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Proceedings Paper

Patient-specific deep deformation models (PsDDM) to register planning and interventional ultrasound volumes in image fusion-guided interventions
Author(s): Jhimli Mitra; Michael MacDonald; David Mills; Soumya Ghose; L. Scott Smith; Shourya Sarcar; Desmond Teck-Beng Yeo; Clare Tempany; Bryan Bednarz; Sydney Jupitz; Thomas K. Foo
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Paper Abstract

Image fusion-guided interventions often require planning MR/CT and interventional U/S images to be registered in realtime. Organ motion, patient breathing and inconsistent ultrasound probe positioning during intervention, all contribute to the challenges of real-time 3D deformable registration, where alignment accuracy and computation time are often mutual trade-offs. In this work, we propose a novel framework to align planning and interventional 3D U/S by training patientspecific deep-deformation models (PsDDM) at the planning stage. During intervention, planning 3D U/S volumes are efficiently warped onto the interventional 3D U/S volumes using the trained deep-deformation model, thus enabling the transfer of other modality (planning MR/CT) information in real-time on interventional images. The alignment of planning MR/CT to planning U/S is not time-critical as these can be aligned before the intervention with desired accuracy using any known multimodal deformable registration method. The feasibility of training PsDDM is shown on liver U/S data acquired with a custom-built MR-compatible, hands-free 3D ultrasound probe that allows simultaneous acquisition of planning MR and U/S. Liver U/S volumes exhibit large motion in time due to respiration and therefore serve as a good anatomy to quantify the accuracy of the PsDDM. For quantitative evaluation of the PsDDM, a large vessel bifurcation was manually annotated on 9 U/S volumes that were not used for training the PsDDM but from the same subject. Mean target registration error (TRE) between the centroids was 0.84mm ± 0.39mm, mean Hausdorff distance (HD) was 1.80mm ± 0.29mm and mean surface distance (MSD) was 0.44mm ± 0.06mm for all volumes. In another experiment, the PsDDM was trained using liver volumes from one scanning session, while the model was tested on data from a separate scanning session of the same patient, for which qualitative alignment results were presented.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150Y (16 March 2020); doi: 10.1117/12.2549352
Show Author Affiliations
Jhimli Mitra, General Electric Research (United States)
Michael MacDonald, General Electric Research (United States)
David Mills, General Electric Research (United States)
Soumya Ghose, General Electric Research (United States)
L. Scott Smith, General Electric Research (United States)
Shourya Sarcar, General Electric Research (United States)
Desmond Teck-Beng Yeo, General Electric Research (United States)
Clare Tempany, Brigham and Women's Hospital (United States)
Bryan Bednarz, Univ. of Wisconsin-Madison (United States)
Sydney Jupitz, Univ. of Wisconsin-Madison (United States)
Thomas K. Foo, General Electric Research (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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