2D-3D rigid registration to compensate for prostate motion during 3D TRUS-guided biopsy

Silva, Tharindu De; Fenster, Aaron; Bax, Jeffrey; Gardi, Lori; Romagnoli, Cesare; Samarabandu, Jagath; Ward, Aaron D.

doi:10.1117/12.912303

Cited by 14 publications

(35 citation statements)

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“…table 1) have to be discussed with respect to related work. In [5], for a total of 16 registrations involving 16 live 2D images for 8 patients, the TRE was 1.87±0.81mm (4.75±2.62 mm before registration). This was evaluated on fiducials extracted in 2D slices and 3D data (3 or 4 per image).…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy we obtained is slightly lower or similar to the one reported in the literature but it must be kept in mind that it corresponds to experiments on a much larger data set obtained from routine recorded data. Also, we com-puted gold standard transforms based on fiducials on 3D volumes, whereas in [8] and [5], the fiducials were identified on 2D slices. Our gold standard transforms computed with 3D fiducials are therefore probably more precise.…”

Section: Discussionmentioning

confidence: 99%

“…FEM-based) registration which is not compatible with real-time navigation. [5] reports mean running time of 1.1 s for the experiments listed above with a GPU implementation of their method. Considering the potential of parallelization of most of our method steps, we are optimistic about our ability to significantly decrease the computational cost of our method also using GPU to reach similar performances.…”

Section: Discussionmentioning

confidence: 99%

“…However using it for real-time navigation is very challenging due to (1) limited information from the 2D image about off-plane displacement and deformation of the prostate, (2) US imaging shortcomings (noise, artifact shadows and US signal attenuation with depth) and (3) the need for short computation time. Based on their previous work [3], De Silva et al [5] describes 2D-3D rigid registration of prostate images using Normalized Cross Correlation maximization by Powell's method. The probe position and orientation, given by a mechanical arm holding the probe, are used for registration initialization.…”

Section: Introductionmentioning

confidence: 99%

“…After incorporating the learned initialization positions and directions in Powell's method, they obtain a registration error of 2.33±1.09 mm with ∼3 s mean execution time per registration. Gillies et al [7] optimize the 2D-3D registration algorithm presented in [5] and obtain a computation times of 31 ms per registration in phantom experiments.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid 2D–3D ultrasound registration for navigated prostate biopsy

2018

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Purpose: We present a hybrid 2D-3D ultrasound (US) rigid registration method for navigated prostate biopsy that enables continuous localization of the biopsy trajectory during the exam.Methods: Current clinical computer-assisted biopsy systems use either sensorbased or image-based approaches. We combine the advantages of both in order to obtain an accurate and real-time navigation based only on an approximate localization of the US probe. Starting with features extracted in both 2D and 3D images, our method introduces a variant of the Iterative Closest Point (ICP) algorithm. Among other differences to ICP, a combination of both the euclidean distance of feature positions and the similarity distance of feature descriptors is used to find matches between 2D and 3D features. The evaluation of the method is twofold. First, an analysis of variance on input parameters is conducted to estimate the sensitivity of our method to their initialization. Secondly, for a selected set of their values, the Target Registration Error (TRE) was calculated on 29,760 (resp. 4,000) registrations in two different experiments. It was obtained using manually identified anatomical fiducials.Results: For 160 US volumes, from 20 patients, recorded during routine biopsy procedures performed in two hospitals by 6 operators, the mean TRE was 3.91±3.22 mm (resp. 4.37±2.62 mm).Conclusion: This work allows envisioning further developments for prostate navigation and their clinical transfer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid 2D–3D ultrasound registration for navigated prostate biopsy

2018

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Mechatronic system for in-bore MRI-guided insertion of needles to the prostate: An in vivo needle guidance accuracy study

Cepek

Lindner

Ghai

et al. 2014

J. Magn. Reson. Imaging

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Real‐time registration of 3D to 2D ultrasound images for image‐guided prostate biopsy

et al. 2017

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The presented method encourages real-time implementation of motion compensation algorithms in prostate biopsy with clinically acceptable registration errors. Continuous motion compensation demonstrated registration accuracy with submillimeter and subdegree error, while performing < 50 ms computation times. Image registration technique approaching the frame rate of an ultrasound system offers a key advantage to be smoothly integrated to the clinical workflow. In addition, this technique could be used further for a variety of image-guided interventional procedures to treat and diagnose patients by improving targeting accuracy.

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2D-3D rigid registration to compensate for prostate motion during 3D TRUS-guided biopsy

Cited by 14 publications

References 0 publications

Hybrid 2D–3D ultrasound registration for navigated prostate biopsy

Hybrid 2D–3D ultrasound registration for navigated prostate biopsy

Mechatronic system for in-bore MRI-guided insertion of needles to the prostate: An in vivo needle guidance accuracy study

Real‐time registration of 3D to 2D ultrasound images for image‐guided prostate biopsy

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