Abstract：
OBJECTIVE: To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction.
METHODS: We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations.
RESULTS: The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20.
CONCLUSIONS: Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds.
CONCLUSIONS: The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible.
CONCLUSIONS: Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.
METHODS: We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations.
RESULTS: The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20.
CONCLUSIONS: Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds.
CONCLUSIONS: The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible.
CONCLUSIONS: Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.
摘要：
目的：探讨基于评分的扩散模型在加速乳腺MRI重建中的应用。
方法：我们在9549例女性乳腺MRI检查中训练了一个基于评分的模型，并利用该模型重建具有2、5和20的欠采样因子的欠采样MRI图像。由两名经验丰富的放射科医生对图像进行评估，他们根据图像的整体质量和诊断价值在100次其他MRI检查的独立测试集上对图像进行了评估。
结果：基于评分的模型产生高质量和诊断价值的MRI图像。T1和T2加权MRI图像都可以高度精确地重建。当加速因子为2时，在100％（放射科医生1）和99％（放射科医生2）的情况下，两名放射科医生将图像评为与原始图像几乎无法区分（5级评分为4或5）。对于5的加速因子，该分数下降到88％和70％，对于20的极端加速因子下降到5％和21％。
结论：基于分数的模型可以以高保真度重建MRI图像，即使在相对较高的加速因子下，但是需要在更大规模的图像上进行进一步的工作，以确保诊断质量。
结论：乳腺MRI检查的数量预计会增加，建议对乳腺致密的女性进行MRI筛查。加速的图像采集方法可以帮助使这种检查更容易访问。
结论：加速乳腺MRI重建在临床环境中仍然是一个重大挑战。基于分数的扩散模型可以对中等欠采样因子实现近乎完美的重构。具有保持图像质量的更快的乳腺MRI扫描可以彻底改变临床工作流程和患者体验。
方法：我们在9549例女性乳腺MRI检查中训练了一个基于评分的模型，并利用该模型重建具有2、5和20的欠采样因子的欠采样MRI图像。由两名经验丰富的放射科医生对图像进行评估，他们根据图像的整体质量和诊断价值在100次其他MRI检查的独立测试集上对图像进行了评估。
结果：基于评分的模型产生高质量和诊断价值的MRI图像。T1和T2加权MRI图像都可以高度精确地重建。当加速因子为2时，在100％（放射科医生1）和99％（放射科医生2）的情况下，两名放射科医生将图像评为与原始图像几乎无法区分（5级评分为4或5）。对于5的加速因子，该分数下降到88％和70％，对于20的极端加速因子下降到5％和21％。
结论：基于分数的模型可以以高保真度重建MRI图像，即使在相对较高的加速因子下，但是需要在更大规模的图像上进行进一步的工作，以确保诊断质量。
结论：乳腺MRI检查的数量预计会增加，建议对乳腺致密的女性进行MRI筛查。加速的图像采集方法可以帮助使这种检查更容易访问。
结论：加速乳腺MRI重建在临床环境中仍然是一个重大挑战。基于分数的扩散模型可以对中等欠采样因子实现近乎完美的重构。具有保持图像质量的更快的乳腺MRI扫描可以彻底改变临床工作流程和患者体验。
