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eess.IV2025

Diffusion Probabilistic Models for Compressive SAR Imaging

Odysseas Pappas, Perla Mayo, Andrew Austin +1

Compressed sensing Synthetic Aperture Radar (SAR) image formation, formulated as an inverse problem and solved with traditional iterative optimization methods can be very computati…

eess.IV2024

Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting

Perla Mayo, Carolin M. Pirkl, Alin Achim +2

Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. Howeve…

eess.IV2024

A Multimodal Approach for Fluid Overload Prediction: Integrating Lung Ultrasound and Clinical Data

Tianqi Yang, Nantheera Anantrasirichai, Oktay Karakuş +2

Managing fluid balance in dialysis patients is crucial, as improper management can lead to severe complications. In this paper, we propose a multimodal approach that integrates vis…

eess.IV2024

Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction

Odysseas Pappas, Jonathan Mamou, Adrian Basarab +2

Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical p…

eess.IV2024

The Quest for Early Detection of Retinal Disease: 3D CycleGAN-based Translation of Optical Coherence Tomography into Confocal Microscopy

Xin Tian, Nantheera Anantrasirichai, Lindsay Nicholson +1

Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, offering distinct advantages and limitations. In vivo OCT offers rapid, non-invasive imag…

eess.IV2024

RoTIR: Rotation-Equivariant Network and Transformers for Fish Scale Image Registration

Ruixiong Wang, Alin Achim, Renata Raele-Rolfe +4

Image registration is an essential process for aligning features of interest from multiple images. With the recent development of deep learning techniques, image registration appro…