ArticleAdvances in radiation oncology2025
Artificial Intelligence-Assisted Compressed Sensing Technique Accelerates Magnetic Resonance Imaging Simulation for Head and Neck Cancer Radiation Therapy.
Article in Advances in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: To explore the potential of artificial intelligence-assisted compressed sensing (ACS) technique, when compared with that of conventional parallel imaging (PI) technique, in magnetic resonance imaging (MRI) simulation for head and neck cancer radiation therapy. Methods and Materials: Fifty-two patients with pathologically confirmed head and neck cancer underwent MRI simulation using a 3.0-T MRI simulation system. For each patient, axial T1-weighted gradient spin echo, T2-weighted fast spin echo sequence, and postcontrast and postcontrast fat-suppressed T1-weighted gradient spin echo sequence were obtained by ACS and PI. Acquisition time, signal-to-noise ratio, contrast-to-noise ratio, and image quality of both sets of MRI simulation images were compared. Image quality analysis was scored with lesion detection, margin sharpness of lesions, artifacts, and overall image quality using the 5-point Likert scale. Moreover, tumor target volume acquired from fusion images of simulation computed tomography with simulation MRI by ACS and from fusion images by PI were compared. Dice similarity coefficient of gross tumor target between fusion images by ACS and those by PI were also measured. Results: Acquisition time of MRI simulation by ACS was significantly shorter than that by PI, whether for the time of individual sequence or the total acquisition time ( Conclusions: Compared to PI, ACS can significantly accelerate MRI simulation for head and neck cancer radiation therapy without compromising image quality and degrading the guidance role of tumor target delineation.
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