Improving Accuracy of Information Extraction from Quantitative Magnetic Resonance Imaging

Quantitative MRI offers the possibility to produce objective measurements of tissue physiology at different scales. Such measurements are highly valuable in applications such as drug development, treatment monitoring or early diagnosis of cancer. From microstructural information in diffusion weighted imaging (DWI) or local perfusion and permeability in dynamic contrast (DCE-) MRI to more macroscopic observations of the local intestinal contraction, a number of aspects of quantitative MRI are considered in this thesis. The main objective of the presented work is to provide pre-processing techniques and model modification in order to improve the reliability of image analysis in quantitative MRI. Firstly, the challenge of clinical DWI signal modelling is investigated to overcome the biasing effect due to noise in the data. Several methods with increasing level of complexity are applied to simulations and a series of clinical datasets. Secondly, a novel Robust Data Decomposition Registration technique is introduced to tackle the problem of image registration in DCE-MRI. The technique allows the separation of tissue enhancement from motion effects so that the latter can be corrected independently. It is successfully applied to DCE-MRI datasets of different organs. This application is extended to the correction of respiratory motion in small bowel motility quantification in dynamic MRI data acquired during free breathing. Finally, a new local model for the arterial input function (AIF) is proposed. The estimation of the arterial blood contrast agent concentration in DCE-MRI is augmented using prior knowledge on local tissue structure from DWI. This work explores several types of imaging using MRI. It contributes to clinical quantitative MRI analysis providing practical solutions aimed at improving the accuracy and consistency of the parameters derived from image data

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.

Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset

Advanced H-1 Lung Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is one of the widely used medical imaging modality, since it can provide both structural and functional assessment in a single imaging session. However, two major challenges should be considered by using MRI for lung imaging. The first challenge is the intrinsic low SNR of H-1 lung MRI due to the low proton density as well as the fast decay of the lung parenchyma signal. And the second challenge is subject motion. To achieve high resolution structural image, MRI requires a long scan time, usually a few minutes or even longer, which make MRI sensitive to subject motion. To address the first challenge, ultra-short echo time (UTE) MRI sequence is used to capture the lung parenchyma signal before decay. As for subject motion, two major strategies are widely used. One strategy is fast breath-holding scan, the subjects are asked to hold their breaths for a short duration, and the fast 3D MR sequence would be used to acquire data within that duration. This dissertation proposes a new acquisition scheme based on the standard UTE sequence, which largely increases the encoding efficiency and improves the breath-holding scan images. The other is free breathing scan with motion correction. The subjects are allowed to breathe during the MR acquisition. After the acquisition, the motion corrupted data would go through the motion correction step to reconstruct the motion free images. In this dissertation, two novel motion corrected reconstruction strategies are proposed to incorporate the motion modeling and compensation into the reconstruction to get high SNR motion corrected 3D and 4D images. When translating the developed techniques to the clinical studies, specifically for pediatric and neonatal studies, more practical problems need to be considered, such as smaller but finer anatomy to image, the different respiratory patterns of the young subjects etc. This dissertation proposes a 5-minute free breathing UTE MRI strategy to achieve a 3D high resolution motion free lung image for pediatric and neonatal studies

Integrated Graph Theoretic, Radiomics, and Deep Learning Framework for Personalized Clinical Diagnosis, Prognosis, and Treatment Response Assessment of Body Tumors

Purpose: A new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer scientists and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework (I GRAD) for personalized clinical diagnosis, prognosis and treatment planning using an integration of graph theory, radiomics, and deep learning. Methods: There are three major components of the I GRAD framework–image segmentation, feature extraction, and clinical decision support. Image Segmentation: I developed the multiparametric deep learning (MPDL) tissue signature model for segmentation of normal and abnormal tissue from multiparametric (mp) radiological images. The segmentation MPDL network was constructed from stacked sparse autoencoders (SSAE) with five hidden layers. The MPDL network parameters were optimized using k-fold cross-validation. In addition, the MPDL segmentation network was tested on an independent dataset. Feature Extraction: I developed the radiomic feature mapping (RFM) and contribution scattergram (CSg) methods for characterization of spatial and inter-parametric relationships in multiparametric imaging datasets. The radiomic feature maps were created by filtering radiological images with first and second order statistical texture filters followed by the development of standardized features for radiological correlation to biology and clinical decision support. The contribution scattergram was constructed to visualize and understand the inter-parametric relationships of the breast MRI as a complex network. This multiparametric imaging complex network was modeled using manifold learning and evaluated using graph theoretic analysis. Feature Integration: The different clinical and radiological features extracted from multiparametric radiological images and clinical records were integrated using a hybrid multiview manifold learning technique termed the Informatics Radiomics Integration System (IRIS). IRIS uses hierarchical clustering in combination with manifold learning to visualize the high-dimensional patient space on a two-dimensional heatmap. The heatmap highlights the similarity and dissimilarity between different patients and variables. Results: All the algorithms and techniques presented in this dissertation were developed and validated using breast cancer as a model for diagnosis and prognosis using multiparametric breast magnetic resonance imaging (MRI). The deep learning MPDL method demonstrated excellent dice similarity of 0.87±0.05 and 0.84±0.07 for segmentation of lesions on malignant and benign breast patients, respectively. Furthermore, each of the methods, MPDL, RFM, and CSg demonstrated excellent results for breast cancer diagnosis with area under the receiver (AUC) operating characteristic (ROC) curve of 0.85, 0.91, and 0.87, respectively. Furthermore, IRIS classified patients with low risk of breast cancer recurrence from patients with medium and high risk with an AUC of 0.93 compared to OncotypeDX, a 21 gene assay for breast cancer recurrence. Conclusion: By integrating advanced computer science methods into the radiological setting, the I-GRAD framework presented in this thesis can be used to model radiological imaging data in combination with clinical and histopathological data and produce new tools for personalized diagnosis, prognosis or treatment planning by physicians

Lung Imaging and Function Assessment using Non-Contrast-Enhanced Magnetic Resonance Imaging

Measurement of pulmonary ventilation and perfusion has significant clinical value for the diagnosis and monitoring of prevalent lung diseases. To this end, non-contrast-enhanced MRI techniques have emerged as a promising alternative to scintigraphical measurements, computed tomography, and contrast-enhanced MRI. Although these techniques allow the acquisition of both structural and functional information in the same scan session, they are prone to robustness issues related to imaging artifacts and post-processing techniques, limiting their clinical utilization. In this work, new acquisition and post-processing techniques were introduced for improving the robustness of non-contrast-enhanced MRI based functional lung imaging. Furthermore, pulmonary functional maps were acquired in 2-year-old congenital diaphragmatic hernia (CDH) patients to demonstrate the feasibility of non-contrast-enhanced MRI methods for functional lung imaging. In the first study, a multi-acquisition framework was developed to improve robustness against field inhomogeneity artifacts. This method was evaluated at 1.5T and 3T field strengths via acquisitions obtained from healthy volunteers. The results demonstrate that the proposed acquisition framework significantly improved ventilation map homogeneity p<0.05. In the second study, a post-processing method based on dynamic mode decomposition (DMD) was developed to accurately identify dominant spatiotemporal patterns in the acquisitions. This method was demonstrated on digital lung phantoms and in vivo acquisitions. The findings indicate that the proposed method led to a significant reduction in dispersion of estimated ventilation and perfusion map amplitudes across different number of measurements when compared with competing methods p<0.05. In the third study, the free-breathing non-contrast-enhanced dynamic acquisitions were obtained from 2-year-old patients after CDH repair, and then processed using the DMD to obtain pulmonary functional maps. Afterwards, functional differences between ipsilateral and contralateral lungs were assessed and compared with results obtained using contrast-enhanced MRI measurements. The results demonstrate that pulmonary ventilation and perfusion maps can be generated from dynamic acquisitions successfully without the need for ionizing radiation or contrast agents. Furthermore, lung perfusion parameters obtained with DMD MRI correlate very strongly with parameters obtained using dynamic contrast-enhanced MRI. In conclusion, the presented work improves the robustness and accuracy of non-contrast-enhanced functional lung imaging using MRI. Overall, the methods introduced in this work may serve as a valuable tool in the clinical adaptation of non-contrast-enhanced imaging methods and may be used for longitudinal assessments of pulmonary functional changes

Model-driven registration for multi-parametric renal MRI

The use of MR imaging biomarkers is a promising technique that may assist towards faster prognosis and more accurate diagnosis of diseases like diabetic kidney disease (DKD). The quantification of MR Imaging renal biomarkers from multiparametric MRI is a process that requires a physiological model to be fitted on the data. This process can provide accurate estimates only under the assumption that there is pixelto-pixel correspondence between images acquired over different time points. However, this is rarely the case due to motion artifacts (breathing, involuntary muscle relaxation) introduced during the acquisition. Hence, it is of vital importance for a biomarkers quantification pipeline to include a motion correctionstep in order to properly align the images and enable a more accurate parameter estimation. This study aims in testing whether a Model Driven Registration (MDR), which integrates physiological models in the registration process itself, can serve as a universal solution for the registration of multiparametric renal MRI. MDR is compared with a state-of-the-art model-free motion correction approach for multiparametric MRI, that minimizes a Principal Components Analysis based metric, performing a groupwise registration. The results of the two methods are compared on T1, DTI and DCE-MRI data for a small cohort of 10 DKD patients, obtained from BEAt-DKD project’s digital database. The majority of the evaluation metrics used to compare the two methods indicated that MDR achieved better registration results, while requiring significantly lower computational times. In conclusion, MDR could be considered as the method of choice for motion correction of multiparametric quantitative renal MRI