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来自投射域的双能量计算机断层扫描的多材料分解的两步框架

Di Xu, Qihui Lyu, Dan Ruan

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 5, 2025

概括

一个新的深度学习框架,rFast-MMDNet,准确地从双能量CT投影数据中分解多种材料. 这种方法可以通过快速高效的处理来改善乳腺组织的分化.

科学领域:

医疗成像医学成像
计算成像技术的成像
人工智能在医学中的应用

背景情况:

双能计算断层扫描 (DECT) 使用不同的X射线光谱来改善多材料分解 (MMD).
对于两个以上的材料,准确的MMD与传统方法具有挑战性.
图像领域的现有深度学习 (DL) 方法在计算上是低效的,或者未充分利用投影数据.

研究的目的:

通过使用原始投影数据,提出rFast-MMDNet,一种用于MMD (>2材料) 的临床适用框架.
用DECT.实现精确的乳腺组织分化 (纤维腺体,脂肪,化).
开发一个非递归的,计算效率高的DL-MMD框架.

主要方法:

rFast-MMDNet采用两阶段算法:SinoNet用于投影分解,FBP-DenoiseNet用于域调整和后处理.
该框架在模拟的DECT数据集 (DL-Spectral-Challenge) 上进行了评估,其中包括乳房幽灵.
性能与直接反转 (AA-MMD),图像域DL (ID-UNet) 和sinogram域DL (三重CBCT) 方法进行了比较.

主要成果:

rFast-MMDNet实现了高保真性分解,平均RMSE为0.004 ± 0和MAE为0.001 ± 0.
对于rFast-MMDNet的推理时间小于1秒.
rFast-MMDNet的性能优于三重CBCT和图像域DL方法,显示出更高的准确性.

结论:

从投影数据开发了一个快速,稳健和可解释的工作流程,以实现高效和精确的MMD.
rFast-MMDNet框架增强了DECT对乳腺组织分化的能力.
这种方法通过先进的基于DL的MMD促进了改进的诊断应用.

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