Motion-corrected Positron Emission Tomography Images Research Articles

Improving diagnostic precision in amyloid brain PET imaging through data-driven motion correction

BackgroundHead motion during brain positron emission tomography (PET)/computed tomography (CT) imaging degrades image quality, resulting in reduced reading accuracy. We evaluated the performance of a head motion correction algorithm using 18F-flutemetamol (FMM) brain PET/CT images.MethodsFMM brain PET/CT images were retrospectively included, and PET images were reconstructed using a motion correction algorithm: (1) motion estimation through 3D time-domain signal analysis, signal smoothing, and calculation of motion-free intervals using a Merging Adjacent Clustering method; (2) estimation of 3D motion transformations using the Summing Tree Structural algorithm; and (3) calculation of the final motion-corrected images using the 3D motion transformations during the iterative reconstruction process. All conventional and motion-corrected PET images were visually reviewed by two readers. Image quality was evaluated using a 3-point scale, and the presence of amyloid deposition was interpreted as negative, positive, or equivocal. For quantitative analysis, we calculated the uptake ratio (UR) of 5 specific brain regions, with the cerebellar cortex as a reference region. The results of the conventional and motion-corrected PET images were statistically compared.ResultsIn total, 108 sets of FMM brain PET images from 108 patients (34 men and 74 women; median age, 78 years) were included. After motion correction, image quality significantly improved (p < 0.001), and there were no images of poor quality. In the visual analysis of amyloid deposition, higher interobserver agreements were observed in motion-corrected PET images for all specific regions. In the quantitative analysis, the UR difference between the conventional and motion-corrected PET images was significantly higher in the group with head motion than in the group without head motion (p = 0.016).ConclusionsThe motion correction algorithm provided better image quality and higher interobserver agreement. Therefore, we suggest that this algorithm be adopted as a routine post-processing protocol in amyloid brain PET/CT imaging and applied to brain PET scans with other radiotracers.

Extended MRI-based PET motion correction for cardiac PET/MRI

PurposeA 2D image navigator (iNAV) based 3D whole-heart sequence has been used to perform MRI and PET non-rigid respiratory motion correction for hybrid PET/MRI. However, only the PET data acquired during the acquisition of the 3D whole-heart MRI is corrected for respiratory motion. This study introduces and evaluates an MRI-based respiratory motion correction method of the complete PET data.MethodsTwelve oncology patients scheduled for an additional cardiac 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI and 15 patients with coronary artery disease (CAD) scheduled for cardiac 18F-Choline (18F-FCH) PET/MRI were included. A 2D iNAV recorded the respiratory motion of the myocardium during the 3D whole-heart coronary MR angiography (CMRA) acquisition (~ 10 min). A respiratory belt was used to record the respiratory motion throughout the entire PET/MRI examination (~ 30–90 min). The simultaneously acquired iNAV and respiratory belt signal were used to divide the acquired PET data into 4 bins. The binning was then extended for the complete respiratory belt signal. Data acquired at each bin was reconstructed and combined using iNAV-based motion fields to create a respiratory motion-corrected PET image. Motion-corrected (MC) and non-motion-corrected (NMC) datasets were compared. Gating was also performed to correct cardiac motion. The SUVmax and TBRmax values were calculated for the myocardial wall or a vulnerable coronary plaque for the 18F-FDG and 18F-FCH datasets, respectively.ResultsA pair-wise comparison showed that the SUVmax and TBRmax values of the motion corrected (MC) datasets were significantly higher than those for the non-motion-corrected (NMC) datasets (8.2 ± 1.0 vs 7.5 ± 1.0, p < 0.01 and 1.9 ± 0.2 vs 1.2 ± 0.2, p < 0.01, respectively). In addition, the SUVmax and TBRmax of the motion corrected and gated (MC_G) reconstructions were also higher than that of the non-motion-corrected but gated (NMC_G) datasets, although for the TBRmax this difference was not statistically significant (9.6 ± 1.3 vs 9.1 ± 1.2, p = 0.02 and 2.6 ± 0.3 vs 2.4 ± 0.3, p = 0.16, respectively). The respiratory motion-correction did not lead to a change in the signal to noise ratio.ConclusionThe proposed respiratory motion correction method for hybrid PET/MRI improved the image quality of cardiovascular PET scans by increased SUVmax and TBRmax values while maintaining the signal-to-noise ratio.Trial registration METC162043 registered 01/03/2017.

MR-guided motion-corrected PET image reconstruction for cardiac PET-MR.

Simultaneous PET-MR imaging has shown potential for the comprehensive assessment of myocardial health from a single examination. Furthermore, MR-derived respiratory motion information has been shown to improve PET image quality by incorporating this information into the PET image reconstruction. Separately, MR-based anatomically guided PET image reconstruction has been shown to perform effective denoising, but this has been so far demonstrated mainly in brain imaging. To date the combined benefits of motion compensation and anatomical guidance have not been demonstrated for myocardial PET-MR imaging. This work addresses this by proposing a single cardiac PET-MR image reconstruction framework which fully utilises MR-derived information to allow both motion compensation and anatomical guidance within the reconstruction. Methods: Fifteen patients underwent a 18F-FDG cardiac PET-MR scan with a previously introduced acquisition framework. The MR data processing and image reconstruction pipeline produces respiratory motion fields and a high-resolution respiratory motion-corrected MR image with good tissue contrast. This MR-derived information was then included in a respiratory motion-corrected, cardiac-gated, anatomically guided image reconstruction of the simultaneously acquired PET data. Reconstructions were evaluated by measuring myocardial contrast and noise and compared to images from several comparative intermediate methods using the components of the proposed framework separately. Results: Including respiratory motion correction, cardiac gating, and anatomical guidance significantly increased contrast. In particular, myocardium-to-blood pool contrast increased by 143% on average (p<0.0001) compared to conventional uncorrected, non-guided PET images. Furthermore, anatomical guidance significantly reduced image noise compared to non-guided image reconstruction by 16.1% (p<0.0001). Conclusion: The proposed framework for MR-derived motion compensation and anatomical guidance of cardiac PET data was shown to significantly improve image quality compared to alternative reconstruction methods. Each component of the reconstruction pipeline was shown to have a positive impact on the final image quality. These improvements have the potential to improve clinical interpretability and diagnosis based on cardiac PET-MR images.

Motion correction for PET data using subspace-based real-time MR imaging in simultaneous PET/MR

Image quality of positron emission tomography (PET) reconstructions is degraded by subject motion occurring during the acquisition. Magnetic resonance (MR)-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g. navigators) to detect motion. However, handling irregular respiratory motion and bulk motion remains challenging. In this work, we propose an MR-based motion correction method relying on subspace-based real-time MR imaging to estimate motion fields used to correct PET reconstructions. We take advantage of the low-rank characteristics of dynamic MR images to reconstruct high-resolution MR images at high frame rates from highly undersampled k-space data. Reconstructed dynamic MR images are used to determine motion phases for PET reconstruction and estimate phase-to-phase nonrigid motion fields able to capture complex motion patterns such as irregular respiratory and bulk motion. MR-derived binning and motion fields are used for PET reconstruction to generate motion-corrected PET images. The proposed method was evaluated on in vivo data with irregular motion patterns. MR reconstructions accurately captured motion, outperforming state-of-the-art dynamic MR reconstruction techniques. Evaluation of PET reconstructions demonstrated the benefits of the proposed method in terms of motion artifacts reduction, improving the contrast-to-noise ratio by up to a factor 3 and achieveing a target-to-background ratio up to 90% superior compared to standard/uncorrected methods. The proposed method can improve the image quality of motion-corrected PET reconstructions in clinical applications.

Correction of head movement by frame-to-frame image realignment for receptor imaging in positron emission tomography studies with [11C]raclopride and [11C]FLB 457

Positron emission tomography (PET) scans of imaging receptors require 60-90-min dynamic acquisition for quantitative analysis. Head movement is often observed during scanning, which hampers the reliable estimation of quantitative parameters. This study evaluated image-based motion correction by frame-to-frame realignment for PET studies with [11C]raclopride and [11C]FLB 457 acquired by an Eminence SET-3000GCT/X and investigated the effect of this correction on the quantitative outcomes. First, an optimal method for estimating motion parameters was evaluated by computer simulation. Simulated emission sinograms were reconstructed to the PET images with or without attenuation correction using a µ-map of the transmission scan. Six motion parameters were estimated frame-by-frame by registering each frame of the PET images to several types of reference images and the reliability of registration was compared. Next, in [11C]raclopride and [11C]FLB 457 studies in normal volunteers, six motion parameters for each frame were estimated by the registration method determined from the simulation results. Head movement was corrected by realigning the PET images reconstructed with a motion-included µ-map in which a mismatch between the transmission and emission scans was corrected. After this correction, time-activity curves (TAC) for the striatum or cerebral cortex were obtained and the binding potentials of the receptors (BPND) were estimated using the simplified reference tissue model. In the simulations, the motion parameters could be reliably estimated by registering each frame of the non-attenuation-corrected PET images to their early-phase frame. The motion parameters in the human studies were also obtained using the same method. After correction, a discontinuity of TACs in the striatum and cerebral cortex was remarkably improved and the BPND values in these regions increased. Compared to the motion-corrected PET images reconstructed using the measured µ-map, the images reconstructed using the motion-included µ-map did not result in a remarkable improvement of BPND in the striatum of [11C]raclopride studies, while the BPND in the cerebral cortex changed in some [11C]FLB 457 studies in which large head movement was observed. In PET receptor imaging, head movement during dynamic scans can be corrected by frame-to-frame realignment. This method is easily applicable to clinical studies and provides reliable TACs and BPND.

Body motion detection and correction in cardiac PET: Phantom and human studies.

Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET. Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies. Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC. The proposed body motion correction method improves image quality of cardiac PET.

Joint cardiac and respiratory motion estimation for motion-corrected cardiac PET-MR

Respiratory and cardiac motion can strongly impair cardiac PET image quality and tracer uptake quantification. Standard gating techniques can minimize these motion artefacts but suffer from low signal-to-noise ratio because only a small percentage of the total data is utilized. Motion correction approaches have been proposed to overcome this problem but require accurate knowledge of such physiological motion. Here we present a joint PET-MR motion estimation approach which combines complimentary dynamic image information from simultaneously acquired MR and PET to ensure improved cardiac and respiratory motion estimation for motion-corrected image reconstruction (MCIR) of PET images. A 3D triple-echo Dixon MR scan is used both for calculation of MR-based attenuation correction (AC) maps and estimation of physiological motion. PET listmode data is obtained simultaneously to the MR acquisition which is used for a joint motion estimation and reconstruction of the final MCIR PET. In a first step, dynamic cardiac and respiratory motion resolved 4D MR and PET images are reconstructed. These image series are used in a joint image registration to estimate non-rigid cardiac and respiratory motion fields. In a second step, the motion fields are utilized in a MR MCIR to obtain cardiac and respiratory resolved dynamic MR-based AC maps. In the last step, the non-rigid motion fields and the dynamic AC maps are applied in a PET MCIR to obtain the final motion-corrected PET images. PET-MR data has been obtained in six patients without any known heart disease. Motion amplitudes were between 5.6 and 16 mm, with higher values in the basal compared to the mid-ventricular and apical segments. The proposed joint PET-MR motion estimation provided more accurate motion estimation than using either modality separately. The underestimation of PET uptake due to respiratory and cardiac motion artefacts in the AC maps was up to 17%. The average increase in uptake values using MCIR was 23% ± 10% (p < 0.0001), with values of 28% ± 11% (p < 0.0001) for basal, 21% ± 8% (p < 0.0001) for mid-cavity and 17% ± 7% (p < 0.0001) for apical segments. With the proposed scheme we could ensure high PET image quality and improve local PET uptake quantification by up to 30%. Attenuation correction and motion information was obtained from the same PET-MR raw data, which was obtained during free-breathing to minimize scan times and to increase patient comfort.

Awake 18F-FDG PET Imaging of Memantine-Induced Brain Activation and Test-Retest in Freely Running Mice.

PET scans of the mouse brain are usually performed with anesthesia to immobilize the animal. However, it is desirable to avoid the confounding factor of anesthesia in mouse-brain response. Methods: We developed and validated brain PET imaging of awake, freely moving mice. Head-motion tracking was performed using radioactive point-source markers, and we used the tracking information for PET-image motion correction. Regional 18F-FDG brain uptake in a test, retest, and memantine-challenge study was measured in awake (n = 8) and anesthetized (n = 8) C57BL/6 mice. An awake uptake period was considered for the anesthesia scans. Results: Awake (motion-corrected) PET images showed an 18F-FDG uptake pattern comparable to the pattern of anesthetized mice. The test–retest variability (represented by the intraclass correlation coefficient) of the regional SUV quantification in the awake animals (0.424–0.555) was marginally lower than that in the anesthetized animals (intraclass correlation coefficient, 0.491–0.629) over the different regions. The increased memantine-induced 18F-FDG uptake was more pronounced in awake (+63.6%) than in anesthetized (+24.2%) animals. Additional behavioral information, acquired for awake animals, showed increased motor activity on a memantine challenge (total distance traveled, 18.2 ± 5.28 m) compared with test–retest (6.49 ± 2.21 m). Conclusion: The present method enables brain PET imaging on awake mice, thereby avoiding the confounding effects of anesthesia on the PET reading. It allows the simultaneous measurement of behavioral information during PET acquisitions. The method does not require any additional hardware, and it can be deployed in typical high-throughput scan protocols.

Abstract 16693: Standalone Quantitative 18 F-NaF Coronary PET Fused With Prior Deep-Inspiration Coronary CT Angiography for Improved Patient Workflow

Introduction: 18 F-NaF PET holds promise in distinguishing high-risk plaques in patients with coronary artery disease. The current imaging protocol requires a hybrid single-session acquisition of coronary computed tomography angiography (CTA) and PET. This approach does not allow high-risk patient selection for 18 F-NaF imaging based on prior CTA findings. We aimed to determine whether prior deep inspiration CTA fused with a subsequent PET-only scan allows reliable evaluation of coronary 18 F-NaF uptake. Methods: Twenty-five patients (age 65±6 years, 68% males) underwent clinical CTA (CTA1) on a dual-source CT scanner and subsequently combined 18 F-NaF PET/CTA (CTA2) imaging on a hybrid PET/CT scanner within 16 [9,28] days. We fused CTA1 with 18 F-NaF PET (PET) and compared the visual pattern of activity, maximal standard uptake values (SUVmax) and target to background ratio (TBR) measurements on prior-CTA (PET/CTA1) versus hybrid (PET/CTA2) cardiac motion corrected PET images. Results: On PET/CTA2, 118 coronary plaques were identified. Thirty-six coronary segments from 14 (56%) patients had high 18 F-NaF uptake (TBR&gt;1.25), while 82 segments had lesions with 18 F-NaF TBR ≤1.25. Uptake in all lesions was categorized identically on co-registered PET/CTA1. There was no difference in 18 F-NaF uptake between PET/CTA1 and PET/CTA2 (SUVmax: 1.46±0.59 vs. 1.45±0.57, p=0.53; TBR:1.28±0.55 vs. 1.26±0.53, p=0.41). Repeatability of uptake measurements was excellent with intraclass correlation coefficients of 0.98 (95% CI 0.97 to 0.99, p&lt;0.001) and 0.94 (95% CI 0.92 to 0.96, p&lt;0.001) and limits of agreement of ±0.18 and ±0.19 for SUVmax and TBR respectively. Conclusions: Coronary CTA/PET protocol with CTA first followed by PET-only allows for reliable and reproducible quantification of 18 F-NaF coronary uptake. This approach may facilitate selection of high-risk patients for PET-only imaging based on results from prior CTA, providing a practical workflow for clinical application.

Respiratory- and cardiac motion-corrected simultaneous whole-heart PET and dual phase coronary MR angiography.

PurposeTo develop a framework for efficient and simultaneous acquisition of motion‐compensated whole‐heart coronary MR angiography (CMRA) and left ventricular function by MR and myocardial integrity by PET on a 3T PET‐MR system.MethodsAn acquisition scheme based on a dual‐phase CMRA sequence acquired simultaneously with cardiac PET data has been developed. The framework is integrated with a motion‐corrected image reconstruction approach, so that non‐rigid respiratory and cardiac deformation fields estimated from MR images are used to correct both the CMRA (respiratory motion correction for each cardiac phase) and the PET data (respiratory and cardiac motion correction). The proposed approach was tested in a cohort of 8 healthy subjects and 6 patients with coronary artery disease. Left ventricular (LV) function estimated from motion‐corrected dual‐phase CMRA was compared to the gold standard estimated from a stack of 2D CINE images for the healthy subjects. Relative increase of signal in motion‐corrected PET images compared to uncorrected images was computed for standard 17‐segment polar maps for each patient.ResultsMotion‐corrected dual‐phase CMRA images allow for visualization of the coronary arteries in both systole and diastole for all healthy subjects and cardiac patients. LV functional indices from healthy subjects result in good agreement with the reference method, underestimating stroke volume by 3.07 ± 3.26 mL and ejection fraction by 0.30 ± 1.01%. Motion correction improved delineation of the myocardium in PET images, resulting in an increased 18F‐FDG signal of up to 28% in basal segments of the myocardial wall compared to uncorrected images.ConclusionThe proposed motion‐corrected dual‐phase CMRA and cardiac PET produces co‐registered good quality images in both modalities in a single efficient examination of ~13 min.

Concurrent Respiratory Motion Correction of Abdominal PET and Dynamic Contrast-Enhanced-MRI Using a Compressed Sensing Approach.

We present an approach for concurrent reconstruction of respiratory motion-compensated abdominal dynamic contrast-enhanced (DCE)-MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields derived from radial MR data; the approach is robust to changes in respiratory pattern and does not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncologic patients were simultaneously acquired for 6 min on an integrated PET/MR system after administration of 18F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases on the basis of a respiratory signal derived directly from the radial MR data. The resulting multidimensional dataset was reconstructed using a compressed sensing approach that exploits sparsity among respiratory phases. Motion vector fields obtained using the full 6-min (MC6-min) and only the last 1 min (MC1-min) of data were incorporated into the PET reconstruction to obtain motion-corrected PET images and in an MR iterative reconstruction algorithm to produce a series of motion-corrected DCE-MR images (moco_GRASP). The motion-correction methods (MC6-min and MC1-min) were evaluated by qualitative analysis of the MR images and quantitative analysis of SUVmax and SUVmean, contrast, signal-to-noise ratio (SNR), and lesion volume in the PET images. Results: Motion-corrected MC6-min PET images demonstrated 30%, 23%, 34%, and 18% increases in average SUVmax, SUVmean, contrast, and SNR and an average 40% reduction in lesion volume with respect to the non-motion-corrected PET images. The changes in these figures of merit were smaller but still substantial for the MC1-min protocol: 19%, 10%, 15%, and 9% increases in average SUVmax, SUVmean, contrast, and SNR; and a 28% reduction in lesion volume. Moco_GRASP images were deemed of acceptable or better diagnostic image quality with respect to conventional breath-hold Cartesian volumetric interpolated breath-hold examination acquisitions. Conclusion: We presented a method that allows the simultaneous acquisition of respiratory motion-corrected diagnostic quality DCE-MRI and quantitatively accurate PET data in an integrated PET/MR scanner with negligible prolongation in acquisition time compared with routine PET/DCE-MRI protocols.

A PET ANALYSIS PIPELINE FOR MEASURING MISFOLDED PROTEIN CONCENTRATIONS IN MULTICENTER IMAGING TRIALS USING MIAKATTM

The pathophysiological process of Alzheimer's disease (AD) begins years before clinical symptoms with the preclinical phase providing the best opportunity for effective therapeutic intervention. New molecular imaging techniques provide the possibility to detect and follow the early disease process using β-amyloid (Aβ) and tau biomarkers to monitor the concentration of these misfolded proteins and the effects of drug therapy. Here we propose a PET image analysis pipeline that can derive quantitative measurements of misfolded protein concentrations for multicentre trials in AD. A particular analytical pipeline is implemented in MIAKAT™ to process static PET (Aβ or Tau) images, along with an associated structural T1-MR image. It uses images in NIFTI file format and derives quantitative imaging outcome measures from PET data, namely regional Standardized Uptake Value Ratios (SUVR) with respect to cerebellum grey as reference region and SUVR parametric images as follows: 1) The subject's T1-MR image is segmented to derive a grey matter (GM) probability map using SPM12. A neuroanatomical atlas containing 125 regions (CIC atlas) is nonlinearly warped to the subject's MR image using non rigid registration. 2) Individual frames within the PET acquisition are corrected for subject motion via a frame-to-frame (rigid) registration algorithm and averaged. The resulting motion corrected PET image is rigid-body registered to the subject's structural MR data. 3) The atlas is applied to the PET data in order to calculate regional SUVR values and SUVR parametric images. The analysis pipeline executes automatically, and allows for appropriate QC of these steps on completion. It provides full audit trailing of the analysis and QC status, maintaining provenance of SUVR outcome measures and enabling reproduction of results in the future from the primary data. Example analysis results of an Aβ ([18F]AV45) and a tau ([18F]AV1451) PET scan from an early AD subject are presented in Figure1 and Table1. The analysis process is fast enabling processing of a single scan in under 10 mins on a standard workstation. The proposed analysis pipeline provides a consistent, efficient and reproducible way to derive quantitative endpoints from misfolded protein PET images with their associated QC status. Four different images produced by the pipeline for an example early AD subject are shown in three orthogonal cross-sections including: subject's MR (1st row), subject's brain grey mask (2nd row), SUVR parametric image of subject's brain with respect to cerebellum grey from Aβ PET scan (3rd row) and from tau PET scan (4th row).

Validating PET segmentation of thoracic lesions—is 4D PET necessary?

Respiratory-induced motions are prone to degrade the positron emission tomography (PET) signal with the consequent loss of image information and unreliable segmentations. This phantom study aims to assess the discrepancies relative to stationary PET segmentations, of widely used semi-automatic PET segmentation methods on heterogeneous target lesions influenced by motion during image acquisition. Three target lesions included dual F-18 Fluoro-deoxy-glucose (FDG) tracer concentrations as high- and low tracer activities relative to the background. Four different tracer concentration arrangements were segmented using three SUV threshold methods (Max40%, SUV40% and 2.5SUV) and a gradient based method (GradientSeg). Segmentations in static 3D-PET scans (PETsta) specified the reference conditions for the individual segmentation methods, target lesions and tracer concentrations. The motion included PET images followed a 4D-PET (PET4D) and a 3D-PET (PETmot) scan protocol. Moreover, motion-corrected PET images (PETdeb) were derived from the PETmot images. Segmentations in PET4D, PETmot and PETdeb were compared to the PETsta segmentations according to volume changes (ΔVol) and an error estimate (lowUptakeerror) for the lesion part covering the low tracer concentration. In PET4D images, all segmentation methods provided lowUptakeerror estimates equivalent to PETsta segmentations and, except for the Max40% segmentations, a slight volume expansion. In the PETmot images, the GradientSeg method results in an average 0.43 increased volume and an overestimation of 0.33 for the lowUptakeerror. The most accurate segmentations in PETmot, relative to PETsta, were accomplished by the 2.5SUV and SUV40% methods. In the PETdeb images, the GradientSeg method solitary provided segmentations equivalent to segmentation in PETsta images. The use of FDG with various tracer concentrations revealed, according to PETsta images, that the most constant segmentations for motion-corrected PET images (PET4D or PETdeb) were achieved using the GradientSeg method. In the absence of PET4D or PETdeb images, the 2.5SUV and SUV40% methods are most consistent to PETsta segmentations.

Respiratory and cardiac motion correction in dual gated PET/MR imaging.

Respiratory and cardiac motion in PET/MR imaging leads to reduced quantitative and qualitative image accuracy. Correction methodologies involve the use of double gated acquisitions which lead to low signal-to-noise ratio (SNR) and to issues concerning the combination of cardiac and respiratory frames. The objective of this work is to use a generalized reconstruction by inversion of coupled systems (GRICS) approach, previously used for PET/MR respiratory motion correction, combined with a cardiac phase signal and a reconstruction incorporated PET motion correction approach in order to reconstruct motion free images from dual gated PET acquisitions. The GRICS method consists of formulating parallel MRI in the presence of patient motion as a coupled inverse problem. Its resolution, using a fixed-point method, allows the reconstructed image to be improved using a motion model constructed from the raw MR data and two respiratory belts. GRICS obtained respiratory displacements are interpolated using the cardiac phase derived from an ECG to model simultaneous cardiac and respiratory motion. Three different volunteer datasets (4DMR acquisitions) were used for evaluation. GATE was used to simulate 4DPET datasets corresponding to the acquired 4DMR images. Simulated data were subsequently binned using 16 cardiac phases (M1) vs diastole only (M2), in combination with 8 respiratory amplitude gates. Respiratory and cardiac motion corrected PET images using either M1 or M2 were compared to respiratory only corrected images and evaluated in terms of SNR and contrast improvement. Significant visual improvements were obtained when correcting simultaneously for respiratory and cardiac motion (using 16 cardiac phase or diastole only) compared to respiratory motion only compensation. Results were confirmed by an associated increased SNR and contrast. Results indicate that using GRICS is an efficient tool for respiratory and cardiac motion correction in dual gated PET/MR imaging.

Practical PET Respiratory Motion Correction in Clinical PET/MR.

Respiratory motion during PET acquisition may lead to blurring in resulting images and underestimation of uptake parameters. The advent of integrated PET/MR scanners allows us to exploit the integration of modalities, using high spatial resolution and high-contrast MR images to monitor and correct PET images degraded by motion. We proposed a practical, anatomy-independent MR-based correction strategy for PET data affected by respiratory motion and showed that it can improve image quality both for PET acquired simultaneously to the motion-capturing MR and for PET acquired up to 1 h earlier during a clinical scan. To estimate the respiratory motion, our method needs only an extra 1-min dynamic MR scan, acquired at the end of the clinical PET/MR protocol. A respiratory signal was extracted directly from the PET list-mode data. This signal was used to gate the PET data and to construct a motion model built from the dynamic MR data. The estimated motion was then incorporated into the PET image reconstruction to obtain a single motion-corrected PET image. We evaluated our method in 2 steps. The PET-derived respiratory signal was compared with an MR measure of diaphragmatic displacement via a pencil-beam navigator. The motion-corrected images were compared with uncorrected images with visual inspection, line profiles, and standardized uptake value (SUV) in focally avid lesions. We showed a strong correlation between the PET-derived and MR-derived respiratory signals for 9 patients, with a mean correlation of 0.89. We then showed 4 clinical case study examples ((18)F-FDG and (68)Ga-DOTATATE) using the motion-correction technique, demonstrating improvements in image sharpness and reduction of respiratory artifacts in scans containing pancreatic, liver, and lung lesions as well as cardiac scans. The mean increase in peak SUV (SUV(peak)) and maximum SUV (SUV(max)) in a patient with 4 pancreatic lesions was 23.1% and 34.5% in PET acquired simultaneously with motion-capturing MR, and 17.6% and 24.7% in PET acquired 50 min before as part of the clinical scan. We showed that a respiratory signal can be obtained from raw PET data and that the clinical PET image quality can be improved using only a short additional PET/MR acquisition. Our method does not need external respiratory hardware or modification of the normal clinical MR sequences.

Accelerated acquisition of tagged MRI for cardiac motion correction in simultaneous PET-MR: phantom and patient studies.

Degradation of image quality caused by cardiac and respiratory motions hampers the diagnostic quality of cardiac PET. It has been shown that improved diagnostic accuracy of myocardial defect can be achieved by tagged MR (tMR) based PET motion correction using simultaneous PET-MR. However, one major hurdle for the adoption of tMR-based PET motion correction in the PET-MR routine is the long acquisition time needed for the collection of fully sampled tMR data. In this work, the authors propose an accelerated tMR acquisition strategy using parallel imaging and/or compressed sensing and assess the impact on the tMR-based motion corrected PET using phantom and patient data. Fully sampled tMR data were acquired simultaneously with PET list-mode data on two simultaneous PET-MR scanners for a cardiac phantom and a patient. Parallel imaging and compressed sensing were retrospectively performed by GRAPPA and kt-FOCUSS algorithms with various acceleration factors. Motion fields were estimated using nonrigid B-spline image registration from both the accelerated and fully sampled tMR images. The motion fields were incorporated into a motion corrected ordered subset expectation maximization reconstruction algorithm with motion-dependent attenuation correction. Although tMR acceleration introduced image artifacts into the tMR images for both phantom and patient data, motion corrected PET images yielded similar image quality as those obtained using the fully sampled tMR images for low to moderate acceleration factors (<4). Quantitative analysis of myocardial defect contrast over ten independent noise realizations showed similar results. It was further observed that although the image quality of the motion corrected PET images deteriorates for high acceleration factors, the images were still superior to the images reconstructed without motion correction. 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.

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-count PET image volume, a procedure called Reconstruct, Register and Average (RRA). Methods: A phantom was constructed using 5 ~ 2 mL 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 activity 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 three 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-count 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 1 mm 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 ungated 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 ungated 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 non-moving 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.

Evaluation of two approaches to motion-corrected PET image reconstruction

Motion correction is a major task in PET. This study aims to understand the behaviour of different approaches to motion-corrected image reconstruction in terms of convergence rates and the properties of the images obtained. We have studied two approaches that have been suggested to motion-correct PET gated data. The first method, RRA, is based on independent reconstructions of each gate which are then registered to a reference gate and averaged. On the other hand, in the second method, MCIR, all data are used with the motion information within a single reconstruction. As a first step, this investigation compares the methods with no motion present. Multiple simulations have been produced with counts approximating a 5 min PET thorax acquisition. The image bias and variance are the main figures of merit. The results show different convergence and statistics performance between the two methods. RRA converges faster than MCIR but not at the correct value. Considering the trade-off between bias and variance, it is seen that MCIR outperforms RRA. It has been also demonstrated that, although at higher iterations RRA has lower variance and higher bias than MCIR, at a low number of iterations the performance of the methods becomes closer. This investigation has revealed the statistical and computational discrepancies between RRA and MCIR when motion is not present and MCIR is shown to be clearly superior.

Multiple Bronchiolar Stenoses in a Patient with Generalized Airways Obstruction

PET scans of the mouse brain are usually performed with anesthesia to immobilize the animal. However, it is desirable to avoid the confounding factor of anesthesia in mouse-brain response. <b>Methods:</b> We developed and validated brain PET imaging of awake, freely moving mice. Head-motion tracking was performed using radioactive point-source markers, and we used the tracking information for PET-image motion correction. Regional ¹⁸F-FDG brain uptake in a test, retest, and memantine-challenge study was measured in awake (<i>n</i> = 8) and anesthetized (<i>n</i> = 8) C57BL/6 mice. An awake uptake period was considered for the anesthesia scans. <b>Results:</b> Awake (motion-corrected) PET images showed an ¹⁸F-FDG uptake pattern comparable to the pattern of anesthetized mice. The test–retest variability (represented by the intraclass correlation coefficient) of the regional SUV quantification in the awake animals (0.424–0.555) was marginally lower than that in the anesthetized animals (intraclass correlation coefficient, 0.491–0.629) over the different regions. The increased memantine-induced ¹⁸F-FDG uptake was more pronounced in awake (+63.6%) than in anesthetized (+24.2%) animals. Additional behavioral information, acquired for awake animals, showed increased motor activity on a memantine challenge (total distance traveled, 18.2 ± 5.28 m) compared with test–retest (6.49 ± 2.21 m). <b>Conclusion:</b> The present method enables brain PET imaging on awake mice, thereby avoiding the confounding effects of anesthesia on the PET reading. It allows the simultaneous measurement of behavioral information during PET acquisitions. The method does not require any additional hardware, and it can be deployed in typical high-throughput scan protocols.