Quantitative Evaluation of PET Respiratory Motion Correction Using MR Derived Simulated Data

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.

Respiratory motion in the chest region during single photon emission computed tomography (SPECT) is a major degrading factor that reduces the accuracy of image quantification. This effect is more notable when the tumor is very small, or the spatial resolution of the imaging system is less than the respiratory motion amplitude. Small animals imaging systems with sub-millimeter spatial resolution need more attention to the respiratory motion for quantitative studies. We developed a motion-embedded four-dimensional (4D)-multi pinhole SPECT (MPS) reconstruction algorithm for respiratory motion correction. This algorithm makes full use of projection statistics for reconstruction of every individual frame. The ROBY phantom with small tumors in liver was generated in eight different phases during one respiratory cycle. The MPS projections were modeled using a fast ray tracing method simulating an MPS acquisition. Individual frames were reconstructed and used for motion estimation. The Demons non-rigid registration algorithm was used to calculate deformation vector fields (DVFs) for simultaneous motion correction and image reconstruction. A motion-embedded 4D-MPS method was used to reconstruct images using all the projections and corresponding DVFs, simultaneously. The 4D-MPS reconstructed images were compared to the low-count single frame (LCSF) reconstructed image, the three-dimensional (3D)-MPS images reconstructed using individual frames, and post reconstruction registration (PRR) that aligns all individual phases to a reference frame using Demons-derived DVFs. The tumor volume relative error (TVE), tumor contrast relative error (TCE), and dice index (DI) for 2, 3, and 4mm liver were calculated and compared for different reconstruction methods. For the 4D-MPS reconstruction method, TVE was reduced and DI was higher compared to PRR, 3D-MPS, and LCSF. The extent of the improvement was higher for the small tumor size (i.e. 2mm). For the biggest tumor in contrast 3 (i.e. 4mm) TVE for 4D-MPS, PRR, 3D-MPS and, LCSF were 1.33%, 8%, 8%, and 14.67%, respectively. The results suggest that motion-embedded 4D-MPS method is an effective and practical way for respiratory motion correction. It reconstructs high quality gated frames while using all projection data to reconstruct each frame.

Purpose: Current hepatic dynamic contrast enhanced CT (DCE‐CT) protocols rely on patients holding their breath either once or multiple times. We propose that hepatic CT perfusion imaging is feasible using a free breathing DCE‐CT scanning protocol without prior coaching on breathing and automatic post acquisition image registration to eliminate motion induced artifacts. Methods: 26 patients (21 males, 5 females, average age 71 years) with primary or metastatic hepatomas were scanned with a free‐ breathing axial shuttle DCE‐CT. One‐dimensional respiratory motion correction in the axial direction was performed automatically by registering high contrast liver features in every image to that of a chosen reference image.CT Perfusion (GE Healthcare) was extended to calculate root mean squared deviation (RMSD) maps to quantify the amount of respiratory motion before and after motion correction. Functional parameters were generated for both corrected and uncorrected scans to determine the effect of motion induced artifacts. Results: Within the liver, background levels of RMSD due to tissue inhomogeneity, imaging noise and reconstruction artifacts was found to be less than 20 HU. A threshold of 200 HU was chosen to indicate organ motion. The mean fraction of voxels in the liver with RMSD above 200 HU (VF) decreased from 8.0% to 4.0% after correction (p=0.01). A strong correlation was found between initial VF (VFI) and reduction in VF (DVF) due to motion correction (DVF = 0.94*VFI − 3.66, R=0.97). Total blood flow was 30 ml/min/kg (p=0.08) higher in tumour than normal tissue prior to correction and 40 ml/min/kg (p=0.008) after correction. Conclusion: Free breathing DCE‐CT in liver is feasible and using a posteriori motion correction reduces potential motion artifacts in functional maps. Motion correction is beneficial in cases where initial VF is greater than 5% or an ROI can not be drawn that includes the portal vein in all volumes. “This research is funded by the Canadian Cancer Society (grant #700386)”.

Infective endocarditis (IE) is a serious and diagnostically challenging condition. [18F]FDG PET/CT is valuable for evaluating suspected IE, but it is susceptible to motion-related artefacts. This study investigated the potential benefits of cardiac motion correction for [18F]FDG PET/CT. In this prospective study, patients underwent [18F]FDG PET/CT for suspected IE, combined with a conventional cardiac gating sequence, a data-driven cardiac and respiratory gating sequence (CardioFreezeTM), or both. Scans were performed in adherence to EANM guidelines and assessors were blinded to patients’ clinical contexts. Final diagnosis of IE was established based on multidisciplinary consensus after a minimum of 4 months follow-up and surgical findings, whenever performed. Seven patients participated in the study, undergoing both an ungated [18F] FDG-PET/CT and a scan with either conventional cardiac gating, CardioFreezeTM, or both. Cardiac motion correction improved the interpretability of [18F]FDG PET/CT in four out of five patients with valvular IE lesions, regardless of the method of motion correction used, which was statistically significant by Wilcoxon’s signed rank test: p = 0.046. In one patient the motion-corrected sequence confirmed the diagnosis of endocarditis, which had been missed on non-gated PET. The performance of the two gating sequences was comparable. In conclusion, in this exploratory study, cardiac motion correction of [18F]FDG PET/CT improved the interpretability of [18F]FDG PET/CT. This may improve the sensitivity of PET/CT for suspected IE. Further larger comparative studies are necessary to confirm the additive value of these cardiac motion correction methods.

The impact of a method for MR-based respiratory motion correction of PET data on lesion visibility and quantification in patients with oncologic findings in the lung was evaluated. Twenty patients with one or more lesions in the lung were included. Hybrid imaging was performed on an integrated PET/MR system using 18F-FDG as radiotracer. The standard thoracic imaging protocol was extended by a free-breathing self-gated acquisition of MR data for motion modelling. PET data was acquired simultaneously in list-mode for 5-10 mins. One experienced radiologist and one experienced nuclear medicine specialist evaluated and compared the post-processed data in consensus regarding lesion visibility (scores 1–4, 4 being best), image noise levels (scores 1–3, 3 being lowest noise), SUVmean and SUVmax. Motion-corrected (MoCo) images were additionally compared with gated images. Non-motion-corrected free-breathing data served as standard of reference in this study. Motion correction generally improved lesion visibility (3.19 ± 0.63) and noise ratings (2.95 ± 0.22) compared to uncorrected (2.81 ± 0.66 and 2.95 ± 0.22, respectively) or gated PET data (2.47 ± 0.93 and 1.30 ± 0.47, respectively). Furthermore, SUVs (mean and max) were compared for all methods to estimate their respective impact on the quantification. Deviations of SUVmax were smallest between the uncorrected and the MoCo lesion data (average increase of 9.1% of MoCo SUVs), while SUVmean agreed best for gated and MoCo reconstructions (MoCo SUVs increased by 1.2%). The studied method for MR-based respiratory motion correction of PET data combines increased lesion sharpness and improved lesion activity quantification with high signal-to-noise ratio in a clinical setting. In particular, the detection of small lesions in moving organs such as the lung and liver may thus be facilitated. These advantages justify the extension of the PET/MR imaging protocol by 5–10 minutes for motion correction.

Objective: To investigate the effect of different respiratory motion correction methods on PET images during chest PET/MRI scans. Methods: The data of 35 patients (24 males and 11 females, aged from 29 to 84 year) of pulmonary lesions with significantly high uptake in thoracic PET/MRI scan were retrospective collected from Jingling Hospital. Four different methods were used to reconstruct the PET data. Group A was the full-time 20 min without respiratory motion correction static acquisition (Static) as a control, group B was the end-expiration static collection (Q.Static), and group C was the multi-bins respiratory gating (Gated-Respiratory). In addition, the influence of the time being considered, group D was added for reconstruction in the first 1/3 period (6 min 40 s) of group A. Then, the maximum value (L-SUVmax) and the mean value (L-SUVmean) of the SUV of the lesion, the mean value (B-SUVmean) and the standard deviation (B-SUVsd) of the SUV of the background under each reconstruction results were measured, and for each lesion the signal-to-noise ratio (L-SNR) was calculated. In order to exclude the interference of the background, the mean of the relative SUV (L-dSUVmean) of the L-SUVmean relative to the B-SUVmean was also calculated. Finally, One-Way Repeated Measures ANOVA was used, and the post-hoc pairwise comparison between groups was tested by Bonferroni's modified test. Results: There was statistically significant difference among group B or group C compared to group A and group D in L-SUVmax, L-SUVmean and L-dSUVmean [L-SUVmax:group B vs group A or group D was 8.06±3.57 vs 7.73±3.45 or 7.61±3.50, group C vs group A or group D was 8.04±3.56 vs 7.73±3.45 or 7.61±3.50 (all P<0.05); L-SUVmean: group B vs group A or group D was 4.12±1.78 vs 3.98±1.72 or 3.91±1.71, group C vs group A or group D was 4.13±1.78 vs 3.98±1.72 or 3.91±1.71 (all P<0.05); L-dSUVmean: group B vs group A or group D was 3.52±0.16 vs 3.39±0.18 or 3.31±0.18, group C vs group A or group D was 3.53±0.18 vs 3.39±0.18 or 3.31±0.18 (all P<0.05)], but there was no statistically significant difference between group B and group C (all P>0.05). There were statistically significant differences between group D and group A in B-SUVsd (0.07±0.00 vs 0.07±0.00, P=0.023) and L-SNR (69.80±44.57 vs 85.35±68.98, P=0.001). There was no statistically significant difference between group D and group A in L-SUVmax, L-SUVmean, B-SUVmean and L-dSUVmean (all P>0.05). Conclusions: There was no significant difference in PET image quality between the Q.static and Gated-Respiratory group, both of the two groups were better than the Static group which with no Gated-Respiratory motion correction. If non-respiratory gated Static is used, the PET acquisition time is recommended to be 6 min 40 s.

Following continuous improvement in PET spatial resolution, respiratory motion correction has become an important task. Two of the most common approaches that utilize all detected PET events to motion-correct PET data are the reconstruct-transform-average method (RTA) and motion-compensated image reconstruction (MCIR). In RTA, separate images are reconstructed for each respiratory frame, subsequently transformed to one reference frame and finally averaged to produce a motion-corrected image. In MCIR, the projection data from all frames are reconstructed by including motion information in the system matrix so that a motion-corrected image is reconstructed directly. Previous theoretical analyses have explained why MCIR is expected to outperform RTA. It has been suggested that MCIR creates less noise than RTA because the images for each separate respiratory frame will be severely affected by noise. However, recent investigations have shown that in the unregularized case RTA images can have fewer noise artefacts, while MCIR images are more quantitatively accurate but have the common salt-and-pepper noise. In this paper, we perform a realistic numerical 4D simulation study to compare the advantages gained by including regularization within reconstruction for RTA and MCIR, in particular using the median-root-prior incorporated in the ordered subsets maximum a posteriori one-step-late algorithm. In this investigation we have demonstrated that MCIR with proper regularization parameters reconstructs lesions with less bias and root mean square error and similar CNR and standard deviation to regularized RTA. This finding is reproducible for a variety of noise levels (25, 50, 100 million counts), lesion sizes (8 mm, 14 mm diameter) and iterations. Nevertheless, regularized RTA can also be a practical solution for motion compensation as a proper level of regularization reduces both bias and mean square error.

To develop a patient-specific respiratory motion correction technique with true 100% acquisition efficiency. A short training scan consisting of a series of single heartbeat images, each acquired with a preceding diaphragmatic navigator, was performed to fit a model relating the patient-specific 3D respiratory motion of the heart-to-diaphragm position. The resulting motion model was then used to update the imaging plane in real-time to correct for translational motion based on respiratory position provided by the navigator. The method was tested in a group of 11 volunteers with 5 separate free-breathing acquisitions: FB, no motion correction; FB-TF, free breathing with a linear tracking factor; Nav Gate, navigator gating; Nav Gate-TF, navigator gating with a tracking factor; and PROCO, prospective motion correction (proposed). Each acquisition lasted for 50 accepted heartbeats, where non-gated scans had a 100% acceptance rate, and gated scans accepted data only within a ±4 mm navigator window. Retrospective image registration was used to measure residual motion and determine the effectiveness of each method. PROCO reduced the range/RMSE of residual motion to 4.08 ± 1.4/0.90 ± 0.3 mm, compared to 10.78 ± 6.9/2.97 ± 2.2 mm for FB, 5.32 ± 2.92/1.24 ± 0.8 mm for FB-TF, 4.08 ± 1.6/0.93 ± 0.4 mm for Nav Gate, and 2.90 ± 1.0/0.63 ± 0.2 mm for Nav Gate-TF. Nav Gate and Nav Gate-TF reduced scan efficiency to 48.84 ± 9.31% and 54.54 ± 10.12%, respectively. PROCO successfully limited the residual motion in single-shot imaging to the level of traditional navigator gating while maintaining 100% acquisition efficiency.

Simultaneous PET and MR imaging is a promising new technique allowing the fusion of functional (PET) and anatomic/functional (MR) information. In the thoracic-abdominal regions, respiratory motion is a major challenge leading to reduced quantitative and qualitative image accuracy. Correction methodologies include the use of gated frames that lead to low signal-to-noise ratio considering the associated low statistics. More advanced correction approaches, previously developed for PET/CT imaging, consist of either registering all the reconstructed gated frames to the reference frame or incorporating motion parameters into the iterative reconstruction process to produce a single motion-compensated PET image. The goal of this work was to compare these two—previously implemented in PET/CT—correction approaches within the context of PET/MR motion correction for oncology applications using clinical 4-dimensional PET/MR acquisitions. Two different correction approaches were evaluated comparing the incorporation of elastic transformations extracted from 4-dimensional MR imaging datasets during PET list-mode image reconstruction to a postreconstruction image-based approach. Eleven patient datasets acquired on a PET/MR system were used. T1-weighted 4D MR images were registered to the end-expiration image using a nonrigid B-spline registration algorithm to derive deformation matrices accounting for respiratory motion. The derived matrices were subsequently incorporated within a PET image reconstruction of the original emission list-mode data (reconstruction space [RS] method). The corrected images were compared with those produced by applying the deformation matrices in the image space (IS method) followed by summing the realigned gated frames, as well as with uncorrected motion-averaged images. Both correction techniques led to significant improvement in accounting for respiratory motion artifacts when compared with uncorrected motion-averaged images. These improvements included signal-to-noise ratio (mean increase of 28.0% and 24.2% for the RS and IS methods, respectively), lesion size (reduction of 60.4% and 47.9%, respectively), lesion contrast (increase of 70.1% and 57.2%, respectively), and lesion position (changes of 60.9% and 46.7%, respectively). Our results demonstrate significant respiratory motion compensation using both methods, with superior results from a 4D PET RS approach.

Purpose 18F-fluorocholine is a positron emission tomography (PET) tracer earlier found to be a marker of macrophage content in carotid plaques. We aimed to assess the feasibility of 18F-choline PET-MRI to non-invasively localize vulnerable coronary plaques, using optical coherence tomography (OCT) as reference standard. Methods Patients with recent myocardial infarction who were scheduled for a secondary angiography of a non-culprit vessel, underwent 18F-fluorocholine coronary PET-MRI. Subsequently, OCT was performed during the secondary angiography. Maximum target-to-background (TBRmax) values of 18F-fluorocholine uptake were determined in two vessel sections that contained either vulnerable or stable plaques as defined by OCT. The OCT-based definition of a vulnerable plaque was a fibrous cap thickness &lt;70µm. To enhance the detectability of coronary plaques using PET, three different motion-correction strategies were used: multigate respiratory gating motion correction (MRG-MOCO), extended MR-based motion correction (eMR-MOCO), and extended MR-based motion correction with ECG gating (eMR-MOCO-ECG). Results Fifteen patients were included in this study, of which eleven entered final analyses. Data of the other four subjects could only be partially analysed. TBRmax values were as follows for three different reconstructions in vulnerable versus stable plaques: MRG-MOCO: mean TBRmax 1.45 vs 1.35, p=0.52; eMR-MOCO mean TBRmax 1.47 vs 1.27, p= 0.26, eMR-MOCO-ECG: mean TBRmax 1.49 vs 1.26, p= 0.21. Conclusion 18F-fluorocholine uptake in vulnerable atherosclerotic plaques in coronary arteries was not significantly different from uptake in stable plaques even though advanced motion-correction methods were applied. That may be caused by multiple factors, such as small coronary plaque size and remaining cardiac motion.

Background/Objectives: 18F-fluorocholine is a positron emission tomography (PET) tracer earlier found to be a marker of macrophage content in carotid plaques. We aimed to assess the feasibility of 18F-choline PET-MRI to non-invasively localize vulnerable coronary plaques, using optical coherence tomography (OCT) as a reference standard. Methods: Patients with recent myocardial infarction who were scheduled for a secondary angiography of a non-culprit vessel underwent 18F-fluorocholine coronary PET-MRI. Subsequently, OCT was performed during the secondary angiography. Maximum target-to-background (TBRmax) values of 18F-fluorocholine uptake were determined in two vessel sections that contained either vulnerable or stable plaques as defined by OCT. The OCT-based definition of a vulnerable plaque was a fibrous cap thickness < 70 µm. To enhance the detectability of coronary plaques using PET, three different motion-correction strategies were used: multigate respiratory gating motion correction (MRG-MOCO), extended MR-based motion correction (eMR-MOCO), and extended MR-based motion correction with ECG gating (eMR-MOCO-ECG). Results: Fifteen patients were included in this study. One patient needed to be excluded due to extravasation of the tracer. In another patient, no region with only a stable plaque could be identified. TBRmax values were as follows for three different reconstructions in vulnerable versus stable plaques: MRG-MOCO: mean TBRmax 1.45 vs. 1.35, p = 0.52 (n = 13); eMR-MOCO: mean TBRmax 1.47 vs. 1.27, p = 0.26 (n = 11); eMR-MOCO-ECG: mean TBRmax 1.49 vs. 1.26, p = 0.21 (n = 11). Conclusions: 18F-fluorocholine uptake in vulnerable atherosclerotic plaques in coronary arteries was not significantly different from uptake in stable plaques, even though advanced motion-correction methods were applied. That may be caused by multiple factors, such as small coronary plaque size, tracer biology, or remaining cardiac motion.

Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12mm, compared to 2.6% for the CoM method and 8.5% for no correction. We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.

O.12 - Determination of the influence of respiratory motion on the SUV accuracy in pulmonary lesions

The aim of this study was to develop a method for tracking respiratory motion throughout full MR or PET/MR studies that requires only minimal additional hardware and no modifications to the sequences. Patient motion that is caused by respiration affects the quality of the signal of the individual radiofrequency receive coil elements. This effect can be detected as a modulation of a monofrequent signal that is emitted by a small portable transmitter placed inside the bore (Pilot Tone). The frequency is selected such that it is located outside of the frequency band of the actual MR readout experiment but well within the bandwidth of the radiofrequency receiver, that is, the oversampling area. Temporal variations of the detected signal indicate motion. After extraction of the signal from the raw data, principal component analysis was used to identify respiratory motion. The approach and potential applications during MR and PET/MR examinations that rely on a continuous respiratory signal were validated with an anthropomorphic, PET/MR-compatible motion phantom as well as in a volunteer study. Respiratory motion detection and correction were presented for MR and PET data in phantom and volunteer studies. The Pilot Tone successfully recovered the ground-truth respiratory signal provided by the phantom. The presented method provides reliable respiratory motion tracking during arbitrary imaging sequences throughout a full PET/MR study. All results can directly be transferred to MR-only applications as well.

Four-Dimensional Image Reconstruction Strategies in Cardiac-Gated and Respiratory-Gated PET Imaging