We propose a learning-based method to register 4D CBCT images from different respiratory phases. An unsupervised spatial transformation network (STN)-based deformable registration method is introduced for registering phases of 4D CBCT. Namely, no ground truth deformation vector filed (DVF) is needed during training. To avoid the scatter artifact effect while learning the trainable parameters, a structural similarity-based loss is used for supervision. To evaluate the proposed method, we retrospectively investigate 20 lung 4D CBCT datasets. Five-fold cross-validation was used to evaluate the proposed method. During each experiment, 16 4D CBCT datasets were used for training and the rest 4 4D CBCTs were used as testing. During training, each two phases from one patient were used as moving and fixed images. The average TRE is 1.67 ±3.30 mm. The proposed method has great potential in quantifying tumor trajectory for making clinical decision during radiation therapy.
KEYWORDS: Prostate, Magnetic resonance imaging, Computed tomography, Image registration, High dynamic range imaging, Prostate cancer, Radiotherapy, Tumors, Rectum, Medical imaging
The use of multiparametric MRI (mp-MRI) can reliably identify dominant intra-prostatic lesions (DILs) within prostate cancer. Dose escalation to DILs using high-dose-rate (HDR) brachytherapy may improve tumor control probability. In this study, we retrospectively investigated a total of 17 patients treated by HDR prostate brachytherapy, each of whom has mp-MRI and CT images acquired pre-treatment. 21 DILs were contoured based on mp-MRI and propagated to CT images after registration using a newly developed deformable image registration method. A boost plan was created for each patient and optimized on the original needle pattern. In addition, separate plans were generated using a virtually implanted needle around the DIL in order to simulate mp-MRI guided needle placement. Both plans were optimized to maximize DIL V150 coverage while meeting OAR sparing constraints. DIL V150, prostate coverage, and OAR sparing were compared with original plan results. Overall, optimized boost plans significantly escalate dose to DILs while meeting OAR constraints. The addition of mp-MRI guided virtual needles facilitate increased coverage of DIL volumes, achieving a V150 <90% in 85% of DILs compared with 57% of boost plan without an additional needle. These results strongly indicate that the proposed mp-MRI guided DIL boost in HDR brachytherapy is feasible without violating OAR constraints. This retrospective study suggests the use of mp-MRI-defined DIL to optimize needle placement through the deformable MRI-ultrasound registration in the operating room may represent a strategy to personalize treatment delivery and improve tumor control.
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