Adaptive Structured Light with Scatter Correction for High-Precision Underwater 3D Measurements.

High-precision underwater 3D cameras are required to automate many of the traditional subsea inspection, maintenance and repair (IMR) operations. In this paper we introduce a novel multi-frequency phase stepping (structured light) method for high-precision 3D estimation even in turbid water. We introduce an adaptive phase-unwrapping procedure which uses the phase-uncertainty to determine the highest frequency that can be reliably unwrapped.

Real-time super-resolved 3D in turbid water using a fast range-gated CMOS camera.

We present a range-gated camera system designed for real-time (10 Hz) 3D estimation underwater. The system uses a fast-shutter CMOS sensor (1280×1024) customized to facilitate gating with 1.67 ns (18.

Design tool for TOF and SL based 3D cameras.

Active illumination 3D imaging systems based on Time-of-flight (TOF) and Structured Light (SL) projection are in rapid development, and are constantly finding new areas of application. In this paper, we present a theoretical design tool that allows prediction of 3D imaging precision. Theoretical expressions are developed for both TOF and SL imaging systems.

Bayesian characterization of uncertainty in intra-subject non-rigid registration.

In settings where high-level inferences are made based on registered image data, the registration uncertainty can contain important information. In this article, we propose a Bayesian non-rigid registration framework where conventional dissimilarity and regularization energies can be included in the likelihood and the prior distribution on deformations respectively through the use of Boltzmann's distribution. The posterior distribution is characterized using Markov Chain Monte Carlo (MCMC) methods with the effect of the Boltzmann temperature hyper-parameters marginalized under broad uninformative hyper-prior distributions.

Two solutions for registration of ultrasound to MRI for image-guided prostate interventions.

Ultrasound-guided prostate interventions could benefit from incorporating the radiologic localization of the tumor which can be acquired from multiparametric MRI. To enable this integration, we propose and compare two solutions for registration of T2 weighted MR images with transrectal ultrasound. Firstly, we propose an innovative and practical approach based on deformable registration of binary label maps obtained from manual segmentation of the gland in the two modalities.

Selection of optimal hyper-parameters for estimation of uncertainty in MRI-TRUS registration of the prostate.

Transrectal ultrasound (TRUS) facilitates intra-treatment delineation of the prostate gland (PG) to guide insertion of brachytherapy seeds, but the prostate substructure and apex are not always visible which may make the seed placement sub-optimal. Based on an elastic model of the prostate created from MRI, where the prostate substructure and apex are clearly visible, we use a Bayesian approach to estimate the posterior distribution on deformations that aligns the pre-treatment MRI with intra-treatment TRUS. Without apex information in TRUS, the posterior prediction of the location of the prostate boundary, and the prostate apex boundary in particular, is mainly determined by the pseudo stiffness hyper-parameter of the prior distribution.

A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization.

Purpose: This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible.

PROBABILISTIC NON-RIGID REGISTRATION OF PROSTATE IMAGES: MODELING AND QUANTIFYING UNCERTAINTY.

Registration of pre- to intra-procedural prostate images needs to handle the large changes in position and shape of the prostate caused by varying rectal filling and patient positioning. We describe a probabilistic method for non-rigid registration of prostate images which can quantify the most probable deformation as well as the uncertainty of the estimated deformation. The method is based on a biomechanical Finite Element model which treats the prostate as an elastic material.

Estimation of delivered dose in radiotherapy: the influence of registration uncertainty.

We present a probabilistic framework to estimate the accumulated radiation dose and the corresponding dose uncertainty that is delivered to important anatomical structures, e.g. the primary tumor and healthy surrounding organs, during radiotherapy.

Probabilistic elastography: estimating lung elasticity.

We formulate registration-based elastography in a probabilistic framework and apply it to study lung elasticity in the presence of emphysematous and fibrotic tissue. The elasticity calculations are based on a Finite Element discretization of a linear elastic biomechanical model. We marginalize over the boundary conditions (deformation) of the biomechanical model to determine the posterior distribution over elasticity parameters.

Multimodal image registration for preoperative planning and image-guided neurosurgical procedures.

Image registration is the process of transforming images acquired at different time points, or with different imaging modalities, into the same coordinate system. It is an essential part of any neurosurgical planning and navigation system because it facilitates combining images with important complementary, structural, and functional information to improve the information based on which a surgeon makes critical decisions. Brigham and Women's Hospital (BWH) has been one of the pioneers in developing intraoperative registration methods for aligning preoperative and intraoperative images of the brain.

Summarizing and visualizing uncertainty in non-rigid registration.

Registration uncertainty may be important information to convey to a surgeon when surgical decisions are taken based on registered image data. However, conventional non-rigid registration methods only provide the most likely deformation. In this paper we show how to determine the registration uncertainty, as well as the most likely deformation, by using an elastic Bayesian registration framework that generates a dense posterior distribution on deformations.

Haptic guided 3-D deformable image registration.

Purpose: We present a system which supports deformable image registration guided by a haptic device.

Methods: The haptic device is tied to a block matching method where a set of uniformly distributed control points determine the block positions. Each control point constitutes a particle in a mass spring grid which limits the space of allowed movements to elastic movements.

A non-rigid registration framework that accommodates resection and retraction.

Traditional non-rigid registration algorithms are incapable of accurately registering intra-operative with pre-operative images whenever tissue has been resected or retracted. In this work we present methods for detecting and handling retraction and resection. The registration framework is based on the bijective Demons algorithm using an anisotropic diffusion smoother.