Computation Core

William Wells Bruno 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)

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.)

Data

MRI acquired to guide Gynecologic Brachytherapy Catheter Placement

Links

3D Slicer

Full Publication List

In NIH/NLMdatabase and in our Abstracts Database.

Select Recent Publications

Toews M, Wells WM. Phantomless Auto-Calibration and Online Calibration Assessment for a Tracked Freehand 2-D Ultrasound Probe. IEEE Trans Med Imaging. 2018;37 (1) :262-72.Abstract
This paper presents a method for automatically calibrating and assessing the calibration quality of an externally tracked 2-D ultrasound (US) probe by scanning arbitrary, natural tissues, as opposed a specialized calibration phantom as is the typical practice. A generative topic model quantifies the posterior probability of calibration parameters conditioned on local 2-D image features arising from a generic underlying substrate. Auto-calibration is achieved by identifying the maximum a-posteriori image-to-probe transform, and calibration quality is assessed online in terms of the posterior probability of the current image-to-probe transform. Both are closely linked to the 3-D point reconstruction error (PRE) in aligning feature observations arising from the same underlying physical structure in different US images. The method is of practical importance in that it operates simply by scanning arbitrary textured echogenic structures, e.g., in-vivo tissues in the context of the US-guided procedures, without requiring specialized calibration procedures or equipment. Observed data take the form of local scale-invariant features that can be extracted and fit to the model in near real-time. Experiments demonstrate the method on a public data set of in vivo human brain scans of 14 unique subjects acquired in the context of neurosurgery. Online calibration assessment can be performed at approximately 3 Hz for the US images of pixels. Auto-calibration achieves an internal mean PRE of 1.2 mm and a discrepancy of [2 mm, 6 mm] in comparison to the calibration via a standard phantom-based method.
Niethammer M, Pohl KM, Janoos F, Wells WM. Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models. SIAM J. Imaging Sci. 2017;10 (3) :1069–1103.Abstract
Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating this uncertainty is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. However, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the active mean fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model, in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the tt icgbench dataset.
Ghafoorian M, Mehrtash A, Kapur T, Karssemeijer N, Marchiori E, Pesteie M, Guttmann CRG, de Leeuw F-E, Tempany CMC, van Ginneken B, et al. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation. Int Conf Med Image Comput Comput Assist Interv. 2017;20 (Pt3) :516-24.Abstract
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, (1) How much data from the new domain is required for a decent adaptation of the original network?; and, (2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset. The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.
Mastmeyer A, Pernelle G, Ma R, Barber L, Kapur T. Accurate Model-based Segmentation of Gynecologic Brachytherapy Catheter Collections in MRI-images. Med Image Anal. 2017;42 :173-88.Abstract
The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49  mm; 51 of the outliers deviated more than two catheter widths (3.4  mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44  mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.
Zhang M, Wells WM, Golland P. Probabilistic Modeling of Anatomical Variability using a Low Dimensional Parameterization of Diffeomorphisms. Med Image Anal. 2017;41 :55-62.Abstract
We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space.
Preiswerk F, Toews M, Cheng C-C, Chiou J-yuan G, Mei C-S, Schaefer LF, Hoge WS, Schwartz BM, Panych LP, Madore B. Hybrid MRI Ultrasound Acquisitions, and Scannerless Real-time Imaging. Magn Reson Med. 2016;78 (3) :897-908.Abstract

PURPOSE: To combine MRI, ultrasound, and computer science methodologies toward generating MRI contrast at the high frame rates of ultrasound, inside and even outside the MRI bore. METHODS: A small transducer, held onto the abdomen with an adhesive bandage, collected ultrasound signals during MRI. Based on these ultrasound signals and their correlations with MRI, a machine-learning algorithm created synthetic MR images at frame rates up to 100 per second. In one particular implementation, volunteers were taken out of the MRI bore with the ultrasound sensor still in place, and MR images were generated on the basis of ultrasound signal and learned correlations alone in a "scannerless" manner. RESULTS: Hybrid ultrasound-MRI data were acquired in eight separate imaging sessions. Locations of liver features, in synthetic images, were compared with those from acquired images: The mean error was 1.0 pixel (2.1 mm), with best case 0.4 and worst case 4.1 pixels (in the presence of heavy coughing). For results from outside the bore, qualitative validation involved optically tracked ultrasound imaging with/without coughing. CONCLUSION: The proposed setup can generate an accurate stream of high-speed MR images, up to 100 frames per second, inside or even outside the MR bore. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Bersvendsen J, Toews M, Danudibroto A, Wells WM, Urheim S, San José Estépar R, Samset E. Robust Spatio-Temporal Registration of 4D Cardiac Ultrasound Sequences. Proc SPIE Int Soc Opt Eng. 2016;9790.Abstract
Registration of multiple 3D ultrasound sectors in order to provide an extended field of view is important for the appreciation of larger anatomical structures at high spatial and temporal resolution. In this paper, we present a method for fully automatic spatio-temporal registration between two partially overlapping 3D ultrasound sequences. The temporal alignment is solved by aligning the normalized cross correlation-over-time curves of the sequences. For the spatial alignment, corresponding 3D Scale Invariant Feature Transform (SIFT) features are extracted from all frames of both sequences independently of the temporal alignment. A rigid transform is then calculated by least squares minimization in combination with random sample consensus. The method is applied to 16 echocardiographic sequences of the left and right ventricles and evaluated against manually annotated temporal events and spatial anatomical landmarks. The mean distances between manually identified landmarks in the left and right ventricles after automatic registration were (mean ± SD) 4.3 ± 1.2 mm compared to a reference error of 2.8 ± 0.6 mm with manual registration. For the temporal alignment, the absolute errors in valvular event times were 14.4 ± 11.6 ms for Aortic Valve (AV) opening, 18.6 ± 16.0 ms for AV closing, and 34.6 ± 26.4 ms for mitral valve opening, compared to a mean inter-frame time of 29 ms.
Sastry R, Bi WL, Pieper S, Frisken S, Kapur T, Wells W, Golby AJ. Applications of Ultrasound in the Resection of Brain Tumors. J Neuroimaging. 2017;27 (1) :5-15.Abstract

Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies.

Preiswerk F, Toews M, Hoge WS, Chiou J-yuan G, Panych LP, Wells WM, Madore B. Hybrid Utrasound and MRI Acquisitions for High-Speed Imaging of Respiratory Organ Motion. Med Image Comput Comput Assist Interv. 2015;9349 :315-22.Abstract

Magnetic Resonance (MR) imaging provides excellent image quality at a high cost and low frame rate. Ultrasound (US) provides poor image quality at a low cost and high frame rate. We propose an instance-based learning system to obtain the best of both worlds: high quality MR images at high frame rates from a low cost single-element US sensor. Concurrent US and MRI pairs are acquired during a relatively brief offine learning phase involving the US transducer and MR scanner. High frame rate, high quality MR imaging of respiratory organ motion is then predicted from US measurements, even after stopping MRI acquisition, using a probabilistic kernel regression framework. Experimental results show predicted MR images to be highly representative of actual MR images.

Chao T-C, Chiou J-yuan G, Maier SE, Madore B. Fast Diffusion Imaging with High Angular Resolution. Magn Reson Med. 2017;77 (2) :696-706.Abstract

PURPOSE: High angular resolution diffusion imaging (HARDI) is a well-established method to help reveal the architecture of nerve bundles, but long scan times and geometric distortions inherent to echo planar imaging (EPI) have limited its integration into clinical protocols. METHODS: A fast imaging method is proposed here that combines accelerated multishot diffusion imaging (AMDI), multiplexed sensitivity encoding (MUSE), and crossing fiber angular resolution of intravoxel structure (CFARI) to reduce spatial distortions and reduce total scan time. A multishot EPI sequence was used to improve geometrical fidelity as compared to a single-shot EPI acquisition, and acceleration in both k-space and diffusion sampling enabled reductions in scan time. The method is regularized and self-navigated for motion correction. Seven volunteers were scanned in this study, including four with volumetric whole brain acquisitions. RESULTS: The average similarity of microstructural orientations between undersampled datasets and their fully sampled counterparts was above 85%, with scan times below 5 min for whole-brain acquisitions. Up to 2.7-fold scan time acceleration along with four-fold distortion reduction was achieved. CONCLUSION: The proposed imaging strategy can generate HARDI results with relatively good geometrical fidelity and low scan duration, which may help facilitate the transition of HARDI from a successful research tool to a practical clinical one. Magn Reson Med, 2016. © 2016 Wiley Periodicals, Inc.