Feasibility of gray-blood late gadolinium enhancement evaluation in young patients with congenital and acquired heart disease.

Background: Late gadolinium enhancement (LGE) sequences have become common in pediatric cardiovascular magnetic resonance (CMR) to assess for myocardial fibrosis. Bright-blood late gadolinium enhancement (BB-LGE) by conventional phase-sensitive inversion recovery (PSIR) is commonly utilized, but similar inversion time (TI) value of fibrosis and left ventricular (LV) blood pool can make subendocardial areas difficult to assess. A gray-blood LGE (GB-LGE) technique has been described, targeting nulling of the LV blood pool and demonstrating improvement in ischemic scar detection over BB-LGE in adult patients. We sought to evaluate the feasibility of the GB-LGE technique in a young population with congenital and acquired heart disease and compare its ability to detect subendocardial scar to conventional BB-LGE. Methods: Seventy-six consecutive patients referred for clinical CMR underwent both BB-LGE and GB-LGE on 1.5 T and 3 T scanners. Conventional PSIR sequences were obtained with TI to null the myocardium (BB-LGE) in short-axis and horizontal long-axis stacks. Same PSIR stacks were immediately repeated with TI to null the blood pool (GB-LGE). Both sequences were reviewed separately a week apart by two readers, blinded to the initial clinical interpretation. Studies were analyzed for overall image quality, confidence in scar detection, confidence in detection of LGE, LGE class, inter- and intra-observer agreement for the presence of scar, and intraclass correlation coefficient (ICC) for total scar burden. Results: Overall confidence in myocardial scar detection by BB-LGE or GB-LGE as well as grading of image quality were not statistically different [(p = 1 and p = 1) and (p = 0.53, p = 0.18), respectively]. There was very good inter-observer agreement for the presence of scar on BB-LGE (K = 0.88, 95% CI 0.77-0.99) and GB-LGE (K = 0.84, 95% CI 0.7-0.96), as well as excellent intra-observer agreement for both readers (K = 0.93, 95% CI 0.87-0.99; and K = 0.81, 95% CI 0.69-0.95). Interclass correlation coefficient for total scar burden was excellent for BB-LGE (ICC = 0.98, 95% CI 0.96-0.99) and GB-LGE (ICC = 0.94, 95% CI 0.91-0.97). Conclusions: The GB-LGE technique is feasible in the pediatric population with congenital and acquired heart disease. It can detect subendocardial/ischemic scar similar to conventional bright-blood PSIR sequences in the pediatric population.

Automated segmentation of 3D cine cardiovascular magnetic resonance imaging.

Introduction: As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish. Methods: Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements. Results: The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml). Discussion: The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.

Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging.

PURPOSE: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. METHODS: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow. RESULTS: For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27°. For pixels with a velocity > 25 cm/s, the angle error significantly degraded to 19.26°. Although the averaged blood flow velocity prediction was under-estimated by 26.69%, the high correlation (PCC = 0.95) of global time-varying velocity and the visual evaluation demonstrate a good agreement between our prediction and 4D flow data. The E/A ratio was derived with minimal bias, but with considerable mean absolute error of 0.39 and wide limits of agreement. The diastolic function classification showed a high accuracy of 86.9%. CONCLUSION: Using a deep learning-based algorithm, intra-cardiac blood flow velocities can be predicted from long-axis cine MRI with high correlation with 4D flow derived velocities. Visualization of the derived velocities provides adjunct functional information and may potentially be used to derive the E/A ratio from conventional CMR exams.

Predicting Hemodynamic Performance of Fontan Operation for Glenn Physiology using Computational Fluid Dynamics: Ten Patient-specific Cases.

Single ventricle hearts have only one ventricle that can pump blood effectively and the treatment requires three stages of operations to reconfigure the heart and circulatory system. At the second stage, Glenn procedure is performed to connect superior vena cava (SVC) to the pulmonary arteries (PA). For the third and most complex operation, called Fontan, an extracardiac conduit is used to connect inferior vena cava (IVC) to the PL and thereafter no deoxygenated blood goes to the heart. Predicting Hemodynamic Performance of Fontan Operation using computational fluid dynamics (CFD) is hypothesized to improve outcomes and optimize this treatment planning in children with single-ventricle heart disease. An important reason for this surgical planning is to reduce the development of pulmonary arteriovenous malformations (PAVM) and the need to perform Fontan revisions. The purpose of this study was to develop amodel for Fontan surgical planning and use this model to compare blood circulation in two designed graft types of Fontan operation known as T-shape and Y-graft. The functionality of grafts was compared in terms of power loss (PL) and hepatic flow distribution (HFD), a known factor in PAVM development. To perform this study, ten single-ventricle children with Glenn physiology were included and a CFD model was developed to estimate the blood flow circulation to the left and right pulmonary arteries. The estimated blood flow by CFD was compared with that measured by cardiovascular magnetic resonance. Results showed that there was an excellent agreement between the net blood flow in the right and left pulmonary arteries computed by CFD and CMR (ICC= 0.98, P-value ≥0.21). After validating the accuracy of each CFD model, Fontan operations using T-shape and Y-graft conduits were performed in silico for each patient and the developed CFD model was used to predict the post-surgical PL and HFD. We found that the PL in the Y-graft was significantly lower than in the T-shape (P-value ≤0.001) and HFD was significantly better balanced in Y-graft compared to the T-shape (P-value=0.004).

Non-contrast free-breathing whole-heart 3D cine cardiovascular magnetic resonance with a novel 3D radial leaf trajectory.

PURPOSE: To develop and validate a non-contrast free-breathing whole-heart 3D cine steady-state free precession (SSFP) sequence with a novel 3D radial leaf trajectory. METHODS: We used a respiratory navigator to trigger acquisition of 3D cine data at end-expiration to minimize respiratory motion in our 3D cine SSFP sequence. We developed a novel 3D radial leaf trajectory to reduce gradient jumps and associated eddy-current artifacts. We then reconstructed the 3D cine images with a resolution of 2.0mm3 using an iterative nonlinear optimization algorithm. Prospective validation was performed by comparing ventricular volumetric measurements from a conventional breath-hold 2D cine ventricular short-axis stack against the non-contrast free-breathing whole-heart 3D cine dataset in each patient (n = 13). RESULTS: All 3D cine SSFP acquisitions were successful and mean scan time was 07:09 ± 01:31 min. End-diastolic ventricular volumes for left ventricle (LV) and right ventricle (RV) measured from the 3D datasets were smaller than those from 2D (LV: 159.99 ± 42.99 vs. 173.16 ± 47.42; RV: 180.35 ± 46.08 vs. 193.13 ± 49.38; p-value≤0.044; bias<8%), whereas ventricular end-systolic volumes were more comparable (LV: 79.12 ± 26.78 vs. 78.46 ± 25.35; RV: 97.18 ± 32.35 vs. 102.42 ± 32.53; p-value≥0.190, bias<6%). The 3D cine data had a lower subjective image quality score. CONCLUSION: Our non-contrast free-breathing whole-heart 3D cine sequence with novel leaf trajectory was robust and yielded smaller ventricular end-diastolic volumes compared to 2D cine imaging. It has the potential to make examinations easier and more comfortable for patients.

Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.

Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset. Implemented as a recurrent neural network, the model evolves a segmentation over multiple steps, from a single user click until reaching an automatically determined stopping point. We develop a novel loss function that evaluates the entire sequence of output segmentations, and use it to learn model parameters. Segmentations evolve predictably according to growth dynamics encapsulated by training data, which consists of images, partially completed segmentations, and the recommended next step. The user can easily refine the final segmentation by examining those that are earlier or later in the output sequence. Using a dataset of 3D cardiac MR scans from patients with a wide range of CHD types, we show that our iterative model offers better generalization to patients with the most severe heart malformations.

Improved cardiac T1 mapping accuracy and precision with a new hybrid MOLLI and SASHA technique: MOSHA.

PURPOSE: To develop and validate a new myocardial T1 mapping sequence (MOSHA) which is based on a combination of the modified Look-Locker inversion recovery (MOLLI) and the saturation recovery single-shot acquisition (SASHA) sequences. METHODS: Prior studies have shown that myocardial T1 mapping by SASHA is more accurate but less precise than MOLLI. A new myocardial T1 mapping technique (MOSHA) based on single-shot acquisitions is developed by combining the MOLLI and SASHA sequences. Phantom and patient studies on 15 patients (9 males, median age 21 years) were performed to validate and compare MOSHA with the MOLLI and SASHA sequences in terms of accuracy and precision. RESULTS: In the phantom study, MOSHA was as accurate as SASHA (P-value = 0.88) and as precise as MOLLI (P-value = 0.59). Similar trends were observed in the patient study. Compared to SASHA, MOSHA accuracy was comparable for blood pre-contrast (P-value≥0.10) and post-contrast (P-value≥0.70), and for myocardium pre-contrast (P-value = 0.70) and post-contrast (P-value = 0.09). Compared to MOLLI, MOSHA precision was lower for blood pre-contrast (P-value<0.01) and higher for blood post-contrast (P-value≤0.01), and comparable for myocardium pre-contrast (P-value = 0.24) and post-contrast (P-value = 0.07). Synthetic Extracellular volume fraction (ECV) calculated by MOSHA was more precise than those of SASHA and MOLLI (P-value≤0.01). CONCLUSION: In phantom studies and patients, MOSHA has comparable accuracy as SASHA and nearly similar precision as MOLLI for T1 mapping. Precision of MOSHA was better than MOLLI and SASHA in synthetic ECV measurements. Therefore, it may be a superior choice in clinical practice for a precise and accurate calculation of T1 and ECV.

Accelerated free-breathing 3D whole-heart magnetic resonance angiography with a radial phyllotaxis trajectory, compressed sensing, and curvelet transform.

PURPOSE: To develop and validate an accelerated free-breathing 3D whole-heart magnetic resonance angiography (MRA) technique using a radial k-space trajectory with compressed sensing and curvelet transform. METHOD: A 3D radial phyllotaxis trajectory was implemented to traverse the centerline of k-space immediately before the segmented whole-heart MRA data acquisition at each cardiac cycle. The k-space centerlines were used to correct the respiratory-induced heart motion in the acquired MRA data. The corrected MRA data were then reconstructed by a novel compressed sensing algorithm using curvelets as the sparsifying domain. The proposed 3D whole-heart MRA technique (radial CS curvelet) was then prospectively validated against compressed sensing with a conventional wavelet transform (radial CS wavelet) and a standard Cartesian acquisition in terms of scan time and border sharpness. RESULTS: Fifteen patients (females 10, median age 34-year-old) underwent 3D whole-heart MRA imaging using a standard Cartesian trajectory and our proposed radial phyllotaxis trajectory. Scan time for radial phyllotaxis was significantly shorter than Cartesian (4.88 ±â€¯0.86 min. vs. 6.84 ±â€¯1.79 min., P-value = 0.004). Radial CS curvelet border sharpness was slightly lower than Cartesian and, for the majority of vessels, was significantly better than radial CS wavelet (P-value < 0.050). CONCLUSION: The proposed technique of 3D whole-heart MRA acquisition with a radial CS curvelet has a shorter scan time and slightly lower vessel sharpness compared to the Cartesian acquisition with radial profile ordering, and has slightly better sharpness than radial CS wavelet. Future work on this technique includes additional clinical trials and extending this technique to 3D cine imaging.

Cardiac self-gating using blind source separation for 2D cine cardiovascular magnetic resonance imaging.

PURPOSE: To develop and validate a new cardiac self-gating algorithm using blind source separation for 2D cine steady-state free precession (SSFP) imaging. METHODS: A standard cine SSFP sequence was modified so that the center point of k-space was sampled with each excitation. The center points of k-space were processed by 4 blind source separation methods, and used to detect heartbeats and assign k-space data to appropriate time points in the cardiac cycle. The proposed self-gating technique was prospectively validated in 8 patients against the standard electrocardiogram (ECG)-gating method by comparing the cardiac cycle lengths, image quality metrics, and ventricular volume measurements. RESULTS: There was close agreement between the cardiac cycle length using the ECG- and self-gating methods (bias 0.0 bpm, 95% limits of agreement ±2.1 bpm). The image quality metrics were not significantly different between the ECG- and self-gated images. The ventricular volumes, stroke volumes, and mass measured from self-gated images were all comparable with those from ECG-gated images (all biases <5%). CONCLUSION: The self-gating method yielded comparable cardiac cycle length, image quality, and ventricular measurements compared with standard ECG-gated cine imaging. It may simplify patient preparation, be more robust when there is arrhythmia, and allow cardiac gating at higher field strengths.

Cardiac magnetic resonance using fused 3D cine and 4D flow sequences:Validation of ventricular and blood flow measurements.

PURPOSE: Current cardiovascular magnetic resonance (CMR) examinations require expert planning, multiple breath holds, and 2D imaging. To address this, we sought to develop and validate a comprehensive free-breathing 3D cine function and flow CMR examination using a steady-state free precession (SSFP) sequence to depict anatomy fused with a spatially registered phase contrast (PC) sequence for blood flow analysis. METHODS: In a prospective study, 25 patients underwent a CMR examination which included a 3D cine SSFP sequence and a 3D cine PC (also known as 4D flow) sequence acquired during free-breathing and after the administration of a gadolinium-based contrast agent. Both 3D sequences covered the heart and mediastinum, and used retrospective vectorcardiogram gating (20 phases/beat interpolated to 30 phases/beat) and prospective respiratory motion compensation confining data acquisition to end-expiration. Cardiovascular measurements derived from the 3D cine SSFP and PC images were then compared with those from standard 2D imaging. RESULTS: All 3D cine SSFP and PC acquisitions were completed successfully. The mean time for the 3D cine sequences including prescription was shorter than that for the corresponding 2D sequences (21 min vs. 36 min, P-value <0.001). Left and right ventricular end-diastolic volumes and stroke volumes by 3D cine SSFP were slightly smaller than those from 2D cine SSFP (all biases ≤5%). The blood flow measurements from the 3D and 2D sequences had close agreement in the ascending aorta (bias -2.6%) but main pulmonary artery flow was lower with the 3D cine sequence (bias -11.2%). CONCLUSION: Compared to the conventional 2D cine approach, a comprehensive 3D cine function and flow examination was faster and yielded slightly lower left and right end-diastolic volumes, stroke volumes, and main pulmonary artery blood flow. This free-breathing 3D cine approach allows flexible post-examination data analysis and has the potential to make examinations more comfortable for patients and easier to perform for the operator.

Free-breathing whole-heart 3D cine magnetic resonance imaging with prospective respiratory motion compensation.

PURPOSE: To develop and validate a new prospective respiratory motion compensation algorithm for free-breathing whole-heart 3D cine steady-state free precession (SSFP) imaging. METHODS: In a 3D cine SSFP sequence, 4 excitations per cardiac cycle are re-purposed to prospectively track heart position. Specifically, their 1D image is reconstructed and routed into the scanner's standard diaphragmatic navigator processing system. If all 4 signals are in end-expiration, cine image data from the entire cardiac cycle is accepted for image reconstruction. Prospective validation was carried out in patients (N = 17) by comparing in each a conventional breath-hold 2D cine ventricular short-axis stack and a free-breathing whole-heart 3D cine data set. RESULTS: All 3D cine SSFP acquisitions were successful and the mean scan time was 5.9 ± 2.7 min. Left and right ventricular end-diastolic, end-systolic, and stroke volumes by 3D cine SSFP were all larger than those from 2D cine SSFP. This bias was < 6% except for right ventricular end-systolic volume that was 12%. The 3D cine images had a lower ventricular blood-to-myocardium contrast ratio, contrast-to-noise ratio, mass, and subjective quality score. CONCLUSION: The novel prospective respiratory motion compensation method for 3D cine SSFP imaging was robust and efficient and yielded slightly larger ventricular volumes and lower mass compared to breath-hold 2D cine imaging. Magn Reson Med 80:181-189, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Accelerated whole-heart MR angiography using a variable-density poisson-disc undersampling pattern and compressed sensing reconstruction.

PURPOSE: To accelerate whole-heart three-dimension MR angiography (MRA) by using a variable-density Poisson-disc undersampling pattern and a compressed sensing (CS) reconstruction algorithm, and compare the results with sensitivity encoding (SENSE). METHODS: For whole-heart MRA, a prospective variable-density Poisson-disc k-space undersampling pattern was developed in which 1-2% of central part of k-space was fully sampled, and sampling in the remainder decreased exponentially toward the periphery. The undersampled data were then estimated using CS reconstruction. In patients, images using this sequence with an undersampling rate of ≈6 were compared with those using a SENSE rate of 2 (n = 15) and a SENSE rate of 6 (n = 13). RESULTS: Compared with SENSE rate 2, CS rate 6 images had similar objective border sharpness, significantly lower subjective image quality scores at all four locations (all P < 0.01), and shorter scan times (P < 0.05). Compared with SENSE rate 6, CS rate 6 had similar objective border sharpness at all four locations, significantly better subjective image quality scores at three of four locations (all P < 0.01), and similar scan times (P = 0.24). CONCLUSION: Compared with SENSE with a comparable acceleration rate, a variable-density Poisson-disc undersampling pattern and CS reconstruction achieved better subjective image quality and similar border sharpness. Magn Reson Med 79:761-769, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease.

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the intermediate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incomplete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Compared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.

Prospective heart tracking for whole-heart magnetic resonance angiography.

PURPOSE: To develop a prospective respiratory-gating technique (Heart-NAV) for use with contrast-enhanced three-dimensional (3D) inversion recovery (IR) whole-heart magnetic resonance angiography (MRA) acquisitions that directly tracks heart motion without creating image inflow artifact. METHODS: With Heart-NAV, one of the startup pulses for the whole-heart steady-state free precession MRA sequence is used to collect the centerline of k-space, and its one-dimensional reconstruction is fed into the standard diaphragm-navigator (NAV) signal analysis process to prospectively gate and track respiratory-induced heart displacement. Ten healthy volunteers underwent non-contrast whole-heart MRA acquisitions using the conventional diaphragm-NAV and Heart-NAV with 5 and 10-mm acceptance windows in a 1.5T scanner. Five patients underwent contrast-enhanced IR whole-heart MRA using a diaphragm-NAV and Heart-NAV with a 5-mm acceptance window. RESULTS: For non-contrast whole-heart MRA with both the 5 and 10-mm acceptance windows, Heart-NAV yielded coronary artery vessel sharpness and subjective visual scores that were not significantly different than those using a conventional diaphragm-NAV. Scan time for Heart-NAV was 10% shorter (p < 0.05). In patients undergoing contrast-enhanced IR whole-heart MRA, inflow artifact was seen with the diaphragm-NAV but not with Heart-NAV. CONCLUSION: Compared with a conventional diaphragm-NAV, Heart-NAV achieves similar image quality in a slightly shorter scan time and eliminates inflow artifact. Magn Reson Med 77:759-765, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Three-dimensional heart locator and compressed sensing for whole-heart MR angiography.

PURPOSE: We sought to develop a whole-heart magnetic resonance angiography technique with three-dimensional (3D) respiratory motion compensation and reduced scan time. METHODS: A novel respiratory motion compensation method was implemented that acquires a 1D navigator (NAV) and a low-resolution 3D-image of the heart (3D-LOC) just before the angiography data. The central 10% of SSFP k-space was fully acquired using NAV-gating, and then 10% of peripheral k-space was randomly undersampled to complete the scan. Spatial registration of the 3D-LOC information was used to correct the central and peripheral k-space lines for the bulk respiratory motion in three dimensions, and then the remaining k-space data was estimated using compressed sensing (CS). Ten volunteers each underwent two angiography acquisitions with 1 mm(3) resolution: (i) conventional NAV with CS, and (ii) the new 3D-LOC with CS. RESULTS: Compared with conventional NAV, the new 3D-LOC with CS technique had a shorter scan time (4.8 ± 1.1 versus 6.3 ± 1.7 min; P < 0.001), better objective vessel sharpness for all three coronary arteries (P < 0.05), and no difference in subjective vessel sharpness for all three coronary arteries. CONCLUSION: Compared with conventional NAV with CS, acceleration and respiratory motion correction using 3D-LOC with CS reduces scan time and improves objective vessel sharpness.

Interactive Whole-Heart Segmentation in Congenital Heart Disease.

We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.

Free-breathing steady-state free precession cine cardiac magnetic resonance with respiratory navigator gating.

PURPOSE: To develop and validate a respiratory motion compensation method for free-breathing cardiac cine imaging. METHODS: A free-breathing navigator-gated cine steady-state free precession acquisition (Cine-Nav) was developed which preserves the equilibrium state of the net magnetization vector, maintains the high spatial and temporal resolutions of standard breath-hold (BH) acquisition, and images entire cardiac cycle. Cine image data is accepted only from cardiac cycles occurring entirely during end-expiration. Prospective validation was performed in 10 patients by obtaining in each three complete ventricular image stacks with different respiratory motion compensation approaches: (1) BH, (2) free-breathing with 3 signal averages (3AVG), and (3) free-breathing with Cine-Nav. RESULTS: The subjective image quality score (1 = worst, 4 = best) for Cine-Nav (3.8 ± 0.4) was significantly better than for 3AVG (2.2 ± 0.5, P = 0.002), and similar to BH (4.0 ± 0.0, P = 0.13). The blood-to-myocardium contrast ratio for Cine-Nav (6.3 ± 1.5) was similar to BH (5.9 ± 1.6, P = 0.52) and to 3AVG (5.6 ± 2.5, P = 0.43). There were no significant differences between Cine-Nav and BH for the ventricular volumes and mass. In contrast, there were significant differences between 3AVG and BH in all of these measurements but right ventricular mass. CONCLUSION: Free-breathing cine imaging with Cine-Nav yielded comparable image quality and ventricular measurements to BH, and was superior to 3AVG.

Three-dimensional heart locator for whole-heart coronary magnetic resonance angiography.

PURPOSE: Coronary magnetic resonance angiography (MRA) is commonly performed with diaphragmatic navigator (NAV) gating to compensate for respiratory motion, but this approach is inefficient as data must be reacquired when it is outside the acceptance window. We therefore developed and validated a motion compensation technique based on three-dimensional (3D) spatial registration in which data are accepted throughout the respiratory cycle. METHODS: A novel respiratory motion compensation method was implemented that acquires a low-resolution 3D-image of the heart (3D-LOC) just prior to coronary MRA data acquisition. 3D-LOC volumes were registered to the first 3D-LOC to estimate the respiratory-induced heart motion and to modify the coronary MRA data and reconstruct motion-corrected images. Whole-heart coronary MRA datasets were acquired from nine healthy subjects using a diaphragmatic NAV and using 3D-LOC. RESULTS: There was no significant difference between the subjective image score of NAV and 3D-LOC in three main coronary branches. The vessel sharpness of 3D-LOC was higher than NAV in the right (0.44 ± 0.08 vs. 0.49 ± 0.08; P = 0.055) and left circumflex arteries (0.49 ± 0.05 vs. 0.52 ± 0.04; P = 0.039). Scan time for 3D-LOC was significantly shorter than NAV (4.3 ± 0.6 vs. 8.3 ± 2.3 min; P = 0.004). CONCLUSION: Compared to NAV gating, 3D-LOC for coronary MRA reduces scan time by nearly 50% without compromising image quality.

Free-breathing 3D cardiac MRI using iterative image-based respiratory motion correction.

Respiratory motion compensation using diaphragmatic navigator gating with a 5 mm gating window is conventionally used for free-breathing cardiac MRI. Because of the narrow gating window, scan efficiency is low resulting in long scan times, especially for patients with irregular breathing patterns. In this work, a new retrospective motion compensation algorithm is presented to reduce the scan time for free-breathing cardiac MRI that increasing the gating window to 15 mm without compromising image quality. The proposed algorithm iteratively corrects for respiratory-induced cardiac motion by optimizing the sharpness of the heart. To evaluate this technique, two coronary MRI datasets with 1.3 mm(3) resolution were acquired from 11 healthy subjects (seven females, 25 ± 9 years); one using a navigator with a 5 mm gating window acquired in 12.0 ± 2.0 min and one with a 15 mm gating window acquired in 7.1 ± 1.0 min. The images acquired with a 15 mm gating window were corrected using the proposed algorithm and compared to the uncorrected images acquired with the 5 and 15 mm gating windows. The image quality score, sharpness, and length of the three major coronary arteries were equivalent between the corrected images and the images acquired with a 5 mm gating window (P-value > 0.05), while the scan time was reduced by a factor of 1.7.

Improved multimodality data fusion of late gadolinium enhancement MRI to left ventricular voltage maps in ventricular tachycardia ablation.

Electroanatomical voltage mapping (EAVM) is commonly performed prior to catheter ablation of scar-related ventricular tachycardia (VT) to locate the arrhythmic substrate and to guide the ablation procedure. EAVM is used to locate the position of the ablation catheter and to provide a 3-D reconstruction of left-ventricular anatomy and scar. However, EAVM measurements only represent the endocardial scar with no transmural or epicardial information. Furthermore, EAVM is a time-consuming procedure, with a high operator dependence and has low sampling density, i.e., spatial resolution. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) allows noninvasive assessment of scar morphology that can depict 3-D scar architecture. Despite the potential use of LGE as a roadmap for VT ablation for identification of arrhythmogenic substrate, its utility has been very limited. To allow for identification of VT substrate, a correlation is needed between the substrates identified by EAVM as the gold standard and LGE-MRI scar characteristics. To do so, a system must be developed to fuse the datasets from these modalities. In this study, a registration pipeline for the fusion of LGE-MRI and EAVM data is presented. A novel surface registration algorithm is proposed, integrating the matching of global scar areas as an additional constraint in the registration process. A preparatory landmark registration is initially performed to expedite the convergence of the algorithm. Numerical simulations were performed to evaluate the accuracy of the registration in the presence of errors in identifying landmarks in EAVM or LGE-MRI datasets as well as additional errors due to respiratory or cardiac motion. Subsequently, the accuracy of the proposed fusion system was evaluated in a cohort of ten patients undergoing VT ablation where both EAVM and LGE-MRI data were available. Compared to landmark registration and surface registration, the presented method achieved significant improvement in registration error. The proposed data fusion system allows the fusion of EAVM and LGE-MRI data in VT ablation with registration errors less than 3.5  mm.