Bojorquez Jorge Zavala, Bricq Stéphanie, Brunotte François, Walker Paul M, Lalande Alain
LE2I, UMR 6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 21000, Dijon, France.
MAGMA. 2016 Oct;29(5):777-88. doi: 10.1007/s10334-016-0562-3. Epub 2016 May 9.
To segment and classify the different attenuation regions from MRI at the pelvis level using the T 1 and T 2 relaxation times and anatomical knowledge as a first step towards the creation of PET/MR attenuation maps.
Relaxation times were calculated by fitting the pixel-wise intensities of acquired T 1- and T 2-weighted images from eight men with inversion-recovery and multi-echo multi-slice spin-echo sequences. A decision binary tree based on relaxation times was implemented to segment and classify fat, muscle, prostate, and air (within the body). Connected component analysis and an anatomical knowledge-based procedure were implemented to localize the background and bone.
Relaxation times at 3 T are reported for fat (T 1 = 385 ms, T 2 = 121 ms), muscle (T 1 = 1295 ms, T 2 = 40 ms), and prostate (T 1 = 1700 ms, T 2 = 80 ms). The relaxation times allowed the segmentation-classification of fat, prostate, muscle, and air, and combined with anatomical knowledge, they allowed classification of bone. The good segmentation-classification of prostate [mean Dice similarity score (mDSC) = 0.70] suggests a viable implementation in oncology and that of fat (mDSC = 0.99), muscle (mDSC = 0.99), and bone (mDSCs = 0.78) advocates for its implementation in PET/MR attenuation correction.
Our method allows the segmentation and classification of the attenuation-relevant structures required for the generation of the attenuation map of PET/MR systems in prostate imaging: air, background, bone, fat, muscle, and prostate.
利用T1和T2弛豫时间以及解剖学知识,对骨盆水平的MRI不同衰减区域进行分割和分类,作为创建PET/MR衰减图的第一步。
通过对8名男性采用反转恢复和多回波多层自旋回波序列采集的T1加权和T2加权图像的逐像素强度进行拟合,计算弛豫时间。实施基于弛豫时间的决策二叉树,对脂肪、肌肉、前列腺和体内空气进行分割和分类。采用连通分量分析和基于解剖学知识的程序来定位背景和骨骼。
报告了3T时脂肪(T1 = 385ms,T2 = 121ms)、肌肉(T1 = 1295ms,T2 = 40ms)和前列腺(T1 = 1700ms,T2 = 80ms)的弛豫时间。弛豫时间有助于对脂肪、前列腺、肌肉和空气进行分割分类,结合解剖学知识,还能对骨骼进行分类。前列腺的良好分割分类(平均骰子相似性分数[mDSC]=0.70)表明在肿瘤学中具有可行的应用,脂肪(mDSC = 0.99)、肌肉(mDSC = 0.99)和骨骼(mDSCs = 0.78)的分割分类则支持其在PET/MR衰减校正中的应用。
我们的方法能够对前列腺成像中PET/MR系统衰减图生成所需的与衰减相关的结构进行分割和分类:空气、背景、骨骼、脂肪、肌肉和前列腺。