A block matching‐based registration algorithm for localization of locally advanced lung tumors. Issue 4 (13th March 2014)

• Record Type: Journal Article
• Authors: Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D.
• Is Part Of: Medical physics. Volume 41:Issue 4(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-03-13
• Subjects: Computed tomography -- Registration -- Spatial resolution -- Cancer -- Therapeutic applications, including brachytherapy; cancer -- computerised tomography -- image matching -- image registration -- image resolution -- lung -- median filters -- medical image processing -- radiation therapy -- tumours; nonsmall‐cell lung cancer -- image registration -- image‐guided radiation therapy; Computerised tomographs -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; Medical imaging -- Cancer -- Lungs -- Computed tomography -- Image registration -- Anatomy -- Cone beam computed tomography -- Image analysis -- Radiation therapy -- Radiation treatment; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4867860
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : To implement and evaluate a block matching‐based registration (BMR) algorithm for locally advanced lung tumor localization during image‐guided radiotherapy. Methods: : Small (1 cm 3 ), nonoverlapping image subvolumes ("blocks") were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on‐treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near‐maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on‐treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on‐treatment computed tomography scans having physician‐delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician‐identified targets to establish residual error after registration. Results: : Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%; p < 0.001). Left‐right, anterior‐posterior, and superior‐inferior systematic BDE were 3.2, 2.4, and 4.4 mm, respectively, with random BDE of 2.4, 2.1, and 2.7 mm. Margins required to include both localization and delineation uncertainties ranged from 5.0 to 11.7 mm, an average of 40% less than required for bony alignment. Conclusions: : BMR is a promising approach for automatic lung tumor localization. Further evaluation is warranted to assess the accuracy and robustness of BMR against other potential localization strategies.