Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.
Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.
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DOI：
10.3390/tomography9030079
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发表时间：
2023-05-10
期刊：
影响因子：
--
通讯作者：
Holmes JH
中科院分区：
文献类型：
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作者：
Allen TJ;Henze Bancroft LC;Wang K;Wang PN;Unal O;Estkowski LD;Cashen TA;Bayram E;Strigel RM;Holmes JH
为了优化图像质量，MRI技术专家通常会放置特定于患者的图形成像体积和局部预扫描体积。然而，由MR技术专家手动放置这些卷是耗时、繁琐的，并且受运营商内部和跨运营商变化的影响。随着用于筛查目的的缩略乳房MRI检查的增加，解决这些瓶颈至关重要。这项工作提出了一种用于乳腺MRI的扫描和扫描前体积的自动放置方法。从10台单独的MRI扫描仪上获得的333例临床乳腺检查中，回顾收集了解剖三平面扫描图像序列和相关扫描体积。三位MR物理学家也生成了双边扫描前体积，并一致进行了审查。训练一个深度卷积神经网络，从3平面侦察图像中预测扫描体积和扫描前体积。使用交集对联合、体积中心之间的绝对距离和体积大小的差异来评估网络预测的体积与临床扫描体积或物理学家放置的扫描前体积之间的一致性。扫描体模型与联合模型相比，3D交集的中值为0.69。扫描体积定位的中位误差为2.7 cm，中位大小误差为2%。扫描前放置位置的3D交点相对于Union的中位数为0.68，左右扫描体积之间的平均值没有显著差异。扫描前体积位置的中位误差为1.3 cm，大小误差的中位数为−2%。两种模型在定位或体积大小方面的平均估计不确定度从0.2到3.4厘米不等。总体而言，这项工作证明了一种基于神经网络模型的自动放置扫描和预扫描体积的方法的可行性。
Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was −2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model.
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影响因子：
3
作者：
Goto, Takao;Kabasawa, Hiroyuki
通讯作者：
Kabasawa, Hiroyuki
影响因子：
4.4
作者：
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通讯作者：
Hernando, Diego
影响因子：
15.2
作者：
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通讯作者：
Beam AL
DOI：
10.1148/ryai.2019180069
发表时间：
2019-11-01
期刊：
RADIOLOGY-ARTIFICIAL INTELLIGENCE
影响因子：
--
作者：
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通讯作者：
Hsiao, Albert
影响因子：
15.2
作者：
通讯作者：
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