Respiratory Motion Correction Method Research Articles

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.

Effect of different respiratory motion correction methods on PET image quality in chest PET/MRI

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.

Focused navigation for respiratory-motion-corrected free-running radial 4D flow MRI.

To validate a respiratory motion correction method called focused navigation (fNAV) for free-running radial whole-heart 4D flow MRI. Using fNAV, respiratory signals derived from radial readouts are converted into three orthogonal displacements, which are then used to correct respiratory motion in 4D flow datasets. Hundred 4D flow acquisitions were simulated with non-rigid respiratory motion and used for validation. The difference between generated and fNAV displacement coefficients was calculated. Vessel area and flow measurements from 4D flow reconstructions with (fNAV) and without (uncorrected) motion correction were compared to the motion-free ground-truth. In 25 patients, the same measurements were compared between fNAV 4D flow, 2D flow, navigator-gated Cartesian 4D flow, and uncorrected 4D flow datasets. For simulated data, the average difference between generated and fNAV displacement coefficients was 0.04 0.32 mm and 0.31 0.35 mm in the x and y directions, respectively. In the z direction, this difference was region-dependent (0.02 0.51 mm up to 5.85 3.41 mm). For all measurements (vessel area, net volume, and peak flow), the average difference from ground truth was higher for uncorrected 4D flow datasets (0.32 0.11 cm2 , 11.1 3.5mL, and 22.3 6.0mL/s) than for fNAV 4D flow datasets (0.10 0.03 cm2 , 2.6 0.7mL, and 5.1 .9mL/s, p < 0.05). In vivo, average vessel area measurements were 4.92 2.95 cm2 , 5.06 2.64 cm2 , 4.87 2.57 cm2 , 4.87 2.69 cm2 , for 2D flow and fNAV, navigator-gated and uncorrected 4D flow datasets, respectively. In the ascending aorta, all 4D flow datasets except for the fNAV reconstruction had significantly different vessel area measurements from 2D flow. Overall, 2D flow datasets demonstrated the strongest correlation to fNAV 4D flow for both net volume (r2 =0.92) and peak flow (r2 =0.94), followed by navigator-gated 4D flow (r2 =0.83 and r2 =0.86, respectively), and uncorrected 4D flow (r2 =0.69 and r2 =0.86, respectively). fNAV corrected respiratory motion in vitro and in vivo, resulting in fNAV 4D flow measurements that are comparable to those derived from 2D flow and navigator-gated Cartesian 4D flow datasets, with improvements over those from uncorrected 4D flow.

Respiratory motion correction for liver contrast-enhanced ultrasound by automatic selection of a reference image.

Respiratory motion correction is necessary for the quantitative analysis of liver contrast-enhanced ultrasound (CEUS) image sequences. Most respiratory motion correction methods are based on the dual mode of CEUS image sequences, including contrast and grayscale image sequences. Due to free-breathing motion, the acquired two-dimensional (2D) ultrasound cine might show the in-plane and out-of-plane motion of tumors. The registration of an entire 2D ultrasound contrast image sequence based on out-of-plane images is ineffective. For the respiratory motion correction of CEUS sequences, the reference image is usually considered the standard for the deletion of any out-of-plane images. Most methods used for the selection of the reference image are subjective in nature. Here, a quantitative selection method for an optimal reference image from CEUS image sequences in the B mode and contrast mode was explored. The original high-dimensional ultrasound grayscale image data were mapped into a two-dimensional space using Laplacian Eigenmaps (LE), and K-means clustering was adopted. The center image of the larger cluster with a near-peak contrast intensity was considered the optimal ultrasound reference image. In the ultrasound grayscale image sequence, the images with the maximum correlations to the reference image in the same time interval were selected as the corrected image sequence. The effectiveness of this proposed method was then validated on 18 CEUS cases of VX2 tumors in rabbit livers. Correction smoothed the time-intensity curves (TICs) extracted from the region of interest of the CEUS image sequences. Before correction, the average of the total mean structural similarity (TMSSIM) and the average of the mean correlation coefficient (MCC) from the image sequences were 0.45±0.11 and 0.67±0.16, respectively, and after correction, the average TMSSIM and MCC increased (P<0.001) by 31% to 0.59±0.11 and by 21% to 0.81±0.11, respectively. The average deviation value (DV) index of the TICs from the image sequences prior to correction was 92.16±18.12, and correction reduced the average to 31.71±7.31. The average TMSSIM and MCC values after correction using the mean frame of the reference image (MBMFRI) were clearly lower than those after correction using the proposed method (P<0.001). Moreover, the average DV after correction using the MBMFRI was obviously higher than that after correction using the proposed method (P<0.001). The breathing frequency of rabbits is notably faster than that of human beings, but the proposed correction method could reduce the effect of the respiratory motion in the CEUS image sequences. The reference image was selected quantitatively, which could improve the accuracy of the quantitative analysis of rabbit liver CEUS sequences using the reference image method based on the current standard of manual selection and the MBMFRI. Thiseasy-to-operatemethod can potentially be used in both animal studies and clinical applications.

Event-by-event non-rigid data-driven PET respiratory motion correction methods: comparison of principal component analysis and centroid of distribution

Respiratory motion is a major cause of degradation of PET image quality. Respiratory gating and motion correction can be performed to reduce the effects of respiratory motion; these methods require motion information, typically obtained from external tracking systems. Various groups have studied data-driven (DD) motion estimation methods. Recently, a DD respiratory motion estimation method was established by calculating the centroid of distribution (COD) of listmode events, which was then used with event-by-event respiratory motion correction (EBE-MC) and showed results comparable to those with an external motion tracking device. The EBE-MC method only corrected for rigid motion, so that non-rigid components still contributed to motion-induced blurring. A non-rigid respiratory motion correction (NRMC) was later developed to overcome this problem, but was only evaluated using signals from an external monitor. Thus, it is desirable to further develop DD motion estimation to achieve the best respiratory motion correction results.We evaluated two DD respiratory motion detection methods, COD and principal component analysis (PCA), by comparing the extracted motion trace to that acquired by the Anzai system in dynamic studies with two tracers. PCA was chosen as a preliminary study indicated that it produced stable results than other DD methods. We then developed and performed DD-EBE-NRMC using either COD- or PCA-derived respiratory motion information. DD correction results were compared with Anzai-based results. For all tested studies, both COD and PCA showed a good-to-excellent match with Anzai signals, with PCA showing a higher correlation with Anzai signals. The DD-EBE-NRMC results showed that both COD and PCA provide comparable image quality improvement to the Anzai-based correction. Although COD showed a lower correlation with Anzai than PCA, COD-based NRMC results are comparable to those of PCA, both of which showed great reduction in motion-induced blurring.

Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution

Data-driven respiratory gating techniques were developed to correct for respiratory motion in PET studies, without the help of external motion tracking systems. Due to the greatly increased image noise in gated reconstructions, it is desirable to develop a data-driven event-by-event respiratory motion correction method. In this study, using the Centroid-of-distribution (COD) algorithm, we established a data-driven event-by-event respiratory motion correction technique using TOF PET list-mode data, and investigated its performance by comparing with an external system-based correction method. Ten human scans with the pancreatic β-cell tracer 18F-FP-(+)-DTBZ were employed. Data-driven respiratory motions in superior–inferior (SI) and anterior–posterior (AP) directions were first determined by computing the centroid of all radioactive events during each short time frame with further processing. The Anzai belt system was employed to record respiratory motion in all studies. COD traces in both SI and AP directions were first compared with Anzai traces by computing the Pearson correlation coefficients. Then, respiratory gated reconstructions based on either COD or Anzai traces were performed to evaluate their relative performance in capturing respiratory motion. Finally, based on correlations of displacements of organ locations in all directions and COD information, continuous 3D internal organ motion in SI and AP directions was calculated based on COD traces to guide event-by-event respiratory motion correction in the MOLAR reconstruction framework. Continuous respiratory correction results based on COD were compared with that based on Anzai, and without motion correction. Data-driven COD traces showed a good correlation with Anzai in both SI and AP directions for the majority of studies, with correlation coefficients ranging from 63% to 89%. Based on the determined respiratory displacements of pancreas between end-expiration and end-inspiration from gated reconstructions, there was no significant difference between COD-based and Anzai-based methods. Finally, data-driven COD-based event-by-event respiratory motion correction yielded comparable results to that based on Anzai respiratory traces, in terms of contrast recovery and reduced motion-induced blur. Data-driven event-by-event respiratory motion correction using COD showed significant image quality improvement compared with reconstructions with no motion correction, and gave comparable results to the Anzai-based method.

Event-by-Event Continuous Respiratory Motion Correction for Dynamic PET Imaging.

Existing respiratory motion-correction methods are applied only to static PET imaging. We have previously developed an event-by-event respiratory motion-correction method with correlations between internal organ motion and external respiratory signals (INTEX). This method is uniquely appropriate for dynamic imaging because it corrects motion for each time point. In this study, we applied INTEX to human dynamic PET studies with various tracers and investigated the impact on kinetic parameter estimation. The use of 3 tracers-a myocardial perfusion tracer, (82)Rb (n = 7); a pancreatic β-cell tracer, (18)F-FP(+)DTBZ (n = 4); and a tumor hypoxia tracer, (18)F-fluoromisonidazole ((18)F-FMISO) (n = 1)-was investigated in a study of 12 human subjects. Both rest and stress studies were performed for (82)Rb. The Anzai belt system was used to record respiratory motion. Three-dimensional internal organ motion in high temporal resolution was calculated by INTEX to guide event-by-event respiratory motion correction of target organs in each dynamic frame. Time-activity curves of regions of interest drawn based on end-expiration PET images were obtained. For (82)Rb studies, K1 was obtained with a 1-tissue model using a left-ventricle input function. Rest-stress myocardial blood flow (MBF) and coronary flow reserve (CFR) were determined. For (18)F-FP(+)DTBZ studies, the total volume of distribution was estimated with arterial input functions using the multilinear analysis 1 method. For the (18)F-FMISO study, the net uptake rate Ki was obtained with a 2-tissue irreversible model using a left-ventricle input function. All parameters were compared with the values derived without motion correction. With INTEX, K1 and MBF increased by 10% ± 12% and 15% ± 19%, respectively, for (82)Rb stress studies. CFR increased by 19% ± 21%. For studies with motion amplitudes greater than 8 mm (n = 3), K1, MBF, and CFR increased by 20% ± 12%, 30% ± 20%, and 34% ± 23%, respectively. For (82)Rb rest studies, INTEX had minimal effect on parameter estimation. The total volume of distribution of (18)F-FP(+)DTBZ and Ki of (18)F-FMISO increased by 17% ± 6% and 20%, respectively. Respiratory motion can have a substantial impact on dynamic PET in the thorax and abdomen. The INTEX method using continuous external motion data substantially changed parameters in kinetic modeling. More accurate estimation is expected with INTEX.

Initial evaluation of a practical PET respiratory motion correction method in clinical simultaneous PET/MRI.

Respiratory motion during PET acquisitions can cause image artefacts, with sharpness and tracer quantification adversely affected due to count ‘smearing’. Motion correction by registration of PET gates becomes increasingly difficult with shorter scan times and less counts. The advent of simultaneous PET/MRI scanners allows the use of high spatial resolution MRI to capture motion states during respiration [1, 2]. In this work, we use a respiratory signal derived from the PET list-mode data [3, 4], with no requirement for an external device or MR sequence modifications. Clinical PET data are grouped into 10 respiratory bins based on respiratory signal amplitude derived from the PET list-mode data (Deep breaths outside defined limits are ignored) (Figure ​(Figure1).1). During an extra post-scan 30s PET/MRI acquisition, rapid 2D Gradient Echo MR images are collected and grouped into these 10 respiratory bins. Images in each bin are averaged to form one image per bin (Figure ​(Figure2),2), which are registered to a reference image, forming a patient-specific motion model. Motion estimates from the model are applied directly within the reconstruction of the clinical PET list-mode data using Motion Compensated Image Reconstruction (MCIR) [5], to form one motion-corrected image. On two human subjects (18F-FDG - 1 multiple liver lesions, 1 cardiac) we present PET data motion-corrected with an MRI motion model. Images are assessed visually, with line profiles through ROIs (Figure ​(Figure3),3), and by change of pixel intensity in regions of high activity. Figure 1 Respiratory signal throughout clinical 15 minute PET acquisition, including ‘free breathing’ and ‘breath-hold’ sections. Horizontal lines used to bin the PET data based on signal amplitude are also shown. Deep inhalation ... Figure 2 Binned and averaged MRI slices for 4 out of the 10 bins; ranging from end-expiration to end-inspiration. Figure 3 Uncorrected image (a) with ROI (b), motion-corrected image (c) with ROI (d), line profiles through the liver lesions in both images (e). Image intensity scale is in arbitrary units. In the liver case we see a decrease in tumor ‘smearing’ after MRI model-based correction (Figure ​(Figure3).3). Other areas of high activity in the liver, only marginally visible in the uncorrected image, become apparent in the motion-corrected image. Average intensity increase over the 3 lesions is 11%, while increase in intensity in the cardiac wall in the cardiac patient is 10%.

Event‐by‐event respiratory motion correction for PET with 3D internal‐1D external motion correlation

Respiratory motion during PET∕CT imaging can cause substantial image blurring and underestimation of tracer concentration for both static and dynamic studies. In this study, the authors developed an event-by-event respiratory motion correction method that used three-dimensional internal-one-dimensional external motion correlation (INTEX3D) in listmode reconstruction. The authors aim to fully correct for organ/tumor-specific rigid motion caused by respiration using all detected events to eliminate both intraframe and interframe motion, and investigate the quantitative improvement in static and dynamic imaging. The positional translation of an internal organ or tumor during respiration was first determined from the reconstructions of multiple phase-gated images. A level set (active contour) method was used to segment the targeted internal organs/tumors whose centroids were determined. The mean displacement of the external respiratory signal acquired by the Anzai system that corresponded to each phase-gated frame was determined. Three linear correlations between the 1D Anzai mean displacements and the 3D centroids of the internal organ/tumor were established. The 3D internal motion signal with high temporal resolution was then generated by applying each of the three correlation functions to the entire Anzai trace (40 Hz) to guide event-by-event motion correction in listmode reconstruction. The reference location was determined as the location where CT images were acquired to facilitate phase-matched attenuation correction and anatomical-based postfiltering. The proposed method was evaluated with a NEMA phantom driven by a QUASAR respiratory motion platform, and human studies with two tracers: pancreatic beta cell tracer [(18)F]FP(+)DTBZ and tumor hypoxia tracer [(18)F]fluoromisonidazole (FMISO). An anatomical-based postreconstruction filter was applied to the motion-corrected images to reduce noise while preserving quantitative accuracy and organ boundaries in the patient studies. The INTEX3D method yielded an increase of 5%-9% and 32%-40% in contrast recovery coefficient on the hot spheres in the NEMA phantom, compared to the reconstructions with only 1D motion correction (INTEX1D) and no motion correction, respectively. The proposed method also increased the mean activities of the pancreas and kidney by 9.3% and 11.2%, respectively, across three subjects in the FPDTBZ studies, and the average lesion-to-blood ratio by 20% across three lesions in the FMISO study, compared to the reconstructions without motion correction. In addition, the proposed method reduced intragate motion as compared to phase-gated images. The application of the anatomical-based postreconstruction filter further reduced noise in the background by >50% compared to reconstructions without postfiltering, while preserving quantitative accuracy and organ boundaries. Finally, the measurements of the time-activity curves from a subject with FPDTBZ showed that INTEX3D yielded 18% and 11% maximum increases in tracer concentration in the pancreas and kidney cortex, respectively. These results suggest that the proposed method can effectively compensate for both intragate and intergate respiratory motion while preserving all the counts, and is applicable to dynamic studies.

Self-guided retrospective motion correction (SEGMO) for free-breathing whole-heart coronary MRA with 100% acquisition efficiency

A respiratory motion correction method (SEGMO) is proposed for whole-heart coronary MRA. It eliminates the need for a diaphragm navigator, reduces imaging setup time, and has more accurate respiratory motion detection using an affine model. Its inherent 100% gating efficiency ensures a shorter and more fixed scan time compared to conventional navigator gated schemes.

Ultra-low dose CT attenuation correction for PET/CT

A challenge for positron emission tomography/computed tomography (PET/CT) quantitation is patient respiratory motion, which can cause an underestimation of lesion activity uptake and an overestimation of lesion volume. Several respiratory motion correction methods benefit from longer duration CT scans that are phase matched with PET scans. However, even with the currently available, lowest dose CT techniques, extended duration cine CT scans impart a substantially high radiation dose. This study evaluates methods designed to reduce CT radiation dose in PET/CT scanning. We investigated selected combinations of dose reduced acquisition and noise suppression methods that take advantage of the reduced requirement of CT for PET attenuation correction (AC). These include reducing CT tube current, optimizing CT tube voltage, adding filtration, CT sinogram smoothing and clipping. We explored the impact of these methods on PET quantitation via simulations on different digital phantoms. CT tube current can be reduced much lower for AC than that in low dose CT protocols. Spectra that are higher energy and narrower are generally more dose efficient with respect to PET image quality. Sinogram smoothing could be used to compensate for the increased noise and artifacts at radiation dose reduced CT images, which allows for a further reduction of CT dose with no penalty for PET image quantitation. When CT is not used for diagnostic and anatomical localization purposes, we showed that ultra-low dose CT for PET/CT is feasible. The significant dose reduction strategies proposed here could enable respiratory motion compensation methods that require extended duration CT scans and reduce radiation exposure in general for all PET/CT imaging.

Combination of compressed sensing and parallel imaging with respiratory motion correction for highly-accelerated cardiac perfusion MRI

Cardiac perfusion MRI requires fast data acquisition to achieve an appropriate combination of temporal resolution, spatial resolution and spatial coverage for clinical studies [1]. We have recently presented a combination of compressed sensing and parallel imaging (k-t SPARSE-SENSE) to highly accelerate perfusion studies [2]. However, this method is sensitive to respiratory motion, which decreases temporal sparsity and produces temporal blurring in the reconstructed images. In this work, we present a rigid respiratory motion correction method which allows highly-accelerated first-pass cardiac perfusion MRI to be performed without strict breath-holding.

Evaluation of Respiratory Motion Effect on Defect Detection in Myocardial Perfusion SPECT: A Simulation Study

The objective of this study is to investigate the effects of respiratory motion (RM) on defect detection in Tc-99m sestamibi myocardial perfusion SPECT (MPS) using a phantom population that includes patient variability. Three RM patterns are included, namely breath-hold, slightly enhanced normal breathing, and deep breathing. For each RM pattern, six 4-D NCAT phantoms were generated, each with anatomical variations. Anterior, lateral and inferior myocardial defects with different sizes and contrasts were inserted. Noise-free SPECT projections were simulated using an analytical projector. Poisson noise was then added to generate noisy realizations. The projection data were reconstructed using the OS-EM algorithm with 1 and 4 subsets/iteration and at 1, 2, 3, 5, 7, and 10 iterations. Short-axis images centered at the centroid of the myocardial defect were extracted, and the channelized Hotelling observer (CHO) was applied for the detection of the defect. The CHO results show that the value of the area under the receiver operating characteristics (ROC) curve (AUC) is affected by the RM amplitude. For all the defect sizes and contrasts studied, the highest or optimal AUC values indicate maximum detectability decrease with the increase of the RM amplitude. With no respiration, the ranking of the optimal AUC value in decreasing order is anterior then lateral, and finally inferior defects. The AUC value of the lateral defect drops more severely as the RM amplitude increases compared to other defect locations. Furthermore, as the RM amplitude increases, the AUC values of the smaller defects drop more quickly than the larger ones. We demonstrated that RM affects defect detectability of MPS imaging. The results indicate that developments of optimal data acquisition methods and RM correction methods are needed to improve the defect detectability in MPS.

Analysis of residual coronary artery motion for breath hold and navigator approaches using real‐time coronary MRI

Coronary artery MRI methods utilize breath holds, or diaphragmatic navigators, to compensate for respiratory motion. To increase image quality and navigator (NAV) gating efficiency, slice tracking is used, with more sophisticated affine motion models recently introduced. This study assesses the extent of remaining coronary artery motion in free breathing NAV and single and multi breath hold coronary artery MRI. Additionally, the effect of the NAV gating window size was examined. To visualize and measure the respiratory induced motion, an image containing a coronary artery cross section was acquired at each heartbeat. The amount of residual coronary artery displacement was used as a direct measure for the performance of the respiratory motion correction method. Free breathing studies with motion compensation (slice tracking with 5 mm gating window) had a similar amount of residual motion (0.76+/-0.17 mm) as a single breath hold (0.52+/-0.20 mm) and were superior to multiple breath holds (1.22+/-0.60 mm). Affine NAV methods allowed for larger gating windows ( approximately 10 mm windows) with similar residual motion (0.74+/-0.17 mm). In this healthy adult cohort (N=10), free-breathing NAV methods offered respiratory motion suppression similar to a single breath hold.