The clinical relevance of distortion correction in presurgical fMRI at 7 T

Presurgical planning with fMRI benefits from increased reliability and the possibility to reduce measurement time introduced by using ultra-high field. Echo-planar imaging suffers, however, from geometric distortions which scale with field strength and potentially give rise to clinically significant displacement of functional activation. We evaluate the effectiveness of a dynamic distortion correction (DDC) method based on unmodified single-echo EPI in the context of simulated presurgical planning fMRI at 7 T and compare it with static distortion correction (SDC). The extent of distortion in EPI and activation shifts are investigated in a group of eleven patients with a range of neuropathologies who performed a motor task. The consequences of neglecting to correct images for susceptibility-induced distortions are assessed in a clinical context. It was possible to generate time series of EPI-based field maps which were free of artifacts in the eloquent brain areas relevant to presurgical fMRI, despite the presence of signal dropouts caused by pathologies and post-operative sites. Distortions of up to 5.1 mm were observed in the primary motor cortex in raw EPI. These were accurately corrected with DDC and slightly less accurately with SDC. The dynamic nature of distortions in UHF clinical fMRI was demonstrated via investigation of temporal variation in voxel shift maps, confirming the potential inadequacy of SDC based on a single reference field map, particularly in the vicinity of pathologies or in the presence of motion. In two patients, the distortion correction was potentially clinically significant in that it might have affected the localization or interpretation of activation and could thereby have influenced the treatment plan. Distortion correction is shown to be effective and clinically relevant in presurgical planning at 7 T

Quantitative MRI correlates of hippocampal and neocortical pathology in intractable temporal lobe epilepsy

Intractable or drug-resistant epilepsy occurs in over 30% of epilepsy patients, with many of these patients undergoing surgical excision of the affected brain region to achieve seizure control. Advances in MRI have the potential to improve surgical treatment of epilepsy through improved identification and delineation of lesions. However, validation is currently needed to investigate histopathological correlates of these new imaging techniques. The purpose of this work is to investigate histopathological correlates of quantitative relaxometry and DTI from hippocampal and neocortical specimens of intractable TLE patients. To achieve this goal I developed and evaluated a pipeline for histology to in-vivo MRI image registration, which finds dense spatial correspondence between both modalities. This protocol was divided in two steps whereby sparsely sectioned histology from temporal lobe specimens was first registered to the intermediate ex-vivo MRI which is then registered to the in-vivo MRI, completing a pipeline for histology to in-vivo MRI registration. When correlating relaxometry and DTI with neuronal density and morphology in the temporal lobe neocortex, I found T1 to be a predictor of neuronal density in the neocortical GM and demonstrated that employing multi-parametric MRI (combining T1 and FA together) provided a significantly better fit than each parameter alone in predicting density of neurons. This work was the first to relate in-vivo T1 and FA values to the proportion of neurons in GM. When investigating these quantitative multimodal parameters with histological features within the hippocampal subfields, I demonstrated that MD correlates with neuronal density and size, and can act as a marker for neuron integrity within the hippocampus. More importantly, this work was the first to highlight the potential of subfield relaxometry and diffusion parameters (mainly T2 and MD) as well as volumetry in predicting the extent of cell loss per subfield pre-operatively, with a precision so far unachievable. These results suggest that high-resolution quantitative MRI sequences could impact clinical practice for pre-operative evaluation and prediction of surgical outcomes of intractable epilepsy

Methodological considerations for neuroimaging in deep brain stimulation of the subthalamic nucleus in Parkinson’s disease patients

Deep brain stimulation (DBS) of the subthalamic nucleus is a neurosurgical intervention for Parkinson’s disease patients who no longer appropriately respond to drug treatments. A small fraction of patients will fail to respond to DBS, develop psychiatric and cognitive side-effects, or incur surgery-related complications such as infections and hemorrhagic events. In these cases, DBS may require recalibration, reimplantation, or removal. These negative responses to treatment can partly be attributed to suboptimal pre-operative planning procedures via direct targeting through low-field and low-resolution magnetic resonance imaging (MRI). One solution for increasing the success and efficacy of DBS is to optimize preoperative planning procedures via sophisticated neuroimaging techniques such as high-resolution MRI and higher field strengths to improve visualization of DBS targets and vasculature. We discuss targeting approaches, MRI acquisition, parameters, and post-acquisition analyses. Additionally, we highlight a number of approaches including the use of ultra-high field (UHF) MRI to overcome limitations of standard settings. There is a trade-off between spatial resolution, motion artifacts, and acquisition time, which could potentially be dissolved through the use of UHF-MRI. Image registration, correction, and post-processing techniques may require combined expertise of traditional radiologists, clinicians, and fundamental researchers. The optimization of pre-operative planning with MRI can therefore be best achieved through direct collaboration between researchers and clinicians

Pattern recognition of brain fMRI images for various physiological states

The development of fMRI (functional Magnetic Resonance Imaging) has led many researchers to localize brain functions using different stimuli. The use of pattern recognition techniques have made it possible to predict the stimuli being presented from the corresponding brain images and activation patterns. The primary objective of the present study was to use pattern recognition methods to develop a model using available fMRJ images and then to use the model to identify the stimulus presented from a large number of unknown images. Two different experimental conditions were used involving both binary and multi-class classification. Bilateral finger tapping data which had two distinct states Active and Rest were used for binary classification. Binary classification was done using Learning Vector Quantization (LVQ) and Least Square Support Vector Machine (LS-SVM). Gas mixture data, which were obtained from rats while ventilated with different gas mixtures for rest and breath hold task, gave various physiological conditions. These multi-class data were also classified using LS-SVM technique. Feature selection was performed on every data to select out patterns made up of significant voxels using statistical techniques like correlation, paired t-test and ANOVA. The accuracies for binary classification were between 90% and 100% while the average accuracy for multi-categorical data was 70%

Methods for Improving MRI-Based Conductivity Mapping

The electrical properties - permittivity and conductivity - of a material describe how electromagnetic waves behave in that material. Electrical properties are frequency-dependent parameters and, for a liquid sample, are measured with a dielectric probe and a network analyzer. This measurement technique is not feasible in vivo, but methods have been developed to make these measurements using magnetic resonance imaging (MRI). This work focuses on measuring conductivity, or the ability to conduct electric current. Mapping the electrical properties within the human body can provide important information for MRI safety and diagnostic applications. First, the specific absorption rate (SAR) in an MRI scan is proportional to conductivity, and limited to minimize the risk of heating in a subject. Knowledge of subject-specific conductivity maps could lead to better, subject-specific SAR estimation. Second, several small studies in recent years have shown that conductivity is elevated in malignant tumors as compared to healthy tissue. There are open research questions regarding the correlation between conductivity and other diagnostic metrics. Both of these applications benefit from accurate conductivity maps. In this work we describe three different methods for improving the accuracy of conductivity maps. The first is a novel regularized, model-based approach which we refer to as the Inverse Laplacian method. The Inverse Laplacian method resulted in lower reconstruction bias and error due to noise in simulations than the conventional filtering method. The Inverse Laplacian method also produced conductivity maps closer to the measured values in a phantom and with reduced noise in the human brain, as compared to the filtering method. The second is a method for combining multi-coil MRI data for conductivity mapping, because the use of multi-coil receivers can drastically improve the SNR in conductivity maps. The noise in the combined phase data using the proposed method was slightly elevated as compared to the optimal combination method, but the conductivity uniformity in a uniform gel phantom was greater than that of the optimal combination method. Furthermore, by visual inspection, the human brain conductivity calculated from data combined using the proposed method had minimal bias and noise amplification. Finally, we present a method for mapping conductivity tensors, as opposed to scalar values, which provides an additional layer of information to conductivity maps. Our proposed mathematical framework yields accurate tensor quantities provided the object can rotate 90 degrees in any direction. However, restricting the object rotation to mimic the constraints on a human subject yields slightly inaccurate results. We also present a dictionary-based approach to tensor calculations to try to improve the tensor estimates using restricted rotations.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144027/1/kropella_1.pd

Medical robots for MRI guided diagnosis and therapy

Magnetic Resonance Imaging (MRI) provides the capability of imaging tissue with fine resolution and superior soft tissue contrast, when compared with conventional ultrasound and CT imaging, which makes it an important tool for clinicians to perform more accurate diagnosis and image guided therapy. Medical robotic devices combining the high resolution anatomical images with real-time navigation, are ideal for precise and repeatable interventions. Despite these advantages, the MR environment imposes constraints on mechatronic devices operating within it. This thesis presents a study on the design and development of robotic systems for particular MR interventions, in which the issue of testing the MR compatibility of mechatronic components, actuation control, kinematics and workspace analysis, and mechanical and electrical design of the robot have been investigated. Two types of robotic systems have therefore been developed and evaluated along the above aspects. (i) A device for MR guided transrectal prostate biopsy: The system was designed from components which are proven to be MR compatible, actuated by pneumatic motors and ultrasonic motors, and tracked by optical position sensors and ducial markers. Clinical trials have been performed with the device on three patients, and the results reported have demonstrated its capability to perform needle positioning under MR guidance, with a procedure time of around 40mins and with no compromised image quality, which achieved our system speci cations. (ii) Limb positioning devices to facilitate the magic angle effect for diagnosis of tendinous injuries: Two systems were designed particularly for lower and upper limb positioning, which are actuated and tracked by the similar methods as the first device. A group of volunteers were recruited to conduct tests to verify the functionality of the systems. The results demonstrate the clear enhancement of the image quality with an increase in signal intensity up to 24 times in the tendon tissue caused by the magic angle effect, showing the feasibility of the proposed devices to be applied in clinical diagnosis

Imaging the subthalamic nucleus in Parkinson’s disease

This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time