containing 31 thorax and abdomen lesions as well as with computer simulations driven by 1295 different patient respiratory traces. Image quality was evaluated in terms of the lesion SUV(max) and the fraction of counts included in each gate as a surrogate for image noise. RESULTS For all the gating methods, image noise artifactually increases SUV(max) when the fraction of counts included in each gate is less than 50%. While simulation data show that H-QPG is superior to C-QPG, the H-QPG and C-QPG methods lead to similar quantification-noise tradeoffs in patient data. Compared to ungated images, both QPG methods yield significantly higher lesion SUV(max). Compared to five-bin phase gating, the QPG methods yield significantly larger fraction of counts with similar SUV(max) improvement. Both QPG methods result in increased lesion SUV(max) for patients whose lesions have longer quiescent periods. CONCLUSIONS Compared to ungated and phase-gated images, the QPG methods lead to images with less motion blurring and an improved compromise between SUV(max) and fraction of counts. The QPG methods for respiratory motion compensation could effectively improve tumor quantification with minimal noise increase.

Comparison of 4-class and continuous fat/water methods for whole-body, MR-based PET attenuation correction

The goal of this study was to compare two approaches for MR-based PET patient attenuation correction (AC) in whole-body FDG-PET imaging using a tri-modality PET/CT & MR setup. Sixteen clinical whole-body FDG patients were included in this study. Mean standard uptake values (SUV) were measured for liver and lung volumes-of-interest for comparison. Maximum SUV values were measured in 18 FDGavid features in ten of the patients. The AC methods compared to gold-standard CT-based AC were segmentation of the CT (air, lung, fat, water), MR image segmentation with 4 tissue classes (air, lung, fat, water) and segmentation with air, lung and a continuous fat/water method. Results: The magnitude of uptake value differences induced by CT-based image segmentation were similar but lower on average than those found using the MRderived AC methods. The average liver SUV difference with that found using CTAC was 1.3%, 10.4% and 5.7% for 4-class segmented CT, 4-class MRAC and continuous fat/water MRAC methods, respectively. The average FDG-avid feature SUV max difference was -0.5%,1.7% and -1.6% for 4-class segmented CT, 4-class MRAC and continuous fat/water MRAC methods, respectively. Conclusion: The results demonstrated that both 4class and continuous fat/water AC methods provided adequate quantitation in the body, and that the continuous fat/water method was within 5.7% on average for SUV mean in liver and 1.6% on average for SUV max for FDG-avid features.

Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET.

Ordered subset expectation maximization (OSEM) is the most widely used algorithm for clinical PET image reconstruction. OSEM is usually stopped early and post-filtered to control image noise and does not necessarily achieve optimal quantitation accuracy. As an alternative to OSEM, we have recently implemented a penalized likelihood (PL) image reconstruction algorithm for clinical PET using the relative difference penalty with the aim of improving quantitation accuracy without compromising visual image quality. Preliminary clinical studies have demonstrated visual image quality including lesion conspicuity in images reconstructed by the PL algorithm is better than or at least as good as that in OSEM images. In this paper we evaluate lesion quantitation accuracy of the PL algorithm with the relative difference penalty compared to OSEM by using various data sets including phantom data acquired with an anthropomorphic torso phantom, an extended oval phantom and the NEMA image quality phantom; clinical data; and hybrid clinical data generated by adding simulated lesion data to clinical data. We focus on mean standardized uptake values and compare them for PL and OSEM using both time-of-flight (TOF) and non-TOF data. The results demonstrate improvements of PL in lesion quantitation accuracy compared to OSEM with a particular improvement in cold background regions such as lungs.

Measured count-rate performance of the Discovery STE PET/CT scanner in 2D, 3D and partial collimation acquisition modes

We measured count rates and scatter fraction on the Discovery STE PET/CT scanner in conventional 2D and 3D acquisition modes, and in a partial collimation mode between 2D and 3D. As part of the evaluation of using partial collimation, we estimated global count rates using a scanner model that combined computer simulations with an empirical live-time function. Our measurements followed the NEMA NU2 count rate and scatter-fraction protocol to obtain true, scattered and random coincidence events, from which noise equivalent count (NEC) rates were calculated. The effect of patient size was considered by using 27 cm and 35 cm diameter phantoms, in addition to the standard 20 cm diameter cylindrical count-rate phantom. Using the scanner model, we evaluated two partial collimation cases: removing half of the septa (2.5D) and removing two-thirds of the septa (2.7D). Based on predictions of the model, a 2.7D collimator was constructed. Count rates and scatter fractions were then measured in 2D, 2.7D and 3D. The scanner model predicted relative NEC variation with activity, as confirmed by measurements. The measured 2.7D NEC was equal or greater than 3D NEC for all activity levels in the 27 cm and 35 cm phantoms. In the 20 cm phantom, 3D NEC was somewhat higher ( approximately 15%) than 2.7D NEC at 100 MBq. For all higher activity concentrations, 2.7D NEC was greater and peaked 26% above the 3D peak NEC. The peak NEC in 2.7D mode occurred at approximately 425 MBq, and was 26-50% greater than the peak 3D NEC, depending on object size. NEC in 2D was considerably lower, except at relatively high activity concentrations. Partial collimation shows promise for improved noise equivalent count rates in clinical imaging without altering other detector parameters.

Dose Optimization in TOF-PET/MR Compared to TOF-PET/CT

Purpose To evaluate the possible activity reduction in FDG-imaging in a Time-of-Flight (TOF) PET/MR, based on cross-evaluation of patient-based NECR (noise equivalent count rate) measurements in PET/CT, cross referencing with phantom-based NECR curves as well as initial evaluation of TOF-PET/MR with reduced activity. Materials and Methods A total of 75 consecutive patients were evaluated in this study. PET/CT imaging was performed on a PET/CT (time-of-flight (TOF) Discovery D 690 PET/CT). Initial PET/MR imaging was performed on a newly available simultaneous TOF-PET/MR (Signa PET/MR). An optimal NECR for diagnostic purposes was defined in clinical patients (NECRP) in PET/CT. Subsequent optimal activity concentration at the acquisition time ([A]0) and target NECR (NECRT) were obtained. These data were used to predict the theoretical FDG activity requirement of the new TOF-PET/MR system. Twenty-five initial patients were acquired with (retrospectively reconstructed) different imaging times equivalent for different activities on the simultaneous PET/MR for the evaluation of clinically realistic FDG-activities. Results The obtained values for NECRP, [A]0 and NECRT were 114.6 (± 14.2) kcps (Kilocounts per second), 4.0 (± 0.7) kBq/mL and 45 kcps, respectively. Evaluating the NECRT together with the phantom curve of the TOF-PET/MR device, the theoretical optimal activity concentration was found to be approximately 1.3 kBq/mL, which represents 35% of the activity concentration required by the TOF-PET/CT. Initial evaluation on patients in the simultaneous TOF-PET/MR shows clinically realistic activities of 1.8 kBq/mL, which represent 44% of the required activity. Conclusion The new TOF-PET/MR device requires significantly less activity to generate PET-images with good-to-excellent image quality, due to improvements in detector geometry and detector technologies. The theoretically achievable dose reduction accounts for up to 65% but cannot be fully translated into clinical routine based on the coils within the FOV and MR-sequences applied at the same time. The clinically realistic reduction in activity is slightly more than 50%. Further studies in a larger number of patients are needed to confirm our findings.

Comparison of different methods for data-driven respiratory gating of PET data

Respiratory movement degrades image quality in PET/CT. The first step in correcting for movement is to gate the data into different motion states. In current practice, the gating is based on information from external devices that measure physical parameters such as the chest position. Various groups have proposed methods to extract a gating signal out of the PET data. Here we compare methods using PCA, Laplacian Eigenmaps, Spectral Analysis and sensitivity. We test the methods on clinical PET list mode data for different tracers. We evaluate correlation with the chest position as measured by the Varian RPM system, and stability under increased noise in the PET data, both by reducing counts and reducing total duration. We also compare SUVmax and lesion displacement when gating the PET data based on the signals extracted by the different methods.

Impact of CT attenuation correction method on quantitative respiratory‐correlated (4D) PET/CT imaging

PURPOSE Respiratory-correlated positron emission tomography (PET/CT) 4D PET/CT is used to mitigate errors from respiratory motion; however, the optimal CT attenuation correction (CTAC) method for 4D PET/CT is unknown. The authors performed a phantom study to evaluate the quantitative performance of CTAC methods for 4D PET/CT in the ground truth setting. METHODS A programmable respiratory motion phantom with a custom movable insert designed to emulate a lung lesion and lung tissue was used for this study. The insert was driven by one of five waveforms: two sinusoidal waveforms or three patient-specific respiratory waveforms. 3DPET and 4DPET images of the phantom under motion were acquired and reconstructed with six CTAC methods: helical breath-hold (3DHEL), helical free-breathing (3DMOT), 4D phase-averaged (4DAVG), 4D maximum intensity projection (4DMIP), 4D phase-matched (4DMATCH), and 4D end-exhale (4DEXH) CTAC. Recovery of SUV(max), SUV(mean), SUV(peak), and segmented tumor volume was evaluated as RC(max), RC(mean), RC(peak), and RC(vol), representing percent difference relative to the static ground truth case. Paired Wilcoxon tests and Kruskal-Wallis ANOVA were used to test for significant differences. RESULTS For 4DPET imaging, the maximum intensity projection CTAC produced significantly more accurate recovery coefficients than all other CTAC methods (p < 0.0001 over all metrics). Over all motion waveforms, ratios of 4DMIP CTAC recovery were 0.2 ± 5.4, -1.8 ± 6.5, -3.2 ± 5.0, and 3.0 ± 5.9 for RC(max), RC(peak), RC(mean), and RC(vol). In comparison, recovery coefficients for phase-matched CTAC were -8.4 ± 5.3, -10.5 ± 6.2, -7.6 ± 5.0, and -13.0 ± 7.7 for RC(max), RC(peak), RC(mean), and RC(vol). When testing differences between phases over all CTAC methods and waveforms, end-exhale phases were significantly more accurate (p = 0.005). However, these differences were driven by the patient-specific respiratory waveforms; when testing patient and sinusoidal waveforms separately, patient waveforms were significantly different between phases (p < 0.0001) while the sinusoidal waveforms were not significantly different (p = 0.98). When considering only the subset of 4DMATCH images that corresponded to the end-exhale image phase, 4DEXH, mean and interquartile range were similar to 4DMATCH but variability was considerably reduced. CONCLUSIONS Comparative advantages in accuracy and precision of SUV metrics and segmented volumes were demonstrated with the use of the maximum intensity projection and end-exhale CT attenuation correction. While respiratory phase-matched CTAC should in theory provide optimal corrections, image artifacts and differences in implementation of 4DCT and 4DPET sorting can degrade the benefit of this approach. These results may be useful to guide the implementation, analysis, and development of respiratory-correlated thoracic PET/CT in the radiation oncology and diagnostic settings.

Performance of a BGO PET/CT with higher resolution PET detectors

A new PET detector block has been designed to replace the standard detector of the Discovery ST PET/CT system. The new detector block is the same size as the original, but consists of an 8/spl times/6 (tangential /spl times/ axial) matrix of crystals rather than the original 6/spl times/6. The new crystal dimensions are 4.7 /spl times/ 6.3 /spl times/ 30 mm/sup 3/ (tangential /spl times/ axial /spl times/ radial). Full PET/CT systems have been built with these detectors (Discovery STE). Most other aspects of the system are identical to the standard Discovery ST, with differences including the low energy threshold for 3D imaging (now 425 keV) and front-end electronics. Initial performance evaluation has been done, including NEMA NU2-2001 tests and imaging of the 3D Hoffman brain phantom and a neck phantom with small lesions. The system sensitivity was 1.90 counts/s/kBq in 2D, and 9.35 counts/s/kBq in 3D. Scatter fractions measured for 2D and 3D, respectively, were 18.6% and 34.5%. In 2D, the peak NEC of 89.9 kcps occurred at 47.0 kBq/cc. In 3D, the peak NEC of 74.3 kcps occurred at 8.5 kBq/cc. Spatial resolution (all expressed in mm FWHM) measured in 2D for 1 cm off-axis source 5.06 transaxial, 5.14 axial and for 10 cm source 5.45 radial, 5.86 tangential, and 6.23 axial. In 3D for 1 cm off-axis source 5.13 transaxial, 5.74 axial, and for 10 cm source 5.92 radial, 5.54 tangential, and 6.16 axial. Images of the brain and neck phantom demonstrate some improvement, compared to measurements on a standard Discovery ST.

Evaluation of the accuracy and robustness of a motion correction algorithm for PET using a novel phantom approach

We introduce the use of a novel physical phantom to quantify the performance of a motion-correction algorithm. The goal of the study was to assess a PET-PET image registration, the final output of which is a motion-corrected high-statistics PET image volume, a procedure called Reconstruct, Register and Average (RRA). Methods: A phantom was constructed using 5 ∼2mL Ge-68 filled spheres suspended in a water-filled tank via lightweight fishing line and driven by a periodic motion. Comparison of maximum and mean concentration and sphere volume was performed. Ground truth data were measured using no-motion. With motion, five replicate datasets of 3-minute phase-gated data for each of 3 different periods of motion were acquired. Gated PET images were registered using a multi-resolution level-sets-based non-rigid registration (NRR). The NRR images were then averaged to form a motion-corrected, high-statistics image volume. Spheres from all images were segmented and compared across the imaging conditions. Results: The average center-of-mass range of motion was 7.35, 5.83 and 2.66 mm for the spheres over the three periods of 8, 6 and 4 seconds. The center-of-mass for all spheres in all conditions was corrected to within 1mm on average using NRR as compared to the gated data. For the RRA data, the sphere maximum activity concentration (MAC) was on average 40.2% higher (−4.0% to 116.7%) and sphere volume was on average 12.0% smaller (−8.2% to 28.1%) as compared to the un-gated data with motion. The RRA results for MAC were on average 70% more accurate and for sphere volume 80% more accurate as compared to the un-gated data. Conclusions: The results show that the novel phantom setup and analysis methods are a promising evaluation technique for the assessment of motion correction algorithms. Benefits include the ability to compare against ground truth data without motion but with control of the statistical data quality and background variability. Use of a nonmoving object adjacent to spheres in motion, the spatial extent of the motion correction algorithm was confirmed to be local to the induced motion and to not affect the stationary object. A further benefit of the assessment technique is the use of ground truth data.

An Efficient Algorithm for Targeted Reconstruction of Tomographic Data

Applications such as cardiac imaging require high-resolution images of a targeted display field-of-view (DFOV) within the patient. While reconstructing only the targeted DFOV is straightforward for analytical reconstruction algorithms, conventional formulations of iterative algorithms require that the DFOV include the entire emitting object, since they work to match the measured data to an estimate formed by applying the system model to the current estimate of the image. If the estimated image does not include activity outside the DFOV, its forward projection cannot correctly match the measurement. A brute force solution is to reconstruct the full FOV at a pixel pitch equal to that desired in the targeted DFOV, then select the pixels corresponding to the targeted DFOV; this is a computationally expensive solution whose cost rises quadratically as the targeted DFOV decreases in size. We present a method to efficiently reconstruct only the DFOV after estimating the contribution of activity outside the DFOV from a first-pass, low-resolution reconstruction. We maintain high image quality and relatively consistent convergence characteristics in the targeted DFOV reconstruction by using the estimate of activity outside the DFOV as an additive correction term in the forward system model, rather than subtracting the estimate from the raw data. We demonstrate artifact-free, quantitatively accurate images with significant speed-up in reconstruction times over the brute-force method.