Semiautomatic registration of 3D transabdominal ultrasound images for patient repositioning during postprostatectomy radiotherapy. Issue 12 (2nd December 2014)

• Record Type: Journal Article
• Authors: Presles, Benoît; Fargier‐Voiron, Marie; Biston, Marie‐Claude; Lynch, Rod; Munoz, Alexandre; Liebgott, Hervé; Pommier, Pascal; Rit, Simon; Sarrut, David
• Is Part Of: Medical physics. Volume 41:Issue 12(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-12-02
• Subjects: biomedical ultrasonics -- computerised tomography -- gradient methods -- image registration -- medical image processing -- optimisation -- radiation therapy -- statistical analysis -- stochastic processes; Therapeutic applications, including brachytherapy -- Treatment strategy -- Registration -- Computed tomography -- Ultrasonography -- Numerical optimization; Computerised tomographs -- Diagnosis using ultrasonic, sonic or infrasonic waves -- 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; postprostatectomy -- radiotherapy -- registration -- ultrasound; Ultrasonography -- Three dimensional image processing -- Computed tomography -- Radiation therapy -- Tissues -- Image registration -- Ultrasound therapy -- Medical image contrast -- Radiation treatment; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4901642
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The aim of the present work is to propose and evaluate registration algorithms of three‐dimensional (3D) transabdominal (TA) ultrasound (US) images to setup postprostatectomy patients during radiation therapy. Methods: Three registration methods have been developed and evaluated to register a reference 3D‐TA‐US image acquired during the planning CT session and a 3D‐TA‐US image acquired before each treatment session. The first method (method A) uses only gray value information, whereas the second one (method B) uses only gradient information. The third one (method C) combines both sets of information. All methods restrict the comparison to a region of interest computed from the dilated reference positioning volume drawn on the reference image and use mutual information as a similarity measure. The considered geometric transformations are translations and have been optimized by using the adaptive stochastic gradient descent algorithm. Validation has been carried out using manual registration by three operators of the same set of image pairs as the algorithms. Sixty‐two treatment US images of seven patients irradiated after a prostatectomy have been registered to their corresponding reference US image. The reference registration has been defined as the average of the manual registration values. Registration error has been calculated by subtracting the reference registration from the algorithm result. For each session, the method has been considered a failure if the registration error was above both the interoperator variability of the session and a global threshold of 3.0 mm. Results: All proposed registration algorithms have no systematic bias. Method B leads to the best results with mean errors of −0.6, 0.7, and −0.2 mm in left–right (LR), superior–inferior (SI), and anterior–posterior (AP) directions, respectively. With this method, the standard deviations of the mean error are of 1.7, 2.4, and 2.6 mm in LR, SI, and AP directions, respectively. The latter are inferior to the interoperator registration variabilities which are of 2.5, 2.5, and 3.5 mm in LR, SI, and AP directions, respectively. Failures occur in 5%, 18%, and 10% of cases in LR, SI, and AP directions, respectively. 69% of the sessions have no failure. Conclusions: Results of the best proposed registration algorithm of 3D‐TA‐US images for postprostatectomy treatment have no bias and are in the same variability range as manual registration. As the algorithm requires a short computation time, it could be used in clinical practice provided that a visual review is performed.