Scalable matching of deformed images Rohit Kumar Jena

Format:

Book

Thesis/Dissertation

Author/Creator:

Jena, Rohit Kumar, author.

Contributor:

University of Pennsylvania. Computer and Information Science., degree granting institution.

Language:

English

Subjects (All):

Medical imaging.

Computer science.

0574.

0984.

0769.

Local Subjects:

Medical imaging.

Computer science.

0574.

0984.

0769.

Genre:

Academic theses

Physical Description:

1 online resource (259 pages)

Contained In:

Dissertations Abstracts International 87-12B

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2026

Language Note:

English

Summary:

Image registration is a fundamental inverse problem ubiquitous across biomedical and life science applications. Over the past three decades, advances in imaging technology have democratized access to unprecedented spatial and temporal detail, while an equally staggering growth in GPU hardware has transformed high-performance computing domains including machine learning, computational fluid dynamics, and molecular dynamics. Despite methodological progress in image registration, algorithms have not scaled in tandem with these advances, constraining researchers to work with heavily down-sampled versions of the rich data they acquire. This dissertation presents a comprehensive investigation into a Pareto-efficient image registration framework that simultaneously optimizes for accuracy, robustness, and scalability. First, we conduct a systematic empirical study comparing classical optimization-based and deep learning paradigms, addressing instrumentation bias in the literature and providing practitioners with a principled framework for selecting appropriate methods. Second, we introduce FireANTs, a GPU-accelerated framework for adaptive Riemannian optimization on the space of diffeomorphisms. We formally quantify the ill-conditioning of deformable registration and develop a novel Eulerian descent formulation enabling powerful adaptive optimization on time-dependent diffeomorphic flows, achieving state-of-the-art zero-shot performance across multiple imaging modalities and benchmark datasets with orders-of-magnitude speedups over existing methods. Third, we develop Deep Implicit Optimization (DIO), which transforms FireANTs into a fully differentiable layer within deep networks. By decoupling feature learning from optimization, DIO inherits task-specific appearance invariances from the optimization objective while enabling end-to-end learning of dense multi-scale features from weak supervision signals, yielding superior generalization to domain shifts and zero-cost plug-and-play of arbitrary transformation representations at test time. Fourth, we develop system-level innovations to scale registration to gigavoxel imaging problems, including IO-aware fused CUDA kernels that reduce memory overheads for bottleneck operations, enabling multimodal registration on previously intractable datasets in minutes. Finally, we demonstrate real-world impact through applications including multimodal in-vivo brain to ex-vivo hemisphere registration, geometry distortion correction for echo-planar MRI, and gradient-free landmark-guided registration for lung CT scans

Notes:

Source: Dissertations Abstracts International, Volume: 87-12, Section: B.

Advisors: Gee, James C.; Chaudhari, Pratik Committee members: Daniilidis, Kostas; Shi, Jianbo; Witschey, Walter R.; Golland, Polina

Ph.D. University of Pennsylvania 2026

Vendor supplied data

Local Notes:

School code: 0175

ISBN:

9798247973027

Access Restriction:

Restricted for use by site license