Cardiac and Respiratory Motion Correction for Simultaneous Cardiac PET/MR.
ABSTRACT: Cardiac PET is a versatile imaging technique providing important diagnostic information about ischemic heart diseases. Respiratory and cardiac motion of the heart can strongly impair image quality and therefore diagnostic accuracy of cardiac PET scans. The aim of this study was to investigate a new cardiac PET/MR approach providing respiratory and cardiac motion-compensated MR and PET images in less than 5 min. Methods: Free-breathing 3-dimensional MR data were acquired and retrospectively binned into multiple respiratory and cardiac motion states. Three-dimensional cardiac and respiratory motion fields were obtained with a nonrigid registration algorithm and used in motion-compensated MR and PET reconstructions to improve image quality. The improvement in image quality and diagnostic accuracy of the technique was assessed in simultaneous 18F-FDG PET/MR scans of a canine model of myocardial infarct and was demonstrated in a human subject. Results: MR motion fields were successfully used to compensate for in vivo cardiac motion, leading to improvements in full width at half maximum of the canine myocardium of 13% ± 5%, similar to cardiac gating but with a 90% ± 57% higher contrast-to-noise ratio between myocardium and blood. Motion correction led to an improvement in MR image quality in all subjects, with an increase in sharpness of the canine coronary arteries of 85% ± 72%. A functional assessment showed good agreement with standard MR cine scans with a difference in ejection fraction of -2% ± 3%. MR-based respiratory and cardiac motion information was used to improve the PET image quality of a human in vivo scan. Conclusion: The MR technique presented here provides both diagnostic and motion information that can be used to improve MR and PET image quality. Reliable respiratory and cardiac motion correction could make cardiac PET results more reproducible.
Project description:Motion of the myocardium deteriorates the quality and quantitative accuracy of cardiac PET images. We present a method for MR-based cardiac and respiratory motion correction of cardiac PET data and evaluate its impact on estimation of activity and kinetic parameters in human subjects. Three healthy subjects underwent simultaneous dynamic 18F-FDG PET and MRI on a hybrid PET/MR scanner. A cardiorespiratory motion field was determined for each subject using navigator, tagging and golden-angle radial MR acquisitions. Acquired coincidence events were binned into cardiac and respiratory phases using electrocardiogram and list mode-driven signals, respectively. Dynamic PET images were reconstructed with MR-based motion correction (MC) and without motion correction (NMC). Parametric images of 18F-FDG consumption rates (Ki) were estimated using Patlak's method for both MC and NMC images. MC alleviated motion artifacts in PET images, resulting in improved spatial resolution, improved recovery of activity in the myocardium wall and reduced spillover from the myocardium to the left ventricle cavity. Significantly higher myocardium contrast-to-noise ratio and lower apparent wall thickness were obtained in MC versus NMC images. Likewise, parametric images of Ki calculated with MC data had improved spatial resolution as compared to those obtained with NMC. Consistent with an increase in reconstructed activity concentration in the frames used during kinetic analyses, MC led to the estimation of higher Ki values almost everywhere in the myocardium, with up to 18% increase (mean across subjects) in the septum as compared to NMC. This study shows that MR-based motion correction of cardiac PET results in improved image quality that can benefit both static and dynamic studies.
Project description:The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.
Project description: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.
Project description:INTRODUCTION:Subject movements lead to severe artifacts in magnetic resonance (MR) brain imaging. In this study we evaluate the diagnostic image quality in T1-weighted, T2-weighted, and time-of-flight angiographic MR sequences when using a flexible, navigator-based prospective motion correction system (iMOCO). METHODS:Five healthy volunteers were scanned during different movement scenarios with and without (+/-) iMOCO activated. An experienced neuroradiologist graded images for image quality criteria (grey-white-matter discrimination, basal ganglia, and small structure and vessel delineation), and general image quality on a four-grade scale. RESULTS:In scans with deliberate motion, there was a significant improvement in the image quality with iMOCO compared to the scans without iMOCO in both general image impression (T1 p<0.01, T2 p<0.01, TOF p = 0.03) and in anatomical grading (T1 p<0.01, T2 p<0.01, TOF p = 0.01). Subjective image quality was considered non-diagnostic in 91% of the scans with motion -iMOCO, but only in 4% of the scans with motion +iMOCO. iMOCO performed best in the T1-weighted sequence and least well in the angiography sequence. iMOCO was not shown to have any negative effect on diagnostic image quality, as no significant difference in diagnostic quality was seen between scans -iMOCO and +iMOCO with no deliberate movement. CONCLUSION:The evaluation showed that iMOCO enables substantial improvements in image quality in scans affected by subject movement, recovering important diagnostic information in an otherwise unusable scan.
Project description:Respiratory and cardiac motion is the most serious limitation to whole-body PET, resulting in spatial resolution close to 1 cm. Furthermore, motion-induced inconsistencies in the attenuation measurements often lead to significant artifacts in the reconstructed images. Gating can remove motion artifacts at the cost of increased noise. This paper presents an approach to respiratory motion correction using simultaneous PET/MRI to demonstrate initial results in phantoms, rabbits, and nonhuman primates and discusses the prospects for clinical application.Studies with a deformable phantom, a free-breathing primate, and rabbits implanted with radioactive beads were performed with simultaneous PET/MRI. Motion fields were estimated from concurrently acquired tagged MR images using 2 B-spline nonrigid image registration methods and incorporated into a PET list-mode ordered-subsets expectation maximization algorithm. Using the measured motion fields to transform both the emission data and the attenuation data, we could use all the coincidence data to reconstruct any phase of the respiratory cycle. We compared the resulting SNR and the channelized Hotelling observer (CHO) detection signal-to-noise ratio (SNR) in the motion-corrected reconstruction with the results obtained from standard gating and uncorrected studies.Motion correction virtually eliminated motion blur without reducing SNR, yielding images with SNR comparable to those obtained by gating with 5-8 times longer acquisitions in all studies. The CHO study in dynamic phantoms demonstrated a significant improvement (166%-276%) in lesion detection SNR with MRI-based motion correction as compared with gating (P < 0.001). This improvement was 43%-92% for large motion compared with lesion detection without motion correction (P < 0.001). CHO SNR in the rabbit studies confirmed these results.Tagged MRI motion correction in simultaneous PET/MRI significantly improves lesion detection compared with respiratory gating and no motion correction while reducing radiation dose. In vivo primate and rabbit studies confirmed the improvement in PET image quality and provide the rationale for evaluation in simultaneous whole-body PET/MRI clinical studies.
Project description:PURPOSE:The scattering matrix (S-matrix) of a parallel transmit (pTx) coil is sensitive to physiological motion but requires additional monitoring RF pulses to be measured. In this work, we present and evaluate pTx RF pulse designs that simultaneously excite for imaging and measure the S-matrix to generate real-time motion signals without prolonging the image sequence. THEORY AND METHODS:Three pTx waveforms for measuring the S-matrix were identified and superimposed onto the imaging excitation RF pulses: (1) time division multiplexing, (2) frequency division multiplexing, and (3) code division multiplexing. These 3 methods were evaluated in healthy volunteers for scattering sensitivity and image artefacts. The S-matrix and real-time motion signals were calculated on the image calculation environment of the MR scanner. Prospective cardiac triggers were identified in early systole as a high rate of change of the cardiac motion signal. Monitoring accuracy was compared against electrocardiogram or the imaged diaphragm position. RESULTS:All 3 monitoring approaches measure the S-matrix during image excitation with quality correlated to input power. No image artefacts were observed for frequency multiplexing, and low energy artefacts were observed in the other methods. The accuracy of the achieved prospective cardiac gating was 15 ± 16 ms for breath hold and 24 ± 17 ms during free breathing. The diaphragm position prediction accuracy was 1.3 ± 0.9 mm. In all volunteers, good quality cine images were acquired for breath hold scans and dual gated CINEs were demonstrated. CONCLUSION:The S-matrix can be measured during image excitation to generate real-time cardiac and respiratory motion signals for prospective gating. No artefacts are introduced when frequency division multiplexing is used.
Project description:Respiratory motion and partial-volume effects are the two main sources of image degradation in whole-body PET imaging. Simultaneous PET-MR allows measurement of respiratory motion using MRI while collecting PET events. Improved PET images may be obtained by modeling respiratory motion and point spread function (PSF) within the PET iterative reconstruction process. In this study, the authors assessed the relative impact of PSF modeling and MR-based respiratory motion correction in phantoms and patient studies using a whole-body PET-MR scanner.An asymmetric exponential PSF model accounting for radially varying and axial detector blurring effects was obtained from point source acquisitions performed in the PET-MR scanner. A dedicated MRI acquisition protocol using single-slice steady state free-precession MR acquisitions interleaved with pencil-beam navigator echoes was developed to track respiratory motion during PET-MR studies. An iterative ordinary Poisson fully 3D OSEM PET reconstruction algorithm modeling all the physical effects of the acquisition (attenuation, scatters, random events, detectors efficiencies, PSF), as well as MR-based nonrigid respiratory deformations of tissues (in both emission and attenuation maps) was developed. Phantom and(18)F-FDG PET-MR patient studies were performed to evaluate the proposed quantitative PET-MR methods.The phantom experiment results showed that PSF modeling significantly improved contrast recovery while limiting noise propagation in the reconstruction process. In patients with soft-tissue static lesions, PSF modeling improved lesion contrast by 19.7%-109%, enhancing the detectability and assessment of small tumor foci. In a patient study with small moving hepatic lesions, the proposed reconstruction technique improved lesion contrast by 54.4%-98.1% and reduced apparent lesion size by 21.8%-34.2%. Improvements were particularly important for the smallest lesion undergoing large motion at the lung-liver interface. Heterogeneous tumor structures delineation was substantially improved. Enhancements offered by PSF modeling were more important when correcting for motion at the same time.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.
Project description:PURPOSE:To develop a flexible method for tracking respiratory and cardiac motions throughout MR and PET-MR body examinations that requires no additional hardware and minimal sequence modification. METHODS:The incorporation of a contrast-neutral rosette navigator module following the RF excitation allows for robust cardiorespiratory motion tracking with minimal impact on the host sequence. Spatial encoding gradients are applied to the FID signal and the desired motion signals are extracted with a blind source separation technique. This approach is validated with an anthropomorphic, PET-MR-compatible motion phantom as well as in 13 human subjects. RESULTS:Both respiratory and cardiac motions were reliably extracted from the proposed rosette navigator in phantom and patient studies. In the phantom study, the MR-derived motion signals were additionally validated against the ground truth measurement of diaphragm displacement and left ventricle model triggering pulse. CONCLUSION:The proposed method yields accurate respiratory and cardiac motion-state tracking, requiring only a short (1.76 ms) additional navigator module, which is self-refocusing and imposes minimal constraints on sequence design.
Project description:Positron Emission Tomography (PET) images are prone to motion artefacts due to the long acquisition time of PET measurements. Recently, simultaneous magnetic resonance imaging (MRI) and PET have become available in the first generation of Hybrid MR-PET scanners. In this work, the elimination of artefacts due to head motion in PET neuroimages is achieved by a new approach utilising MR-based motion tracking in combination with PET list mode data motion correction for simultaneous MR-PET acquisitions. The method comprises accurate MR-based motion measurements, an intra-frame motion minimising and reconstruction time reducing temporal framing algorithm, and a list mode based PET reconstruction which utilises the Ordinary Poisson Algorithm and avoids axial and transaxial compression. Compared to images uncorrected for motion, an increased image quality is shown in phantom as well as in vivo images. In vivo motion corrected images show an evident increase of contrast at the basal ganglia and a good visibility of uptake in tiny structures such as superior colliculi.
Project description:BACKGROUND: Respiratory gating and gate optimization strategies present solutions for overcoming image degradation caused by respiratory motion in PET and traditionally utilize hardware systems and/or employ complex processing algorithms. In this work, we aimed to advance recently emerging data-driven gating methods and introduce a new strategy for optimizing the four-dimensional data based on information contained in that data. These algorithms are combined to form an automated motion correction workflow. METHODS: Software-based gating methods were applied to a nonspecific population of 84 small-animal rat PET scans to create respiratory gated images. The gated PET images were then optimized using an algorithm we introduce as 'gating+' to reduce noise and optimize signal; the technique was also tested using simulations. Gating+ is based on a principle of only using gated information if and where it adds a net benefit, as evaluated in temporal frequency space. Motion-corrected images were assessed quantitatively and qualitatively. RESULTS: Of the small-animal PET scans, 71% exhibited quantifiable motion after software gating. The mean liver displacement was 3.25 mm for gated and 3.04 mm for gating+ images. The (relative) mean percent standard deviations measured in background ROIs were 1.53, 1.05, and 1.00 for the gated, gating+, and ungated values, respectively. Simulations confirmed that gating+ image voxels had a higher probability of being accurate relative to the corresponding ungated values under varying noise and motion scenarios. Additionally, we found motion mapping and phase decoupling models that readily extend from gating+ processing. CONCLUSIONS: Raw PET data contain information about motion that is not currently utilized. In our work, we showed that through automated processing of standard (ungated) PET acquisitions, (motion-) information-rich images can be constructed with minimal risk of noise introduction. Such methods have the potential for implementation with current PET technology in a robust and reproducible way.