Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas.
Medical Physics Graduate Program, University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas.
Magn Reson Med. 2019 Jun;81(6):3888-3900. doi: 10.1002/mrm.27677. Epub 2019 Feb 8.
To develop and evaluate a sliding-window convolutional neural network (CNN) for radioactive seed identification in MRI of the prostate after permanent implant brachytherapy.
Sixty-eight patients underwent prostate cancer low-dose-rate (LDR) brachytherapy using radioactive seeds stranded with positive contrast MR-signal seed markers and were scanned using a balanced steady-state free precession pulse sequence with and without an endorectal coil (ERC). A sliding-window CNN algorithm (SeedNet) was developed to scan the prostate images using 3D sub-windows and to identify the implanted radioactive seeds. The algorithm was trained on sub-windows extracted from 18 patient images. Seed detection performance was evaluated by computing precision, recall, F -score, false discovery rate, and false-negative rate. Seed localization performance was evaluated by computing the RMS error (RMSE) between the manually identified and algorithm-inferred seed locations. SeedNet was implemented into a clinical software package and evaluated on sub-windows extracted from 40 test patients.
SeedNet achieved 97.6 ± 2.2% recall and 97.2 ± 1.9% precision for radioactive seed detection and 0.19 ± 0.04 mm RMSE for seed localization in the images acquired with an ERC. Without the ERC, the recall remained high, but the false-positive rate increased; the RMSE of the seed locations increased marginally. The clinical integration of SeedNet slightly increased the run-time, but the overall run-time was still low.
SeedNet can be used to perform automated radioactive seed identification in prostate MRI after LDR brachytherapy. Image quality improvement through pulse sequence optimization is expected to improve SeedNet's performance when imaging without an ERC.
开发并评估一种滑动窗口卷积神经网络（CNN），用于经永久性植入近距离放射治疗后前列腺 MRI 中的放射性种子识别。
68 例患者接受放射性种子植入低剂量率（LDR）近距离放射治疗，这些种子用具有正对比磁共振信号种子标记物的线串起来，并使用平衡稳态自由进动脉冲序列进行扫描，该序列带有和不带有直肠内线圈（ERC）。开发了一种滑动窗口 CNN 算法（SeedNet），用于扫描前列腺图像的 3D 子窗口，并识别植入的放射性种子。该算法在 18 例患者图像的子窗口上进行训练。通过计算精度、召回率、F 分数、假阳性率和假阴性率来评估种子检测性能。通过计算手动识别和算法推断的种子位置之间的均方根误差（RMSE）来评估种子定位性能。将 SeedNet 实现到临床软件包中，并在从 40 例测试患者中提取的子窗口上进行评估。
SeedNet 在 ERC 采集的图像中实现了 97.6±2.2%的召回率和 97.2±1.9%的精度以及 0.19±0.04mm 的种子定位 RMSE。没有 ERC 时，召回率仍然很高，但假阳性率增加；种子位置的 RMSE 略有增加。SeedNet 的临床整合略微增加了运行时间，但总体运行时间仍然较低。
SeedNet 可用于在 LDR 近距离放射治疗后进行前列腺 MRI 中的自动放射性种子识别。通过脉冲序列优化提高图像质量有望改善 SeedNet 在无 ERC 成像时的性能。
