Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

Robust and Fast Quantitative MRI for Clinical Deployment

Within this thesis, my work carried out in order to prepare an existing quantitative imaging method, multi-parameter mapping, for clinical use, is summarized. My tasks were to improve the motion-robustness of the acquisitions used in this protocol, and to reduce the scan time of the protocol to a clinically viable level. In order to reduce acquisition times, I investigated the use of higher parallel imaging acceleration factors, compared to those used in the protocol to date. I found that increasing the acceleration factor from 2 to 2-by-2 is a viable approach to decrease scan time, as is elliptical k-space coverage. In order to improve the robustness versus inter-scan motion, I investigated the effect of inter-scan motion on the quantitative maps derived from the protocol. I found that, while rigid-body motion correction is not sufficient in cases where a map is calculated from more than one scan, as the changes in the receive field are unaccounted for. I introduced a correction method, based on measuring the receive field for each structural scan, and showed that it improves image quality in the presence of inter-scan motion. Motion robustness was also improved by selecting a relatively motioninsensitive acquisition trajectory, from a set of clinically available trajectories. To further address the issue of intra-scan motion, I developed a novel navigator technique, based on acquiring data concurrent with gradient spoiling. Crucially, the acquisition of this navigator did not require additional scan time. I found that this navigator is sufficiently sensitive to motion, such that outlier rejection can be used to identify motion-corrupted data points. I implemented a data re-acquisition approach, based on the outlier rejection, and showed that image quality can be improved by this method

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management

Development of an optimised non-invasive MRI method to measure renal perfusion in patients with impaired renal function

Arterial Spin Labelling (ASL) is a unique Magnetic Resonance Imaging (MRI) approach for quantifying tissue perfusion non-invasively. More than two decades of technical developments established ASL as a valuable tool in neuroimaging, having more recently began its translation to the clinic. ASL holds great potential for the assessment of kidney disease given that it does not require contrast agents which are typically contraindicated for patients with impaired renal function. However, renal ASL applications remain limited and the technique has yet to be incorporated into clinical practice. The sensitivity of ASL to patient movement, which severely corrupts the renal perfusion estimates, is arguably one of the greatest factors hindering a wide adoption of this technique. This thesis begins with an overview of the main concepts addressed in this work (kidney physiology, MRI and ASL) and a thorough literature review of previous renal ASL work. The problem of patient movement is then addressed at all levels of the ASL framework by combining a motion-insensitive ASL acquisition scheme with a specifically tailored image processing pipeline. The feasibility of this technique to provide repeatable renal perfusion measurements is demonstrated in the first paediatric cohort with impaired renal function to undergo renal ASL. Finally, the critical findings of this thesis are summarised and prospective future research directions are outlined