Radiology Commons^™

Ai-Driven Multi-Modal Mr For Precision Oncology, Maneet K. Marwaha

Radiologic Sciences

Purpose To examine the role of artificial intelligence (AI) in multiparametric magnetic resonance (MR) imaging for precision oncology, with a focus on automated tumor segmentation, treatment response prediction, and workflow efficiency.

Method A literature review was conducted to examine current evidence on AI applications in multiparametric MR imaging for oncology. Studies evaluating deep learning models for tumor segmentation, longitudinal imaging analysis, and treatment response prediction were synthesized using a thematic analysis approach.

Results Three primary themes emerged: (a) AI-driven tumor segmentation improved accuracy, reproducibility, and efficiency across multiparametric MR sequences; (b) deep learning models enhanced prediction of treatment response through …

High-Definition Healing: Investigating Ai-Driven Mr Reconstruction Methods, Jerell R. Jones

Radiologic Sciences

Purpose To analyze recent artificial intelligence (AI)-driven magnetic resonance (MR) reconstruction methods aimed at addressing long acquisition times, image artifacts, and workflow inefficiencies.

Method A systematic literature review was conducted using PubMed, Scopus, and Scopus AI, targeting peer‑reviewed studies published between 2015 and 2026. Search terms included “MRI,” “magnetic resonance imaging,” “artificial intelligence,” “deep learning,” and “machine learning.” Eligible studies included methodology, validation, and review papers. Extracted data included algorithm type, study design, key findings, and limitations. Themes were identified through content analysis and generative AI use.

Results Prominent themes identified were the emergence of generative adversarial networks, advanced neural …

Mri-Based Deep Learning Radiomics Model For Automated Classification Of Disc Degeneration In The Lumbar Spine, Shiv Patil, Om Gandhi, Mert Karabacak, Matthew Carr, Konstantinos Margetis

Student Papers, Posters & Projects

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging (MRI) is critical for clinical management and patient selection for spine surgery. This study aims to develop and evaluate machine learning (ML) models that combine features from deep learning (DL) and radiomics for the automated prediction of Pfirrmann grade (PG), a measure of disc degeneration, using multi-parametric lumbar spine MRI. Sagittal T1, T2, and T2 SPACE MRIs of 218 patients with LBP were acquired from the SPIDER dataset. For each intervertebral disc and available sequence, …

Deep Learning Based Approaches For Low Cost Defense Detection, Adele J. Noel-Rickert

All NMU Master's Theses

Pulmonary fibrosis is a progressive interstitial lung disease characterized by the accumulation of fibrotic tissue within the lungs, leading to impaired respiratory function and reduced quality of life. Early detection is important for disease management; however, accurate diagnosis often relies on high-resolution computed tomography (CT), which may not be accessible in all clinical settings. Chest radiography provides a lower-cost and widely available imaging modality, but interpretation of chest X-rays for fibrotic disease can be challenging due to subtle radiographic patterns and overlapping anatomical structures. This thesis investigates the use of multimodal deep learning techniques to assist in pul- monary fibrosis …

Associations Between Contralesional Neuroplasticity And Motor Impairment Through Deep Learning-Derived Mri Regional Brain Age In Chronic Stroke (Enigma): A Multicohort, Retrospective, Observational Study, Gilsoon Park, Mahir Khan, Justin Andrushko, Nerisa Banaj, Michael Borich, Lara Boyd, Amy Brodtmann, Truman Brown, Cathrin Buetefisch, Adriana Conforto, Steven Cramer, Michael Dimyan, Martin Domin, Miranda Donnelly, Natalia Egorova-Brumley, Elsa Ermer, Wuwei Feng, Fatemeh Geranmayeh, Colleen Hanlon, Brenton Hordacre, Neda Jahanshad, Steven Kautz, Mohamed Salah Khlif, Jingchun Liu, Martin Lotze, Bradley Macintosh, Feroze Mohamed, Jan Nordvik, Fabrizio Piras, Kate Revill, Andrew Robertson, Christian Schranz, Nicolas Schweighofer, Na Jin Seo, Surjo Soekadar, Shraddha Srivastava, Bethany Tavenner, Gregory Thielman, Sophia Thomopoulos, Daniela Vecchio, Emilio Werden, Lars Westlye, Carolee Winstein, George Wittenberg, Jennifer Ferris, Chunshui Yu, Paul Thompson, Sook-Lei Liew, Hosung Kim

Department of Radiology Faculty Papers

BACKGROUND: Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain predicted age difference (PAD) has emerged as a sensitive biomarker of both sensorimotor and cognitive function after stroke. Our previous study showed a higher global brain PAD associated with poorer motor function after stroke. However, the association between local stroke lesion load, regional brain age, and motor impairment is unclear. This study aimed to investigate the associations between focal lesion damage, regional brain PAD in both hemispheres, and motor outcomes in chronic stroke, and to identify key predictors of motor impairment.

METHODS: …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Checklist For Reproducibility Of Deep Learning In Medical Imaging, Mana Moassefi, Yashbir Singh, Gian Marco Conte, Bardia Khosravi, Pouria Rouzrokh, Sanaz Vahdati, Nabile Safdar, Linda Moy, Felipe Kitamura, Amilcare Gentili, Paras Lakhani, Nina Kottler, Safwan Halabi, Joseph Yacoub, Yuankai Hou, Khaled Younis, Bradley Erickson, Elizabeth Krupinski, Shahriar Faghani

Department of Radiology Faculty Papers

The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 experts in medical imaging and DL assessed these items using Likert scales, with two survey …

Interpretation Models For Prostate Lesion: Detecting, Explaining, And Understanding., Mehmet Akif Gulum

Electronic Theses and Dissertations

Prostate cancer is a major public health concern, affecting millions of men worldwide. While early detection and treatment of prostate cancer is critical for improving patient outcomes, the detection of prostate lesions is even more important for timely intervention and management of the disease. Prostate lesions are abnormal growths or lumps within the prostate gland, which may or may not be cancerous. The timely detection and accurate diagnosis of prostate lesions is crucial for effective treatment and management of the disease. In recent years, deep learning models have shown promise in accurately detecting and characterizing prostate lesions using advanced imaging …

Applications Of Deep Learning With Detecting Intracranial Aneurysms On Ct Angiograms: A Literature Review, Christian Fang, Emily Wang

Rowan-Virtua Research Day

INTRODUCTION

Deep learning is a method of artificial intelligence involving progressively layered neural networks to extrapolate patterns from data to provide predictions. Moreover, given the arduous nature required for examining CT scans for intracranial aneurysms, discovering ways to expedite this process is beneficial. The use of deep learning to evaluate CT angiograms for intracranial aneurysms has been sparsely studied. This literature review aims to determine the accuracy and reliability of deep learning to analyze CT angiograms in patients suspected to have intracranial aneurysms.

METHODS

A qualitative review of literature using PubMed, SCOPUS, and EMBASE was conducted. Inclusion criteria comprised articles …

Deep Learning-Based Multimodality Classification Of Chronic Mild Traumatic Brain Injury Using Resting-State Functional Mri And Pet Imaging, Faezeh Vedaei, Najmeh Mashhadi, Mahdi Alizadeh, George Zabrecky, Daniel A. Monti, Md, Nancy Wintering, Emily Navarreto, Chloe Hriso, Andrew B. Newberg, Feroze B. Mohamed

Department of Radiology Faculty Papers

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and …

Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho

Mathematics & Statistics Faculty Publications

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region …

Clinical Acceptability Of Automatically Generated Lymph Node Levels And Structures Of Deglutition And Mastication For Head And Neck Radiation Therapy, Sean Maroongroge, Abdallah Sr Mohamed, Callistus Nguyen, Jean Guma De La Vega, Steven J Frank, Adam S Garden, Brandon G Gunn, Anna Lee, Lauren Mayo, Amy Moreno, William H Morrison, Jack Phan, Michael T Spiotto, Laurence E Court, Clifton D Fuller, David I Rosenthal, Tucker J Netherton

Faculty, Staff and Student Publications

BACKGROUND AND PURPOSE: Auto-contouring of complex anatomy in computed tomography (CT) scans is a highly anticipated solution to many problems in radiotherapy. In this study, artificial intelligence (AI)-based auto-contouring models were clinically validated for lymph node levels and structures of swallowing and chewing in the head and neck.

MATERIALS AND METHODS: CT scans of 145 head and neck radiotherapy patients were retrospectively curated. One cohort (n = 47) was used to analyze seven lymph node levels and the other (n = 98) used to analyze 17 swallowing and chewing structures. Separate nnUnet models were trained and validated using the separate …

Auto-Segmentation In Pancreatic And Liver Radiation Therapy, Cenji Yu

Dissertations and Theses (Open Access)

Background

Gastrointestinal cancers exhibit a high mortality rate compared to other cancer types. Among these, pancreatic cancer ranks as the fourth leading cause of cancer-related deaths worldwide. The five-year survival rate remains alarmingly low at a mere 9%. Hepatocellular carcinoma (HCC), another aggressive form of cancer, is rapidly becoming the primary cause of cancer-related deaths in the United States. The treatment of both liver cancer and pancreatic cancer heavily relies on a multidisciplinary approach. Innovative treatment strategies involving dose-escalated regimens, such as stereotactic body radiation therapy (SBRT), are emerging as an important pillar of the management of liver and pancreatic …

Opportunistic Detection Of Type 2 Diabetes Using Deep Learning From Frontal Chest Radiographs, Ayis Pyrros, Stephen M. Borstelmann, Ramana Mantravadi, Zachary Zaiman, Kaesha Thomas, Brandon Price, Eugene Greenstein, Nasir Siddiqui, Melinda Willis, Ihar Shulhan, John Hines-Shah, Jeanne M. Horowitz, Paul Nikolaidis, Matthew P. Lungren, Jorge Mario Rodríguez-Fernández, Judy Wawira Gichoya, Sanmi Koyejo, Adam E. Flanders, Nishith Khandwala, Amit Gupta, John W. Garrett, Joseph Paul Cohen, Brian T. Layden, Perry J. Pickhardt, William Galanter

Department of Radiology Faculty Papers

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of …

Effective Connectivity Of Functional Mri Via Deep Learning Methods, Kai-Cheng Chuang

LSU Doctoral Dissertations

Effective connectivity, based on functional magnetic resonance imaging (fMRI) time series signals, is the quantification of how strongly brain activity in a certain source brain region contributes to brain activity in a target brain region, independent of the contributions of other source regions. Current methods to solve this problem have several limitations. They are either unable to model nonlinear relationships between source and target signals, unable to efficiently quantify time lags in source-target relationships, unable to identify time-varying relationships, or fail to account for variability in the hemodynamic response function that converts neuron activity to a measurable signal. In this …

Contouring Quality Assurance Methodology Based On Multiple Geometric Features Against Deep Learning Auto-Segmentation, Jingwei Duan, Mark E. Bernard, James Castle, Xue Feng, Chi Wang, Mark C. Kenamond, Quan Chen

Markey Cancer Center Faculty Publications

Background: Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors.Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics.

Purpose: The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors.

Methods and materials: DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were …

Understanding Deep Learning - Challenges And Prospects, Niha Adnan, Fahad Umer

Department of Surgery Docs

The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and will improve the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. …

Characterization Of Indeterminate Breast Lesions On B-Mode Ultrasound Using Automated Machine Learning Models, Shuo Wang, Sihua Niu, Enze Qu, Flemming Forsberg, Annina Wilkes, Alexander Sevrukov, Kibo Nam, Robert F. Mattrey, Haydee Ojeda-Fournier, John R. Eisenbrey

Department of Radiology Faculty Papers

Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue, but also increases false positives, resulting in unnecessary biopsies. This study investigated the use of Google AutoML Vision (Mountain View, CA), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound.

Methods: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations and Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Artificial Intelligence In Ultrasound Imaging: Current Research And Applications, Shuo Wang, Bs, Ji-Bin Liu, Md, Ziyin Zhu, Md, John Eisenbrey, Phd

Department of Radiology Faculty Papers

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent software or system based on big data information, machine learning and deep learning technologies. The rapid development of science and technology as well as internet communication has enabled AI and big data to gradually apply to many fields of health care. The modern imaging medicine is one of the first areas that AI can play an important role and applications. As cross-sectional imaging, ultrasound (US) is well suitable for AI technology to standardize imaging protocols and improve diagnostic accuracy. This article reviews current AI technology …

Automated Assessment Of Acute Aortic Dissection On Thoracic Ct Using Deep Learning, Varun Singh, Richard Gorniak, Md, Adam Flanders, Md, Paras Lakhani, Md

Department of Radiology Posters

Purpose

To assess the efficacy of deep convolutional neural networks (DCNNs ) in differentiating acute aortic dissections from non-dissected aortas on thoracic CT.

Deep Gaze Velocity Analysis During Mammographic Reading For Biometric Identification Of Radiologists, Hong-Jun Yoon, Folami Alamudun, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi

Journal of Human Performance in Extreme Environments

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that …