Computation Project

William WellsBruno Madore
William Wells, PhD
Core Lead
Bruno Madore, PhD
Project Lead

The computation project is leveraging recent progress in ultrasound-ultrasound (US) registration and in hybrid US-MRI technology to develop synergistic software and hardware technology that is aimed at improving surgical and interventional guidance in the presence of tissue deformation or motion, issues that complicate treatment monitoring or comparisons to pre-operative images and treatment plans.  Our approach to addressing deformation problems in image guided therapy (IGT) leverages our recent work in feature-based US-US registration, where image content is modeled in terms of local scale-invariant image features, i.e., distinctive patterns of echogenic anatomical tissue that can be automatically extracted from images and used as the basis for registration. Our solution for motion in IGT is built upon our recent developments in hybrid US-MRI technology that acquires MRI and ultrasound simultaneously to exploit the relative strengths of MRI (high spatial resolution and excellent soft tissue contrast), and US (high frame rate). Much of the proposed research deals with providing solutions to registration problems for IGT applications, such as tissue deformation fields, and we believe that in this context it is important to characterize the potential uncertainties in these solutions, similarly to providing error bars in other estimation problems.To this end we are developing registration-with-uncertainty algorithms that incorporate random process models of spatial uncertainty. The technology is evaluated in the context of our testbed clinical projects, image-guided neurosurgery and abdominal cryotherapy, in the AMIGO suite, our advanced interventional suite that includes intra-operative 3T MRI, ultrasound and PET/CT. The hybrid US-MRI approach enables rapid updates to MRI images to accommodate, e.g., breathing motions during cryoablation procedures.In addition, US-US registration algorithms facilitate improvements in US-updated neurosurgical guidance, and have potential IGT applications in our program or elsewhere, for example in prostate biopsies. In order to facilitate dissemination of these algorithms to the broader IGT community, we distribute software components in the open-source SlicerIGT platform. Our projects are:

Registration algorithms for MRI and US with emphasis on uncertainty and algorithm performance. We continue algorithm developments aimed at characterizing uncertainty and accuracy in image registration,and tissue deformation estimation from implanted trackers,that are based on Gaussian Random Fields (GRF). We are also developing algorithms that estimate surgical tissue deformations from our feature-based ultrasound / ultrasound registration technology. Finally, we translate the developed algorithms into AMIGO using the SlicerIGT platform by providing extensions that visualize deformed MRI based on intraoperative US, associated registration uncertainty, and integrated laser surface scanning for neurosurgery. (Contact: William Wells)

Technology for simultaneous US-MRI acquisition for monitoring procedures. We are developing machine learning techniques that use high bandwidth US data to estimate motion and deformation in MRI images. We are also further generalizing the hybrid US-MRI approach by exploiting information from 256 independent channels, from a custom-built MR-compatible 256-element 2D US transducer array provided by an industrial partner. We are developing a pre-scan calibration (“learning”) phase that employs simultaneously-acquired MRI and USdata. We will deploy on-line deformation-corrected updates of MR as they become available from the scanner, for monitoring cryoablations. (Contact: Bruno Madore)


Select Recent Publications

  1. Sastry R., Bi W.L., Pieper S., Frisken S., Kapur T., Wells III W.M., Golby A.J. Applications of Ultrasound in the Resection of Brain Tumors. J Neuroimaging. 2016 Aug 19. PMID: 27541694.
  2. Bersvendsen J., Toews M., Danudibroto A., Wells III W.M., Urheim S., San Jose Estepar R., Samset E. Robust Spatio-Temporal Registration of 4D Cardiac Ultrasound Sequences. Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9790. PMID: 27516706. PMC4976768.
  3. Preiswerk F., Toews M., Cheng C-C., Chiou J-Y.G., Mei C-S., Schaefer L.F., Hoge W.S., Schwartz B.M., Panych L.P., Madore B. Hybrid MRI Ultrasound Acquisitions, and Scannerless Real-time Imaging. Magn Reson Med. 2016 Oct 13. PMID: 27739101.

Full Publication List

Peer-reviewed publications on our research in Computational Methods for Image-Guided Therapy can be found in the NIH/NLM database of biomedical literature by clicking here.

In addition, abstracts presented at national and international meetings are available online here.


Collaborations are a key component of our research program; We closely interact with researchers within our institution, across the US, and internationally. Active collaborations on funded projects are tabulated below, along with resultant publications.


Multimodal Registration of the Brain's Cortical Surface

Collaborating Investigator:  Michael Miga, PhD
Collaborating Institutions:  Vanderbilt University, Nashville, TN
Grant Number: R01NS049251
Grant Period: 08/01/2004-04/30/2019 
NCIGT Team and Project: Alexandra Golby, Neurosurgery, William M. Wells III., Computation
Joint Publications

Optimization of High Dose Conformal Therapy

Collaborating Investigators:  James Balter, PhD, Kristy Brock, PhD
Collaborating Institutions: University of Michigan, Ann Arbor, MI   
Grant Number: P01CA059827
Grant Period: 02/01/1997-06/30/2019
NCIGT Team and Project: William M. Wells III., Matthew Toews, Computation
Joint Publications


OpenIGTLink: A Network Communication Interface For Closed-Loop Image-Guided Interventions

Collaborating Investigator:  Junichi Tokuda, PhD
Collaborating Institution:  Brigham & Women's Hospital, Boston, MA
Grant Number: R01EB020667
Grant Period: 07/01/2015-06/30/2018
NCIGT Project: Guidance, Prostate, Computation, Neurosurgery
Joint Publications

NAC- Neuroimage Analysis Center

Collaborating Investigator:  Ron Kikinis, MD
Collaborating Institution:  Brigham & Women's Hospital, Boston, MA
Grant Number: P41EB015902
Grant Period: 08/01/2013-05/31/2018
NCIGT Project: Computation, Neurosurgery
Joint Publications

Software and Documentation

3D Slicer, a comprehensive open source platform for medical image analysis, contains several modules and functions that have been contributed by us for Computation. These include:Source Code for the Paper Titled: Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features (Med Image Anal. 2013 Apr;17(3):271-82.)


MRI acquired to guide Gynecologic Brachytherapy Catheter Placement


3D Slicer