Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace Models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916660352385024 |
|---|---|
| author | Sheagren, Calder D. Kadota, Brenden T. Patel, Jaykumar H. Chiew, Mark Wright, Graham A. |
| author_facet | Sheagren, Calder D. Kadota, Brenden T. Patel, Jaykumar H. Chiew, Mark Wright, Graham A. |
| contents | Cardiac parametric mapping is useful for evaluating cardiac fibrosis and edema. Parametric mapping relies on single-shot heartbeat-by-heartbeat imaging, which is susceptible to intra-shot motion during the imaging window. However, reducing the imaging window requires undersampled reconstruction techniques to preserve image fidelity and spatial resolution. The proposed approach is based on a low-rank tensor model of the multi-dimensional data, which jointly estimates spatial basis images and temporal basis time-courses from an auxiliary parallel imaging reconstruction. The tensor-estimated spatial basis is then further refined using a deep neural network, trained in a fully supervised fashion, improving the fidelity of the spatial basis using learned representations of cardiac basis functions. This two-stage spatial basis estimation will be compared against Fourier-based reconstructions and parallel imaging alone to demonstrate the sharpening and denoising properties of the deep learning-based subspace analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace Models Sheagren, Calder D. Kadota, Brenden T. Patel, Jaykumar H. Chiew, Mark Wright, Graham A. Image and Video Processing Cardiac parametric mapping is useful for evaluating cardiac fibrosis and edema. Parametric mapping relies on single-shot heartbeat-by-heartbeat imaging, which is susceptible to intra-shot motion during the imaging window. However, reducing the imaging window requires undersampled reconstruction techniques to preserve image fidelity and spatial resolution. The proposed approach is based on a low-rank tensor model of the multi-dimensional data, which jointly estimates spatial basis images and temporal basis time-courses from an auxiliary parallel imaging reconstruction. The tensor-estimated spatial basis is then further refined using a deep neural network, trained in a fully supervised fashion, improving the fidelity of the spatial basis using learned representations of cardiac basis functions. This two-stage spatial basis estimation will be compared against Fourier-based reconstructions and parallel imaging alone to demonstrate the sharpening and denoising properties of the deep learning-based subspace analysis. |
| title | Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace Models |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2503.17852 |