Shaaer Amani, Davidson Melanie, Semple Mark, Nicolae Alexandru, Mendez Lucas Castro, Chung Hans, Loblaw Andrew, Tseng Chia-Lin, Morton Gerard, Ravi Ananth
Department of Physics, Ryerson University, Toronto, Ontario, Canada.
Department of Medical Physics, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
Brachytherapy. 2019 Jan-Feb;18(1):95-102. doi: 10.1016/j.brachy.2018.08.006. Epub 2018 Oct 2.
Identifying dominant intraprostatic lesions (DILs) on transrectal ultrasound (TRUS) images during prostate high-dose-rate brachytherapy (HDR-BT) treatment planning is challenging. Multiparametric MRI (mpMRI) is the tool of choice for DIL identification; however, the geometry of the prostate on mpMRI and on the TRUS may differ significantly, requiring image registration. This study evaluates the efficacy of an in-house software for MRI-to-TRUS DIL registration (MR2US) and compares its results to rigid and B-Spline deformable registration.
Ten patients with intermediate-risk prostate cancer, each with mpMRI and TRUS data sets, were included in this study. Five radiation oncologists (ROs) with expertise in TRUS-based HDR-BT were asked to cognitively contour the DIL onto the TRUS image using mpMRI as reference. The contours were analyzed for concordance using simultaneous truth and performance level estimation algorithm. Similarity indices, DIL volumes, and distance between centroid positions were measured to compare the consensus contours against the contours from ROs and the automated algorithms; registration time between all contouring methods was recorded.
MR2US registration had the highest dice coefficients among all patients with a mean of 0.80 ± 0.13 in comparison to rigid (0.65 ± 0.20) and B-Spline (0.51 ± 0.30). The distance between centroid positions between simultaneous truth and performance level estimation contour and MR2US, rigid, and B-Spline contours were 5 ± 2, 7 ± 5, and 18 ± 11 mm, respectively. The average registration time was significantly shorter for MR2US (11 ± 2 s) and rigid algorithm (7 ± 1 s) compared to ROs (227 ± 27 s) and B-Spline (199 ± 38 s).
The efficacy of integrating an MRI-delineated DIL into a TRUS-based BT workflow has been validated in this study. The MR2US software is fast and accurate enough to be used for DIL identification in prostate HDR-BT.
在前列腺高剂量率近距离放射治疗（HDR - BT）治疗计划期间，在经直肠超声（TRUS）图像上识别前列腺内主要病变（DILs）具有挑战性。多参数磁共振成像（mpMRI）是识别DILs的首选工具；然而，mpMRI和TRUS上前列腺的几何形状可能存在显著差异，这需要图像配准。本研究评估了一种用于MRI到TRUS的DIL配准（MR2US）的内部软件的有效性，并将其结果与刚性配准和B样条可变形配准进行比较。
本研究纳入了10例中度风险前列腺癌患者，每位患者均有mpMRI和TRUS数据集。邀请了5位在基于TRUS的HDR - BT方面有专业知识的放射肿瘤学家（ROs），以mpMRI作为参考，在TRUS图像上凭认知勾勒出DILs的轮廓。使用同步真相和性能水平估计算法分析轮廓的一致性。测量相似性指数、DIL体积和质心位置之间的距离，以将共识轮廓与ROs和自动算法的轮廓进行比较；记录所有轮廓绘制方法之间的配准时间。
在所有患者中，MR2US配准的骰子系数最高，平均值为0.80±0.13，相比之下刚性配准为0.65±0.20，B样条配准为0.51±0.30。同步真相和性能水平估计轮廓与MR2US、刚性和B样条轮廓之间的质心位置距离分别为5±2、7±5和18±11毫米。与ROs（227±27秒）和B样条（199±38秒）相比，MR2US（11±2秒）和刚性算法（7±1秒）的平均配准时间明显更短。
本研究验证了将MRI勾勒的DIL整合到基于TRUS的BT工作流程中的有效性。MR2US软件速度快且足够准确，可用于前列腺HDR - BT中的DIL识别。
