Multimodal MRI analysis using deep learning methods

Magnetic resonance imaging (MRI) has been widely used in scientific and clinical research. It is a non-invasive medical imaging technique that reveals anatomical structures and provides useful information for investigators to explore aging and pathological processes. Different MR modalities offer different useful properties. Automatic MRI analysis algorithms have been developed to address problems in many applications such as classification, segmentation, and disease diagnosis. Segmentation and labeling algorithms applied to brain MRIs enable evaluations of the volumetric changes of specific structures in neurodegenerative diseases. Reconstruction of fiber orientations using diffusion MRI is beneficial to obtain better understanding of the underlying structures. In this thesis, we focused on development of deep learning methods for MRI analysis using different image modalities. Specifically, we applied deep learning techniques on different applications, including segmentation of brain structures and reconstruction of tongue muscle fiber orientations. For segmentation of brain structures, we developed an end-to-end deep learning algorithm for ventricle parcellation of brains with ventriculomegaly using T1-w MR images. The deep network provides robust and accurate segmentation results in subjects with high variability in ventricle shapes and sizes. We developed another deep learning method to automatically parcellate the thalamus into a set of thalamic nuclei using T1-w MRI and features from diffusion MRI. The algorithm incorporates a harmonization step to make the network adapt to input images with different contrasts. We also studied the strains associated with tongue muscles during speech production using multiple MRI modalities. To enable this study, we first developed a deep network to reconstruct crossing tongue muscle fiber orientations using diffusion MRI. The network was specifically designed for the human tongue and accounted for the orthogonality property of the tongue muscles. Next, we proposed a comprehensive pipeline to analyze the strains associated with tongue muscle fiber orientations during speech using diffusion MRI, and tagged and cine MRI. The proposed pipeline provides a solution to analyze the cooperation between muscle groups during speech production

DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue

Tagged magnetic resonance imaging (MRI) has been used for decades to observe and quantify the detailed motion of deforming tissue. However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields. To address these issues, this paper presents a novel unsupervised phase-based 3D motion estimation technique for tagged MRI. We introduce two key innovations. First, we apply a sinusoidal transformation to the harmonic phase input, which enables end-to-end training and avoids the need for phase interpolation. Second, we propose a Jacobian determinant-based learning objective to encourage incompressible flow fields for deforming biological tissues. Our method efficiently estimates 3D motion fields that are accurate, dense, and approximately diffeomorphic and incompressible. The efficacy of the method is assessed using human tongue motion during speech, and includes both healthy controls and patients that have undergone glossectomy. We show that the method outperforms existing approaches, and also exhibits improvements in speed, robustness to tag fading, and large tongue motion.Comment: Accepted to MIDL 2023 (full paper

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Publisher's version (útgefin grein)Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly (p < 0.005) higher than four state-of-the-art segmentation methods we compared with. Furthermore, the typical processing time on CPU-base implementation of the proposed method is 2 min, which is much lower than the several hours required by the other methods. Results indicate that our method provides: 1) highly robust parcellation of the ventricular system that is comparable in accuracy to state-of-the-art methods on healthy controls; 2) greater robustness and significantly more accurate results on cases of ventricular enlargement; and 3) a tool that enables computation of novel imaging biomarkers for dilated ventricular spaces that characterize the ventricular system. © 2019 The AuthorsThis work was supported by the NIH/NINDS under grant R21-NS096497 . Support was also provided by the National Multiple Sclerosis Society grant RG-1507-05243 , the Department of Defense in the Center for Neuroscience and Regenerative Medicine , and the Icelandic Centre for Research (RANNIS) under grant 173942051 . The author Shuo Han is in part supported by the Intramural Research Program of the NIH , National Institute on Aging . This research project was conducted using computational resources at the Maryland Advanced Research Computing Center (MARCC).Peer Reviewe

Two-Step Elution Recovery of Cyanide Platinum Using Functional Metal Organic Resin

A novel functional ion-exchange/adsorption metal organic resin (MOR), TEBAC-HKUST-1, was prepared and characterized. Ethanedithiol was used as the grafting agent to introduce thiol groups onto HKUST-1, and 4-vinylbenzyl chloride was then grafted onto SH-HKUST-1 using thiol groups. Finally, the quaternary ammonium functional group was immobilized onto the carrier by performing a quaternization reaction. The structure and property of TEBAC-HKUST-1 MOR were characterized by TGA, N2 adsorption&ndash;desorption, FTIR, SEM, and XRD. TEBAC-HKUST-1 MOR was used to remove metal cyanide complexes from wastewater. The adsorption was rapid, and the metal cyanide complexes including Pt(CN)42&minus;, Co(CN)63&minus;, Cu(CN)32&minus;, and Fe(CN)63&minus; were removed in 30 min. TEBAC-HKUST-1 MOR exhibited a high stability in neutral and weak basic aqueous solutions. Furthermore, Pt(II) could be efficiently recovered through two-step elution. The recovery rate of Pt(II) for five cycles were over 92.0% in the mixture solution containing Pt(CN)42&minus;, Co(CN)63&minus;, Cu(CN)32&minus;, and Fe(CN)63&minus;. The kinetic data were best fitted with the pseudo second-order model. Moreover, the isothermal data were best fitted with the Langmuir model. The thermodynamic results show that the adsorption is a spontaneous and exothermic process. TEBAC-HKUST-1 MOR not only exhibited excellent ability for the rapid removal of metal cyanide complexes, but also provided a new idea for the extraction of noble metals from cyanide-contaminated water

Tongue muscle strain analysis from multimodal MRI (Shao et al., 2023)

Purpose: Muscle groups within the tongue in healthy and diseased populations show different behaviors during speech. Visualizing and quantifying strain patterns of these muscle groups during tongue motion can provide insights into tongue motor control and adaptive behaviors of a patient. Method: We present a pipeline to estimate the strain along the muscle fiber directions in the deforming tongue during speech production. A deep convolutional network estimates the crossing muscle fiber directions in the tongue using diffusion-weighted magnetic resonance imaging (MRI) data acquired at rest. A phase-based registration algorithm is used to estimate motion of the tongue muscles from tagged MRI acquired during speech. After transforming both muscle fiber directions and motion fields into a common atlas space, strain tensors are computed and projected onto the muscle fiber directions, forming so-called strains in the line of actions (SLAs) throughout the tongue. SLAs are then averaged over individual muscles that have been manually labeled in the atlas space using high-resolution T2-weighted MRI. Data were acquired, and this pipeline was run on a cohort of eight healthy controls and two glossectomy patients. Results: The crossing muscle fibers reconstructed by the deep network show orthogonal patterns. The strain analysis results demonstrate consistency of muscle behaviors among some healthy controls during speech production. The patients show irregular muscle patterns, and their tongue muscles tend to show more extension than the healthy controls. Conclusions: The study showed visual evidence of correlation between two muscle groups during speech production. Patients tend to have different strain patterns compared to the controls. Analysis of variations in muscle strains can potentially help develop treatment strategies in oral diseases. Supplemental Material S1. Histograms of the bottom and top 10% SLA values along the 1st and 2nd fiber directions from /ə/ to /θ/, /i/, and /ŋ/. The control data are grouped together. The ratio of the flap volume to the whole tongue volume is 0.17 and 0.26 for Patient 1 and 2, respectively. Shao, M., Xing, F., Carass, A., Liang, X., Zhuo, J., Stone, M., Woo, J., & Prince, J. L. (2023). Analysis of tongue muscle strain during speech from multimodal magnetic resonance imaging. Journal of Speech, Language, and Hearing Research. Advance online publication. https://doi.org/10.1044/2022_JSLHR-22-00329</p