R. Lemaire , C. Raboutet , T. Leleu , C. Jaudet , L. Dessoude , F. Missohou , Y. Poirier , P.-Y. Deslandes , A. Lechervy , J. Lacroix , I. Moummad , S. Bardet , J. Thariat , D. Stefan , A. Corroyer-Dulmont
{"title":"加速磁共振成像图像采集的人工智能解决方案：对放射科和放疗科肿瘤治疗护理的影响。","authors":"R. Lemaire , C. Raboutet , T. Leleu , C. Jaudet , L. Dessoude , F. Missohou , Y. Poirier , P.-Y. Deslandes , A. Lechervy , J. Lacroix , I. Moummad , S. Bardet , J. Thariat , D. Stefan , A. Corroyer-Dulmont","doi":"10.1016/j.canrad.2023.11.004","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™, can increase the resolution of acquired images. The objective of this prospective study was to evaluate the impact of this algorithm that halves the acquisition time on the detectability of brain lesions in radiology and radiotherapy.</p></div><div><h3>Material and methods</h3><p>The T1/T2 MRI of 33 patients with brain metastases or meningiomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pixel intensity and lesions size.</p></div><div><h3>Results</h3><p>The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for radiology and radiotherapy respectively. Undetected lesions on the IA image are lesions with a diameter less than 4<!--> <!-->mm and statistically low average gadolinium-enhancement contrast.</p></div><div><h3>Conclusion</h3><p>It is possible to reduce MRI acquisition times by half using the commercial algorithm to restore the characteristics of the image and obtain good specificity and sensitivity for lesions with a diameter greater than 4<!--> <!-->mm.</p></div>","PeriodicalId":9504,"journal":{"name":"Cancer Radiotherapie","volume":"28 3","pages":"Pages 251-257"},"PeriodicalIF":1.5000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments\",\"authors\":\"R. Lemaire , C. Raboutet , T. Leleu , C. Jaudet , L. Dessoude , F. Missohou , Y. Poirier , P.-Y. Deslandes , A. Lechervy , J. Lacroix , I. Moummad , S. Bardet , J. Thariat , D. Stefan , A. Corroyer-Dulmont\",\"doi\":\"10.1016/j.canrad.2023.11.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™, can increase the resolution of acquired images. The objective of this prospective study was to evaluate the impact of this algorithm that halves the acquisition time on the detectability of brain lesions in radiology and radiotherapy.</p></div><div><h3>Material and methods</h3><p>The T1/T2 MRI of 33 patients with brain metastases or meningiomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pixel intensity and lesions size.</p></div><div><h3>Results</h3><p>The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for radiology and radiotherapy respectively. Undetected lesions on the IA image are lesions with a diameter less than 4<!--> <!-->mm and statistically low average gadolinium-enhancement contrast.</p></div><div><h3>Conclusion</h3><p>It is possible to reduce MRI acquisition times by half using the commercial algorithm to restore the characteristics of the image and obtain good specificity and sensitivity for lesions with a diameter greater than 4<!--> <!-->mm.</p></div>\",\"PeriodicalId\":9504,\"journal\":{\"name\":\"Cancer Radiotherapie\",\"volume\":\"28 3\",\"pages\":\"Pages 251-257\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Radiotherapie\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S127832182400060X\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Radiotherapie","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S127832182400060X","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments
Purpose
MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™, can increase the resolution of acquired images. The objective of this prospective study was to evaluate the impact of this algorithm that halves the acquisition time on the detectability of brain lesions in radiology and radiotherapy.
Material and methods
The T1/T2 MRI of 33 patients with brain metastases or meningiomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pixel intensity and lesions size.
Results
The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for radiology and radiotherapy respectively. Undetected lesions on the IA image are lesions with a diameter less than 4 mm and statistically low average gadolinium-enhancement contrast.
Conclusion
It is possible to reduce MRI acquisition times by half using the commercial algorithm to restore the characteristics of the image and obtain good specificity and sensitivity for lesions with a diameter greater than 4 mm.
期刊介绍：
Cancer/radiothérapie se veut d''abord et avant tout un organe francophone de publication des travaux de recherche en radiothérapie. La revue a pour objectif de diffuser les informations majeures sur les travaux de recherche en cancérologie et tout ce qui touche de près ou de loin au traitement du cancer par les radiations : technologie, radiophysique, radiobiologie et radiothérapie clinique.
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