Real-Time Magnetic Resonance Imaging

Real‐time magnetic resonance imaging (RT‐MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast‐switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady‐state free precession, and single‐shot rapid acquisition with relaxation enhancement. RT‐MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft‐tissue contrast, as well as flow information. In this review, we discuss the history of RT‐MRI, fundamental tradeoffs, enabling technology, established applications, and current trends

Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions

Cardiac magnetic resonance assessment of central and peripheral vascular function in patients undergoing renal sympathetic denervation as predictor for blood pressure response

Background: Most trials regarding catheter-based renal sympathetic denervation (RDN) describe a proportion of patients without blood pressure response. Recently, we were able to show arterial stiffness, measured by invasive pulse wave velocity (IPWV), seems to be an excellent predictor for blood pressure response. However, given the invasiveness, IPWV is less suitable as a selection criterion for patients undergoing RDN. Consequently, we aimed to investigate the value of cardiac magnetic resonance (CMR) based measures of arterial stiffness in predicting the outcome of RDN compared to IPWV as reference. Methods: Patients underwent CMR prior to RDN to assess ascending aortic distensibility (AAD), total arterial compliance (TAC), and systemic vascular resistance (SVR). In a second step, central aortic blood pressure was estimated from ascending aortic area change and flow sequences and used to re-calculate total arterial compliance (cTAC). Additionally, IPWV was acquired. Results: Thirty-two patients (24 responders and 8 non-responders) were available for analysis. AAD, TAC and cTAC were higher in responders, IPWV was higher in non-responders. SVR was not different between the groups. Patients with AAD, cTAC or TAC above median and IPWV below median had significantly better BP response. Receiver operating characteristic (ROC) curves predicting blood pressure response for IPWV, AAD, cTAC and TAC revealed areas under the curve of 0.849, 0.828, 0.776 and 0.753 (p = 0.004, 0.006, 0.021 and 0.035). Conclusions: Beyond IPWV, AAD, cTAC and TAC appear as useful outcome predictors for RDN in patients with hypertension. CMR-derived markers of arterial stiffness might serve as non-invasive selection criteria for RDN

Deep learning for fast and robust medical image reconstruction and analysis

Medical imaging is an indispensable component of modern medical research as well as clinical practice. Nevertheless, imaging techniques such as magnetic resonance imaging (MRI) and computational tomography (CT) are costly and are less accessible to the majority of the world. To make medical devices more accessible, affordable and efficient, it is crucial to re-calibrate our current imaging paradigm for smarter imaging. In particular, as medical imaging techniques have highly structured forms in the way they acquire data, they provide us with an opportunity to optimise the imaging techniques holistically by leveraging data. The central theme of this thesis is to explore different opportunities where we can exploit data and deep learning to improve the way we extract information for better, faster and smarter imaging. This thesis explores three distinct problems. The first problem is the time-consuming nature of dynamic MR data acquisition and reconstruction. We propose deep learning methods for accelerated dynamic MR image reconstruction, resulting in up to 10-fold reduction in imaging time. The second problem is the redundancy in our current imaging pipeline. Traditionally, imaging pipeline treated acquisition, reconstruction and analysis as separate steps. However, we argue that one can approach them holistically and optimise the entire pipeline jointly for a specific target goal. To this end, we propose deep learning approaches for obtaining high fidelity cardiac MR segmentation directly from significantly undersampled data, greatly exceeding the undersampling limit for image reconstruction. The final part of this thesis tackles the problem of interpretability of the deep learning algorithms. We propose attention-models that can implicitly focus on salient regions in an image to improve accuracy for ultrasound scan plane detection and CT segmentation. More crucially, these models can provide explainability, which is a crucial stepping stone for the harmonisation of smart imaging and current clinical practice.Open Acces

Optimizing and method development for clinical MR imaging near metallic implants

Metallic hip prosthesis is an implant that gets more common and comes with problems like metallosis (inflammation due to metallic debris). Magnetic resonance imaging (MRI) which is a superior method when imaging soft tissue (compared to other medical imaging techniques) is affected by metal implants and will result in a distorted image. View Angle Tilting (VAT) and Slice Encoding for Metal Artifact Correction (SEMAC) are techniques which can reduce both in-plane and through-plane distortions. Unfortunately do the SEMAC technique come with a drawback of increased scan times. To acheive more acceptable scan times in the clinic,a new method, called Compressed Sensing can be used in combinationwith VAT and SEMAC. This method which reconstructs data using fewersamples than before thought was required will reduce the scan time. A phantom (hip prosthesis surrounded with agarose gel) was used toinvestigate how much the sampling can be reduced, while still retainingan image with good quality. The data was presented in different domainswhich were individually investigated for an optimized performance of thereconstruction algorithm. Fully sampled data was imported into Matlaband afterwards undersampled. Compressed Sensing was used to reconstructthe images and a comparison was done with the original images. Even with low sampling (40% data) Compressed Sensing can reconstructimages with no significant loss of image quality. SEMAC imagestoday fit the restriction of Compressed Sensing and by implementing themethod the SEMAC technique can be more acessible in clinical practice,thereby improving the diagnosis of patients with metallic prosteses

Compressed Sensing Accelerated Magnetic Resonance Spectroscopic Imaging

abstract: Magnetic resonance spectroscopic imaging (MRSI) is a valuable technique for assessing the in vivo spatial profiles of metabolites like N-acetylaspartate (NAA), creatine, choline, and lactate. Changes in metabolite concentrations can help identify tissue heterogeneity, providing prognostic and diagnostic information to the clinician. The increased uptake of glucose by solid tumors as compared to normal tissues and its conversion to lactate can be exploited for tumor diagnostics, anti-cancer therapy, and in the detection of metastasis. Lactate levels in cancer cells are suggestive of altered metabolism, tumor recurrence, and poor outcome. A dedicated technique like MRSI could contribute to an improved assessment of metabolic abnormalities in the clinical setting, and introduce the possibility of employing non-invasive lactate imaging as a powerful prognostic marker. However, the long acquisition time in MRSI is a deterrent to its inclusion in clinical protocols due to associated costs, patient discomfort (especially in pediatric patients under anesthesia), and higher susceptibility to motion artifacts. Acceleration strategies like compressed sensing (CS) permit faithful reconstructions even when the k-space is undersampled well below the Nyquist limit. CS is apt for MRSI as spectroscopic data are inherently sparse in multiple dimensions of space and frequency in an appropriate transform domain, for e.g. the wavelet domain. The objective of this research was three-fold: firstly on the preclinical front, to prospectively speed-up spectrally-edited MRSI using CS for rapid mapping of lactate and capture associated changes in response to therapy. Secondly, to retrospectively evaluate CS-MRSI in pediatric patients scanned for various brain-related concerns. Thirdly, to implement prospective CS-MRSI acquisitions on a clinical magnetic resonance imaging (MRI) scanner for fast spectroscopic imaging studies. Both phantom and in vivo results demonstrated a reduction in the scan time by up to 80%, with the accelerated CS-MRSI reconstructions maintaining high spectral fidelity and statistically insignificant errors as compared to the fully sampled reference dataset. Optimization of CS parameters involved identifying an optimal sampling mask for CS-MRSI at each acceleration factor. It is envisioned that time-efficient MRSI realized with optimized CS acceleration would facilitate the clinical acceptance of routine MRSI exams for a quantitative mapping of important biomarkers.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

A magnet-compatible isometric handgrip protocol with real-time visual feedback for coronary endothelial function MRI assessment

Il tessuto endoteliale delle arterie coronariche è il principale regolatore dell’ omeostasi vascolare. Al fine di investigare in modo non invasivo la funzione endoteliale delle arterie coronariche, e' stato implementato un nuovo e robusto strumento clinico di MRI affiancato ad un esercizio di handgrip digitale. E' stata sviluppata una interfaccia grafica che fornisce un feedback a tempo reale della forza esercitata con l' handgrip. Il set-up e' stato testato e validato in volontari sani

3D spatio-temporal analysis for compressive sensing in magnetic resonance imaging of the murine cardiac cycle

This thesis consists of two major contributions, each of which has been prepared in a conference paper. These papers will be submitted for publication in the SPIE 2013 Medical Imaging Conference and the ASEE 2013 Annual Conference. The first paper explores a three-dimensional compressive sensing (CS) technique for reducing measurement time in MR imaging of the murine (mouse) cardiac cycle. By randomly undersampling a single 2D slice of a mouse heart at regular time intervals as it expands and contracts through the stages of a heartbeat, a CS reconstruction algorithm can be made to exploit transform sparsity in time as well as space. For the purposes of measuring the left ventricular volume in the mouse heart, this 3D approach offers significant advantages against classical 2D spatial compressive sensing. The second paper describes the modification and testing of a set of laboratory exercises for developing an undergraduate level understanding of Simulink. An existing partial set of lab exercises for Simulink was obtained and improved considerably in pedagogical utility, and then the completed set of pilot exercises was taught as a part of a communications course at the Missouri University of Science and Technology in order to gauge student responses and learning experiences. In this paper, the content of the laboratory exercises with corresponding educational approaches are discussed, along with student feedback and future improvements. --Abstract, page iv