Cardiac motion and Partial Volume Effects (PVE) are two of the main causes of image degradation in cardiac PET. Motion generates artifacts and blurring while PVE lead to erroneous myocardial activity measurements. Newly available simultaneous PET-MR scanners offer new possibilities in cardiac imaging as MRI can assess wall contractility while collecting PET perfusion data. In this perspective, we develop a list-mode iterative reconstruction framework incorporating both tagged-MR derived non-rigid myocardial wall motion and position dependent detector Point Spread Function (PSF) directly into the PET system matrix. In this manner, our algorithm performs both motion “deblurring” and PSF deconvolution while reconstructing images with all available PET counts. The proposed methods are evaluated in a beating non-rigid cardiac phantom whose hot myocardial compartment contains small transmural and non-transmural cold defects. In order to accelerate imaging time, we investigate collecting full and half k-space tagged MR data to obtain tagged volumes that are registered using non-rigid B-spline registration to yield wall motion information. Our experimental results show that tagged-MR based motion correction yielded an improvement in defect/myocardium contrast recovery of 34-206% as compared to motion uncorrected studies. Likewise, lesion detectability improved by respectively 115-136% and 62-235% with MR-based motion compensation as compared to gating and no motion correction and made it possible to distinguish non-transmural from transmural defects, which has clinical significance given inherent limitations of current single modality imaging in identifying the amount of residual ischemia. The incorporation of PSF modeling within the framework of MR-based motion compensation significantly improved defect/myocardium contrast recovery (5.1-8.5%, p<0.01) and defect detectability (39-56%, p<0.01). No statistical difference was found in PET contrast and lesion detectability based on motion fields obtained with half and full k-space tagged data.

This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.

The results suggest that the proposed quantitative PET-MR methods can significantly enhance the performance of tumor diagnosis and staging as compared to conventional methods. This approach may enable utilization of the full potential of the scanner in oncologic studies of both the lower abdomen, with moving lesions, as well as other parts of the body unaffected by motion.

Accelerated tMR images obtained with more than 4 times acceleration can still provide relatively accurate motion fields and yield tMR-based motion corrected PET images with similar image quality as those reconstructed using fully sampled tMR data. The reduction of tMR acquisition time makes it more compatible with routine clinical cardiac PET-MR studies.

Coronary atherosclerotic plaque rupture is the main cause of myocardial infarction and the leading killer in the US. Inflammation is a known bio-marker of plaque vulnerability and can be assessed non-invasively using FDG-PET imaging. However, cardiac and respiratory motion of the heart makes PET detection of coronary plaque very challenging. Fat surrounding coronary arteries allow the use of MRI to track plaque motion during simultaneous PET-MR examination. In this study, we proposed and assessed the performance of a fat-MR based coronary motion correction technique for improved FDG-PET coronary plaque imaging in simultaneous PET-MR. The proposed methods were evaluated in a realistic four-dimensional PET-MR simulation study obtained by combining patient water-fat separated MRI and XCAT anthropomorphic phantom. Five small lesions were digitally inserted inside the patient coronary vessels to mimic coronary atherosclerotic plaques. The heart of the XCAT phantom was digitally replaced with the patient’s heart. Motion-dependent activity distributions, attenuation maps, and fat MR volumes of the heart, were generated using the XCAT cardiac and respiratory motion fields. A full Monte Carlo simulation using Siemens mMR’s geometry was performed for each motion phase. Cardiac/respiratory motion fields were estimated using non-rigid registration of the transformed fat MR volumes and incorporated directly into the system matrix of PET reconstruction along with motion-dependent attenuation maps. The proposed motion correction method was compared to conventional PET reconstruction techniques such as no motion correction, cardiac gating, and dual cardiac-respiratory gating. Compared to uncorrected reconstructions, fat-MR based motion compensation yielded an average improvement of plaque-to-background contrast (PBC) of 29.6%, 43.7%, 57.2%, and 70.6% for true plaque-to-blood ratios of 10, 15, 20 and 25:1, respectively. Channelized Hotelling Observer (CHO) Signal to Noise Ratio (SNR) was used to quantify plaque detectability. CHO-SNR improvement ranged from 105% to 128% for fat MR-based motion correction as compared to no motion correction. Likewise, CHO-SNR improvement ranged from 348% to 396% as compared to both cardiac and dual cardiac-respiratory gating approaches. Based on this study, our approach, a fat-MR based motion correction for coronary plaque PET imaging using simultaneous PET-MR, offers great potential for clinical practice. The ultimate performance and limitation of our approach, however, must be fully evaluated in patient studies.

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semiquantitative "hot spot" imaging for oncologic applications has meant that the capability of PET for quantifying biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total body PET, have opened up the ability to address a vast range of new research questions from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next 10 years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

PET is an established modality for myocardial perfusion imaging (MPI) which enables quantification of absolute myocardial blood flow (MBF) using dynamic imaging and kinetic modeling. However, heart motion and Partial Volume Effects (PVE) significantly limit the spatial resolution and quantitative accuracy of PET MPI. Simultaneous PET-MR offers a solution to the motion problem in PET by enabling MR-based motion correction of PET data. The aim of this study was to develop a motion and PVE correction methodology for PET MPI using simultaneous PET-MR, and to assess its impact on both static and dynamic PET MPI using 18F-Flurpiridaz, a novel 18F-labeled perfusion tracer. Two dynamic 18F-Flurpiridaz MPI scans were performed on healthy pigs using a PET-MR scanner. Cardiac motion was tracked using a dedicated tagged-MRI (tMR) sequence. Motion fields were estimated using non-rigid registration of tMR images and used to calculate motion-dependent attenuation maps. Motion correction of PET data was achieved by incorporating tMR-based motion fields and motion-dependent attenuation coefficients into image reconstruction. Dynamic and static PET datasets were created for each scan. Each dataset was reconstructed as (i) Ungated, (ii) Gated (end-diastolic phase), and (iii) Motion-Corrected (MoCo), each without and with point spread function (PSF) modeling for PVE correction. Myocardium-to-blood concentration ratios (MBR) and apparent wall thickness were calculated to assess image quality for static MPI. For dynamic MPI, segment- and voxel-wise myocardial blood flow (MBF) values were estimated by non-linear fitting of a 2-tissue compartment model to tissue time-activity-curves. MoCo and Gating respectively decreased mean apparent wall thickness by 15.1% and 14.4% and increased MBR by 20.3% and 13.6% compared to Ungated images (P<0.01). Combined motion and PSF correction (MoCo-PSF) yielded 30.9% (15.7%) lower wall thickness and 82.2% (20.5%) higher MBR compared to Ungated data reconstructed without (with) PSF modeling (P<0.01). For dynamic PET, mean MBF across all segments were comparable for MoCo (0.72±0.21 mL/min/mL) and Gating (0.69±0.18 mL/min/mL). Ungated data yielded significantly lower mean MBF (0.59±0.16 mL/min/mL). Mean MBF for MoCo-PSF was 0.80±0.22 mL/min/mL, which was 37.9% (25.0%) higher than that obtained from Ungated data without (with) PSF correction (P<0.01). The developed methodology holds promise to improve the image quality and sensitivity of PET MPI studies performed using PET-MR.

Purpose: Artifacts caused by head motion present a major challenge in brain positron emission tomography (PET) imaging. The authors investigated the feasibility of using wired active MR microcoils to track head motion and incorporate the measured rigid motion fields into iterative PET reconstruction. Methods: Several wired active MR microcoils and a dedicated MR coil-tracking sequence were developed. The microcoils were attached to the outer surface of an anthropomorphic 18 F-filled Hoffman phantom to mimic a brain PET scan. Complex rotation/translation motion of the phantom was induced by a balloon, which was connected to a ventilator. PET list-mode and MR tracking data were acquired simultaneously on a PET-MR scanner. The acquired dynamic PET data were reconstructed iteratively with and without motion correction. Additionally, static phantom data were acquired and used as the gold standard. Results: Motion artifacts in PET images were effectively removed by wired active MR microcoil based motion correction. Motion correction yielded an activity concentration bias ranging from −0.6% to 3.4% as compared to a bias ranging from −25.0% to 16.6% if no motion correction was applied. The contrast recovery values were improved by 37%-156% with motion correction as compared to no motion correction. The image correlation (mean ± standard deviation) between the motion corrected (uncorrected) images of 20 independent noise realizations and static reference was R 2 = 0.978 ± 0.007 (0.588 ± 0.010, respectively). Conclusions: Wired active MR microcoil based motion correction significantly improves brain PET quantitative accuracy and image contrast.