Respiratory organ motion in interventional MRI : tracking, guiding and modeling

Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy. In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities. Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well. Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence

Doctor of Philosophy

dissertationEach year in the United States, a quarter million cases of stroke are caused directly by atherosclerotic disease of the cervical carotid artery. This represents a significant portion of health care costs that could be avoided if high-risk carotid artery lesions could be detected early on in disease progression. There is mounting evidence that Magnetic Resonance Imaging of the carotid artery can better classify subjects who would benefit from interventions. Turbo Spin Echo sequences are a class of Magnetic Resonance Imaging sequences that provide a variety of tissue contrasts. While high resolution Turbo Spin Echo images have demonstrated important details of carotid artery morphology, it is evident that pulsatile blood and wall motion related to the cardiac cycle are still significant sources of image degradation. In addition, patient motion artifacts due to the relatively long scan times of Turbo Spin Echo sequences result in an unacceptable fraction of noninterpretable studies. This dissertation presents work done to detect and correct for types of voluntary and physiological patient motion

Fat and water signals in nuclear magnetic resonance imaging

This thesis is intended to explore fat and water differentiation in nuclear magnetic resonance imaging. The need to create separate fat and water images is discussed and a critical review of current practices in the field is presented. These techniques include chemical shift imaging, coupled spin mapping and methods based on relaxation time differences. As an extension of this review, alternative slice cycling procedures are proposed that afford an improvement in the conventional chemical shift selective presaturation sequence. A new, hybrid fat or water suppression sequence is studied in detail, including a theoretical description of the role of the sequence parameters, as well as direct experimental comparison with its most closely related conventional fat and water differentiation techniques. The proposed scheme is shown to be robust in normal use and more tolerant than the conventional methods to mis-settings of experimental parameters. In vivo demonstration of the method is also performed. Further work involves the generation of differential fat and water relaxation time maps. A critical review of current, conventional techniques that allow production of longitudinal relaxation calculated images is presented. Novel pulse sequence schemes for the measurement of fat and water longitudinal relaxation times are described, and the accuracy of these measurements is evaluated using phantoms. The results obtained are also being compared with conventional spectroscopic and imaging methods

Diagnostic accuracy of 3.0-T magnetic resonance T1 and T2 mapping and T2-weighted dark-blood imaging for the infarct-related coronary artery in Non-ST-segment elevation myocardial infarction

Background: Patients with recent non–ST‐segment elevation myocardial infarction commonly have heterogeneous characteristics that may be challenging to assess clinically. Methods and Results: We prospectively studied the diagnostic accuracy of 2 novel (T1, T2 mapping) and 1 established (T2‐weighted short tau inversion recovery [T2W‐STIR]) magnetic resonance imaging methods for imaging the ischemic area at risk and myocardial salvage in 73 patients with non–ST‐segment elevation myocardial infarction (mean age 57±10 years, 78% male) at 3.0‐T magnetic resonance imaging within 6.5±3.5 days of invasive management. The infarct‐related territory was identified independently using a combination of angiographic, ECG, and clinical findings. The presence and extent of infarction was assessed with late gadolinium enhancement imaging (gadobutrol, 0.1 mmol/kg). The extent of acutely injured myocardium was independently assessed with native T1, T2, and T2W‐STIR methods. The mean infarct size was 5.9±8.0% of left ventricular mass. The infarct zone T1 and T2 times were 1323±68 and 57±5 ms, respectively. The diagnostic accuracies of T1 and T2 mapping for identification of the infarct‐related artery were similar (P=0.125), and both were superior to T2W‐STIR (P<0.001). The extent of myocardial injury (percentage of left ventricular volume) estimated with T1 (15.8±10.6%) and T2 maps (16.0±11.8%) was similar (P=0.838) and moderately well correlated (r=0.82, P<0.001). Mean extent of acute injury estimated with T2W‐STIR (7.8±11.6%) was lower than that estimated with T1 (P<0.001) or T2 maps (P<0.001). Conclusions: In patients with non–ST‐segment elevation myocardial infarction, T1 and T2 magnetic resonance imaging mapping have higher diagnostic performance than T2W‐STIR for identifying the infarct‐related artery. Compared with conventional STIR, T1 and T2 maps have superior value to inform diagnosis and revascularization planning in non–ST‐segment elevation myocardial infarction. Clinical Trial Registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02073422

Linear motion correction in three dimensions applied to dynamic gadolinium enhanced breast imaging

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134776/1/mp8576.pd

Artifacts in magnetic resonance imaging: how it can really affect diagnostic image quality and confuse clinical diagnosis?

Different kinds of artifacts can occur during a magnetic resonance imaging (MRI) scans due to hardware or software related problems, human physiologic phenomenon or physical restrictions. Some of them can seriously affecting diagnostic image quality, while others may simulate or be confused with different pathology. On another word artifact as an artificial feature appearing in an image that is not present in the original investigative object. It is important to recognize these artifacts according to a basic understanding of their origin, especially those mimicking pathology, as they can lead to incorrect diagnosis and cause serious after-effects on patient’s health and outcomes. We presented an overview of the most common MRI artifacts and methods to fix or rectify them. We also provide the original artifacts images and statistics from the Lithuanian University of Health Sciences Kaunas Clinical Hospital, Dept of Radiology, mainly obtained from image databases and some images from data base of other Lithuanian hospitals

Resting-state Connectivity Dynamics in the Human Brain using High-speed fMRI

Resting-state fMRI using seed-based connectivity analysis (SCA) typically involves regression of the confounding signals resulting from movement and physiological noise sources. This not only adds additional complexity to the analysis but may also introduce possible regression bias. We recently introduced a computationally efficient real-time SCA approach without confound regression, which employs sliding-window correlation analysis with running mean and standard deviation (meta-statistics). The present study characterizes the confound tolerance of this windowed seed-based connectivity analysis (wSCA), which combines efficient decorrelation of confounding signal events with high-pass filter characteristics that reduce sensitivity to drifts. The confound suppression and the strength of resting-state network (RSN) connectivity were characterized for a range of confounding signal profiles as a function of sliding-window width and scan duration, using simulation and in vivo data. The connectivity strength in six resting-state networks (RSNs) and artifactual connectivity in white matter were compared between wSCA and conventional regression-based SCA (cSCA). The wSCA approach demonstrated scalable confound suppression that increased with decreasing sliding-window width and increasing scan duration in both simulations and in vivo. The confound suppression for sliding-window widths ≤ 15 s was comparable to that of cSCA. Twenty-eight RSNs that were previously reported in a group-ICA study were detected in real-time at scan durations as short as 30 s and with sliding-window widths as short as 4 s. The inter- and intra- network connectivity dynamics of the 28 resting-state networks were studied in real-time and self-repeating connectivity patterns were identified. The wSCA is further investigated offline to study the strength and temporal fluctuations in connectivity using 28 single-region seeds and 28 multi-region seed clusters to measure inter-regional connectivity (IRC) in 140 functional brain regions and inter-network connectivity (INC) among the hubs of 28 RSNs. Multi-region seed IRC maps displayed smaller temporal fluctuations and stronger resting-state connectivity compared with single-region seed IRC maps. Dual thresholding of the meta-statistics maps demonstrated higher spatio-temporal IRC stability in auditory, sensorimotor, and visual cortices compared to other brain regions. The group averaged INC matrices for single-region seeds were consistent with the functional network connectivity matrices (FNCMs) presented in the aforementioned group-ICA study. Furthermore, we extended the mapping of functional connectivity to the whole-brain connectivity fingerprints. In combination with novel brain parcellation methods and advanced machine learning algorithms, wSCA can aid in studying the spatial and temporal connectivity dynamics of the resting-state connectivity. The robust confound tolerance, high temporal resolution, and compatibility with real-time high-speed fMRI, make this approach suitable for monitoring data quality, neurofeedback, and clinical research studies involving disease related changes in functional connectomics

Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver

An area of rapid advancement in abdominal MRI is diffusion-weighted imaging (DWI). By measuring diffusion properties of water molecules, DWI is capable of non-invasively probing tissue properties and physiology at cellular and macromolecular level. The integration of DWI as part of abdominal MRI exam allows better lesion characterization and therefore more accurate initial diagnosis and treatment monitoring. One of the most technical challenging, but also most useful abdominal DWI applications is in liver and therefore requires special attention and careful optimization. In this article, the latest technical developments of DWI and its liver applications are reviewed with the explanations of the technical principles, recommendations of the imaging parameters, and examples of clinical applications. More advanced DWI techniques, including Intra-Voxel Incoherent Motion (IVIM) diffusion imaging, anomalous diffusion imaging, and Diffusion Kurtosis Imaging (DKI) are discussed

Traumatic and Overuse Injuries of the Elbow

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145344/1/cpmia2701.pd