Balanced Steady State Research Articles

Dynamic Cardiac Magnetic Resonance Fingerprinting During Vasoactive Breathing Maneuvers: First Results.

Cardiac magnetic resonance fingerprinting (cMRF) enables simultaneous mapping of myocardial T1 and T2 with very short acquisition times. Breathing maneuvers have been utilized as a vasoactive stress test to dynamically characterize myocardial tissue in vivo. We tested the feasibility of sequential, rapid cMRF acquisitions during breathing maneuvers to quantify myocardial T1 and T2 changes. We measured T1 and T2 values using conventional T1 and T2-mapping techniques (modified look locker inversion [MOLLI] and T2-prepared balanced-steady state free precession), and a 15 heartbeat (15-hb) and rapid 5-hb cMRF sequence in a phantom and in 9 healthy volunteers. The cMRF5-hb sequence was also used to dynamically assess T1 and T2 changes over the course of a vasoactive combined breathing maneuver. In healthy volunteers, the mean myocardial T1 of the different mapping methodologies were: MOLLI 1,224 ± 81 ms, cMRF15-hb 1,359 ± 97 ms, and cMRF5-hb 1,357 ± 76 ms. The mean myocardial T2 measured with the conventional mapping technique was 41.7 ± 6.7 ms, while for cMRF15-hb 29.6 ± 5.8 ms and cMRF5-hb 30.5 ± 5.8 ms. T2 was reduced with vasoconstriction (post-hyperventilation compared to a baseline resting state) (30.15 ± 1.53 ms vs. 27.99 ± 2.07 ms, p = 0.02), while T1 did not change with hyperventilation. During the vasodilatory breath-hold, no significant change of myocardial T1 and T2 was observed. cMRF5-hb enables simultaneous mapping of myocardial T1 and T2, and may be used to track dynamic changes of myocardial T1 and T2 during vasoactive combined breathing maneuvers.

Grain-size dependence of water retention in a model aggregated soil

We experimentally examined the amount of water retention in a model soil composed of aggregated glass beads. The model soil was characterized by two size parameters: size of aggregates D and size of monomer particles (composing aggregates) d. In the experiment, water was sprinkled on the model-soil system that has an open top surface and drainable sieve bottom. When the sprinkled water amount exceeded a threshold (retainable limit), draining flux balanced with the sprinkled flux. The weight variations of retained and drained water were measured to confirm this balanced (steady) state and quantify the retained water. We defined the weight of the retained water in this steady state as W0 and examined the relationship among W0,d and D. As a result, it was revealed that W0 increases as d decreases simply due to the capillary effects. Regarding D dependence, it turned out that W0 becomes the maximum around D≃500μm. The value of D maximizing water retention is determined by the void formation due to the aggregated structure, capillary effect, and gravity.

DCNet: Diversity convolutional network for ventricle segmentation on short-axis cardiac magnetic resonance images

To accurately and simultaneously segment myocardium, left and right ventricles at the end-diastolic (ED) and end-systolic (ES) phases from short-axis cardiac magnetic resonance (CMR) images with inherent variability in appearance, shape, and location of the region of interest (ROI), we propose a diversity convolutional network (DCNet) that aims to solve ventricle under- and over-segmentation problems. DCNet is composed of three stages: the integration of diversity features, recoding of diversity features, and decoding of integrated features. To enhance the representational capacity and enrich the feature space of a single convolution, we design a diversity convolution block during the first stage. During the second stage, we design a dual-path channel attention mechanism to simultaneously select average and maximum features. In addition, we use a soft Dice loss function to assist in the network’s training. We conducted experiments on the 2017 Automated Cardiac Diagnosis Challenge (ACDC 2017), 2019 Multi-Sequence Cardiac MR Segmentation Challenge (MS-CMRSeg 2019), and 2020 Myocardial Pathology Segmentation Challenge (MyoPS 2020) datasets. We submitted our test results on the ACDC dataset to an online test platform, the proposed DCNet achieved Dice scores of 95.80%, 91.77%, and 91.57% in the left ventricle, right ventricle, and myocardium segmentation tasks, respectively. Compared with four representative networks, the proposed DCNet achieves the best results on balanced steady state free precession (bSSFP) cine sequence and late gadolinium enhancement (LGE) CMR sequences on the MS-CMRSeg and MyoPS datasets. Therefore, the proposed method is promising for automatic ventricle segmentation in clinical applications. We uploaded the code to https://github.com/fly1995/DCNet.

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images.

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone.

Inter-field strength agreement of cardiovascular magnetic resonance cine-derived strain and strain rate measures: a randomised study

Abstract Background Left ventricular (LV) strain and strain rate measurements can be derived from routinely acquired cardiovascular magnetic resonance (CMR) cine images by feature tracking techniques. However, the inter-field strength agreement of strain measurements derived from these techniques is not known. We hypothesised that there would be excellent inter-field strength agreement (between 1.5 and 3 Tesla [T]) for the measurement of global strain and strain rate derived from cine imaging. Methods Prospective, randomised cross-over observational study. Healthy volunteers each underwent CMR scans at 1.5T and 3T within 30 minutes on the same day in a randomised order. Retrospectively ECG gated, short and long-axis balanced steady state free precession cine images were obtained using standardised acquisition parameters at both field strengths. Two software packages were used to derive LV global longitudinal, circumferential and long and short axis radial systolic strain, peak systolic, early diastolic and late diastolic strain rates. All strain values are expressed as positive numbers. Results Twenty-two subjects (mean age 36±12 years; 45% male) were studied. No differences in heart rate and blood pressure measurements during scanning were observed between field strengths. The abstract figure shows an example of strain analysis and Bland-Altman plots for global longitudinal and circumferential strain. Minimal bias was seen in all strain and strain rate measurements between field strengths using the first software package. Strain and strain rate values derived from long axis images (longitudinal and long axis radial) showed poor to fair agreement (intraclass correlation co-efficient (ICC) range 0.39–0.71), whereas measures derived from short axis images (circumferential and short axis radial) showed good to excellent agreement between field strengths (ICC range 0.78–0.91). Similar results were observed with the second software package, though the differences in agreement between long and short axis derived measures were less pronounced. Conclusion Longitudinal strain and strain rate measures derived from CMR feature tracking have poor inter-field strength agreement between 1.5T and 3T. By contrast, agreement of circumferential and short axis radial strain and strain rate measurements at 1.5T and 3T is good. These results need to be considered when assessing strain at different field strengths. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): United Kingdom National Institute for Health Research

LA-Net: A Multi-Task Deep Network for the Segmentation of the Left Atrium.

Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross attention modules (CAMs) and enhanced decoder modules (EDMs) to purposefully select the most meaningful edge information for segmentation and smoothly incorporate it into segmentation masks at multiple-scales. We evaluate the performance of LA-Net on two MR sequences: late gadolinium enhanced (LGE) atrial MRI and atrial short axis balanced steady state free precession (bSSFP) MRI. LA-Net gives Hausdorff distances of 12.43 mm and Dice scores of 0.92 on the LGE (STACOM 2018) dataset and Hausdorff distances of 17.41 mm and Dice scores of 0.90 on the bSSFP (in-house) dataset without any post-processing, surpassing previously proposed segmentation networks, including U-Net and SEGANet. Our method allows automatic extraction of information about the LA from MR images, which can play an important role in the management of AF patients.

Improved catheter tracking during cardiovascular magnetic resonance-guided cardiac catheterization using overlay visualization

IntroductionCardiovascular magnetic resonance (CMR)-guided cardiac catheterization is becoming more widespread due to the ability to acquire both functional CMR measurements and diagnostic catheterization data without exposing patients to ionizing radiation. However, the real-time imaging sequences used for catheter guidance during these procedures are limited in resolution and the anatomical detail they can provide. In this study, we propose a passive catheter tracking approach which simultaneously improves catheter tracking and visualization of the anatomy.Methods60 patients with congenital heart disease underwent CMR-guided cardiac catheterization on a 1.5T CMR scanner (Ingenia, Philips Healthcare, Best the Netherlands) using the Philips iSuite system. The proposed T1-overlay technique uses a commercially available heavily T1-weighted sequence to image the catheter, and overlays it on a high-resolution 3D dataset within iSuite in real-time. Suppressed tissue in the real-time images enables the use of a thick imaging slab to assist in tracking of the catheter. Improvement in catheter visualization time was compared between T1-overlay and the conventional invasive CMR (iCMR) balanced steady state free precession (bSSFP) sequence. This technique also enabled selective angiography visualization for real-time evaluation of blood flow dynamics (such as pulmonary transit time), similar to direct contrast injection under standard fluoroscopy. Estimates of pulmonary transit time using iCMR were validated using x-ray fluoroscopy in 16 patients.ResultsThe T1-overlay approach significantly increased the time that the catheter tip was kept in view by the technologist compared to the bSSFP sequence conventionally used for iCMR. The resulting images received higher ratings for blood/balloon contrast, anatomy visualization, and overall suitability for iCMR guidance by three cardiologists. iCMR selective angiography using T1-overlay also provided accurate estimates of pulmonary transit time that agreed well with x-ray fluoroscopy.ConclusionWe demonstrate a new passive catheter tracking technique using the iSuite platform that improves visualization of the catheter and cardiac anatomy. These improvements significantly increase the time that the catheter tip is seen throughout the procedure. We also demonstrate the feasibility of iCMR selective angiography for the measurement of pulmonary transit time.

Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy

BackgroundMyocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), has important prognostic value in hypertrophic cardiomyopathy (HCM). However, nearly 50% of HCM patients have no scar but undergo repeated gadolinium-based CMR over their life span. We sought to develop an artificial intelligence (AI)-based screening model using radiomics and deep learning (DL) features extracted from balanced steady state free precession (bSSFP) cine sequences to identify HCM patients without scar.MethodsWe evaluated three AI-based screening models using bSSFP cine image features extracted by radiomics, DL, or combined DL-Radiomics. Images for 759 HCM patients (50 ± 16 years, 66% men) in a multi-center/vendor study were used to develop and test model performance. An external dataset of 100 HCM patients (53 ± 14 years, 70% men) was used to assess model generalizability. Model performance was evaluated using area-under-receiver-operating curve (AUC).ResultsThe DL-Radiomics model demonstrated higher AUC compared to DL and Radiomics in the internal (0.83 vs 0.77, p = 0.006 and 0.78, p = 0.05; n = 159) and external (0.74 vs 0.64, p = 0.006 and 0.71, p = 0.27; n = 100) datasets. The DL-Radiomics model correctly identified 43% and 28% of patients without scar in the internal and external datasets compared to 42% and 16% by Radiomics model and 42% and 23% by DL model, respectively.ConclusionsA DL-Radiomics AI model using bSSFP cine images outperforms DL or Radiomics models alone as a scar screening tool prior to gadolinium administration. Despite its potential, the clinical utility of the model remains limited and further investigation is needed to improve the accuracy and generalizability.

Efficient non-contrast enhanced 3D Cartesian cardiovascular magnetic resonance angiography of the thoracic aorta in 3\xa0min

BackgroundThe 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.Methods35 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.ResultsThe 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.ConclusionsThoracic 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.

A Deep Learning Approach for Automated Volume Delineation on Daily MRI Scans in Glioblastoma Patients

Recently identified changes in brain during daily MRI-guided radiation therapy (MRgRT) of patients with glioblastoma (GBM) can permit adaptive radiotherapy. However, daily changes can be subtle to detect and difficult to evaluate volumetrically without significant manual effort. The aim of this study is to develop a deep learning (DL) algorithm for automatic volume delineation of daily MRI scans from a hybrid MRI-RT system as an early warning system of tumor change and to facilitate analysis of delta radiomics and clinical trials based on MRI changes during RT.GBM patients undergoing 30 fractions (6 weeks) of RT delivered by a combination MRI-RT system with concurrent temozolomide (TMZ) treatment were considered for study. The MRI data for each patient consisted of one predominantly T2-weighted balanced steady state free precession (bSSFP) planning scan (isotropic 1.5 mm3 resolution, 128 sec) followed by 30 daily bSSFP setup scans for 30 fractions of RT for each patient. Tumor lesion (TL) with/without surrounding edema, and resection cavity (RC) were manually contoured for each patient on each of the 31 scans in MIM. The DL-generated masks were converted to 3D volumes and compared using Dice Similarity Coefficient (DSC) between manual and automatic contours. In addition, the correlation between the automatic and manual volumes was examined with Person Correlation Coefficient (r).The MRIs of 14 patients undergoing MRgRT with biopsy, subtotal resection, or gross total resection of GBM were incorporated into a dataset. Seven patients had only TL, 3 patients had both TL and RC, and 4 patients had RC only. MRIs from 11 patients were used in training and 3 patients were left for testing. Total of 8,505 axial slices were used to train the network, with 1,021 slices used as validation. DSC for TL and RC on the training and test datasets were: mean ± standard deviation: 0.87 ± 0.128 and 0.9 ± 0.122; 0.74 ± 0.233 and 0.8 ± 0.277, respectively. The CNN achieved DSC greater than 0.8 in 2 of the testing patients but less than 0.8 on 1, indicating that while successful, the network was not yet well generalized with the current training data. The automated lesion volumes correlated significantly with the manual volumes with r value of 0.9.Contouring of brain lesions is a complex and time-consuming task, which impacts research throughput as it needs to be performed for every patient and scan. We have shown the promise of MASK R-CNN to produce automated contours for brain tumors. As more patients are treated with MRI-RT and with additional contrasts, the network will improve the accuracy and its generalizability. The use of DL in RT research improves the feasibility of visualizing tumor kinetics trends in real time to alert the physician to significant changes and further study of delta radiomics.

CMMCSegNet: Cross-Modality Multicascade Indirect LGE Segmentation on Multimodal Cardiac MR.

Since Late-Gadolinium Enhancement (LGE) of cardiac magnetic resonance (CMR) visualizes myocardial infarction, and the balanced-Steady State Free Precession (bSSFP) cine sequence can capture cardiac motions and present clear boundaries; multimodal CMR segmentation has played an important role in the assessment of myocardial viability and clinical diagnosis, while automatic and accurate CMR segmentation still remains challenging due to a very small amount of labeled LGE data and the relatively low contrasts of LGE. The main purpose of our work is to learn the real/fake bSSFP modality with ground truths to indirectly segment the LGE modality of cardiac MR by using a proposed cross-modality multicascade framework: cross-modality translation network and automatic segmentation network, respectively. In the segmentation stage, a novel multicascade pix2pix network is designed to segment the fake bSSFP sequence obtained from a cross-modality translation network. Moreover, we propose perceptual loss measuring features between ground truth and prediction, which are extracted from the pretrained vgg network in the segmentation stage. We evaluate the performance of the proposed method on the multimodal CMR dataset and verify its superiority over other state-of-the-art approaches under different network structures and different types of adversarial losses in terms of dice accuracy in testing. Therefore, the proposed network is promising for Indirect Cardiac LGE Segmentation in clinical applications.

Right Ventricular Function and T1-Mapping in Boys With Duchenne Muscular Dystrophy.

Clinical management of boys with Duchenne muscular dystrophy (DMD) relies on in-depth understanding of cardiac involvement, but right ventricular (RV) structural and functional remodeling remains understudied. To evaluate several analysis methods and identify the most reliable one to measure RV pre- and postcontrast T1 (RV-T1) and to characterize myocardial remodeling in the RV of boys with DMD. Prospective. Boys with DMD (N=27) and age-/sex-matched healthy controls (N=17) from two sites. 3.0 T using balanced steady state free precession, motion-corrected phase sensitive inversion recovery and modified Look-Locker inversion recovery sequences. Biventricular mass (Mi), end-diastolic volume (EDVi) and ejection fraction (EF) assessment, tricuspid annular excursion (TAE), late gadolinium enhancement (LGE), pre- and postcontrast myocardial T1 maps. The RV-T1 reliability was assessed by three observers in four different RV regions of interest (ROI) using intraclass correlation (ICC). The Wilcoxon rank sum test was used to compare RV-T1 differences between DMD boys with negative LGE(-) or positive LGE(+) and healthy controls. Additionally, correlation of precontrast RV-T1 with functional measures was performed. A P-value <0.05 was considered statistically significant. A 1-pixel thick RV circumferential ROI proved most reliable (ICC > 0.91) for assessing RV-T1. Precontrast RV-T1 was significantly higher in boys with DMD compared to controls. Both LGE(-) and LGE(+) boys had significantly elevated precontrast RV-T1 compared to controls (1543 [1489-1597] msec and 1550 [1402-1699] msec vs. 1436 [1399-1473] msec, respectively). Compared to healthy controls, boys with DMD had preserved RVEF (51.8 [9.9]% vs. 54.2 [7.2]%, P=0.31) and significantly reduced RVMi (29.8 [9.7]g vs. 48.0 [15.7]g), RVEDVi (69.8 [29.7]mL/m2 vs. 89.1 [21.9]mL/m2 ), and TAE (22.0 [3.2]cm vs. 26.0 [4.7]cm). Significant correlations were found between precontrast RV-T1 and RVEF (β=-0.48%/msec) and between LV-T1 and LVEF (β=-0.51%/msec). Precontrast RV-T1 is elevated in boys with DMD compared to healthy controls and is negatively correlated with RVEF. 1 TECHNICAL EFFICACY: Stage 2.

All-systolic first-pass myocardial rest perfusion at a long saturation time using simultaneous multi-slice imaging and compressed sensing acceleration.

PurposeTo enable all-systolic first-pass rest myocardial perfusion with long saturation times. To investigate the change in perfusion contrast and dark rim artefacts through simulations and surrogate measurements.MethodsSimulations were employed to investigate optimal saturation time for myocardium-perfusion defect contrast and blood-to-myocardium signal ratios. Two saturation recovery blocks with long/short saturation times (LTS/STS) were employed to image 3 slices at end-systole and diastole. Simultaneous multi-slice balanced steady state free precession imaging and compressed sensing acceleration were combined. The sequence was compared to a 3 slice-by-slice clinical protocol in 10 patients. Quantitative assessment of myocardium-peak pre contrast and blood-to-myocardium signal ratios, as well as qualitative assessment of perceived SNR, image quality, blurring, and dark rim artefacts, were performed.ResultsSimulations showed that with a bolus of 0.075 mmol/kg, a LTS of 240470 ms led to a relative increase in myocardium-perfusion defect contrast of 34% ± 9%-28% ± 27% than a STS = 120 ms, while reducing blood-to-myocardium signal ratio by 18% ± 10%-32% ± 14% at peak myocardium. With a bolus of 0.05 mmol/kg, LTS was 320-570 ms with an increase in myocardium-perfusion defect contrast of 63% ± 13%-62% ± 29%. Across patients, LTS led to an average increase in myocardium-peak pre contrast of 59% (P < .001) at peak myocardium and a lower blood-to-myocardium signal ratio of 47% (P < .001) and 15% (P < .001) at peak blood/myocardium. LTS had improved motion robustness (P = .002), image quality (P < .001), and decreased dark rim artefacts (P = .008) than the clinical protocol.ConclusionAll-systolic rest perfusion can be achieved by combining simultaneous multi-slice and compressed sensing acceleration, enabling 3-slice cardiac coverage with reduced motion and dark rim artefacts. Numerical simulations indicate that myocardium-perfusion defect contrast increases at LTS.

Endogenous assessment of myocardial injury with single-shot model-based non-rigid motion-corrected T1 rho mapping

BackgroundCardiovascular magnetic resonance T1ρ mapping may detect myocardial injuries without exogenous contrast agent. However, multiple co-registered acquisitions are required, and the lack of robust motion correction limits its clinical translation. We introduce a single breath-hold myocardial T1ρ mapping method that includes model-based non-rigid motion correction.MethodsA single-shot electrocardiogram (ECG)-triggered balanced steady state free precession (bSSFP) 2D adiabatic T1ρ mapping sequence that collects five T1ρ-weighted (T1ρw) images with different spin lock times within a single breath-hold is proposed. To address the problem of residual respiratory motion, a unified optimization framework consisting of a joint T1ρ fitting and model-based non-rigid motion correction algorithm, insensitive to contrast change, was implemented inline for fast (~ 30 s) and direct visualization of T1ρ maps. The proposed reconstruction was optimized on an ex vivo human heart placed on a motion-controlled platform. The technique was then tested in 8 healthy subjects and validated in 30 patients with suspected myocardial injury on a 1.5T CMR scanner. The Dice similarity coefficient (DSC) and maximum perpendicular distance (MPD) were used to quantify motion and evaluate motion correction. The quality of T1ρ maps was scored. In patients, T1ρ mapping was compared to cine imaging, T2 mapping and conventional post-contrast 2D late gadolinium enhancement (LGE). T1ρ values were assessed in remote and injured areas, using LGE as reference.ResultsDespite breath holds, respiratory motion throughout T1ρw images was much larger in patients than in healthy subjects (5.1 ± 2.7 mm vs. 0.5 ± 0.4 mm, P < 0.01). In patients, the model-based non-rigid motion correction improved the alignment of T1ρw images, with higher DSC (87.7 ± 5.3% vs. 82.2 ± 7.5%, P < 0.01), and lower MPD (3.5 ± 1.9 mm vs. 5.1 ± 2.7 mm, P < 0.01). This resulted in significantly improved quality of the T1ρ maps (3.6 ± 0.6 vs. 2.1 ± 0.9, P < 0.01). Using this approach, T1ρ mapping could be used to identify LGE in patients with 93% sensitivity and 89% specificity. T1ρ values in injured (LGE positive) areas were significantly higher than in the remote myocardium (68.4 ± 7.9 ms vs. 48.8 ± 6.5 ms, P < 0.01).ConclusionsThe proposed motion-corrected T1ρ mapping framework enables a quantitative characterization of myocardial injuries with relatively low sensitivity to respiratory motion. This technique may be a robust and contrast-free adjunct to LGE for gaining new insight into myocardial structural disorders.

Radial self-navigated native magnetic resonance angiography in comparison to navigator-gated contrast-enhanced MRA of the entire thoracic aorta in an aortic patient collective

BackgroundThe native balanced steady state with free precession (bSSFP) magnetic resonance angiography (MRA) technique has been shown to provide high diagnostic image quality for thoracic aortic disease. This study compares a 3D radial respiratory self-navigated native MRA (native-SN-MRA) based on a bSSFP sequence with conventional Cartesian, 3D, contrast-enhanced MRA (CE-MRA) with navigator-gated respiration control for image quality of the entire thoracic aorta.MethodsThirty-one aortic native-SN-MRA were compared retrospectively (63.9 ± 10.3 years) to 61 CE-MRA (63.1 ± 11.7 years) serving as a reference standard. Image quality was evaluated at the aortic root/ascending aorta, aortic arch and descending aorta. Scan time was recorded. In 10 patients with both MRA sequences, aortic pathologies were evaluated and normal and pathologic aortic diameters were measured. The influence of artifacts on image quality was analyzed.ResultsCompared to the overall image quality of CE-MRA, the overall image quality of native-SN-MRA was superior for all segments analyzed (aortic root/ascending, p < 0.001; arch, p < 0.001, and descending, p = 0.005). Regarding artifacts, the image quality of native-SN-MRA remained superior at the aortic root/ascending aorta and aortic arch before and after correction for confounders of surgical material (i.e., susceptibility-related artifacts) (p = 0.008 both) suggesting a benefit in terms of motion artifacts. Native-SN-MRA showed a trend towards superior intraindividual image quality, but without statistical significance. Intraindividually, the sensitivity and specificity for the detection of aortic disease were 100% for native-SN-MRA. Aortic diameters did not show a significant difference (p = 0.899). The scan time of the native-SN-MRA was significantly reduced, with a mean of 05:56 ± 01:32 min vs. 08:51 ± 02:57 min in the CE-MRA (p < 0.001).ConclusionsSuperior image quality of the entire thoracic aorta, also regarding artifacts, can be achieved with native-SN-MRA, especially in motion prone segments, in addition to a shorter acquisition time.

Lymphatic pathway evaluation in congenital heart disease using 3D whole-heart balanced steady state free precession and T2-weighted cardiovascular magnetic resonance

BackgroundDue to passive blood flow in palliated single ventricle, central venous pressure increases chronically, ultimately impeding lymphatic drainage. Early visualization and treatment of these malformations is essential to reduce morbidity and mortality. Cardiovascular magnetic resonance (CMR) T2-weighted lymphangiography (T2w) is used for lymphatic assessment, but its low signal-to-noise ratio may result in incomplete visualization of thoracic duct pathway. 3D-balanced steady state free precession (3D-bSSFP) is commonly used to assess congenital cardiac disease anatomy. Here, we aimed to improve diagnostic imaging of thoracic duct pathway using 3D-bSSFP.MethodsPatients underwent CMR during single ventricle or central lymphatic system assessment using T2w and 3D-bSSFP. T2w parameters included 3D-turbo spin echo (TSE), TE/TR = 600/2500 ms, resolution = 1 × 1 × 1.8 mm, respiratory triggering with bellows. 3D-bSSFP parameters included electrocardiogram triggering and diaphragm navigator, 1.6 mm isotropic resolution, TE/TR = 1.8/3.6 ms. Thoracic duct was identified independently in T2w and 3D-bSSFP images, tracked completely from cisterna chyli to its drainage site, and classified based on severity of lymphatic abnormalities.ResultsForty-eight patients underwent CMR, 46 of whom were included in the study. Forty-five had congenital heart disease with single ventricle physiology. Median age at CMR was 4.3 year (range 0.9–35.1 year, IQR 2.4 year), and median weight was 14.4 kg (range, 7.9–112.9 kg, IQR 5.2 kg). Single ventricle with right dominant ventricle was noted in 31 patients. Thirty-eight patients (84%) were status post bidirectional Glenn and 7 (16%) were status post Fontan anastomosis. Thoracic duct visualization was achieved in 45 patients by T2w and 3D-bSSFP. Complete tracking to drainage site was attained in 11 patients (24%) by T2w vs 25 (54%) by 3D-bSSFP and in 28 (61%) by both. Classification of lymphatics was performed in 31 patients.ConclusionThoracic duct pathway can be visualized by 3D-bSSFP combined with T2w lymphangiography. Cardiac triggering and respiratory navigation likely help retain lymphatic signal in the retrocardiac area by 3D-bSSFP. Visualizing lymphatic system leaks is challenging on 3D-bSSFP images alone, but 3D-bSSFP offers good visualization of duct anatomy and landmark structures to help plan interventions. Together, these sequences can define abnormal lymphatic pathway following single ventricle palliative surgery, thus guiding lymphatic interventional procedures.

Joint Deep Learning Framework for Image Registration and Segmentation of Late Gadolinium Enhanced MRI and Cine Cardiac MRI.

Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) imaging, the current benchmark for assessment of myocardium viability, enables the identification and quantification of the compromised myocardial tissue regions, as they appear hyper-enhanced compared to the surrounding, healthy myocardium. However, in LGE CMR images, the reduced contrast between the left ventricle (LV) myocardium and LV blood-pool hampers the accurate delineation of the LV myocardium. On the other hand, the balanced-Steady State Free Precession (bSSFP) cine CMR imaging provides high resolution images ideal for accurate segmentation of the cardiac chambers. In the interest of generating patient-specific hybrid 3D and 4D anatomical models of the heart, to identify and quantify the compromised myocardial tissue regions for revascularization therapy planning, in our previous work, we presented a spatial transformer network (STN) based convolutional neural network (CNN) architecture for registration of LGE and bSSFP cine CMR image datasets made available through the 2019 Multi-Sequence Cardiac Magnetic Resonance Segmentation Challenge (MS-CMRSeg). We performed a supervised registration by leveraging the region of interest (RoI) information using the manual annotations of the LV blood-pool, LV myocardium and right ventricle (RV) blood-pool provided for both the LGE and the bSSFP cine CMR images. In order to reduce the reliance on the number of manual annotations for training such network, we propose a joint deep learning framework consisting of three branches: a STN based RoI guided CNN for registration of LGE and bSSFP cine CMR images, an U-Net model for segmentation of bSSFP cine CMR images, and an U-Net model for segmentation of LGE CMR images. This results in learning of a joint multi-scale feature encoder by optimizing all three branches of the network architecture simultaneously. Our experiments show that the registration results obtained by training 25 of the available 45 image datasets in a joint deep learning framework is comparable to the registration results obtained by stand-alone STN based CNN model by training 35 of the available 45 image datasets and also shows significant improvement in registration performance when compared to the results achieved by the stand-alone STN based CNN model by training 25 of the available 45 image datasets.

Non-contrast MR angiography versus contrast enhanced MR angiography for detection of renal artery stenosis: a comparative analysis in 400 renal arteries.

In this study, we compared non-contrast MR angiography (NC-MRA) with conventional 3D contrast-enhanced MRA (CE-MRA) in patients suspected to have renal artery stenosis (RAS). From March 2014 to March 2020, patients who were evaluated for RAS and had a glomerular filtration rate > 30ml/min/1.73 m2 underwent MR imaging on a 3T MR Scanner (Signa Hdxt General Electrics, Milwaukee, USA) using a Torso PA coil. The NC-MRA sequence was performed using a 3D fat-suppressed inflow inversion recovery balanced steady state free precession (SSFP) sequence (Inhance 3D Inflow IR, GE Medical) whereas the CE-MRA sequence was a 3D fast spoiled gradient echo (FSPGR). Overall quality of images was rated 1 to 4. Stenosis was reported as grade 1 (Normal), 2 (< 50% narrowing), 3 (> 50% narrowing) and 4 (Total occlusion). Grade 3 and 4 were considered haemodynamically significant. During the study period, 201 patients were enrolled (400 renal arteries). For hemodynamically significant (grade 3/4) stenosis, NC-MRA correctly diagnosed 72 patients (95 arteries) while in 2 patients (2 arteries), NC-MRA underdiagnosed the stenosis as grade 2 (these were found to have grade 3 stenosis on CE-MRA). The kappa value of agreement between NC-MRA and CE-MRA for detection of RAS showing excellent agreement (p < 0.001). In one of the largest series of patients so far, we found that NC-MRA is a viable alternative to CE-MRA for detection of RAS, highly correlating with CE-MRA for grade of stenosis and with additional advantage of lack of gadolinium based contrast agents toxicity.

Edema Progression during MRI-Guided Glioblastoma Radiotherapy

There are limited studies evaluating brain changes on MRI during the standard six weeks of concurrent chemotherapy and radiotherapy (RT) for glioma. Existing studies suggest that MRI during chemoRT could be useful to determine response to treatment. At our institution, we have been treating select gliomas with one of the first commercially available 0.35T MRI-RT systems. The system includes one clinically useable pulse sequence called balanced steady state free precession (bSSFP), which produces a highly T2-weighted 3D image of the brain. The bSSFP MRIs are obtained daily for treatment setup and have demonstrated changes occurring in and around gliomas during patients’ chemoradiotherapy course. Our objective is to quantify the frequency, magnitude, and time course of these changes. Under a prospective registry protocol, we reviewed 14 patients at our institution who were treated for high grade glioma with chemoradiation, 60 Gy in 30 fractions plus temozolomide, on our institution’s MRI-RT system. We contoured the T2-weighted hyper-intense volumes from the MRI-RT set-up scans for all 30 fractions of treatment for each patient and then calculated the contoured MRI volumes. MRI-RT set-up scans during the last week of treatment were compared to the patients’ diagnostic MRIs obtained 3-4 weeks post-treatment completion to determine whether there were similar findings. 5 of 14 (36%) patients demonstrated greater than a 10% increase in T2-weighted MRI volume during the course of their treatment. Alternatively, 3 of 14 (21%) patients demonstrated greater than a 10% decrease in T2-weighted MRI volume during treatment. In 3 of the patients with volume growth, changes predominantly were observed during weeks 5 and 6 of treatment. The bSSFP images obtained during the last week of treatment demonstrated similar abnormalities compared to the diagnostic FLAIR MRIs obtained 3-4 weeks post-treatment. Patients who had volume expansion during treatment that resolved on serial follow-up MRIs were counted as pseudoprogressors. The time to true progression in the cohort was 19 months. Median follow-up was 14.7 months. 36% of high-grade glioma patients had growth of T2-weighted MRI volume during chemoradiotherapy, and these changes were similar to those observed on post-treatment diagnostic scans. Tumor volume expansion could be due to pseudoprogression, true progression, or changes in edema. Our future work aims to differentiate these possibilities with additional patients, follow-up, and MRI pulse sequences on the MRI-RT system. Groups using limited CTV margins for treatment planning should be aware that MRI volumes could significantly change during radiotherapy. Patients demonstrated T2-weighted MRI volume increase during weeks 5-6 of treatment, suggesting that studies using diagnostic MRI scans mid-treatment (week 3-4) may not fully capture the extent of progression.

Edema Progression during MRI-Guided Glioblastoma Radiotherapy

There are limited studies evaluating brain changes on MRI during the six weeks of concurrent chemotherapy and radiotherapy (RT) for glioma. Existing studies suggest that MRI during chemoradiotherapy could be useful to determine early response to treatment. At our center, we have been treating selected gliomas with one of the first commercially available 0.35T MRI-RT systems. The system includes one clinically useable pulse sequence, balanced steady state free precession (bSSFP), which obtains a 1.5 mm isotropic highly T2-weighted 3D image volume through the brain with minimal distortions. The bSSFP MRIs are obtained daily for treatment setup, and we have seen changes occurring in and around gliomas during chemoradiotherapy. Our objective is to quantify the frequency, magnitude, and time course of these changes. Under a prospective registry protocol, we reviewed 14 patients at our institution who were treated for high grade glioma with chemoradiation 60 Gy in 30 fractions plus temozolomide on our MRI-RT system. We contoured the T2-weighted hyperintense volumes from the MRI-RT bSSFP images for all 30 treatment fractions for each patient and measured the volumes of those MRIs. The MRIs from fraction 30 were compared to the diagnostic MRIs obtained 3-4 weeks post-treatment to determine whether there were similar findings. 5 of 14 (36%) patients demonstrated greater than a 10% increase in tumor volume during the course of their treatment. In 3 of these patients with growth, changes predominantly were observed during week 5 and 6 of treatment. One patient’s non-enhancing unresected tumor decreased 24% in volume during the course of treatment. The bSSFP images obtained at the end of treatment demonstrated similar FLAIR abnormalities compared to diagnostic MRIs 3-4 weeks post treatment completion. Patients who had volume expansion during treatment that resolved on serial follow-up MRIs were counted as pseudoprogression, and excluding pseudoprogressors, the progression free survival in the cohort was 19 months. Median follow-up was 14.7 months. 36% of high grade glioma patients had growth of T2-weighted volume during chemoradiotherapy, and these changes were similar to those observed on post-treatment diagnostic scans. Tumor volume expansion could be due to pseudoprogression, true progression, or solely due to changes in edema. Our future work aims to differentiate these possibilities with additional patients and MRI pulse sequences. Changes were greatest during weeks 5-6 of the treatment course, suggesting that studies using diagnostic MRI scans during RT should be obtained late in the treatment course.