Multimodal Registration Research Articles

BOLD response to working memory not related to cortical thickness during early adolescence

BackgroundSignificant cortical thinning and neural resource allocation changes emerge during adolescence; however, little is known of how morphometric changes influence neural response to cognitive demands. This study used a novel multimodal imaging registration technique to examine the relationship between brain structure and function during adolescence. Methods156 healthy 12–14 year-olds (44% female) participants underwent structural and functional magnetic resonance imaging. Cortical surface reconstruction was performed via FreeSurfer, and neural activation was measured from a blood oxygen level dependent (BOLD) contrast during visual working memory (VWM) via AFNI. AFNI Surface Mapper aligned segmented volumetric and functional datasets to a common template space. Hierarchical linear regressions determined the effect of cortical thickness on VWM BOLD contrast in brain regions that activated during the VWM task, controlling for age, pubertal development, gender, IQ, and intracranial volume. ResultsPower analyses suggest this study was able to detect small effect sizes. However, in no region was cortical thickness related to BOLD activation (ps>.01; R2Δ<.02). Gender did not moderate effects. ConclusionsCortical thickness, although variable across individuals, was not related to BOLD response, suggesting that structural and functional maturation do not have the same developmental trajectory during early adolescence. These findings are important, as imaging studies that report group differences in regards to cortical thickness should not necessarily assume co-occurring behavioral or functional changes. The methodology used in this study could be of interest to other developmental neuroimaging researchers using multimodal imaging to understand adolescent brain development.

Multimodal deformable registration of traumatic brain injury MR volumes via the Bhattacharyya distance.

An important problem of neuroimaging data analysis for traumatic brain injury (TBI) is the task of coregistering MR volumes acquired using distinct sequences in the presence of widely variable pixel movements which are due to the presence and evolution of pathology. We are motivated by this problem to design a numerically stable registration algorithm which handles large deformations. To this end, we propose a new measure of probability distributions based on the Bhattacharyya distance, which is more stable than the widely used mutual information due to better behavior of the square root function than the logarithm at zero. Robustness is illustrated on two TBI patient datasets, each containing 12 MR modalities. We implement our method on graphics processing units (GPU) so as to meet the clinical requirement of time-efficient processing of TBI data. We find that 6 sare required to register a pair of volumes with matrix sizes of 256 × 256 × 60 on the GPU. In addition to exceptional time efficiency via its GPU implementation, this methodology provides a clinically informative method for the mapping and evaluation of anatomical changes in TBI.

Calibration of time-of-flight cameras for accurate intraoperative surface reconstruction

In image-guided surgery (IGS) intraoperative image acquisition of tissue shape, motion, and morphology is one of the main challenges. Recently, time-of-flight (ToF) cameras have emerged as a new means for fast range image acquisition that can be used for multimodal registration of the patient anatomy during surgery. The major drawbacks of ToF cameras are systematic errors in the image acquisition technique that compromise the quality of the measured range images. In this paper, we propose a calibration concept that, for the first time, accounts for all known systematic errors affecting the quality of ToF range images. Laboratory and in vitro experiments assess its performance in the context of IGS. For calibration the camera-related error sources depending on the sensor, the sensor temperature and the set integration time are corrected first, followed by the scene-specific errors, which are modeled as function of the measured distance, the amplitude and the radial distance to the principal point of the camera. Accounting for the high accuracy demands in IGS, we use a custom-made calibration device to provide reference distance data, the cameras are calibrated too. To evaluate the mitigation of the error, the remaining residual error after ToF depth calibration was compared with that arising from using the manufacturer routines for several state-of-the-art ToF cameras. The accuracy of reconstructed ToF surfaces was investigated after multimodal registration with computed tomography (CT) data of liver models by assessment of the target registration error (TRE) of markers introduced in the livers. For the inspected distance range of up to 2 m, our calibration approach yielded a mean residual error to reference data ranging from 1.5±4.3 mm for the best camera to 7.2±11.0 mm. When compared to the data obtained from the manufacturer routines, the residual error was reduced by at least 78% from worst calibration result to most accurate manufacturer data. After registration of the CT data with the ToF data, the mean TRE ranged from 3.7±2.1 mm for point-based and 5.7±1.9 mm for surface-based registration for the best camera to 6.2±3.4 and 11.1±2.8 mm, respectively. Compared to data provided by the manufacturer, the mean TRE decreased by 8%-60% for point-based and by 18%-74% for surface-based registration. Using the proposed calibration approach improved the measurement accuracy of all investigated ToF cameras. Although evaluated in the context of intraoperative image acquisition, the proposed calibration procedure can easily be applied to other medical applications using ToF cameras, such as patient positioning or respiratory motion tracking in radiotherapy.

Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base-of-tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam computed tomography (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e. volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC) and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid and Demons steps was 4.6, 2.1 and 1.7 mm, respectively. The respective ECC was 0.57, 0.70 and 0.73, and NPMI was 0.46, 0.57 and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support safer, high-precision base-of-tongue robotic surgery.

Breast lesion co-localisation between X-ray and MR images using finite element modelling

This paper presents a novel X-ray and MR image registration technique based on individual-specific biomechanical finite element (FE) models of the breasts. Information from 3D magnetic resonance (MR) images was registered to X-ray mammographic images using non-linear FE models subject to contact mechanics constraints to simulate the large compressive deformations between the two imaging modalities. A physics-based perspective ray-casting algorithm was used to generate 2D pseudo-X-ray projections of the FE-warped 3D MR images. Unknown input parameters to the FE models, such as the location and orientation of the compression plates, were optimised to provide the best match between the pseudo and clinical X-ray images. The methods were validated using images taken before and during compression of a breast-shaped phantom, for which 12 inclusions were tracked between imaging modalities. These methods were then applied to X-ray and MR images from six breast cancer patients. Error measures (such as centroid and surface distances) of segmented tumours in simulated and actual X-ray mammograms were used to assess the accuracy of the methods. Sensitivity analysis of the lesion co-localisation accuracy to rotation about the anterior-posterior axis was then performed. For 10 of the 12 X-ray mammograms, lesion localisation accuracies of 14 mm and less were achieved. This analysis on the rotation about the anterior-posterior axis indicated that, in cases where the lesion lies in the plane parallel to the mammographic compression plates, that cuts through the nipple, such rotations have relatively minor effects.This has important implications for clinical applicability of this multi-modality lesion registration technique, which will aid in the diagnosis and treatment of breast cancer.

OP0233 QCT indicates locally increased bone mineral density (BMD) in regions of bone marrow lesions (BML) segmented on MR images

BackgroundWhile the association of BMLs with joint space narrowing and pain in knee OA is verified, the correlation between BMLs and local BMD is a topic of major interest with...

Hybrid Ultrasound/Magnetic Resonance Simultaneous Acquisition and Image Fusion for Motion Monitoring in the Upper Abdomen

The combination of ultrasound (US) and magnetic resonance imaging (MRI) may provide a complementary description of the investigated anatomy, together with improved guidance and assessment of image-guided therapies. The aim of the present study was to integrate a clinical setup for simultaneous US and magnetic resonance (MR) acquisition to obtain synchronized monitoring of liver motion. The feasibility of this hybrid imaging and the precision of image fusion were evaluated. Ultrasound imaging was achieved using a clinical US scanner modified to be MR compatible, whereas MRI was achieved on 1.5- and 3-T clinical scanners. Multimodal registration was performed between a high-resolution T1 3-dimensional (3D) gradient echo (volume interpolated gradient echo) during breath-hold and a simultaneously acquired 2D US image, or equivalent, retrospective registration of US imaging probe in the coordinate frame of MRI. A preliminary phantom study was followed by 4 healthy volunteer acquisitions, performing simultaneous 4D MRI and 2D US harmonic imaging (Fo = 2.2 MHz) under free breathing. No characterized radiofrequency mutual interferences were detected under the tested conditions with commonly used MR sequences in clinical routine, during simultaneous US/MRI acquisition. Accurate spatial matching between the 2D US and the corresponding MRI plane was obtained during breath-hold. In situ fused images were delivered. Our 4D MRI sequence permitted the dynamic reconstruction of the intra-abdominal motion and the calculation of high temporal resolution motion field vectors. This study demonstrates that, truly, simultaneous US/MR dynamic acquisition in the abdomen is achievable using clinical instruments. A potential application is the US/MR hybrid guidance of high-intensity focused US therapy in the liver.

Simultaneous Registration of Multiple Images: Similarity Metrics and Efficient Optimization

We address the alignment of a group of images with simultaneous registration. Therefore, we provide further insights into a recently introduced framework for multivariate similarity measures, referred to as accumulated pair-wise estimates (APE), and derive efficient optimization methods for it. More specifically, we show a strict mathematical deduction of APE from a maximum-likelihood framework and establish a connection to the congealing framework. This is only possible after an extension of the congealing framework with neighborhood information. Moreover, we address the increased computational complexity of simultaneous registration by deriving efficient gradient-based optimization strategies for APE: Gauss-Newton and the efficient second-order minimization (ESM). We present next to SSD the usage of intrinsically nonsquared similarity measures in this least squares optimization framework. The fundamental assumption of ESM, the approximation of the perfectly aligned moving image through the fixed image, limits its application to monomodal registration. We therefore incorporate recently proposed structural representations of images which allow us to perform multimodal registration with ESM. Finally, we evaluate the performance of the optimization strategies with respect to the similarity measures, leading to very good results for ESM. The extension to multimodal registration is in this context very interesting because it offers further possibilities for evaluations, due to publicly available datasets with ground-truth alignment.

Abstract 734: Water diffusion decreased in low collagen containing hypoxic regions of breast cancer xenograft.

Abstract Tumors display chaotic vasculature that leads to hypoxia. Hypoxia is associated with an aggressive phenotype and increased resistance to radiation and chemotherapy. Hypoxic tumor regions also contain very few collagen 1 fibers, which is a major component of the tumor extracellular matrix (ECM) and plays an important role in molecular movement through the ECM. Here we examined the effects of hypoxia on water diffusion in a breast cancer xenograft model engineered to fluoresce under hypoxia. We observed, for the first time, that in hypoxic regions water diffusion and diffusion anisotropy was lower than in normoxic regions that may be explained by the few collagen fibers in hypoxic regions. Severe combined immunodeficient female mice were inoculated in the mammary fat pad with 2x106 MDA-MB-231 cells stably expressing red fluorescent protein (RFP) under the control of a hypoxia response element. Once tumor volume was approximately 300-400 mm3 the tumor was excised, fixed and imaged using high-resolution diffusion tensor imaging (DTI). The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) maps were calculated. Following DTI, the tumor was sliced at 1 mm thick sections for optical imaging. Using a 1x lens to cover the entire tumor section, bright field and fluorescence optical images were acquired to detect the RFP-containing hypoxic regions. Multimodality registration was performed by feature extraction to co-register the optical images to the DTI images. Statistical dice similarity indices were calculated as a measure of the registration error. Hypoxic regions were identified from the optical fluorescence field images and the ADC and FA values were calculated in the corresponding hypoxic and normoxic regions. ADC and FA value distributions were identified in the DTI data and red fluorescent hypoxic regions were identified from the optical images. Co-registration accuracy confirmed by dice similarity index of 0.88 indicates a good overlap of optical and DTI images. Overall, hypoxic regions had lower water diffusion (ADC) and diffusion anisotropy (FA) values than normoxic regions. We have consistently observed that collagen fibers are significantly decreased in hypoxic regions, which could explain the lower ADC and FA values observed in hypoxic regions. In separate studies we have also observed high ADC in high collagen fiber containing regions in human breast cancer tissue. Low ADC and FA values observed in low collagen containing hypoxic regions indicate a functional role of these fibers in molecular transport. Decreased diffusion of molecules due to fewer collagen fibers in hypoxic regions may also contribute to poor drug delivery and tumor recurrence in hypoxic regions. Increased cellularity is thought to be one reason why lower ADC and FA values are observed in tumors. An even lower ADC within this backdrop may be due to hypoxic tumor regions. This work was supported by NIH P50CA103175 P30CA006973 Citation Format: Samata M. Kakkad, Jiangyang Zhang, Alireza Akhbardeh, Meiyappan Solaiyappan, Venu Raman, Dieter Leibfritz, Kristine Glunde, Zaver M. Bhujwalla. Water diffusion decreased in low collagen containing hypoxic regions of breast cancer xenograft. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 734. doi:10.1158/1538-7445.AM2013-734

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations

We present a novel approach for the registration of pre-operative magnetic resonance images to intra-operative ultrasound images for the context of image-guided neurosurgery. Our technique relies on the maximization of gradient orientation alignment in a reduced set of high confidence locations of interest and allows for fast, accurate, and robust registration. Performance is compared with multiple state-of-the-art techniques including conventional intensity-based multi-modal registration strategies, as well as other context-specific approaches. All methods were evaluated on fourteen clinical neurosurgical cases with brain tumors, including low-grade and high-grade gliomas, from the publicly available MNI BITE dataset. Registration accuracy of each method is evaluated as the mean distance between homologous landmarks identified by two or three experts. We provide an analysis of the landmarks used and expose some of the limitations in validation brought forward by expert disagreement and uncertainty in identifying corresponding points. The proposed approach yields a mean error of 2.57 mm across all cases (the smallest among all evaluated techniques). Additionally, it is the only evaluated technique that resolves all cases with a mean distance of less than 1 mm larger than the theoretical minimal mean distance when using a rigid transformation. Finally, our proposed method provides reduced processing times with an average registration time of 0.76 s in a GPU-based implementation, thereby facilitating its integration into the operating room.

Toward physiologically motivated registration of diagnostic CT and PET/CT of lung volumes

Current clinical practice for lung cancer diagnosis and staging requires the acquisition of a diagnostic computed tomography (CT) as well as positron emission tomography (PET)/CT volumes from a hybrid scanner, where the CT is used for attenuation correction (AC-CT). The PET and AC-CT images are implicitly aligned, however, image registration between the diagnostic CT and PET volumes is needed to relate the anatomical correspondences. This is an important but difficult task due to the absence of a direct or functional relationship between the intensities. Alternatively, here we propose the diagnostic CT can be aligned with the PET image through an indirect registration process that uses the AC-CT. The resultant deformation field can then be used to align the PET image to the diagnostic CT. The registration of the diagnostic CT to AC-CT registration still presents two major challenges: (a) it is a multimodal registration problem since the diagnostic CT is acquired after the injection of a contrast agent, and (b) the type and amplitude of the deformations require a registration process that includes physically motivated properties to achieve an accurate and physiologically plausible alignment. The authors propose a new framework based on fluid registration including three physiologically motivated properties: (i) sliding motion of the lungs against the pleura; (ii) preservation of rigid structures; (iii) preservation of topology. The sliding motion is modeled using direction dependent regularization that decouples the tangential and the normal components of the external force term. The rigid shape of the bones is preserved using a spatially varying filter for the deformations. Finally, the topology is maintained using the concept of log-unbiased deformations. To solve the multimodal problem, the authors use local cross correlation (LCC) as the similarity measure. The proposed framework is first evaluated on CT lung image pairs representing several phases of the respiratory cycle. The authors show that their proposed framework has a superior performance compared to the classic fluid registration, both in quantitative and qualitative terms. The authors then evaluate the framework using ten real patient scans, where the authors also demonstrate how their physiologically motivated registration framework can be successfully applied to the task of fusing diagnostic CT with the PET/CT image volumes. The proposed registration framework has better results for the fusion of diagnostic CT with PET images in comparison to the classic fluid registration framework.

Non-Rigid Multimodal Medical Image Registration Based on the Conditional Statistics of the Joint Intensity Distribution

In this paper, we present a new methodology for multimodal non-rigid medical image registration. The proposed approach is based on combining a rigid registration achieved by a global optimization method, and a multimodal optical flow technique based on the conditional statistics of the joint intensity distribution (CS-JID) of the images to register. The methodology is essentially composed of two steps: first, the global deformation is approximated by using a rigid registration based on particle filtering; second, the optical flow is applied iteratively between the target and sequentially registered source image, by optimizing a new energy function that penalizes the difference between the intensities in one image with respect to the mean of the conditional intensity distribution of the other image, weighted by the conditional variance. After these steps, the non-rigid registration is made up by adding the resulting vector fields, computed by the rigid registration, and the sequential optical flow. The proposed algorithm was tested with three pairs of computed tomography (CT) and magnetic resonance (MR) images, aligned in the acquisition process, and subsequently warped with a synthetic non-rigid deformation. Preliminary results show that the methodology presents a good alternative for non-rigid multimodal registration, obtaining an average error of less than one pixel in the estimation of the deformation vector field in the majority of the cases.

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients

Accurate registration of 18F−FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from 18F−FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information‐based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application.PACS numbers: 87.57.nj, 87.57.Q‐, 87.57.uk

A variational approach to image registration in dynamic contrast-enhanced MRI of the human kidney

Kidney function can be accessed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measurements which yield spatially resolved maps of physiological parameters like perfusion or filtration. The motion of the kidneys during the scan is a dominant limitation of the measurement quality, and image registration is necessary for accurate quantification. We analyzed the feasibility of applying an algorithm, originally developed for multimodal registration, to kidney perfusion time series. The algorithm uses a variational calculation scheme to align the images. In four out of five data sets, kidney motion could be reduced to below the spatial resolution of the images of 1.6mm while preserving the enhancement pattern of kidney perfusion. Fitting a pharmacokinetic model to the data showed an average reduction of the Akaike fit error of 10% for the registered data, suggesting more stable parameters. We conclude that this image registration algorithm is feasible for correcting kidney motion in renal DCE-MRI.

A Novel Hybrid Multimodal Registration for Lung Radiation Therapy Response Evaluation

New imaging methods for quantifying physiological characteristics of tumor sub-volumes require robust registration techniques for the comparison of images acquired at multiple time points. A significant obstacle to this is heterogeneous deformability among the structures to be assessed. Applying a rigid transform only can result in gross misalignment of the lung tissue, while a purely deformable algorithm induces unphysical changes in the target volume. We propose a hybrid rigid/deformable registration algorithm that enables a more physically realistic assessment of response in both the target and the normal structures.

Multi-Modal Image Registration Based on Gradient Orientations of Minimal Uncertainty

In this paper, we propose a new multi-scale technique for multi-modal image registration based on the alignment of selected gradient orientations of reduced uncertainty. We show how the registration robustness and accuracy can be improved by restricting the evaluation of gradient orientation alignment to locations where the uncertainty of fixed image gradient orientations is minimal, which we formally demonstrate correspond to locations of high gradient magnitude. We also embed a computationally efficient technique for estimating the gradient orientations of the transformed moving image (rather than resampling pixel intensities and recomputing image gradients). We have applied our method to different rigid multi-modal registration contexts. Our approach outperforms mutual information and other competing metrics in the context of rigid multi-modal brain registration, where we show sub-millimeter accuracy with cases obtained from the retrospective image registration evaluation project. Furthermore, our approach shows significant improvements over standard methods in the highly challenging clinical context of image guided neurosurgery, where we demonstrate misregistration of less than 2 mm with relation to expert selected landmarks for the registration of pre-operative brain magnetic resonance images to intra-operative ultrasound images.

Review of multi-modality medical image registration

Multimodality medical image registration has become increasingly popular in the field of image processing,and it has significant meaning for the clinical diagnosis and treatment.In this article,the process of image registration is described,and a full review and comparison of some typical medical image registration methods are given according to the features of the images including internal and external characteristics. Key words: Medical image registration; Extrinsic feature-based registration; Intrinsic feature-based registration; Intensity-based registration

A new set distance and its application to shape registration

We propose a new distance measure, called Complement weighted sum of minimal distances, between finite sets in $${\mathbb Z }^n$$ and evaluate its usefulness for shape registration and matching. In this set distance the contribution of each point of each set is weighted according to its distance to the complement of the set. In this way, outliers and noise contribute less to the new similarity measure. We evaluate the performance of the new set distance for registration of shapes in binary images and compare it to a number of often used set distances found in the literature. The most extensive evaluation uses a set of synthetic 2D images. We also show three examples of real problems: registering a set of 2D images extracted from synchrotron radiation micro-computed tomography (SR $$\upmu $$ CT) volumes depicting bone implants; the difficult multi-modal registration task of finding the exact location of a 2D slice of a bone implant, as imaged by a light microscope, within a 3D SR $$\upmu $$ CT volume of the same implant; and finally recognition of handwritten characters. The evaluation shows that our new set distance performs well for all tasks and outperforms the other observed distance measures in most cases. It is therefore useful in many image registration and shape comparison tasks.

Mono- and multimodal registration of optical breast images

Optical breast imaging offers the possibility of noninvasive, low cost, and high sensitivity imaging of breast cancers. Poor spatial resolution and a lack of anatomical landmarks in optical images of the breast make interpretation difficult and motivate registration and fusion of these data with subsequent optical images and other breast imaging modalities. Methods used for registration and fusion of optical breast images are reviewed. Imaging concerns relevant to the registration problem are first highlighted, followed by a focus on both monomodal and multimodal registration of optical breast imaging. Where relevant, methods pertaining to other imaging modalities or imaged anatomies are presented. The multimodal registration discussion concerns digital x-ray mammography, ultrasound, magnetic resonance imaging, and positron emission tomography.