A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification

Quantitative numerical analysis of g strain in the EPR of distributed systems and its importance for multicenter metalloproteins

A method for simulation of inhomogeneously broadened EPR of metallo-proteins based on recent theoretical advances is surveyed critically in terms of efficiency and accuracy. From the quality of the experimental spectrum, minimal boundary conditions are established for the spatial integration over the g-strained polycrystal. Computational efficiency is achieved by generating the spectrum as an absorption in g space, reducing the number of molecular orientations computed by filtering mosaic artifacts from the Fourier-transformed spectrum, and generating the lineshape due to g strain from a tabulated distribution function. These techniques provide a reduction in computation time by some two orders of magnitude and make the data analysis of EPR of metalloproteins by minimization practical. The resulting simulation program is superior to current approaches in that it does not introduce artifactual multiplicities, and it is expected to require a smaller number of fitting parameters for the quantitative analysis of most cases. To illustrate its potential, the method is applied to EPR data from the iron-sulfur centers in NADH:Q oxidoreductase and in QH2:ferricytochrome c oxidoreductase, clarifying existing controversies on the stoichiometries of these centers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25873/1/0000436.pd

A statistical theory for powder EPR in distributed systems

A statistical interpretation is presented for "g strain," the dominant broadening in the EPR spectra of metallo-proteins. The direct cause of g strain is described by a three-dimensional tensor p, whose principal elements are random variables. The p and g tensors are not necessarily colinear. The observed EPR linewidth results from a distribution in the effective g value as a function of (a) the joint distribution function of the elements of the p tensor and (b) the spatial relationship between the two principal axis systems involved. The theory is reformulated in terms of matrices that facilitate a direct comparison with earlier work. Two previous theories of g strain represent different subsets of the general theory, namely, the case of zero rotation between axis systems and the case with nonzero rotation and full correlation between elements of the p tensor.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25870/1/0000433.pd

The Statistical Nature of G Strain in Epr Linewidths of Frozen Metalloprotein Solutions.

A statistical theory is presented for "g-strain", the major source broadening in the EPR spectra of metalloproteins. The linewidth due to g-strain is described by a three dimensional tensor p, whose principal elements are r and om variables. The principal axes of the p- and g-tensors are not necessarily colinear. Accordingly, the "g-strain" linewidth is a function of the joint distribution function of the elements of the p-tensor with its associated covariance matrix, the principal values of the g-tensor, and the Euler rotation relating the two principal axis systems involved. The linewidth is derived algebraically, then reformulated in terms of matrices, resulting in a convenient notation, which facilitates a direct comparison with earlier work. It is shown that the two previous theories of g-strain represent different subsets of the general theory, namely, the case of zero rotation between axis systems, and a case with rotation and full correlation between elements of the p-tensor. The importance of the covariance matrix in discovering the underlying cause of g-strain is discussed. The theory is applied to EPR spectra of selected {2Fe-2S} ferredoxins and possible physical models for g-strain are analyzed in terms of the results. As a result of full negative correlation, the underlying cause of g-strain is predicted to originate from a single variable. The statistical theory is also applied to the EPR spectrum of a {4Fe-4S} ferredoxin and the limitations of the method are discussed.Ph.D.BiophysicsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/159564/1/8324199.pd

Segmentation of MRS signals using ASPECT (Analysis of SPectra using Eigenvector deComposition of Targets)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134932/1/mp7289.pd

Nuclear Magnetic Resonance: Current and Future Clinical Applications

Nuclear magnetic resonance has evolved from a laboratory analytical tool to become a rapidly developing discipline in clinical medicine. We present a brief historical overview, an introduction to the basic principles of the phenomenon, and a statement of the current status of clinical imaging. We have elected to use the traditional terminology nuclear magnetic resonance to refer to the imaging component of the field rather than the American College of Radiology (ACR) modification magnetic resonance. We do this out of respect for the founders of the field

Motion artifacts in quantitative magnetic resonance imaging

Several investigators have emphasized the potential value of quantitative relaxation times in the assessment of diseases. In performing such measurements using the spin-echo technique, we have encountered several anomalous results, whereby the intensity of the organ parenchyma on second-echo images is greater than on first echo images. This is most likely a result of respiratory motion, and it occurs only rarely. Several volunteers were studied before and after exercise to see if respiratory motion could reproduce the anomalous intensity reverse; a reversal of intensities in renal parenchyma was observed in two of five individuals. We conclude that respiratory motion artifacts will seriously limit quantitative magnetic resonance imaging of the upper abdomen if respiratory gating during imaging is not used.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26373/1/0000460.pd

Detection of Dominant Intra-prostatic Lesions in Patients With Prostate Cancer Using an Artificial Neural Network and MR Multi-modal Radiomics Analysis

Purpose: The aim of this study was to identify and rank discriminant radiomics features extracted from MR multi-modal images to construct an adaptive model for characterization of Dominant Intra-prostatic Lesions (DILs) from normal prostatic gland tissues (NT). Methods and Materials: Two cohorts were retrospectively studied: Group A consisted of 98 patients and Group B 19 patients. Two image modalities were acquired using a 3.0T MR scanner: Axial T2 Weighted (T2W) and axial diffusion weighted (DW) imaging. A linear regression method was used to construct apparent diffusion coefficient (ADC) maps from DW images. DILs and the NT in the mirrored location were drawn on each modality. One hundred and sixty-eight radiomics features were extracted from DILs and NT. A Partial-Least-Squares-Correlation (PLSC) with one-way ANOVA along with bootstrapping ratio techniques were recruited to identify and rank the most discriminant latent variables. An artificial neural network (ANN) was constructed based on the optimal latent variable feature to classify the DILs and NTs. Nineteen patients were randomly chosen to test the contour variability effect on the radiomics analysis and the performance of the ANN. Finally, the trained ANN and a two dimension (2D) convolutional sampling method were combined and used to estimate DIL-NT probability map for two test cases. Results: Among 168 radiomics-based latent variables, only the first four variables of each modality in the PLSC space were found to be significantly different between the DILs and NTs. Area Under Receiver Operating Characteristic (AUROC), Positive Predictive and Negative Predictive values (PPV and NPV) for the conventional method were 94%, 0.95, and 0.92, respectively. When the feature vector was randomly permuted 10,000 times, a very strong permutation-invariant efficiency (p \u3c 0.0001) was achieved. The radiomic-based latent variables of the NTs and DILs showed no statistically significant differences (Fstatistic \u3c Fc = 4.11 with Confidence Level of 95% for all 8 variables) against contour variability. Dice coefficients between DIL-NT probability map and physician contours for the two test cases were 0.82 and 0.71, respectively. Conclusion: This study demonstrates the high performance of combining radiomics information extracted from multimodal MR information such as T2WI and ADC maps, and adaptive models to detect DILs in patients with PCa