Real-time Assessment of Right and Left Ventricular Volumes and Function in Children Using High Spatiotemporal Resolution Spiral bSSFP with Compressed Sensing

Background: Real-time (RT) assessment of ventricular volumes and function enables data acquisition during free-breathing. However, in children the requirement for high spatiotemporal resolution requires accelerated imaging techniques. In this study, we implemented a novel RT bSSFP spiral sequence reconstructed using Compressed Sensing (CS) and validated it against the breath-hold (BH) reference standard for assessment of ventricular volumes in children with heart disease. Methods: Data was acquired in 60 children. Qualitative image scoring and evaluation of ventricular volumes was performed by 3 clinical cardiac MR specialists. 30 cases were reassessed for intra-observer variability, and the other 30 cases for inter-observer variability. Results: Spiral RT images were of good quality, however qualitative scores reflected more residual artefact than standard BH images and slightly lower edge definition. Quantification of Left Ventricular (LV) and Right Ventricular (RV) metrics showed excellent correlation between the techniques with narrow limits of agreement. However, we observed small but statistically significant overestimation of LV end-diastolic volume, underestimation of LV end-systolic volume, as well as a small overestimation of RV stroke volume and ejection fraction using the RT imaging technique. No difference in inter-observer or intra-observer variability were observed between the BH and RT sequences. Conclusions: Real-time bSSFP imaging using spiral trajectories combined with a compressed sensing reconstruction is feasible. The main benefit is that it can be acquired during free breathing. However, another important secondary benefit is that a whole ventricular stack can be acquired in ~20 seconds, as opposed to ~6 minutes for standard BH imaging. Thus, this technique holds the potential to significantly shorten MR scan times in children

Advanced H-1 Lung Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is one of the widely used medical imaging modality, since it can provide both structural and functional assessment in a single imaging session. However, two major challenges should be considered by using MRI for lung imaging. The first challenge is the intrinsic low SNR of H-1 lung MRI due to the low proton density as well as the fast decay of the lung parenchyma signal. And the second challenge is subject motion. To achieve high resolution structural image, MRI requires a long scan time, usually a few minutes or even longer, which make MRI sensitive to subject motion. To address the first challenge, ultra-short echo time (UTE) MRI sequence is used to capture the lung parenchyma signal before decay. As for subject motion, two major strategies are widely used. One strategy is fast breath-holding scan, the subjects are asked to hold their breaths for a short duration, and the fast 3D MR sequence would be used to acquire data within that duration. This dissertation proposes a new acquisition scheme based on the standard UTE sequence, which largely increases the encoding efficiency and improves the breath-holding scan images. The other is free breathing scan with motion correction. The subjects are allowed to breathe during the MR acquisition. After the acquisition, the motion corrupted data would go through the motion correction step to reconstruct the motion free images. In this dissertation, two novel motion corrected reconstruction strategies are proposed to incorporate the motion modeling and compensation into the reconstruction to get high SNR motion corrected 3D and 4D images. When translating the developed techniques to the clinical studies, specifically for pediatric and neonatal studies, more practical problems need to be considered, such as smaller but finer anatomy to image, the different respiratory patterns of the young subjects etc. This dissertation proposes a 5-minute free breathing UTE MRI strategy to achieve a 3D high resolution motion free lung image for pediatric and neonatal studies

Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging.

Quantitative cardiovascular magnetic resonance (CMR) imaging can be used to characterize fibrosis, oedema, ischaemia, inflammation and other disease conditions. However, the need to reduce artefacts arising from body motion through a combination of electrocardiography (ECG) control, respiration control, and contrast-weighting selection makes CMR exams lengthy. Here, we show that physiological motions and other dynamic processes can be conceptualized as multiple time dimensions that can be resolved via low-rank tensor imaging, allowing for motion-resolved quantitative imaging with up to four time dimensions. This continuous-acquisition approach, which we name cardiovascular MR multitasking, captures - rather than avoids - motion, relaxation and other dynamics to efficiently perform quantitative CMR without the use of ECG triggering or breath holds. We demonstrate that CMR multitasking allows for T1 mapping, T1-T2 mapping and time-resolved T1 mapping of myocardial perfusion without ECG information and/or in free-breathing conditions. CMR multitasking may provide a foundation for the development of setup-free CMR imaging for the quantitative evaluation of cardiovascular health

Consensus report from the 8th International Forum for Liver Magnetic Resonance Imaging.

ObjectivesThe 8th International Forum for Liver Magnetic Resonance Imaging (MRI), held in Basel, Switzerland, in October 2017, brought together clinical and academic radiologists from around the world to discuss developments in and reach consensus on key issues in the field of gadoxetic acid-enhanced liver MRI since the previous Forum held in 2013.MethodsTwo main themes in liver MRI were considered in detail at the Forum: the use of gadoxetic acid for contrast-enhanced MRI in patients with liver cirrhosis and the technical performance of gadoxetic acid-enhanced liver MRI, both opportunities and challenges. This article summarises the expert presentations and the delegate voting on consensus statements discussed at the Forum.Results and conclusionsIt was concluded that gadoxetic acid-enhanced MRI has higher sensitivity for the diagnosis of hepatocellular carcinoma (HCC), when compared with multidetector CT, by utilising features of hyperenhancement in the arterial phase and hypointensity in the hepatobiliary phase (HBP). Recent HCC management guidelines recognise an increasing role for gadoxetic acid-enhanced MRI in early diagnosis and monitoring post-resection. Additional research is needed to define the role of HBP in predicting microvascular invasion, to better define washout during the transitional phase in gadoxetic acid-enhanced MRI for HCC diagnosis, and to reduce the artefacts encountered in the arterial phase. Technical developments are being directed to shortening the MRI protocol for reducing time and patient discomfort and toward utilising faster imaging and non-Cartesian free-breathing approaches that have the potential to improve multiphasic dynamic imaging.Key points• Gadoxetic acid-enhanced MRI provides higher diagnostic sensitivity than CT for diagnosing HCC. • Gadoxetic acid-enhanced MRI has roles in early-HCC diagnosis and monitoring post-resection response. • Faster imaging and free-breathing approaches have potential to improve multiphasic dynamic imaging

Rigid‐body motion correction of the liver in image reconstruction for golden‐angle stack‐of‐stars DCE MRI

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141403/1/mrm26782_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141403/2/mrm26782.pd

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion

Objective: Cardiac cine magnetic resonance imaging (MRI) is one of the important means to assess cardiac functions and vascular abnormalities. However, due to cardiac beat, blood flow, or the patient's involuntary movement during the long acquisition, the reconstructed images are prone to motion artifacts that affect the clinical diagnosis. Therefore, accelerated cardiac cine MRI acquisition to achieve high-quality images is necessary for clinical practice. Approach: A novel end-to-end deep learning network is developed to improve cardiac cine MRI reconstruction under free breathing conditions. First, a U-Net is adopted to obtain the initial reconstructed images in k-space. Further to remove the motion artifacts, the Motion-Guided Deformable Alignment (MGDA) method with second-order bidirectional propagation is introduced to align the adjacent cine MRI frames by maximizing spatial-temporal information to alleviate motion artifacts. Finally, the Multi-Resolution Fusion (MRF) module is designed to correct the blur and artifacts generated from alignment operation and obtain the last high-quality reconstructed cardiac images. Main results: At an 8$\times$ acceleration rate, the numerical measurements on the ACDC dataset are SSIM of 78.40%$\pm$4.57%, PSNR of 30.46$\pm$1.22 dB, and NMSE of 0.0468$\pm$0.0075. On the ACMRI dataset, the results are SSIM of 87.65%$\pm$4.20%, PSNR of 30.04$\pm$1.18 dB, and NMSE of 0.0473$\pm$0.0072. Significance: The proposed method exhibits high-quality results with richer details and fewer artifacts for cardiac cine MRI reconstruction on different accelerations under free breathing conditions.Comment: 28 pages, 5 tables, 11 figure