2D‐3D rigid registration to compensate for prostate motion during 3D TRUS‐guided biopsy. Issue 2 (18th January 2013)

• Record Type: Journal Article
• Authors: De Silva, Tharindu; Fenster, Aaron; Cool, Derek W.; Gardi, Lori; Romagnoli, Cesare; Samarabandu, Jagath; Ward, Aaron D.
• Is Part Of: Medical physics. Volume 40:Issue 2(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-01-18
• Subjects: Ultrasonographic imaging -- Registration; biomedical ultrasonics -- image registration -- medical image processing -- motion compensation; prostate motion compensation -- image registration -- 3D transrectal ultrasound‐guided prostate biopsy -- prostate cancer; Diagnosis using ultrasonic, sonic or infrasonic waves -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of motion; Medical imaging -- Ultrasonography -- High pressure -- Image registration -- Motion compensation -- Cancer -- Image detection systems -- Tissues -- Sound pressure -- Computer software; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4773873
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Three‐dimensional (3D) transrectal ultrasound (TRUS)‐guided systems have been developed to improve targeting accuracy during prostate biopsy. However, prostate motion during the procedure is a potential source of error that can cause target misalignments. The authors present an image‐based registration technique to compensate for prostate motion by registering the live two‐dimensional (2D) TRUS images acquired during the biopsy procedure to a preacquired 3D TRUS image. The registration must be performed both accurately and quickly in order to be useful during the clinical procedure. Methods: : The authors implemented an intensity‐based 2D‐3D rigid registration algorithm optimizing the normalized cross‐correlation (NCC) metric using Powell's method. The 2D TRUS images acquired during the procedure prior to biopsy gun firing were registered to the baseline 3D TRUS image acquired at the beginning of the procedure. The accuracy was measured by calculating the target registration error (TRE) using manually identified fiducials within the prostate; these fiducials were used for validation only and were not provided as inputs to the registration algorithm. They also evaluated the accuracy when the registrations were performed continuously throughout the biopsy by acquiring and registering live 2D TRUS images every second. This measured the improvement in accuracy resulting from performing the registration, continuously compensating for motion during the procedure. To further validate the method using a more challenging data set, registrations were performed using 3D TRUS images acquired by intentionally exerting different levels of ultrasound probe pressures in order to measure the performance of our algorithm when the prostate tissue was intentionally deformed. In this data set, biopsy scenarios were simulated by extracting 2D frames from the 3D TRUS images and registering them to the baseline 3D image. A graphics processing unit (GPU)‐based implementation was used to improve the registration speed. They also studied the correlation between NCC and TREs. Results: : The root‐mean‐square (RMS) TRE of registrations performed prior to biopsy gun firing was found to be 1.87 ± 0.81 mm. This was an improvement over 4.75 ± 2.62 mm before registration. When the registrations were performed every second during the biopsy, the RMS TRE was reduced to 1.63 ± 0.51 mm. For 3D data sets acquired under different probe pressures, the RMS TRE was found to be 3.18 ± 1.6 mm. This was an improvement from 6.89 ± 4.1 mm before registration. With the GPU based implementation, the registrations were performed with a mean time of 1.1 s. The TRE showed a weak correlation with the similarity metric. However, the authors measured a generally convex shape of the metric around the ground truth, which may explain the rapid convergence of their algorithm to accurate results. Conclusions: : Registration to compensate for prostate motion during 3D TRUS‐guided biopsy can be performed with a measured accuracy of less than 2 mm and a speed of 1.1 s, which is an important step toward improving the targeting accuracy of a 3D TRUS‐guided biopsy system.