Keywords: Acquisition Methods, AI/ML Image Reconstruction, MRA Synthesis
Motivation: Time-of-Flight (TOF) Magnetic Resonance Angiography (MRA) is critical for diagnosing vascular diseases such as aneurysms, but long acquisition time and noise susceptibility limit its clinical efficiency.
Goal(s): To avoid the long scan time, we aim to synthesize high-quality TOF-MRA images using faster T1-weighted (T1w) and T2-weighted (T2w) sequences.
Approach: We propose a 2.5D Kolmogorov–Arnold Networks (KAN)-based model, termed sKAN, which integrates KAN with snake convolution.
Results: The Synthesized MRA (Syn-MRA) images closely resemble standard-of-care (SOC) MRA, displaying realistic vessel textures and reduced noise. They can serve as a potential alternative to SOC-TOF-MRA, reducing the need for prolonged scan time.
Impact: The proposed method enables TOF-MRA synthesis from faster T1- and T2-weighted MRI images. The synthesized images closely match standard-of-care (SOC) TOF-MRA, accurately depicting vessels in major central areas, thus eliminating need for lengthy acquisition process of SOC-TOF-MRA in clinical settings.
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