arXiv:2605.20687v1 Announce Type: cross Abstract: Conventional cardiac cine MRI relies on breath-hold Cartesian acquisitions, which are vulnerable to motion artifacts and can be uncomfortable or infeasible, particularly for pediatric and other noncompliant patients who cannot reliably hold their breath. Free-breathing radial acquisitions can alleviate these limitations, but robust reconstruction at high acceleration remains challenging due to prominent streak artifacts. To address these limitations, we propose Cine-DL, a clinically oriented framework that couples targeted k-space preprocessing
Advances in deep learning and computational imaging techniques are enabling new approaches to address long-standing challenges in medical diagnostics, moving beyond traditional acquisition limitations.
This development improves diagnostic imaging for a broader patient population, including those who cannot comply with breath-holding protocols, increasing the accessibility and reliability of cardiac MRI.
The ability to perform motion-robust, free-breathing cardiac MRI reduces patient discomfort and expands the applicability of these critical diagnostic tools, potentially leading to earlier and more accurate diagnoses for vulnerable groups.
- · Medical AI companies
- · Diagnostic imaging centers
- · Pediatric cardiology
- · Non-compliant patients
Improved diagnostic accuracy and patient experience in cardiac MRI.
Reduced need for sedation in pediatric imaging and increased throughput in MRI facilities.
Acceleration of AI integration into other complex medical imaging modalities facing similar motion artifact challenges.
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Read at arXiv cs.LG