Evidence map›Paper›PMID 40760871›Full record

ArticleNMR in biomedicine2025

Multifrequency Time-Dependent Deep Image Prior for Real-Time Free-Breathing Cardiac Imaging.

Jesse I Hamilton, Gastao Cruz, William Truesdell, Prachi Agarwal, Imran Rashid, Nicole Seiberlich

Abstract read
In one paragraph

Article in NMR in biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jesse I HamiltonDepartment of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
Gastao CruzDepartment of Radiology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-7397-9104
William TruesdellDepartment of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
Prachi AgarwalDepartment of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
Imran RashidSchool of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
Nicole SeiberlichDepartment of Radiology, University of Michigan, Ann Arbor, Michigan, USA.

Funding

Magnetic Resonance Imaging Guided Robotic Catheter System for Left Atrial Appendage Occlusion ProceduresR01HL163991 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI BEZERRA, HIRAM, CAVUSOGLU, M. CENK · 2022 to 2025
$3.7M
3D Cine Magnetic Resonance Fingerprinting for Rapid Phenotyping of CardiomyopathyR01HL163030 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI JESSE Ian HAMILTON, Imran Rashid · 2022 to 2026
$3.2M
Real-Time MRI Guided Robotic Catheter System for Atrial Fibrillation AblationR01HL153034 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI CAVUSOGLU, M. CENK, GRISWOLD, MARK · 2020 to 2023
$2.7M
NHLBI NIH HHS R01HL153034NHLBI NIH HHS R01HL163030NHLBI NIH HHS R01HL163991Siemens Healthineers
6 · The paper itself

Abstract

The aim of this study is to enable high temporal resolution functional cardiac imaging without breathholds or electrocardiogram (ECG) gating. Real-time MRI is essential for assessing heart function in patients with limited breathhold capacity or arrhythmias that preclude breathheld ECG-gated cine scans. The Time-Dependent Deep Image Prior (Time-DIP) method is a promising reconstruction for dynamic MRI, combining a nonlinear manifold with zero-shot deep learning. However, while Time-DIP has been demonstrated for breathheld cine imaging, it employs a helical manifold that assumes quasi-periodic motion and thus may not be suitable for free-breathing real-time scans, particularly in arrhythmia patients. This study proposes a Multifrequency Time-DIP technique to extend this framework to free-breathing real-time cardiac imaging. First, a "multifrequency manifold" is introduced that parameterizes time using multiple sinusoids spanning various frequencies, enabling dynamic imaging without assuming motion periodicity. Second, joint estimation of coil sensitivities using zero-shot deep learning is used to improve the reconstruction of multichannel data. Simulations and scans of healthy subjects and patients, including those with arrhythmias, were performed using a 2D free-breathing ungated golden angle spiral bSSFP sequence. Image quality and left ventricular (LV) functional measurements were compared to real-time scans reconstructed with compressed sensing and the original Time-DIP implementation, as well as conventional breathheld ECG-gated cine scans. Multifrequency Time-DIP outperformed other real-time techniques in simulations of various motion scenarios. In vivo scans using Multifrequency Time-DIP exhibited reduced aliasing artifacts, achieving temporal resolutions as high as a single TR (4.2 ms/frame), with no significant differences in LV functional measurements compared to conventional scans. While conventional scans had better edge sharpness and image contrast scores, Multifrequency Time-DIP exhibited overall higher image quality metrics among real-time scans. In conclusion, a generalized Time-DIP reconstruction was shown to enable high temporal resolution free-breathing real-time cardiac imaging in healthy subjects and patients, including those with arrhythmias.

Indexed as

Deep LearningHeartMagnetic Resonance Imaging, CineRespirationAdultFemaleHumansMaleTime Factorscardiac imagingdeep learningmanifoldnon‐Cartesianreal‐time

Identifiers

PMID40760871
PMCPMC12322519

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.