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Quantitative Characterizations of Ultrashort Echo (UTE) Images for Supporting Air-Bone Separation in the Head

机译:支持头部空气分离的超短回波(UTE)图像的定量表征

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摘要

Accurate separation of air and bone is critical for creating synthetic CT from MRI to support Radiation Oncology workflow. This study compares two different ultrashort echo-time sequences in the separation of air from bone, and evaluates post-processing methods that correct intensity nonuniformity of images and account for intensity gradients at tissue boundaries to improve this discriminatory power. CT and MRI scans were acquired on 12 patients under an institution review board-approved prospective protocol. The two MRI sequences tested were ultra-short TE imaging using 3D radial acquisition (UTE), and using pointwise encoding time reduction with radial acquisition (PETRA). Gradient nonlinearity correction was applied to both MR image volumes after acquisition. MRI intensity nonuniformity was corrected by vendor-provided normalization methods, and then further corrected using the N4itk algorithm. To overcome the intensity-gradient at air-tissue boundaries, spatial dilations, from 0 to 4 mm, were applied to threshold-defined air regions from MR images. Receiver operating characteristic (ROC) analyses, by comparing predicted (defined by MR images) versus “true” regions of air and bone (defined by CT images), were performed with and without residual bias field correction and local spatial expansion. The post-processing corrections increased the areas under the ROC curves (AUC) from 0.944 ± 0.012 to 0.976 ± 0.003 for UTE images, and from 0.850 ± 0.022 to 0.887 ± 0.012 for PETRA images, compared to without corrections. When expanding the threshold-defined air volumes, as expected, sensitivity of air identification decreased with an increase in specificity of bone discrimination, but in a non-linear fashion. A 1-mm air mask expansion yielded AUC increases of 1% and 4% for UTE and PETRA images, respectively. UTE images had significantly greater discriminatory power in separating air from bone than PETRA images. Post-processing strategies improved the discriminatory power of air from bone for both UTE and PETRA images, and reduced the difference between the two imaging sequences. Both postprocessed UTE and PETRA images demonstrated sufficient power to discriminate air from bone to support synthetic CT generation from MRI data.
机译:空气和骨骼的正确分离对于从MRI创建合成CT以支持放射肿瘤学工作流程至关重要。这项研究在空气与骨骼的分离中比较了两种不同的超短回波时间序列,并评估了后处理方法,这些方法可以校正图像的强度不均匀性并说明组织边界处的强度梯度,以提高这种区分能力。根据机构审查委员会批准的前瞻性方案,对12例患者进行了CT和MRI扫描。测试的两个MRI序列是使用3D径向采集(UTE)和使用径向采集的逐点编码时间减少(PETRA)的超短TE成像。采集后将梯度非线性校正应用于两个MR图像体积。 MRI强度不均匀性可以通过供应商提供的归一化方法进行校正,然后使用N4itk算法进行进一步校正。为了克服空气组织边界处的强度梯度,将0到4 mm的空间膨胀应用于MR图像的阈值定义的空气区域。通过比较预测的(由MR图像定义)与“真实”的空气和骨骼区域(由CT图像定义),可以对接收器工作特性(ROC)进行分析,并进行和不进行残余偏置场校正和局部空间扩展。与未进行校正相比,对于UTE图像,后处理校正将ROC曲线(AUC)下的面积从0.944±0.012增加到0.976±0.003,对于PETRA图像,则从0.850±0.022增加到0.887±0.012。如预期的那样,在扩展阈值定义的空气量时,空气识别的灵敏度会随着骨识别特异性的增加而降低,但呈非线性方式。 1 mm防毒面具的扩展使UTE和PETRA图像的AUC分别增加了1%和4%。与PETRA图像相比,UTE图像在将空气与骨骼分离时具有更大的辨别力。后处理策略改善了UTE和PETRA图像对骨骼的辨别能力,并减小了两个成像序列之间的差异。后处理的UTE和PETRA图像均显示出足够的能力来区分骨骼中的空气,以支持从MRI数据生成合成CT。

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