Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Oct 15, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

DYNAMIC IMAGING USING DEEP BILINEAR UNSUPERVISED LEARNING (DEBLUR).

Abdul Haseeb Ahmed¹, Prashant Nagpal¹, Stanley Kruger¹

¹Department of Electrical and Computer Engineering, University of IOWA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|October 25, 2021

Summary

This summary is machine-generated.

Related Concept Videos

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Immersive Three-Dimensional Visualization as a Routine Tool for Complex Congenital Cardiac Surgical Planning: A Perspective.

Pediatric cardiology·2026

Same author

Rethinking the "D-" and " L-" prefix paradigm for transposition.

Radiology case reports·2026

Same author

Ability of Deep Learning Image Reconstruction to Preserve Detail and Remove Noise in Coronary Computed Tomography Angiography: A Clinical Analysis.

Journal of computer assisted tomography·2026

Same author

AI-Driven Quantitative Coronary CT Angiography in Suspected Coronary Artery Disease: Multicenter CONFIRM2 Registry.

JACC. Advances·2026

Same author

Deducing cardiorespiratory motion of cardiac substructures using a novel 5D-MRI workflow for radiotherapy.

Physics in medicine and biology·2026

Same author

ACR Appropriateness Criteria® Suspected and Known Renal or Splanchnic Artery Aneurysm.

Journal of the American College of Radiology : JACR·2026

Same journal

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

Same journal

DUAL CROSS-ATTENTION SIAMESE TRANSFORMER FOR RECTAL TUMOR REGROWTH ASSESSMENT IN WATCH-AND-WAIT ENDOSCOPY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

Same journal

LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

Same journal

OVERVIEW OF THE CXR-LT 2026 CHALLENGE: MULTI-CENTER LONG-TAILED AND ZERO SHOT CHEST X-RAY CLASSIFICATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

Same journal

CROSS-MODAL FINE-TUNING OF 3D CONVOLUTIONAL FOUNDATION MODELS FOR ADHD CLASSIFICATION WITH LOW-RANK ADAPTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

Same journal

AN IN SILICO STUDY OF LOW-INTENSITY FOCUSED ULTRASOUND DISPLACEMENT MAPPING WITH A 220 KHZ CLINICAL PHASED-ARRAY TRANSDUCER.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

See all related articles

This study introduces a novel deep learning approach for dynamic MRI reconstruction, enhancing image quality by reducing spatial blurring. The method uses convolutional neural networks for bilinear model regularization, improving dynamic MRI recovery.

Area of Science:

Medical Imaging
Magnetic Resonance Imaging
Computational Imaging

Background:

Bilinear models like low-rank and compressed sensing are memory-efficient for dynamic MRI reconstruction.
These methods utilize sparsity and energy compaction priors for regularization.
Existing techniques can suffer from spatial blurring in reconstructed dynamic MRI data.

Purpose of the Study:

To introduce a novel bilinear model for dynamic MRI reconstruction.
To regularize model factors using convolutional neural networks (CNNs) inspired by deep image prior.
To improve the spatial resolution and reduce blurring in dynamic MRI reconstructions.

Main Methods:

A novel bilinear model is proposed, regularizing its spatial and temporal factors with CNNs.

Keywords:

Cardiac MRI bilinear model dynamic imaging image reconstruction unsupervised learning

Related Experiment Videos

Last Updated: Oct 15, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

CNN parameters are pre-trained on undersampled data in an unsupervised manner to reduce runtime.

Sparsity regularization is applied to CNN parameters to prevent overfitting to measurement noise.

Main Results:

The proposed method demonstrates reduced spatial blurring compared to traditional low-rank and SToRM reconstructions.
Experiments were conducted on free-breathing and ungated cardiac CINE data acquired with a navigated golden-angle radial sequence.
The CNN-regularized bilinear model effectively recovers dynamic MRI data with improved fidelity.

Conclusions:

The novel CNN-regularized bilinear model offers superior performance in dynamic MRI reconstruction.
This approach effectively mitigates spatial blurring, leading to enhanced image quality.
The method shows promise for accelerated and high-fidelity dynamic MRI acquisition.