A graphical approach to optimizing variable-kernel smoothing parameters for improved deformable registration of CT and cone beam CT images

• Published in: Physics in medicine & biology 2017-07, Vol.62 (15), p.6246-6260, Article 6246
• Main Authors: Hart, Vern, Burrow, Damon, Allen Li, X
• Format: Article
• Language: English
• Subjects: adaptive radiotherapy; Algorithms; cone beam CT; Cone-Beam Computed Tomography - methods; convergence criteria; deformable image registration; Humans; Image Processing, Computer-Assisted - methods; Male; Pelvis - diagnostic imaging; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - radiotherapy; Radiotherapy Planning, Computer-Assisted - methods; Rectum - diagnostic imaging; registration assessment; Urinary Bladder - diagnostic imaging
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/aa7ccb

A systematic method is presented for determining optimal parameters in variable-kernel deformable image registration of cone beam CT and CT images, in order to improve accuracy and convergence for potential use in online adaptive radiotherapy. Assessed conditions included the noise constant (symmetric force demons), the kernel reduction rate, the kernel reduction percentage, and the kernel adjustment criteria. Four such parameters were tested in conjunction with reductions of 5, 10, 15, 20, 30, and 40%. Noise constants ranged from 1.0 to 1.9 for pelvic images in ten prostate cancer patients. A total of 516 tests were performed and assessed using the structural similarity index. Registration accuracy was plotted as a function of iteration number and a least-squares regression line was calculated, which implied an average improvement of 0.0236% per iteration. This baseline was used to determine if a given set of parameters under- or over-performed. The most accurate parameters within this range were applied to contoured images. The mean Dice similarity coefficient was calculated for bladder, prostate, and rectum with mean values of 98.26%, 97.58%, and 96.73%, respectively; corresponding to improvements of 2.3%, 9.8%, and 1.2% over previously reported values for the same organ contours. This graphical approach to registration analysis could aid in determining optimal parameters for Demons-based algorithms. It also establishes expectation values for convergence rates and could serve as an indicator of non-physical warping, which often occurred in cases  >0.6% from the regression line.