Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

PURPOSE: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF). METHODS: A total of 273 [18F]-FDG PET scans were used, including data from 6 centres equipped with GE Discovery MI ToF scanners. PET data were reconstructed using the block-sequential-regularised-expectation-maximisation (BSREM) algorithm with and without ToF. The images were then split into training (n = 208), validation (n = 15), and testing (n = 50) sets. Three DL-ToF models were trained to transform non-ToF BSREM images to their target ToF images with different levels of DL-ToF strength (low, medium, high). The models were objectively evaluated using the testing set based on standardised uptake value (SUV) in 139 identified lesions, and in normal regions of liver and lungs. Three radiologists subjectively rated the models using testing sets based on lesion detectability, diagnostic confidence, and image noise/quality. RESULTS: The non-ToF, DL-ToF low, medium, and high methods resulted in - 28 ± 18, - 28 ± 19, - 8 ± 22, and 1.7 ± 24% differences (mean; SD) in the SUVmax for the lesions in testing set, compared to ToF-BSREM image. In background lung VOIs, the SUVmean differences were 7 ± 15, 0.6 ± 12, 1 ± 13, and 1 ± 11% respectively. In normal liver, SUVmean differences were 4 ± 5, 0.7 ± 4, 0.8 ± 4, and 0.1 ± 4%. Visual inspection showed that our DL-ToF improved feature sharpness and convergence towards ToF reconstruction. Blinded clinical readings of testing sets for diagnostic confidence (scale 0-5) showed that non-ToF, DL-ToF low, medium, and high, and ToF images scored 3.0, 3.0, 4.1, 3.8, and 3.5 respectively. For this set of images, DL-ToF medium therefore scored highest for diagnostic confidence. CONCLUSION: Deep learning-based image enhancement models may provide converged ToF-equivalent image quality without ToF reconstruction. In clinical scoring DL-ToF-enhanced non-ToF images (medium and high) on average scored as high as, or higher than, ToF images. The model is generalisable and hence, could be applied to non-ToF images from BGO-based PET/CT scanners.

Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise.

PURPOSE: To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. METHODS: List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and »-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). RESULTS: OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. CONCLUSION: Deep learning-based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.

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.

Real-Time Gain Control of PET Detectors and Evaluation With Challenging Radionuclides.

Accurate gain control of PET detectors is a prerequisite for quantitative accuracy. A shift in the 511 keV peak position can lead to errors in scatter correction, degrading quantitation. The PET detectors in a PET/MR scanner are subject to thermal transients due to eddy currents induced during gradient-intensive MRI sequences. Since the gain of silicon photomultiplier-based detectors changes with temperature, good gain control is particularly challenging. In this paper we describe a method that utilizes information from the entire singles spectrum to create a real-time gain control method that maintains gain of PET detectors stable within approximately ±0.5% (±2.5 keV) with varying levels of scatter and in the presence of significant thermal transients. We describe the methods used to combine information about multiple peaks and how this algorithm is implemented in a way that permits real-time processing on a field-programmable gate array. Simulations demonstrate rapid response time and stability. A method ("virtual scatter filter") is also described that extracts unscattered photopeak events from phantom data and demonstrates the accuracy of the photopeak for various radionuclides that emit energies in addition to the pure 511 keV annihilation peak. Radionuclides 52 Mn, 55 Co, 64 Cu, 89 Zr, 90 Y, and 124 I are included in the study for their various forms of spectral contamination.

Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers.

Accurate and robust attenuation correction remains challenging in hybrid PET/MR particularly for torsos because it is difficult to segment bones, lungs and internal air in MR images. Additionally, MR suffers from susceptibility artifacts when a metallic implant is present. Recently, joint estimation (JE) of activity and attenuation based on PET data, also known as maximum likelihood reconstruction of activity and attenuation, has gained considerable interest because of (1) its promise to address the challenges in MR-based attenuation correction (MRAC), and (2) recent advances in time-of-flight (TOF) technology, which is known to be the key to the success of JE. In this paper, we implement a JE algorithm using an MR-based prior and evaluate the algorithm using whole-body PET/MR patient data, for both FDG and non-FDG tracers, acquired from GE SIGNA PET/MR scanners with TOF capability. The weight of the MR-based prior is spatially modulated, based on MR signal strength, to control the balance between MRAC and JE. Large prior weights are used in strong MR signal regions such as soft tissue and fat (i.e. MR tissue classification with a high degree of certainty) and small weights are used in low MR signal regions (i.e. MR tissue classification with a low degree of certainty). The MR-based prior is pragmatic in the sense that it is convex and does not require training or population statistics while exploiting synergies between MRAC and JE. We demonstrate the JE algorithm has the potential to improve the robustness and accuracy of MRAC by recovering the attenuation of metallic implants, internal air and some bones and by better delineating lung boundaries, not only for FDG but also for more specific non-FDG tracers such as 68Ga-DOTATOC and 18F-Fluoride.

PET Imaging Stability Measurements During Simultaneous Pulsing of Aggressive MR Sequences on the SIGNA PET/MR System.

The recent introduction of simultaneous whole-body PET/MR scanners has enabled new research taking advantage of the complementary information obtainable with PET and MRI. One such application is kinetic modeling, which requires high levels of PET quantitative stability. To accomplish the required PET stability levels, the PET subsystem must be sufficiently isolated from the effects of MR activity. Performance measurements have previously been published, demonstrating sufficient PET stability in the presence of MR pulsing for typical clinical use; however, PET stability during radiofrequency (RF)-intensive and gradient-intensive sequences has not previously been evaluated for a clinical whole-body scanner. In this work, PET stability of the GE SIGNA PET/MR was examined during simultaneous scanning of aggressive MR pulse sequences. Methods: PET performance tests were acquired with MR idle and during simultaneous MR pulsing. Recent system improvements mitigating RF interference and gain variation were used. A fast recovery fast spin echo MR sequence was selected for high RF power, and an echo planar imaging sequence was selected for its high heat-inducing gradients. Measurements were performed to determine PET stability under varying MR conditions using the following metrics: sensitivity, scatter fraction, contrast recovery, uniformity, count rate performance, and image quantitation. A final PET quantitative stability assessment for simultaneous PET scanning during functional MRI studies was performed with a spiral in-and-out gradient echo sequence. Results: Quantitation stability of a 68Ge flood phantom was demonstrated within 0.34%. Normalized sensitivity was stable during simultaneous scanning within 0.3%. Scatter fraction measured with a 68Ge line source in the scatter phantom was stable within the range of 40.4%-40.6%. Contrast recovery and uniformity were comparable for PET images acquired simultaneously with multiple MR conditions. Peak noise equivalent count rate was 224 kcps at an effective activity concentration of 18.6 kBq/mL, and the count rate curves and scatter fraction curve were consistent for the alternating MR pulsing states. A final test demonstrated quantitative stability during a spiral functional MRI sequence. Conclusion: PET stability metrics demonstrated that PET quantitation was not affected during simultaneous aggressive MRI. This stability enables demanding applications such as kinetic modeling.

The future of SPECT in a time of PET.

As positron emission tomography (PET) imaging is becoming more prevalent in clinical practice, it is reasonable to ask if there will be a role for single photon emission computed tomography (SPECT) in the future. This article considers that question, focusing on areas where SPECT can differentiate itself from PET for fundamental reasons: breadth of available radionuclides, simultaneous imaging of multiple agents, cost-effectiveness and adaptability to specific imaging situations. The conclusion is that SPECT will continue to evolve and exist alongside PET and will grow the field of molecular imaging with improved efficiency and patient workflow.