Catheter position prediction using deep‐learning‐based multi‐atlas registration for high‐dose rate prostate brachytherapy. Issue 11 (18th September 2021)

• Record Type: Journal Article
• Authors: Lei, Yang; Wang, Tonghe; Fu, Yabo; Roper, Justin; Jani, Ashesh B.; Liu, Tian; Patel, Pretesh; Yang, Xiaofeng
• Is Part Of: Medical physics. Volume 48:Issue 11(2021)
• Page Start: 7261
• Page End: 7270
• Publication Date: 2021-09-18
• Subjects: catheter -- deep learning -- HDR -- prostate; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15206
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: High‐dose‐rate (HDR) prostate brachytherapy involves treatment catheter placement, which is currently empirical and physician dependent. The lack of proper catheter placement guidance during the procedure has left the physicians to rely on a heuristic thinking‐while‐doing technique, which may cause large catheter placement variation and increased plan quality uncertainty. Therefore, the achievable dose distribution could not be quantified prior to the catheter placement. To overcome this challenge, we proposed a learning‐based method to provide HDR catheter placement guidance for prostate cancer patients undergoing HDR brachytherapy. Methods: The proposed framework consists of deformable registration via registration network (Reg‐Net), multi‐atlas ranking, and catheter regression. To model the global spatial relationship among multiple organs, binary masks of the prostate and organs‐at‐risk are transformed into distance maps, which describe the distance of each local voxel to the organ surfaces. For a new patient, the generated distance map is used as fixed image. Reg‐Net is utilized to deformably register the distance maps from multi‐atlas set to match this patient's distance map and then bring catheter maps from multi‐atlas to this patient via spatial transformation. Several criteria, namely prostate volume similarity, multi‐organ semantic image similarity, and catheter position criteria (far from the urethra and within the partial prostate), are used for multi‐atlas ranking. The top‐ranked atlas' deformed catheter positions are selected as the predicted catheter positions for this patient. Finally, catheter regression is used to refine the final catheter positions. A retrospective study on 90 patients with a fivefold cross‐validation scheme was used to evaluate the proposed method's feasibility. In order to investigate the impact of plan quality from the predicted catheter pattern, we optimized the source dwell position and time for both the clinical catheter pattern and predicted catheter pattern with the same optimization settings. Comparisons of clinically relevant dose volume histogram (DVH) metrics were completed. Results: For all patients, on average, both the clinical plan dose and predicted plan dose meet the common dose constraints when prostate dose coverage is kept at V100 = 95%. The plans from the predicted catheter pattern have slightly higher hotspot in terms of V150 by 5.0% and V200 by 2.9% on average. For bladder V75, rectum V75, and urethra V125, the average difference is close to zero, and the range of most patients is within ±1 cc. Conclusion: We developed a new catheter placement prediction method for HDR prostate brachytherapy based on a deep‐learning‐based multi‐atlas registration algorithm. It has great clinical potential since it can provide catheter location estimation prior to catheter placement, which could reduce the dependence on physicians' experience in catheter implantation and improve the quality of prostate HDR treatment plans. This approach merits further clinical evaluation and validation as a method of quality control for HDR prostate brachytherapy.