A Robinson’s research while affiliated with Johns Hopkins University and other places

Radiochromic film dosimetry has been widely adopted in the clinic as it is a convenient option for dose measurement and verification. Film dosimetry analysis is typically performed using expensive commercial software, or custom made scripts in Matlab. However, common clinical film analysis software is not transparent regarding what corrections/optimizations are running behind the scenes. In this work, an extension to the open-source medical imaging platform 3D Slicer was developed and implemented in our centre for film dosimetry analysis. This extension streamlines importing treatment planning system dose and film imaging data, film calibration, registration, and comparison of 2D dose distributions, enabling greater accessibility to film analysis and higher reliability.

The purpose of this study is to develop an accurate and effective technique to predict and monitor volume changes of the tumor and organs at risk (OARs) from daily cone-beam CTs (CBCTs). While CBCT is typically used to minimize the patient setup error, its poor image quality impedes accurate monitoring of daily anatomical changes in radiotherapy. Reconstruction artifacts in CBCT often cause undesirable errors in registration-based contour propagation from the planning CT, a conventional way to estimate anatomical changes. To improve the registration and segmentation accuracy, we developed a new deformable image registration (DIR) that iteratively corrects CBCT intensities using slice-based histogram matching during the registration process. Three popular DIR algorithms (hierarchical B-spline, demons, optical flow) augmented by the intensity correction were implemented on a graphics processing unit for efficient computation, and their performances were evaluated on six head and neck (HN) cancer cases. Four trained scientists manually contoured nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs for each case, to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial software, VelocityAI (Varian Medical Systems Inc.). Manual contouring showed significant variations, [-76, +141]% from the mean of all four sets of contours. The volume differences (mean±std in cc) between the average manual segmentation and four automatic segmentations are 3.70±2.30(B-spline), 1.25±1.78(demons), 0.93±1.14(optical flow), and 4.39±3.86 (VelocityAI). In comparison to the average volume of the manual segmentations, the proposed approach significantly reduced the estimation error by 9%(B-spline), 38%(demons), and 51%(optical flow) over the conventional mutual information based method (VelocityAI). The proposed CT-CBCT registration with local CBCT intensity correction can accurately predict the tumor volume change with reduced errors. Although demonstrated only on HN nodal GTVs, the results imply improved accuracy for other critical structures. This work was supported by NIH/NCI under grant R42CA137886.

In this study, we develop an integrated software platform for adaptive radiation therapy (ART) that combines fast and accurate image registration, segmentation, and dose computation/accumulation methods. The proposed system consists of three key components; 1) deformable image registration (DIR), 2) automatic segmentation, and 3) dose computation/accumulation. The computationally intensive modules including DIR and dose computation have been implemented on a graphics processing unit (GPU). All required patient-specific data including the planning CT (pCT) with contours, daily cone-beam CTs, and treatment plan are automatically queried and retrieved from their own databases. To improve the accuracy of DIR between pCT and CBCTs, we use the double force demons DIR algorithm in combination with iterative CBCT intensity correction by local intensity histogram matching. Segmentation of daily CBCT is then obtained by propagating contours from the pCT. Daily dose delivered to the patient is computed on the registered pCT by a GPU-accelerated superposition/convolution algorithm. Finally, computed daily doses are accumulated to show the total delivered dose to date. Since the accuracy of DIR critically affects the quality of the other processes, we first evaluated our DIR method on eight head-and-neck cancer cases and compared its performance. Normalized mutual-information (NMI) and normalized cross-correlation (NCC) computed as similarity measures, and our method produced overall NMI of 0.663 and NCC of 0.987, outperforming conventional methods by 3.8% and 1.9%, respectively. Experimental results show that our registration method is more consistent and roust than existing algorithms, and also computationally efficient. Computation time at each fraction took around one minute (30-50 seconds for registration and 15-25 seconds for dose computation). We developed an integrated GPU-accelerated software platform that enables accurate and efficient DIR, auto-segmentation, and dose computation, thus supporting an efficient ART workflow. This work was supported by NIH/NCI under grant R42CA137886.

Purpose: Transrectal-ultrasound (TRUS) is the most common image modality used in permanent prostate brachytherapy (PPI), while MRI images can provide additional anatomical information. Our goals were to develop a novel method to register post-implant MRI to intra-operative ultrasound (US) images and demonstrate its potential usage for retrospective implant assessment. Methods: TRUS images of prostate and non-isocentric C-arm fluoroscopy (FL) images are captured intraoperatively right after implant. The reconstructed 3D seed cloud from FL images (seeds_FL) will be used as a bridge to register post MRI to US images. The Registration of Ultrasound and Fluoroscopy (RUF) images is done by an intensity-based point to volume algorithm. The day-one post CT and T2-MRI images are co-registered and the 3D seed cloud is segmented from CT (seeds_CT). The iterative-closest-point algorithm is used to compute rigid transformation between seeds_FL and seed_CT. MRI can be transferred to FL coordinate using registration of two seed clouds and then to US coordinate using RUF registration. A thin-plate-spline algorithm is used to deform the MRI contours and images according to deformation of two seed clouds. To demonstrate this registration method, post MRI images from ten patients were registered to US images. The prostate contours were compared between post US and MRI. The planning needle interferences with critical structure like neurovascular bundles (NVB) were investigated. Results: After registration, center of mass of both prostate and urethra were within 3mm between two contour sets. The anterior boundaries of prostate were often overestimated in post US contours. When planning needles were superimposed over contours, there were about 3-4 needles passing through or in close vicinity of NVB in all patients. Conclusion: A novel method to register post-implant MRI to intra-operative US images was developed and demonstrated in evaluating accuracy of intra-operative US contours and accessing needle passages to NVB during PPI.