A Path to Qualification of PET/MRI Scanners for Multicenter Brain Imaging Studies: Evaluation of MRI-Based Attenuation Correction Methods Using a Patient Phantom.

PET/MRI scanners cannot be qualified in the manner adopted for hybrid PET/CT devices. The main hurdle with qualification in PET/MRI is that attenuation correction (AC) cannot be adequately measured in conventional PET phantoms because of the difficulty in converting the MR images of the physical structures (e.g., plastic) into electron density maps. Over the last decade, a plethora of novel MRI-based algorithms has been developed to more accurately derive the attenuation properties of the human head, including the skull. Although promising, none of these techniques has yet emerged as an optimal and universally adopted strategy for AC in PET/MRI. In this work, we propose a path for PET/MRI qualification for multicenter brain imaging studies. Specifically, our solution is to separate the head AC from the other factors that affect PET data quantification and use a patient as a phantom to assess the former. The emission data collected on the integrated PET/MRI scanner to be qualified should be reconstructed using both MRI- and CT-based AC methods, and whole-brain qualitative and quantitative (both voxelwise and regional) analyses should be performed. The MRI-based approach will be considered satisfactory if the PET quantification bias is within the acceptance criteria specified here. We have implemented this approach successfully across 2 PET/MRI scanner manufacturers at 2 sites.

Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation.

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.

Evaluation of attenuation correction in PET/MRI with synthetic lesion insertion.

Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images. Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC). Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About < 3 % CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of - 5 % to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis. Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.

Quantitative and Qualitative Improvement of Low-Count [68Ga]Citrate and [90Y]Microspheres PET Image Reconstructions Using Block Sequential Regularized Expectation Maximization Algorithm.

PURPOSE: There are several important positron emission tomography (PET) imaging scenarios that require imaging with very low photon statistics, for which both quantitative accuracy and visual quality should not be neglected. For example, PET imaging with the low photon statistics is closely related to active efforts to significantly reduce radiation exposure from radiopharmaceuticals. We investigated two examples of low-count PET imaging: (a) imaging [90Y]microsphere radioembolization that suffers the very small positron emission fraction of Y-90's decay processes, and (b) cancer imaging with [68Ga]citrate with uptake time of 3-4 half-lives, necessary for visualizing tumors. In particular, we investigated a type of penalized likelihood reconstruction algorithm, block sequential regularized expectation maximization (BSREM), for improving both image quality and quantitative accuracy of these low-count PET imaging cases. PROCEDURES: The NEMA/IEC Body phantom filled with aqueous solution of Y-90 or Ga-68 was scanned to mimic the low-count scenarios of corresponding patient data acquisitions on a time-of-flight (TOF) PET/magnetic resonance imaging system. Contrast recovery, background variation, and signal-to-noise ratio were evaluated in different sets of count densities using both conventional TOF ordered subset expectation (TOF-OSEM) and TOF-BSREM algorithms. The regularization parameter, beta, in BSREM that controls the tradeoff between image noise and resolution was evaluated to find a value for improved confidence in image interpretation. Visual quality assessment of the images obtained from patients administered with [68Ga]citrate (n = 6) was performed. We also made preliminary visual image quality assessment for one patient with [90Y]microspheres. In Y-90 imaging, the effect of 511-keV energy window selection for minimizing the number of random events was also evaluated. RESULTS: Quantitatively, phantom images reconstructed with TOF-BSREM showed improved contrast recovery, background variation, and signal-to-noise ratio values over images reconstructed with TOF-OSEM. Both phantom and patient studies of delayed imaging of [68Ga]citrate show that TOF-BSREM with beta = 500 gives the best tradeoff between image noise and image resolution based on visual assessment by the readers. The NEMA-IQ phantom study with [90Y]microspheres shows that the narrow energy window (460-562 keV) recovers activity concentrations in small spheres better than the regular energy window (425-650 keV) with the beta value of 2000 using the TOF-BSREM algorithm. For the images obtained from patients with [68Ga]citrate using TOF-BSREM with beta = 500, the visual analogue scale (VAS) was improved by 17 % and the Likert score was increased by 1 point on average, both in comparison to corresponding scores for images reconstructed using TOF-OSEM. CONCLUSION: Our investigation shows that the TOF-BSREM algorithm improves the image quality and quantitative accuracy in low-count PET imaging scenarios. However, the beta value in this algorithm needed to be adjusted for each radiopharmaceutical and counting statistics at the time of scans.

Simultaneous Estimation of Bias and Resolution in PET Images With a Long-Lived "Pocket" Phantom System.

A challenge in multicenter trials that use quantitative positron emission tomography (PET) imaging is the often unknown variability in PET image values, typically measured as standardized uptake values, introduced by intersite differences in global and resolution-dependent biases. We present a method for the simultaneous monitoring of scanner calibration and reconstructed image resolution on a per-scan basis using a PET/computed tomography (CT) "pocket" phantom. We use simulation and phantom studies to optimize the design and construction of the PET/CT pocket phantom (120 × 30 × 30 mm). We then evaluate the performance of the PET/CT pocket phantom and accompanying software used alongside an anthropomorphic phantom when known variations in global bias (±20%, ±40%) and resolution (3-, 6-, and 12-mm postreconstruction filters) are introduced. The resulting prototype PET/CT pocket phantom design uses 3 long-lived sources (15-mm diameter) containing germanium-68 and a CT contrast agent in an epoxy matrix. Activity concentrations varied from 30 to 190 kBq/mL. The pocket phantom software can accurately estimate global bias and can detect changes in resolution in measured phantom images. The pocket phantom is small enough to be scanned with patients and can potentially be used on a per-scan basis for quality assurance for clinical trials and quantitative PET imaging in general. Further studies are being performed to evaluate its performance under variations in clinical conditions that occur in practice.

Clinical Evaluation of 68Ga-PSMA-II and 68Ga-RM2 PET Images Reconstructed With an Improved Scatter Correction Algorithm.

OBJECTIVE: Gallium-68-labeled radiopharmaceuticals pose a challenge for scatter estimation because their targeted nature can produce high contrast in these regions of the kidneys and bladder. Even small errors in the scatter estimate can result in washout artifacts. Administration of diuretics can reduce these artifacts, but they may result in adverse events. Here, we investigated the ability of algorithmic modifications to mitigate washout artifacts and eliminate the need for diuretics or other interventions. MATERIALS AND METHODS: The model-based scatter algorithm was modified to account for PET/MRI scanner geometry and challenges of non-FDG tracers. Fifty-three clinical 68Ga-RM2 and 68Ga-PSMA-11 whole-body images were reconstructed using the baseline scatter algorithm. For comparison, reconstruction was also processed with modified sampling in the single-scatter estimation and with an offset in the scatter tail-scaling process. None of the patients received furosemide to attempt to decrease the accumulation of radiopharmaceuticals in the bladder. The images were scored independently by three blinded reviewers using the 5-point Likert scale. RESULTS: The scatter algorithm improvements significantly decreased or completely eliminated the washout artifacts. When comparing the baseline and most improved algorithm, the image quality increased and image artifacts were reduced for both 68Ga-RM2 and for 68Ga-PSMA-11 in the kidneys and bladder regions. CONCLUSION: Image reconstruction with the improved scatter correction algorithm mitigated washout artifacts and recovered diagnostic image quality in 68Ga PET, indicating that the use of diuretics may be avoided.

A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy.

We developed a method to evaluate variations in the PET imaging process in order to characterize the relative ability of static and dynamic metrics to measure breast cancer response to therapy in a clinical trial setting. We performed a virtual clinical trial by generating 540 independent and identically distributed PET imaging study realizations for each of 22 original dynamic fluorodeoxyglucose (18F-FDG) breast cancer patient studies pre- and post-therapy. Each noise realization accounted for known sources of uncertainty in the imaging process, such as biological variability and SUV uptake time. Four definitions of SUV were analyzed, which were SUVmax, SUVmean, SUVpeak, and SUV50%. We performed a ROC analysis on the resulting SUV and kinetic parameter uncertainty distributions to assess the impact of the variability on the measurement capabilities of each metric. The kinetic macro parameter, K i , showed more variability than SUV (mean CV K i = 17%, SUV = 13%), but K i pre- and post-therapy distributions also showed increased separation compared to the SUV pre- and post-therapy distributions (mean normalized difference K i = 0.54, SUV = 0.27). For the patients who did not show perfect separation between the pre- and post-therapy parameter uncertainty distributions (ROC AUC < 1), dynamic imaging outperformed SUV in distinguishing metabolic change in response to therapy, ranging from 12 to 14 of 16 patients over all SUV definitions and uptake time scenarios (p < 0.05). For the patient cohort in this study, which is comprised of non-high-grade ER+ tumors, K i outperformed SUV in an ROC analysis of the parameter uncertainty distributions pre- and post-therapy. This methodology can be applied to different scenarios with the ability to inform the design of clinical trials using PET imaging.

Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method.

We have previously developed a convergent penalized likelihood (PL) image reconstruction algorithm using the relative difference prior (RDP) and showed that it achieves more accurate lesion quantitation compared to ordered subsets expectation maximization (OSEM). We evaluated the detectability of low-contrast liver and lung lesions using the PL-RDP algorithm compared to OSEM. We performed a two-alternative forced choice study using a channelized Hotelling observer model that was previously validated against human observers. Lesion detectability showed a stronger dependence on lesion size for PL-RDP than OSEM. Lesion detectability was improved using time-of-flight (TOF) reconstruction, with greater benefit for the liver compared to the lung and with increasing benefit for decreasing lesion size and contrast. PL detectability was statistically significantly higher than OSEM for 20 mm liver lesions when contrast was [Formula: see text] ([Formula: see text]), and TOF PL detectability was statistically significantly higher than TOF OSEM for 15 and 20 mm liver lesions with contrast [Formula: see text] and [Formula: see text], respectively. For all other cases, there was no statistically significant difference between PL and OSEM ([Formula: see text]). For the range of studied lesion properties, lesion detectability using PL-RDP was equivalent or improved compared to using OSEM.

Multicenter Clinical Trials Using 18F-FDG PET to Measure Early Response to Oncologic Therapy: Effects of Injection-to-Acquisition Time Variability on Required Sample Size.

UNLABELLED: Uptake time (interval between tracer injection and image acquisition) affects the SUV measured for tumors in (18)F-FDG PET images. With dissimilar uptake times, changes in tumor SUVs will be under- or overestimated. This study examined the influence of uptake time on tumor response assessment using a virtual clinical trials approach. METHODS: Tumor kinetic parameters were estimated from dynamic (18)F-FDG PET scans of breast cancer patients and used to simulate time-activity curves for 45-120 min after injection. Five-minute uptake time frames followed 4 scenarios: the first was a standardized static uptake time (the SUV from 60 to 65 min was selected for all scans), the second was uptake times sampled from an academic PET facility with strict adherence to standardization protocols, the third was a distribution similar to scenario 2 but with greater deviation from standards, and the fourth was a mixture of hurried scans (45- to 65-min start of image acquisition) and frequent delays (58- to 115-min uptake time). The proportion of out-of-range scans (<50 or >70 min, or >15-min difference between paired scans) was 0%, 20%, 44%, and 64% for scenarios 1, 2, 3, and 4, respectively. A published SUV correction based on local linearity of uptake-time dependence was applied in a separate analysis. Influence of uptake-time variation was assessed as sensitivity for detecting response (probability of observing a change of ≥30% decrease in (18)F-FDG PET SUV given a true decrease of 40%) and specificity (probability of observing an absolute change of <30% given no true change). RESULTS: Sensitivity was 96% for scenario 1, and ranged from 73% for scenario 4 (95% confidence interval, 70%-76%) to 92% (90%-93%) for scenario 2. Specificity for all scenarios was at least 91%. Single-arm phase II trials required an 8%-115% greater sample size for scenarios 2-4 than for scenario 1. If uptake time is known, SUV correction methods may raise sensitivity to 87%-95% and reduce the sample size increase to less than 27%. CONCLUSION: Uptake-time deviations from standardized protocols occur frequently, potentially decreasing the performance of (18)F-FDG PET response biomarkers. Correcting SUV for uptake time improves sensitivity, but algorithm refinement is needed. Stricter uptake-time control and effective correction algorithms could improve power and decrease costs for clinical trials using (18)F-FDG PET endpoints.

Effect of 18F-FDG uptake time on lesion detectability in PET imaging of early stage breast cancer.

Prior reports have suggested that delayed FDG-PET oncology imaging can improve the contrast-to-noise ratio (CNR) for known lesions. Our goal was to estimate realistic bounds for lesion detectability for static measurements with one to four hours between FDG injection and image acquisition. Tumor and normal tissue kinetic model parameters were estimated from dynamic PET studies of patients with early stage breast cancer. These were used to generate time-activity curves (TACs) out to four hours, for which we assumed both nonreversible and reversible models with different rates of FDG dephosphorylation (k4). For each pair of tumor and normal tissue TACs, 600 PET sinogram realizations were generated, and images were reconstructed using OSEM. Test statistics for each tumor and normal tissue region of interest were output from the computer model observers and evaluated using an ROC analysis with the calculated AUC providing a measure of lesion detectability. For the nonreversible model (k4 = 0), the AUC increased in 11/23 (48%) of patients for one to two hours after the current standard post-radiotracer injection imaging window of one hour. This improvement was driven by increased tumor/normal tissue contrast before the impact of increased noise due to radiotracer decay began to dominate the imaging signal. As k4 was increased from 0 to 0.01 min-1, the time of maximum detectability shifted earlier, as the decreasing FDG concentration in the tumor lowered the CNR. These results imply that delayed PET imaging may reveal low-conspicuity lesions that would have otherwise gone undetected.