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Purpose: The detection of clinically significant prostate cancer (PCa) is shown to greatly benefit from MRI-ultrasound fusion biopsy, which involves overlaying pre-biopsy MRI volumes (or targets) with real-time ultrasound images. In previous literature, machine learning models trained on either MRI or ultrasound data have been proposed to improve biopsy guidance and PCa detection. However, quantitative fusion of information from MRI and ultrasound has not been explored in depth in a large study. This paper investigates information fusion approaches between MRI and ultrasound to improve targeting of PCa foci in biopsies., Methods: We build models of fully convolutional networks (FCN) using data from a newly proposed ultrasound modality, temporal enhanced ultrasound (TeUS), and apparent diffusion coefficient (ADC) from 107 patients with 145 biopsy cores. The architecture of our models is based on U-Net and U-Net with attention gates. Models are built using joint training through intermediate and late fusion of the data. We also build models with data from each modality, separately, to use as baseline. The performance is evaluated based on the area under the curve (AUC) for predicting clinically significant PCa., Results: Using our proposed deep learning framework and intermediate fusion, integration of TeUS and ADC outperforms the individual modalities for cancer detection. We achieve an AUC of 0.76 for detection of all PCa foci, and 0.89 for PCa with larger foci. Results indicate a shared representation between multiple modalities outperforms the average unimodal predictions., Conclusion: We demonstrate the significant potential of multimodality integration of information from MRI and TeUS to improve PCa detection, which is essential for accurate targeting of cancer foci during biopsy. By using FCNs as the architecture of choice, we are able to predict the presence of clinically significant PCa in entire imaging planes immediately, without the need for region-based analysis. This reduces the overall computational time and enables future intra-operative deployment of this technology.

Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperineal MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-D fully convolutional neural network model was developed, trained, and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80-mm average in needle tip detection and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared with the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.

Purpose: In the current standard of care, real-time transrectal ultrasound (TRUS) is commonly used for prostate brachytherapy guidance. As TRUS provides limited soft tissue contrast, segmenting the prostate gland in TRUS images is often challenging and subject to inter-observer and intra-observer variability, especially at the base and apex where the gland boundary is hard to define. Magnetic resonance imaging (MRI) has higher soft tissue contrast allowing the prostate to be contoured easily. In this paper, we aim to show that prostate segmentation in TRUS images informed by MRI priors can improve on prostate segmentation that relies only on TRUS images., Methods: First, we compare the TRUS-based prostate segmentation used in the treatment of 598 patients with a high-quality MRI prostate atlas and observe inconsistencies at the apex and base. Second, motivated by this finding, we propose an alternative TRUS segmentation technique that is fully automatic and uses MRI priors. The algorithm uses a convolutional neural network to segment the prostate in TRUS images at mid-gland, where the gland boundary can be clearly seen. It then reconstructs the gland boundary at the apex and base with the aid of a statistical shape model built from an MRI atlas of 78 patients., Results: Compared to the clinical TRUS segmentation, our method achieves similar mid-gland segmentation results in the 598-patient database. For the seven patients who had both TRUS and MRI, our method achieved more accurate segmentation of the base and apex with the MRI segmentation used as ground truth., Conclusion: Our results suggest that utilizing MRI priors in TRUS prostate segmentation could potentially improve the performance at base and apex.

PURPOSE  : Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. METHODS  : In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core. In this hypothesis-generating study, we utilize deep learning based feature visualization as a means to obtain insight into the physical phenomenon governing the interaction of temporal ultrasound with tissue. RESULTS  : Based on the evidence derived from our feature visualization, and the structure of tissue from digital pathology, we build a simulation framework for studying the physical phenomenon underlying TeUS-based tissue characterization. CONCLUSION  : Results from simulation and feature visualization corroborated with the hypothesis that micro-vibrations of tissue microstructure, captured by low-frequency spectral features of TeUS, can be used for detection of prostate cancer.

Purpose: We present a method for prostate cancer (PCa) detection using temporal enhanced ultrasound (TeUS) data obtained either from radiofrequency (RF) ultrasound signals or B-mode images., Methods: For the first time, we demonstrate that by applying domain adaptation and transfer learning methods, a tissue classification model trained on TeUS RF data (source domain) can be deployed for classification using TeUS B-mode data alone (target domain), where both data are obtained on the same ultrasound scanner. This is a critical step for clinical translation of tissue classification techniques that primarily rely on accessing RF data, since this imaging modality is not readily available on all commercial scanners in clinics. Proof of concept is provided for in vivo characterization of PCa using TeUS B-mode data, where different nonlinear processing filters in the pipeline of the RF to B-mode conversion result in a distribution shift between the two domains., Results: Our in vivo study includes data obtained in MRI-guided targeted procedure for prostate biopsy. We achieve comparable area under the curve using TeUS RF and B-mode data for medium to large cancer tumor sizes in biopsy cores (>4 mm)., Conclusion: Our result suggests that the proposed adaptation technique is successful in reducing the divergence between TeUS RF and B-mode data.

Purpose: Epidural and spinal needle insertions, as well as facet joint denervation and injections are widely performed procedures on the lumbar spine for delivering anesthesia and analgesia. Ultrasound (US)-based approaches have gained popularity for accurate needle placement, as they use a non-ionizing, inexpensive and accessible modality for guiding these procedures. However, due to the inherent difficulties in interpreting spinal US, they yet to become the clinical standard-of-care., Methods: A novel statistical shape [Formula: see text] pose [Formula: see text] scale (s [Formula: see text] p [Formula: see text] s) model of the lumbar spine is jointly registered to preoperative magnetic resonance (MR) and US images. An instance of the model is created for each modality. The shape and scale model parameters are jointly computed, while the pose parameters are estimated separately for each modality., Results: The proposed method is successfully applied to nine pairs of preoperative clinical MR volumes and their corresponding US images. The results are assessed using the target registration error (TRE) metric in both MR and US domains. The s [Formula: see text] p [Formula: see text] s model in the proposed joint registration framework results in a mean TRE of 2.62 and 4.20 mm for MR and US images, respectively, on different landmarks., Conclusion: The joint framework benefits from the complementary features in both modalities, leading to significantly smaller TREs compared to a model-to-US registration approach. The s [Formula: see text] p [Formula: see text] s model also outperforms our previous shape [Formula: see text] pose model of the lumbar spine, as separating scale from pose allows to better capture pose and guarantees equally-sized vertebrae in both modalities. Furthermore, the simultaneous visualization of the patient-specific models on the MR and US domains makes it possible for clinicians to better evaluate the local registration accuracy.

Purpose: This paper presents the results of a large study involving fusion prostate biopsies to demonstrate that temporal ultrasound can be used to accurately classify tissue labels identified in multi-parametric magnetic resonance imaging (mp-MRI) as suspicious for cancer., Methods: We use deep learning to analyze temporal ultrasound data obtained from 255 cancer foci identified in mp-MRI. Each target is sampled in axial and sagittal planes. A deep belief network is trained to automatically learn the high-level latent features of temporal ultrasound data. A support vector machine classifier is then applied to differentiate cancerous versus benign tissue, verified by histopathology. Data from 32 targets are used for the training, while the remaining 223 targets are used for testing., Results: Our results indicate that the distance between the biopsy target and the prostate boundary, and the agreement between axial and sagittal histopathology of each target impact the classification accuracy. In 84 test cores that are 5 mm or farther to the prostate boundary, and have consistent pathology outcomes in axial and sagittal biopsy planes, we achieve an area under the curve of 0.80. In contrast, all of these targets were labeled as moderately suspicious in mp-MR., Conclusion: Using temporal ultrasound data in a fusion prostate biopsy study, we achieved a high classification accuracy specifically for moderately scored mp-MRI targets. These targets are clinically common and contribute to the high false-positive rates associated with mp-MRI for prostate cancer detection. Temporal ultrasound data combined with mp-MRI have the potential to reduce the number of unnecessary biopsies in fusion biopsy settings.

A common challenge when performing surface-based registration of images is ensuring that the surfaces accurately represent consistent anatomical boundaries. Image segmentation may be difficult in some regions due to either poor contrast, low slice resolution, or tissue ambiguities. To address this, we present a novel non-rigid surface registration method designed to register two partial surfaces, capable of ignoring regions where the anatomical boundary is unclear. Our probabilistic approach incorporates prior geometric information in the form of a statistical shape model (SSM), and physical knowledge in the form of a finite element model (FEM). We validate results in the context of prostate interventions by registering pre-operative magnetic resonance imaging (MRI) to 3D transrectal ultrasound (TRUS). We show that both the geometric and physical priors significantly decrease net target registration error (TRE), leading to TREs of 2.35 ± 0.81 mm and 2.81 ± 0.66 mm when applied to full and partial surfaces, respectively. We investigate robustness in response to errors in segmentation, varying levels of missing data, and adjusting the tunable parameters. Results demonstrate that the proposed surface registration method is an efficient, robust, and effective solution for fusing data from multiple modalities.

In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.

Purpose: In recent years, fusion of multi-parametric MRI (mp-MRI) with transrectal ultrasound (TRUS)-guided biopsy has enabled targeted prostate biopsy with improved cancer yield. Target identification is solely based on information from mp-MRI, which is subsequently transferred to the subject coordinates through an image registration approach. mp-MRI has shown to be highly sensitive to detect higher-grade prostate cancer, but suffers from a high rate of false positives for lower-grade cancer, leading to unnecessary biopsies. This paper utilizes a machine-learning framework to further improve the sensitivity of targeted biopsy through analyzing temporal ultrasound data backscattered from the prostate tissue., Methods: Temporal ultrasound data were acquired during targeted fusion prostate biopsy from suspicious cancer foci identified in mp-MRI. Several spectral features, representing the signature of backscattered signal from the tissue, were extracted from the temporal ultrasound data. A supervised support vector machine classification model was trained to relate the features to the result of histopathology analysis of biopsy cores obtained from cancer foci. The model was used to predict the label of biopsy cores for mp-MRI-identified targets in an independent group of subjects., Results: Training of the classier was performed on data obtained from 35 biopsy cores. A fivefold cross-validation strategy was utilized to examine the consistency of the selected features from temporal ultrasound data, where we achieved the classification accuracy and area under receiver operating characteristic curve of 94 % and 0.98, respectively. Subsequently, an independent group of 25 biopsy cores was used for validation of the model, in which mp-MRI had identified suspicious cancer foci. Using the trained model, we predicted the tissue pathology using temporal ultrasound data: 16 out of 17 benign cores, as well as all three higher-grade cancer cores, were correctly identified., Conclusion: The results show that temporal analysis of ultrasound data is potentially an effective approach to complement mp-MRI-TRUS-guided prostate cancer biopsy, specially to reduce the number of unnecessary biopsies and to reliably identify higher-grade cancers.

Classification of individuals based on patterns of brain activity observed in functional MRI contrasts may be helpful for diagnosis of neurological disorders. Prior work for classification based on these patterns have primarily focused on using a single contrast, which does not take advantage of complementary information that may be available in multiple contrasts. Where multiple contrasts are used, the objective has been only to identify the joint, distinct brain activity patterns that differ between groups of subjects; not to use the information to classify individuals. Here, we use joint Independent Component Analysis (jICA) within a Support Vector Machine (SVM) classification method, and take advantage of the relative contribution of activation patterns generated from multiple fMRI contrasts to improve classification accuracy. Young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise alone and to speech degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Functional contrasts based on these conditions (and a silent baseline condition) were used within jICA to generate spatially independent joint activation sources and their corresponding modulation profiles. Modulation profiles were used within a non-linear SVM framework to classify individuals as young or older. Results demonstrate that a combination of activation maps across the multiple contrasts yielded an area under ROC curve of 0.86, superior to classification resulting from individual contrasts. Moreover, class separability, measured by a divergence criterion, was substantially higher when using the combination of activation maps.

Purpose: We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI-TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems., Methods: The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation., Results: The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license., Conclusions: We release open-source tools that may be used for registration during MRI-TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools.

Conventional group analysis of functional MRI (fMRI) data usually involves spatial alignment of anatomy across participants by registering every brain image to an anatomical reference image. Due to the high degree of inter-subject anatomical variability, a low-resolution average anatomical model is typically used as the target template, and/or smoothing kernels are applied to the fMRI data to increase the overlap among subjects' image data. However, such smoothing can make it difficult to resolve small regions such as subregions of auditory cortex when anatomical morphology varies among subjects. Here, we use data from an auditory fMRI study to show that using a high-dimensional registration technique (HAMMER) results in an enhanced functional signal-to-noise ratio (fSNR) for functional data analysis within auditory regions, with more localized activation patterns. The technique is validated against DARTEL, a high-dimensional diffeomorphic registration, as well as against commonly used low-dimensional normalization techniques such as the techniques provided with SPM2 (cosine basis functions) and SPM5 (unified segmentation) software packages. We also systematically examine how spatial resolution of the template image and spatial smoothing of the functional data affect the results. Only the high-dimensional technique (HAMMER) appears to be able to capitalize on the excellent anatomical resolution of a single-subject reference template, and, as expected, smoothing increased fSNR, but at the cost of spatial resolution. In general, results demonstrate significant improvement in fSNR using HAMMER compared to analysis after normalization using DARTEL, or conventional normalization such as cosine basis function and unified segmentation in SPM, with more precisely localized activation foci, at least for activation in the region of auditory cortex.

We present a novel technique for creating template-free probabilistic maps of the cytoarchitectonic areas using a groupwise registration. We use the technique to transform 10 human post-mortem structural MR data sets, together with their corresponding cytoarchitectonic information, to a common space. We have targeted the cytoarchitectonically defined subregions of the primary auditory cortex. Thanks to the template-free groupwise registration, the created maps are not macroanatomically biased towards a specific geometry/topology. The advantage of the group-wise versus pairwise registration in avoiding such anatomical bias is better revealed in studies with small number of subjects and a high degree of variability among the individuals such as the post-mortem data. A leave-one-out cross-validation method was used to compare the sensitivity, specificity and positive predictive value of the proposed and published maps. We observe a significant improvement in localization of cytoarchitectonically defined subregions in primary auditory cortex using the proposed maps. The proposed maps can be tailored to any subject space by registering the subject image to the average of the groupwise-registered post-mortem images.

A novel method is presented for creating a probability map from histologically defined cytoarchitectonic data, customised for the anatomy of individual fMRI volunteers. Postmortem structural and cytoarchitectonic information from a published dataset is combined with high resolution structural MR images using deformable registration of a region of interest. In this paper, we have targeted the three sub-areas of the primary auditory cortex (located on Heschl's gyrus); however, the method could be applied to any other cytoarchitectonic region. The resulting probability maps show a significantly higher overlap than previously generated maps using the same cytoarchitectonic data, and more accurately span the macroanatomical structure of the auditory cortex. This improvement indicates a high potential for spatially accurate fMRI analysis, allowing more reliable correlation between anatomical structure and function. We validate the approach using fMRI data from nine individuals, taken from a published dataset. We compare activation for stimuli evoking a pitch percept to activation for acoustically matched noise, and demonstrate that the primary auditory cortex (Te1.0) and the lateral region Te1.2 are sensitive to pitch, whereas Te1.1 is not.

Current deep supervised learning methods typically require large amounts of labeled data for training. Since there is a significant cost associated with clinical data acquisition and labeling, medical datasets used for training these models are relatively small in size. In this paper, we aim to alleviate this limitation by proposing a variational generative model along with an effective data augmentation approach that utilizes the generative model to synthesize data. In our approach, the model learns the probability distribution of image data conditioned on a latent variable and the corresponding labels. The trained model can then be used to synthesize new images for data augmentation. We demonstrate the effectiveness of the approach on two independent clinical datasets consisting of ultrasound images of the spine and magnetic resonance images of the brain. For the spine dataset, a baseline and a residual model achieve an accuracy of 85% and 92%, respectively, using our method compared to 78% and 83% using a conventional training approach for image classification task. For the brain dataset, a baseline and a U-net network achieve an accuracy of 84% and 88%, respectively, in Dice coefficient in tumor segmentation compared to 80% and 83% for the convention training approach. [ABSTRACT FROM AUTHOR]

Accurate detection of end-systolic (ES) and end-diastolic (ED) frames in an echocardiographic cine series can be difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is challenging due to variations in cardiac anatomy and heart rate often associated with pathological conditions. We formulate this problem as a regression problem and propose several deep learning-based architectures that minimize a novel global extrema structured loss function to localize the ED and ES frames. The proposed architectures integrate convolution neural networks (CNNs)-based image feature extraction model and recurrent neural networks (RNNs) to model temporal dependencies between each frame in a sequence. We explore two CNN architectures: DenseNet and ResNet, and four RNN architectures: long short-term memory, bi-directional LSTM, gated recurrent unit (GRU), and Bi-GRU, and compare the performance of these models. The optimal deep learning model consists of a DenseNet and GRU trained with the proposed loss function. On average, we achieved 0.20 and 1.43 frame mismatch for the ED and ES frames, respectively, which are within reported inter-observer variability for the manual detection of these frames. [ABSTRACT FROM AUTHOR]

Abstract: We present a groupwise US to CT registration algorithm for guiding percutaneous spinal interventions. In addition, we introduce a comprehensive validation scheme that accounts for changes in the curvature of the spine between preoperative and intraoperative imaging. In our registration methodology, each vertebra in CT is treated as a sub-volume and transformed individually. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. The sub-volumes are then reconstructed into a single volume. During each iteration of registration, an US image is simulated from the reconstructed CT volume and an intensity-based similarity metric is calculated with the real US image. Validation studies are performed on CT and US images from a sheep cadaver, five patient-based phantoms designed to preserve realistic curvatures of the spine and a sixth patient-based phantom where the curvature of the spine is changed between preoperative and intraoperative imaging. For datasets where the spine curve between two imaging modalities was artificially perturbed, the proposed methodology was able to register initial misalignments of up to 20mm with a success rate of 95%. For the phantom with a physical change in the curvature of the spine introduced between the US and CT datasets, the registration success rate was 98.5%. Finally, the registration success rate for the sheep cadaver with soft-tissue information was 87%. The results demonstrate that our algorithm allows for robust registration of US and CT datasets, regardless of a change in the patients pose between preoperative and intraoperative image acquisitions. [Copyright &y& Elsevier]

We study the effect of point dependent (heteroscedastic) and identically distributed anisotropic fiducial localization noise on fiducial target registration error (TRE). We derive an analytic expression, based on the concept of mechanism spatial stiffness, for predicting TRE. The accuracy of the predicted TRE is compared to simulated values where the optimal registration transformation is computed using the heteroscedastic errors in variables algorithm. The predicted values are shown to be contained by the 95% confidence intervals of the root mean square TRE obtained from the simulations. [ABSTRACT FROM AUTHOR]

Abstract: A new technique for elastic registration of ultrasound images is proposed and evaluated. The algorithm is fast and robust, and has potential for intra-operative applications. It is a modified version of HAMMER technique, which was developed to elastically register magnetic resonance images of brain. A general concept of attribute vectors is employed to find correspondence across images, and new elements are suggested for the vectors to be able to distinguish between different features in ultrasound images. Registration results of simulated and actual deformation of ultrasound images of liver are presented. [Copyright &y& Elsevier]

Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi-automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real-time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp-MRI and ultrasound data. In addition to using convolutional networks for extracting spatial features, the proposed approach employs recurrent networks to exploit the temporal information among a series of ultrasound images. One of the key contributions in the architecture is to use residual convolution in the recurrent networks to improve optimization. We also exploit recurrent connections within and across different layers of the deep networks to maximize the utilization of the temporal information. Furthermore, we perform dense and sparse sampling of the input ultrasound sequence to make the network robust to ultrasound artifacts. Our architecture is trained on 2,238 labeled transrectal ultrasound images, with an additional 637 and 1,017 unseen images used for validation and testing, respectively. We obtain a mean Dice similarity coefficient of 93%, a mean surface distance error of 1.10 mm and a mean Hausdorff distance error of 3.0 mm. A comparison of the reported results with those of a state-of-the-art technique indicates statistically significant improvement achieved by the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: A method for registration of speckle-tracked freehand 3D ultrasound (US) to preoperative CT volumes of the spine is proposed. We register the US volume to the CT volume by creating individual US “sub-volumes”, each consisting of a small section of the entire US volume. The registration proceeds incrementally from the beginning of the US volume to the end, registering every sub-volume, where each sub-volume contains overlapping images with the previous sub-volume. Each registration is performed by generating simulated US images from the CT volume. As a by-product of our registration, the significant drift error common in speckle-tracked US volumes is corrected for. Results are validated through a phantom study of plastic spine phantoms created from clinical patient CT data as well as an animal study using a lamb cadaver. Results demonstrate that we were able to successfully register a speckle-tracked US volume to CT volume in four out of five phantoms with a success rate of greater than 98%. The final error of the registered US volumes decreases by over 50 percent from the speckle tracking error to consistently below 3mm. Studies on the lamb cadaver showed a mean registration error consistently below 2mm. [Copyright &y& Elsevier]