Free-breathing 3D whole-heart joint T1/T2 mapping and water/fat imaging at 0.55 T.

PURPOSE: To develop and validate a highly efficient motion compensated free-breathing isotropic resolution 3D whole-heart joint T1/T2 mapping sequence with anatomical water/fat imaging at 0.55 T. METHODS: The proposed sequence takes advantage of shorter T1 at 0.55 T to acquire three interleaved water/fat volumes with inversion-recovery preparation, no preparation, and T2 preparation, respectively. Image navigators were used to facilitate nonrigid motion-compensated image reconstruction. T1 and T2 maps were jointly calculated by a dictionary matching method. Validations were performed with simulation, phantom, and in vivo experiments on 10 healthy volunteers and 1 patient. The performance of the proposed sequence was compared with conventional 2D mapping sequences including modified Look-Locker inversion recovery and T2-prepared balanced steady-SSFP sequence. RESULTS: The proposed sequence has a good T1 and T2 encoding sensitivity in simulation, and excellent agreement with spin-echo reference T1 and T2 values was observed in a standardized T1/T2 phantom (R2 = 0.99). In vivo experiments provided good-quality co-registered 3D whole-heart T1 and T2 maps with 2-mm isotropic resolution in a short scan time of about 7 min. For healthy volunteers, left-ventricle T1 mean and SD measured by the proposed sequence were both comparable with those of modified Look-Locker inversion recovery (640 ± 35 vs. 630 ± 25 ms [p = 0.44] and 49.9 ± 9.3 vs. 54.4 ± 20.5 ms [p = 0.42]), whereas left-ventricle T2 mean and SD measured by the proposed sequence were both slightly lower than those of T2-prepared balanced SSFP (53.8 ± 5.5 vs. 58.6 ± 3.3 ms [p < 0.01] and 5.2 ± 0.9 vs. 6.1 ± 0.8 ms [p = 0.03]). Myocardial T1 and T2 in the patient measured by the proposed sequence were in good agreement with conventional 2D sequences and late gadolinium enhancement. CONCLUSION: The proposed sequence simultaneously acquires 3D whole-heart T1 and T2 mapping with anatomical water/fat imaging at 0.55 T in a fast and efficient 7-min scan. Further investigation in patients with cardiovascular disease is now warranted.

Evaluation of Deep Learning Estimation of Whole Heart Anatomy from Automated Cardiovascular Magnetic Resonance Short- and Long-Axis Analyses in UK Biobank.

AIMS: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank. METHODS: A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1mm isotropic resolution from CMR short and long axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). RESULTS: Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P values), particularly for sex, age, and body mass index. AUC for all logistic regressions were higher for deep learning volumes than standard volumes (p<0.001 for all four chambers at ED and ES). CONCLUSIONS: Neural network reconstructions of whole heart volumes had significantly stronger associations with cardiovascular disease and risk factors than standard volume estimation methods in an automatic processing pipeline.

Inversion recovery and saturation recovery pulmonary vein MR angiography using an image based navigator fluoro trigger and variable-density 3D cartesian sampling with spiral-like order.

Contrast enhanced pulmonary vein magnetic resonance angiography (PV CE-MRA) has value in atrial ablation pre-procedural planning. We aimed to provide high fidelity, ECG gated PV CE-MRA accelerated by variable density Cartesian sampling (VD-CASPR) with image navigator (iNAV) respiratory motion correction acquired in under 4 min. We describe its use in part during the global iodinated contrast shortage. VD-CASPR/iNAV framework was applied to ECG-gated inversion and saturation recovery gradient recalled echo PV CE-MRA in 65 patients (66 exams) using .15 mmol/kg Gadobutrol. Image quality was assessed by three physicians, and anatomical segmentation quality by two technologists. Left atrial SNR and left atrial/myocardial CNR were measured. 12 patients had CTA within 6 months of MRA. Two readers assessed PV ostial measurements versus CTA for intermodality/interobserver agreement. Inter-rater/intermodality reliability, reproducibility of ostial measurements, SNR/CNR, image, and anatomical segmentation quality was compared. The mean acquisition time was 3.58 ± 0.60 min. Of 35 PV pre-ablation datasets (34 patients), mean anatomical segmentation quality score was 3.66 ± 0.54 and 3.63 ± 0.55 as rated by technologists 1 and 2, respectively (p = 0.7113). Good/excellent anatomical segmentation quality (grade 3/4) was seen in 97% of exams. Each rated one exam as moderate quality (grade 2). 95% received a majority image quality score of good/excellent by three physicians. Ostial PV measurements correlated moderate to excellently with CTA (ICCs range 0.52-0.86). No difference in SNR was observed between IR and SR. High quality PV CE-MRA is possible in under 4 min using iNAV bolus timing/motion correction and VD-CASPR.

Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance.

PURPOSE: The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. METHODS: Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. RESULTS: Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600 smm - 2 $$ {\mathrm{smm}}^{-2} $$ . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes. CONCLUSION: Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.

Highly efficient free-breathing 3D whole-heart imaging in 3-min: single center study in adults with congenital heart disease.

BACKGROUND: Three dimensional, whole-heart (3DWH) MRI is an established non-invasive imaging modality in patients with congenital heart disease (CHD) for the diagnosis of cardiovascular morphology and for clinical decision making. Current techniques utilise diaphragmatic navigation (dNAV) for respiratory motion correction and gating and are frequently limited by long acquisition times. This study proposes and evaluates the diagnostic performance of a respiratory gating-free framework, which considers respiratory image-based navigation (iNAV), and highly accelerated variable density Cartesian sampling in concert with non-rigid motion correction and low-rank patch-based denoising (iNAV-3DWH-PROST). The method is compared to the clinical dNAV-3DWH sequence in adult patients with CHD. METHODS: In this prospective single center study, adult patients with CHD who underwent the clinical dNAV-3DWH MRI were also scanned with the iNAV-3DWH-PROST. Diagnostic confidence (4-point Likert scale) and diagnostic accuracy for common cardiovascular lesions was assessed by three readers. Scan times and diagnostic confidence were compared using the Wilcoxon-signed rank test. Co-axial vascular dimensions at three anatomic landmarks were measured, and agreement between the research and the corresponding clinical sequence was assessed with Bland-Altman analysis. RESULTS: The study included 60 participants (mean age ± [SD]: 33 ± 14 years; 36 men). The mean acquisition time of iNAV-3DWH-PROST was significantly lower compared with the conventional clinical sequence (3.1 ± 0.9 min vs 13.9 ± 3.9 min, p < 0.0001). Diagnostic confidence was higher for the iNAV-3DWH-PROST sequence compared with the clinical sequence (3.9 ± 0.2 vs 3.4 ± 0.8, p < 0.001), however there was no significant difference in diagnostic accuracy. Narrow limits of agreement and mean bias less than 0.08 cm were found between the research and the clinical vascular measurements. CONCLUSIONS: The iNAV-3DWH-PROST framework provides efficient, high quality and robust 3D whole-heart imaging in significantly shorter scan time compared to the standard clinical sequence.

Non-rigid motion-compensated 3D whole-heart T2 mapping in a hybrid 3T PET-MR system.

PURPOSE: Simultaneous PET-MRI improves inflammatory cardiac disease diagnosis. However, challenges persist in respiratory motion and mis-registration between free-breathing 3D PET and 2D breath-held MR images. We propose a free-breathing non-rigid motion-compensated 3D T2 -mapping sequence enabling whole-heart myocardial tissue characterization in a hybrid 3T PET-MR system and provides non-rigid respiratory motion fields to correct also simultaneously acquired PET data. METHODS: Free-breathing 3D whole-heart T2 -mapping was implemented on a hybrid 3T PET-MRI system. Three datasets were acquired with different T2 -preparation modules (0, 28, 55 ms) using 3-fold undersampled variable-density Cartesian trajectory. Respiratory motion was estimated via virtual 3D image navigators, enabling multi-contrast non-rigid motion-corrected MR reconstruction. T2 -maps were computed using dictionary-matching. Approach was tested in phantom, 8 healthy subjects, 14 MR only and 2 PET-MR patients with suspected cardiac disease and compared with spin echo reference (phantom) and clinical 2D T2 -mapping (in-vivo). RESULTS: Phantom results show a high correlation (R2 = 0.996) between proposed approach and gold standard 2D T2 mapping. In-vivo 3D T2 -mapping average values in healthy subjects (39.0 ± 1.4 ms) and patients (healthy tissue) (39.1 ± 1.4 ms) agree with conventional 2D T2 -mapping (healthy = 38.6 ± 1.2 ms, patients = 40.3 ± 1.7 ms). Bland-Altman analysis reveals bias of 1.8 ms and 95% limits of agreement (LOA) of -2.4-6 ms for healthy subjects, and bias of 1.3 ms and 95% LOA of -1.9 to 4.6 ms for patients. CONCLUSION: Validated efficient 3D whole-heart T2 -mapping at hybrid 3T PET-MRI provides myocardial inflammation characterization and non-rigid respiratory motion fields for simultaneous PET data correction. Comparable T2 values were achieved with both 3D and 2D methods. Improved image quality was observed in the PET images after MR-based motion correction.

Cardiac magnetic resonance for early atrial lesion visualization post atrial fibrillation radiofrequency catheter ablation.

BACKGROUND: Incomplete atrial lesions resulting in pulmonary vein-left atrium reconnection after pulmonary vein antrum isolation (PVAI), are related to atrial fibrillation (AF) recurrence. Unfortunately, during the PVAI procedure, fluoroscopy and electroanatomic mapping cannot accurately determine the location and size of the ablation lesions in the atrial wall and this can result in incomplete PVAI lesions (PVAI-L) after radiofrequency catheter ablation (RFCA). AIM: We seek to evaluate whether cardiac magnetic resonance (CMR), immediately after RFCA of AF, can identify PVAI-L by characterizing the left atrial tissue. METHODS: Ten patients (63.1 ± 5.7 years old, 80% male) receiving a RFCA for paroxysmal AF underwent a CMR before (<1 week) and after (<1 h) the PVAI. Two-dimensional dark-blood T2-weighted short tau inversion recovery (DB-STIR), Three-dimensional inversion-recovery prepared long inversion time (3D-TWILITE) and three-dimensional late gadolinium enhancement (3D-LGE) images were performed to visualize PVAI-L. RESULTS: The PVAI-L was visible in 10 patients (100%) using 3D-TWILITE and 3D-LGE. Conversely, On DB-STIR, the ablation core of the PAVI-L could not be identified because of a diffuse high signal of the atrial wall post-PVAI. Microvascular obstruction was identified in 7 (70%) patients using 3D-LGE. CONCLUSION: CMR can visualize PVAI-L immediately after the RFCA of AF even without the use of contrast agents. Future studies are needed to understand if the use of CMR for PVAI-L detection after RFCA can improve the results of ablation procedures.

Combining generative modelling and semi-supervised domain adaptation for whole heart cardiovascular magnetic resonance angiography segmentation.

BACKGROUND: Quantification of three-dimensional (3D) cardiac anatomy is important for the evaluation of cardiovascular diseases. Changes in anatomy are indicative of remodeling processes as the heart tissue adapts to disease. Although robust segmentation methods exist for computed tomography angiography (CTA), few methods exist for whole-heart cardiovascular magnetic resonance angiograms (CMRA) which are more challenging due to variable contrast, lower signal to noise ratio and a limited amount of labeled data. METHODS: Two state-of-the-art unsupervised generative deep learning domain adaptation architectures, generative adversarial networks and variational auto-encoders, were applied to 3D whole heart segmentation of both conventional (n = 20) and high-resolution (n = 45) CMRA (target) images, given segmented CTA (source) images for training. An additional supervised loss function was implemented to improve performance given 10%, 20% and 30% segmented CMRA cases. A fully supervised nn-UNet trained on the given CMRA segmentations was used as the benchmark. RESULTS: The addition of a small number of segmented CMRA training cases substantially improved performance in both generative architectures in both standard and high-resolution datasets. Compared with the nn-UNet benchmark, the generative methods showed substantially better performance in the case of limited labelled cases. On the standard CMRA dataset, an average 12% (adversarial method) and 10% (variational method) improvement in Dice score was obtained. CONCLUSIONS: Unsupervised domain-adaptation methods for CMRA segmentation can be boosted by the addition of a small number of supervised target training cases. When only few labelled cases are available, semi-supervised generative modelling is superior to supervised methods.

Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography.

BACKGROUND: Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection. METHODS: Thirty individuals (female, n = 10) were investigated using a 0.9 mm isotropic image-navigator (iNAV)-based motion-corrected coronary MRA sequence. Each individual was scanned three times utilising different strategies for determination of the optimal trigger delay: (1) the DL software, (2) an experienced operator decision, and (3) a previously utilised formula for determining the trigger delay. Methodologies were compared using custom-made analysis software to assess visible coronary vessel length and coronary vessel sharpness for the entire vessel length and the first 4 cm of each vessel. RESULTS: There was no difference in image quality between any of the methodologies for determination of the optimal trigger delay, as assessed by visible coronary vessel length, coronary vessel sharpness for each entire vessel and vessel sharpness for the first 4 cm of the left mainstem, left anterior descending or right coronary arteries. However, vessel length of the left circumflex was slightly greater using the formula method. The time taken to calculate the trigger delay was significantly lower for the DL-method as compared to the operator-led approach (106 ± 38.0 s vs 168 ± 39.2 s, p < 0.01, 95% CI of difference 25.5-98.1 s). CONCLUSIONS: Deep learning-derived automated software can effectively and efficiently determine the optimal trigger delay for acquisition of coronary MRA and thus may simplify workflow and improve reproducibility.

Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional neural networks.

BACKGROUND: Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images. METHODS: 2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods. RESULTS: Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myocardial contours showed good to excellent agreement with manual pipelines, and remained within the limits of inter-user variability estimated in previous studies. CONCLUSION: Spatio-temporal deep learning shows increased robustness for the segmentation of cine DENSE images. It provides excellent agreement with manual segmentation for strain extraction. Deep learning will facilitate the analysis of DENSE data, bringing it one step closer to clinical routine.

Free-breathing, Contrast Agent-free Whole-Heart MTC-BOOST Imaging: Single-Center Validation Study in Adult Congenital Heart Disease.

Purpose: To assess the clinical performance of the three-dimensional, free-breathing, Magnetization Transfer Contrast Bright-and-black blOOd phase-SensiTive (MTC-BOOST) sequence in adult congenital heart disease (ACHD). Materials and Methods: In this prospective study, participants with ACHD undergoing cardiac MRI between July 2020 and March 2021 were scanned with the clinical T2-prepared balanced steady-state free precession sequence and proposed MTC-BOOST sequence. Four cardiologists scored their diagnostic confidence on a four-point Likert scale for sequential segmental analysis on images acquired with each sequence. Scan times and diagnostic confidence were compared using the Mann-Whitney test. Coaxial vascular dimensions at three anatomic landmarks were measured, and agreement between the research sequence and the corresponding clinical sequence was assessed with Bland-Altman analysis. Results: The study included 120 participants (mean age, 33 years ± 13 [SD]; 65 men). The mean acquisition time of the MTC-BOOST sequence was significantly lower compared with that of the conventional clinical sequence (9 minutes ± 2 vs 14 minutes ± 5; P < .001). Diagnostic confidence was higher for the MTC-BOOST sequence compared with the clinical sequence (mean, 3.9 ± 0.3 vs 3.4 ± 0.7; P < .001). Narrow limits of agreement and mean bias less than 0.08 cm were found between the research and clinical vascular measurements. Conclusion: The MTC-BOOST sequence provided efficient, high-quality, and contrast agent-free three-dimensional whole-heart imaging in ACHD, with shorter, more predictable acquisition time and improved diagnostic confidence compared with the reference standard clinical sequence.Keywords: MR Angiography, Cardiac Supplemental material is available for this article. Published under a CC BY 4.0 license.

Deep learning estimation of three-dimensional left atrial shape from two-chamber and four-chamber cardiac long axis views.

AIMS: Left atrial volume is commonly estimated using the bi-plane area-length method from two-chamber (2CH) and four-chamber (4CH) long axes views. However, this can be inaccurate due to a violation of geometric assumptions. We aimed to develop a deep learning neural network to infer 3D left atrial shape, volume and surface area from 2CH and 4CH views. METHODS AND RESULTS: A 3D UNet was trained and tested using 2CH and 4CH segmentations generated from 3D coronary computed tomography angiography (CCTA) segmentations (n = 1700, with 1400/100/200 cases for training/validating/testing). An independent test dataset from another institution was also evaluated, using cardiac magnetic resonance (CMR) 2CH and 4CH segmentations as input and 3D CCTA segmentations as the ground truth (n = 20). For the 200 test cases generated from CCTA, the network achieved a mean Dice score value of 93.7%, showing excellent 3D shape reconstruction from two views compared with the 3D segmentation Dice of 97.4%. The network also showed significantly lower mean absolute error values of 3.5 mL/4.9 cm2 for LA volume/surface area respectively compared to the area-length method errors of 13.0 mL/34.1 cm2 respectively (P < 0.05 for both). For the independent CMR test set, the network achieved accurate 3D shape estimation (mean Dice score value of 87.4%), and a mean absolute error values of 6.0 mL/5.7 cm2 for left atrial volume/surface area respectively, significantly less than the area-length method errors of 14.2 mL/19.3 cm2 respectively (P < 0.05 for both). CONCLUSIONS: Compared to the bi-plane area-length method, the network showed higher accuracy and robustness for both volume and surface area.

Simultaneous Highly Efficient Contrast-Free Lumen and Vessel Wall MR Imaging for Anatomical Assessment of Aortic Disease.

BACKGROUND: Bright-blood lumen and black-blood vessel wall imaging are required for the comprehensive assessment of aortic disease. These images are usually acquired separately, resulting in long examinations and potential misregistration between images. PURPOSE: To characterize the performance of an accelerated and respiratory motion-compensated three-dimensional (3D) cardiac MRI technique for simultaneous contrast-free aortic lumen and vessel wall imaging with an interleaved T2 and inversion recovery prepared sequence (iT2Prep-BOOST). STUDY TYPE: Prospective. POPULATION: A total of 30 consecutive patients with aortopathy referred for a clinically indicated cardiac MRI examination (9 females, mean age ± standard deviation: 32 ± 12 years). FIELD STRENGTH/SEQUENCE: 1.5-T; bright-blood MR angiography (diaphragmatic navigator-gated T2-prepared 3D balanced steady-state free precession [bSSFP], T2Prep-bSSFP), breath-held black-blood two-dimensional (2D) half acquisition single-shot turbo spin echo (HASTE), and 3D bSSFP iT2Prep-BOOST. ASSESSMENT: iT2Prep-BOOST bright-blood images were compared to T2prep-bSSFP images in terms of aortic vessel dimensions, lumen-to-myocardium contrast ratio (CR), and image quality (diagnostic confidence, vessel sharpness and presence of artifacts, assessed by three cardiologists on a 4-point scale, 1: nondiagnostic to 4: excellent). The iT2Prep-BOOST black-blood images were compared to 2D HASTE images for quantification of wall thickness. A visual comparison between computed tomography (CT) and iT2Prep-BOOST was performed in a patient with chronic aortic dissection. STATISTICAL TESTS: Paired t-tests, Wilcoxon signed-rank tests, intraclass correlation coefficient (ICC), Bland-Altman analysis. A P value < 0.05 was considered statistically significant. RESULTS: Bright-blood iT2Prep-BOOST resulted in significantly improved image quality (mean ± standard deviation 3.8 ± 0.5 vs. 3.3 ± 0.8) and CR (2.9 ± 0.8 vs. 1.8 ± 0.5) compared with T2Prep-bSSFP, with a shorter scan time (7.8 ± 1.7 minutes vs. 12.9 ± 3.4 minutes) while providing a complementary 3D black-blood image. Aortic lumen diameter and vessel wall thickness measurements in bright-blood and black-blood images were in good agreement with T2Prep-bSSFP and HASTE images (<0.02 cm and <0.005 cm bias, respectively) and good intrareader (ICC > 0.96) and interreader (ICC > 0.94) agreement was observed for all measurements. DATA CONCLUSION: iT2Prep-BOOST might enable time-efficient simultaneous bright- and black-blood aortic imaging, with improved image quality compared to T2Prep-bSSFP and HASTE imaging, and comparable measurements for aortic wall and lumen dimensions. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

Simultaneous multi-slice steady-state free precession myocardial perfusion with iterative reconstruction and integrated motion compensation.

PURPOSE: Simultaneous multi-slice (SMS) balanced steady-state free precession (bSSFP) acquisition and iterative reconstruction can provide high spatial resolution and coverage for cardiac magnetic resonance (CMR) perfusion. However, respiratory motion remains a challenge for iterative reconstruction techniques employing temporal regularisation. The aim of this study is to evaluate an iterative reconstruction with integrated motion compensation for SMS-bSSFP first-pass myocardial stress perfusion in the presence of respiratory motion. METHODS: Thirty-one patients with suspected coronary artery disease were prospectively recruited and imaged at 1.5 T. A SMS-bSSFP prototype myocardial perfusion sequence was acquired at stress in all patients. All datasets were reconstructed using an iterative reconstruction with temporal regularisation, once with and once without motion compensation (MC and NMC, respectively). Three readers scored each dataset in terms of: image quality (1:poor; 4:excellent), motion/blurring (1:severe motion/blurring; 3:no motion/blurring), and diagnostic confidence (1:poor confidence; 3:high confidence). Quantitative assessment of sharpness was performed. The number of uncorrupted first-pass dynamics was measured on the NMC datasets to classify patients into 'suboptimal breath-hold (BH)' and 'good BH' groups. RESULTS: Compared across all cases, MC performed better than NMC in terms of image quality (3.5 ± 0.5 vs. 3.0 ± 0.8, P = 0.002), motion/blurring (2.9 ± 0.1 vs. 2.2 ± 0.8, P < 0.001), diagnostic confidence (2.9 ± 0.1 vs. 2.3 ± 0.7, P < 0.001) and sharpness index (0.34 ± 0.05 vs. 0.31 ± 0.06, P < 0.001). Fourteen patients with a suboptimal BH were identified. For the suboptimal BH group, MC performed better than NMC in terms of image quality (3.8 ± 0.4 vs. 2.6 ± 0.8, P < 0.001), motion/blurring (3.0 ± 0.1 vs. 1.6 ± 0.7, P < 0.001), diagnostic confidence (3.0 ± 0.1 vs. 1.9 ± 0.7, P < 0.001) and sharpness index (0.34 ± 0.05 vs. 0.30 ± 0.06, P = 0.004). For the good BH group, sharpness index was higher for MC than NMC (0.34 ± 0.06 vs 0.31 ± 0.07, P = 0.03), while there were no significant differences observed for the other three metrics assessed (P > 0.11). There were no significant differences between suboptimal BH MC and good BH MC for any of the reported metrics (P > 0.06). CONCLUSIONS: Integrated motion compensation significantly reduces motion/blurring and improves image quality, diagnostic confidence and sharpness index of SMS-bSSFP perfusion with iterative reconstruction in the presence of motion.

High-resolution non-contrast free-breathing coronary cardiovascular magnetic resonance angiography for detection of coronary artery disease: validation against invasive coronary angiography.

BACKGROUND: Coronary artery disease (CAD) is the single most common cause of death worldwide. Recent technological developments with coronary cardiovascular magnetic resonance angiography (CCMRA) allow high-resolution free-breathing imaging of the coronary arteries at submillimeter resolution without contrast in a predictable scan time of ~ 10 min. The objective of this study was to determine the diagnostic accuracy of high-resolution CCMRA for CAD detection against the gold standard of invasive coronary angiography (ICA). METHODS: Forty-five patients (15 female, 62 ± 10 years) with suspected CAD underwent sub-millimeter-resolution (0.6 mm3) non-contrast CCMRA at 1.5T in this prospective clinical study from 2019-2020. Prior to CCMR, patients were given an intravenous beta blockers to optimize heart rate control and sublingual glyceryl trinitrate to promote coronary vasodilation. Obstructive CAD was defined by lesions with ≥ 50% stenosis by quantitative coronary angiography on ICA. RESULTS: The mean duration of image acquisition was 10.4 ± 2.1 min. On a per patient analysis, the sensitivity, specificity, positive predictive value and negative predictive value (95% confidence intervals) were 95% (75-100), 54% (36-71), 60% (42-75) and 93% (70-100), respectively. On a per vessel analysis the sensitivity, specificity, positive predictive value and negative predictive value (95% confidence intervals) were 80% (63-91), 83% (77-88), 49% (36-63) and 95% (90-98), respectively. CONCLUSION: As an important step towards clinical translation, we demonstrated a good diagnostic accuracy for CAD detection using high-resolution CCMRA, with high sensitivity and negative predictive value. The positive predictive value is moderate, and combination with CMR stress perfusion may improve the diagnostic accuracy. Future multicenter evaluation is now required.

Simultaneous multislice steady-state free precession myocardial perfusion with full left ventricular coverage and high resolution at 1.5 T.

PURPOSE: To implement and evaluate a simultaneous multi-slice balanced SSFP (SMS-bSSFP) perfusion sequence and compressed sensing reconstruction for cardiac MR perfusion imaging with full left ventricular (LV) coverage (nine slices/heartbeat) and high spatial resolution (1.4 × 1.4 mm2 ) at 1.5T. METHODS: A preliminary study was performed to evaluate the performance of blipped controlled aliasing in parallel imaging (CAIPI) and RF-CAIPI with gradient-controlled local Larmor adjustment (GC-LOLA) in the presence of fat. A nine-slice SMS-bSSFP sequence using RF-CAIPI with GC-LOLA with high spatial resolution (1.4 × 1.4 mm2 ) and a conventional three-slice sequence with conventional spatial resolution (1.9 × 1.9 mm2 ) were then acquired in 10 patients under rest conditions. Qualitative assessment was performed to assess image quality and perceived signal-to-noise ratio (SNR) on a 4-point scale (0: poor image quality/low SNR; 3: excellent image quality/high SNR), and the number of myocardial segments with diagnostic image quality was recorded. Quantitative measurements of myocardial sharpness and upslope index were performed. RESULTS: Fat signal leakage was significantly higher for blipped CAIPI than for RF-CAIPI with GC-LOLA (7.9% vs. 1.2%, p = 0.010). All 10 SMS-bSSFP perfusion datasets resulted in 16/16 diagnostic myocardial segments. There were no significant differences between the SMS and conventional acquisitions in terms of image quality (2.6 ± 0.6 vs. 2.7 ± 0.2, p = 0.8) or perceived SNR (2.8 ± 0.3 vs. 2.7 ± 0.3, p = 0.3). Inter-reader variability was good for both image quality (ICC = 0.84) and perceived SNR (ICC = 0.70). Myocardial sharpness was improved using the SMS sequence compared to the conventional sequence (0.37 ± 0.08 vs 0.32 ± 0.05, p < 0.001). There was no significant difference between measurements of upslope index for the SMS and conventional sequences (0.11 ± 0.04 vs. 0.11 ± 0.03, p = 0.84). CONCLUSION: SMS-bSSFP with multiband factor 3 and compressed sensing reconstruction enables cardiac MR perfusion imaging with three-fold increased spatial coverage and improved myocardial sharpness compared to a conventional sequence, without compromising perceived SNR, image quality, upslope index or number of diagnostic segments.

Efficient non-contrast enhanced 3D Cartesian cardiovascular magnetic resonance angiography of the thoracic aorta in 3 min.

BACKGROUND: The application of cardiovascular magnetic resonance angiography (CMRA) for the assessment of thoracic aortic disease is often associated with prolonged and unpredictable acquisition times and residual motion artefacts. To overcome these limitations, we have integrated undersampled acquisition with image-based navigators and inline non-rigid motion correction to enable a free-breathing, contrast-free Cartesian CMRA framework for the visualization of the thoracic aorta in a short and predictable scan of 3 min. METHODS: 35 patients with thoracic aortic disease (36 ± 13y, 14 female) were prospectively enrolled in this single-center study. The proposed 3D T2-prepared balanced steady state free precession (bSSFP) sequence with image-based navigator (iNAV) was compared to the clinical 3D T2-prepared bSSFP with diaphragmatic-navigator gating (dNAV), in terms of image acquisition time. Three cardiologists blinded to iNAV vs. dNAV acquisition, recorded image quality scores across four aortic segments and their overall diagnostic confidence. Contrast ratio (CR) and relative standard deviation (RSD) of signal intensity (SI) in the corresponding segments were estimated. Co-axial aortic dimensions in six landmarks were measured by two readers to evaluate the agreement between the two methods, along with inter-observer and intra-observer agreement. Kolmogorov-Smirnov test, Mann-Whitney U (MWU), Bland-Altman analysis (BAA), intraclass correlation coefficient (ICC) were used for statistical analysis. RESULTS: The scan time for the iNAV-based approach was significantly shorter (3.1 ± 0.5 min vs. 12.0 ± 3.0 min for dNAV, P = 0.005). Reconstruction was performed inline in 3.0 ± 0.3 min. Diagnostic confidence was similar for the proposed iNAV versus dNAV for all three reviewers (Reviewer 1: 3.9 ± 0.3 vs. 3.8 ± 0.4, P = 0.7; Reviewer 2: 4.0 ± 0.2 vs. 3.9 ± 0.3, P = 0.4; Reviewer 3: 3.8 ± 0.4 vs. 3.7 ± 0.6, P = 0.3). The proposed method yielded higher image quality scores in terms of artefacts from respiratory motion, and non-diagnostic images due to signal inhomogeneity were observed less frequently. While the dNAV approach outperformed the iNAV method in the CR assessment, the iNAV sequence showed improved signal homogeneity along the entire thoracic aorta [RSD SI 5.1 (4.4, 6.5) vs. 6.5 (4.6, 8.6), P = 0.002]. BAA showed a mean difference of < 0.05 cm across the 6 landmarks between the two datasets. ICC showed excellent inter- and intra-observer reproducibility. CONCLUSIONS: Thoracic aortic iNAV-based CMRA with fast acquisition (~ 3 min) and inline reconstruction (3 min) is proposed, resulting in high diagnostic confidence and reproducible aortic measurements.

Whole-heart non-rigid motion corrected coronary MRA with autofocus virtual 3D iNAV.

PURPOSE: Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction. METHODS: Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC. RESULTS: Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 ± 1% vs 52 ± 11%, LAD: 49 ± 8% vs 49 ± 7%; visible vessel length: RCA: 5.98 ± 1.37 cm vs 5.81 ± 1.62 cm, LAD: 5.95 ± 1.85 cm vs 4.83 ± 1.56 cm), however these improvements were not statistically significant. CONCLUSION: The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent.

Quantification of balanced SSFP myocardial perfusion imaging at 1.5 T: Impact of the reference image.

PURPOSE: To investigate the use of a high flip-angle (HFA) balanced SSFP (bSSFP) reference image (in comparison to conventional proton density [PD]-weighted reference images) for conversion of bSSFP myocardial perfusion images into dynamic T1 maps for improved myocardial blood flow (MBF) quantification at 1.5 T. METHODS: The HFA-bSSFP (flip angle [FA] = 50°), PD gradient-echo (PD-GRE; FA = 5°), and PD-bSSFP (FA = 8°) reference images were acquired before a dual-sequence bSSFP perfusion acquisition. Simulations were used to study accuracy and precision of T1 and MBF quantification using the three techniques. The accuracy and precision of T1 , and the precision and intersegment variability of MBF were compared among the three techniques in 8 patients under rest conditions. RESULTS: In simulations, HFA-bSSFP demonstrated improved T1 /MBF precision (higher T1 /MBF SD of 30%-80%/50%-100% and 30%-90%/60%-115% for PD-GRE and PD-bSSFP, respectively). Proton density-GRE and PD-bSSFP were more sensitive to effective FA than HFA-bSSFP (maximum T1 /MBF errors of 13%/43%, 20%/43%, and 1%/3%, respectively). Sensitivity of all techniques (defined as T1 /MBF errors) to native T1 , native T2 , and effective saturation efficiency were negligible (<1%/<1%), moderate (<14%/<19%), and high (<63%/<94%), respectively. In vivo, no difference in T1 accuracy was observed among HFA-bSSFP, PD-GRE, and PD-bSSFP (-9 ± 44 ms vs -28 ± 55 ms vs -22 ± 71 ms, respectively; p > .08). The HFA-bSSFP led to improved T1 /MBF precision (T1 /MBF SD: 41 ± 19 ms/0.24 ± 0.08 mL/g/min vs PD-GRE: 48 ± 20 ms/0.29 ± 0.09 mL/g/min and PD-bSSFP: 59 ± 23 ms/0.33 ± 0.11 mL/g/min; p ≤ .02) and lower MBF intersegment variability (0.14 ± 0.09 mL/g/min vs PD-GRE: 0.21 ± 0.09 mL/g/min and PD-bSSFP: 0.20 ± 0.10 mL/g/min; p ≤ .046). CONCLUSION: We have demonstrated the feasibility of using a HFA-bSSFP reference image for MBF quantification of bSSFP perfusion imaging at 1.5 T. Results from simulations demonstrate that the HFA-bSSFP reference image results in improved precision and reduced sensitivity to effective FA compared with conventional techniques using a PD reference image. Preliminary in vivo data acquired at rest also demonstrate improved precision and intersegment variability using the HFA-bSSFP technique compared with PD techniques; however, a clinical study in patients with coronary artery disease under stress conditions is required to determine the clinical significance of this finding.

Evaluation of accelerated motion-compensated 3d water/fat late gadolinium enhanced MR for atrial wall imaging.

OBJECTIVE: 3D late gadolinium enhancement (LGE) imaging is a promising non-invasive technique for the assessment of atrial fibrosis. However, current techniques result in prolonged and unpredictable scan times and high rates of non-diagnostic images. The purpose of this study was to compare the performance of a recently proposed accelerated respiratory motion-compensated 3D water/fat LGE technique with conventional 3D LGE for atrial wall imaging. MATERIALS AND METHODS: 18 patients (age: 55.7±17.1 years) with atrial fibrillation underwent conventional diaphragmatic navigator gated inversion recovery (IR)-prepared 3D LGE (dNAV) and proposed image-navigator motion-corrected water/fat IR-prepared 3D LGE (iNAV) imaging. Images were assessed for image quality and presence of fibrosis by three expert observers. The scan time for both techniques was recorded. RESULTS: Image quality scores were improved with the proposed compared to the conventional method (iNAV: 3.1 ± 1.0 vs. dNAV: 2.6 ± 1.0, p = 0.0012, with 1: Non-diagnostic to 4: Full diagnostic). Furthermore, scan time for the proposed method was significantly shorter with a 59% reduction is scan time (4.5 ± 1.2 min vs. 10.9 ± 3.9 min, p < 0.0001). The images acquired with the proposed method were deemed as inconclusive less frequently than the conventional images (expert 1/expert 2: 4/7 dNAV and 2/4 iNAV images inconclusive). DISCUSSION: The motion-compensated water/fat LGE method enables atrial wall imaging with diagnostic quality comparable to the current conventional approach with a significantly shorter scan of about 5 min.