AI-assisted compressed sensing technology in accelerated MR simulation for radiotherapy of nasopharyngeal carcinoma
10.3760/cma.j.cn113030-20241219-00484
- VernacularTitle:人工智能辅助压缩感知技术加速鼻咽癌放疗MR模拟定位
- Author:
Shuhan ZHOU
1
;
Yu LUO
;
Chuyan LIN
;
Jianhui SHAO
;
Shaojin WANG
;
Wenjun FAN
;
Feng CHI
Author Information
1. 华南恶性肿瘤防治全国重点实验室，广东省鼻咽癌诊治研究重点实验室，广东省恶性肿瘤临床医学研究中心，中山大学肿瘤防治中心放疗科，广州 510060
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Compressed sensing;
MR simulation;
Radiotherapy;
Nasopharyngeal carcinoma
- From:
Chinese Journal of Radiation Oncology
2025;34(9):929-936
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the feasibility and clinical value of artificial intelligence-assisted compressed sensing (ACS) technology in accelerating MR simulation (MR-sim) for radiotherapy of nasopharyngeal carcinoma (NPC).Methods:Thirty patients with NPC scheduled to receive radical radiotherapy at Sun Yat-sen University Cancer Center were prospectively enrolled. All patients underwent head and neck MR-sim on a 3.0 T scanner, with axial T 1 weighted imaging (WI), T 2WI, contrast-enhanced T 1WI, and fat-suppressed contrast-enhanced T 1WI images acquired using both ACS and parallel imaging (PI) techniques. Paired-sample t tests or rank-sum tests were used to compare scan time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of MR-sim images between the two techniques. A 5-point Likert scale was applied to evaluate tumor lesion visualization, lesion margin clarity, artifacts, and overall image quality, with chi-square tests used to compare subjective image quality scores between the two techniques. Tumor target volumes were delineated on MR-sim images obtained by both ACS and PI techniques after fusion with CT simulation images, and consistency was assessed using the Dice similarity coefficient (DSC). Results:For both individual sequences and overall protocols, ACS significantly reduced MR-sim acquisition time compared with PI ( P < 0.001). The total acquisition time with ACS was (378.60±17.07) s versus (694.93±17.07) s with PI, representing a 45.52% time reduction. SNR, CNR, tumor lesion identification, margin clarity, artifacts, and overall image quality scores of MR-sim images did not differ significantly between ACS and PI ( P > 0.05). Tumor target volumes delineated from ACS- and PI-based MR-sim images showed high consistency after fusion with CT simulation images ( P > 0.05), with mean DSC values of primary tumors and metastatic cervical lymph nodes approaching 1. Conclusion:Compared with conventional MR acceleration methods (PI), ACS enables faster MR-sim acquisition in NPC without compromising image quality or the accuracy of tumor target delineation.
