Automatic bone detection and soft tissue aware ultrasound-CT registration for computer-aided orthopedic surgery.

PURPOSE: The transfer of preoperative CT data into the tracking system coordinates within an operating room is of high interest for computer-aided orthopedic surgery. In this work, we introduce a solution for intra-operative ultrasound-CT registration of bones. METHODS: We have developed methods for fully automatic real-time bone detection in ultrasound images and global automatic registration to CT. The bone detection algorithm uses a novel bone-specific feature descriptor and was thoroughly evaluated on both in-vivo and ex-vivo data. A global optimization strategy aligns the bone surface, followed by a soft tissue aware intensity-based registration to provide higher local registration accuracy. RESULTS: We evaluated the system on femur, tibia and fibula anatomy in a cadaver study with human legs, where magnetically tracked bone markers were implanted to yield ground truth information. An overall median system error of 3.7 mm was achieved on 11 datasets. CONCLUSION: Global and fully automatic registration of bones aquired with ultrasound to CT is feasible, with bone detection and tracking operating in real time for immediate feedback to the surgeon.

Multi-scale tubular structure detection in ultrasound imaging.

We propose a novel, physics-based method for detecting multi-scale tubular features in ultrasound images. The detector is based on a Hessian-matrix eigenvalue method, but unlike previous work, our detector is guided by an optimal model of vessel-like structures with respect to the ultrasound-image formation process. Our method provides a voxel-wise probability map, along with estimates of the radii and orientations of the detected tubes. These results can then be used for further processing, including segmentation and enhanced volume visualization. Most Hessian-based algorithms, including the well-known Frangi filter, were developed for CTA or MRA; they implicitly assume symmetry about the vessel centerline. This is not consistent with ultrasound data. We overcome this limitation by introducing a novel filter that allows multi-scale estimation both with respect to the vessel's centerline and with respect to the vessel's border. We use manually-segmented ultrasound imagery from 35 patients to show that our method is superior to standard Hessian-based methods. We evaluate the performance of the proposed methods based on the sensitivity and specificity like measures, and finally demonstrate further applicability of our method to vascular ultrasound images of the carotid artery, as well as ultrasound data for abdominal aortic aneurysms.

Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound.

Intravascular Ultrasound (IVUS) is a predominant imaging modality in interventional cardiology. It provides real-time cross-sectional images of arteries and assists clinicians to infer about atherosclerotic plaques composition. These plaques are heterogeneous in nature and constitute fibrous tissue, lipid deposits and calcifications. Each of these tissues backscatter ultrasonic pulses and are associated with a characteristic intensity in B-mode IVUS image. However, clinicians are challenged when colocated heterogeneous tissue backscatter mixed signals appearing as non-unique intensity patterns in B-mode IVUS image. Tissue characterization algorithms have been developed to assist clinicians to identify such heterogeneous tissues and assess plaque vulnerability. In this paper, we propose a novel technique coined as Stochastic Driven Histology (SDH) that is able to provide information about co-located heterogeneous tissues. It employs learning of tissue specific ultrasonic backscattering statistical physics and signal confidence primal from labeled data for predicting heterogeneous tissue composition in plaques. We employ a random forest for the purpose of learning such a primal using sparsely labeled and noisy samples. In clinical deployment, the posterior prediction of different lesions constituting the plaque is estimated. Folded cross-validation experiments have been performed with 53 plaques indicating high concurrence with traditional tissue histology. On the wider horizon, this framework enables learning of tissue-energy interaction statistical physics and can be leveraged for promising clinical applications requiring tissue characterization beyond the application demonstrated in this paper.

Hunting for necrosis in the shadows of intravascular ultrasound.

Coronary artery disease leads to failure of coronary circulation secondary to accumulation of atherosclerotic plaques. In adjunction to primary imaging of such vascular plaques using coronary angiography or alternatively magnetic resonance imaging, intravascular ultrasound (IVUS) is used predominantly for diagnosis and reporting of their vulnerability. In addition to plaque burden estimation, necrosis detection is an important aspect in reporting of IVUS. Since necrotic regions generally appear as hypoechic, with speckle appearance in these regions resembling true shadows or severe signal dropout regions, it contributes to variability in diagnosis. This dilemma in clinical assessment of necrosis imaged with IVUS is addressed in this work. In our approach, fidelity of the backscattered ultrasonic signal received by the imaging transducer is initially estimated. This is followed by identification of true necrosis using statistical physics of ultrasonic backscattering. A random forest machine learning framework is used for the purpose of learning the parameter space defining ultrasonic backscattering distributions related to necrotic regions and discriminating it from non-necrotic shadows. Evidence of hunting down true necrosis in shadows of intravascular ultrasound is presented with ex vivo experiments along with cross-validation using ground truth obtained from histology. Nevertheless, in some rare cases necrosis is marginally over-estimated, primarily on account of non-reliable statistics estimation. This limitation is due to sparse spatial sampling between neighboring scan-lines at location far from the transducer. We suggest considering the geometrical location of detected necrosis together with estimated signal confidence during clinical decision making in view of such limitation.

Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI.

In this paper, a new segmentation framework with prior knowledge is proposed and applied to the left ventricles in cardiac Cine MRI sequences. We introduce a new formulation of the random walks method, coined as guided random walks, in which prior knowledge is integrated seamlessly. In comparison with existing approaches that incorporate statistical shape models, our method does not extract any principal model of the shape or appearance of the left ventricle. Instead, segmentation is accompanied by retrieving the closest subject in the database that guides the segmentation the best. Using this techniques, rare cases can also effectively exploit prior knowledge from few samples in training set. These cases are usually disregarded in statistical shape models as they are outnumbered by frequent cases (effect of class population). In the worst-case scenario, if there is no matching case in the database to guide the segmentation, performance of the proposed method reaches to the conventional random walks, which is shown to be accurate if sufficient number of seeds is provided. There is a fast solution to the proposed guided random walks by using sparse linear matrix operations and the whole framework can be seamlessly implemented in a parallel architecture. The method has been validated on a comprehensive clinical dataset of 3D+t short axis MR images of 104 subjects from 5 categories (normal, dilated left ventricle, ventricular hypertrophy, recent myocardial infarction, and heart failure). The average segmentation errors were found to be 1.54 mm for the endocardium and 1.48 mm for the epicardium. The method was validated by measuring different algorithmic and physiologic indices and quantified with manual segmentation ground truths, provided by a cardiologist.

Ultrasound confidence maps using random walks.

Advances in ultrasound system development have led to a substantial improvement of image quality and to an increased use of ultrasound in clinical practice. Nevertheless, ultrasound attenuation and shadowing artifacts cannot be entirely avoided and continue to challenge medical image computing algorithms. We introduce a method for estimating a per-pixel confidence in the information depicted by ultrasound images, referred to as an ultrasound confidence map, which emphasizes uncertainty in attenuated and/or shadow regions. Our main novelty is the modeling of the confidence estimation problem within a random walks framework by taking into account ultrasound specific constraints. The solution to the random walks equilibrium problem is global and takes the entire image content into account. As a result, our method is applicable to a variety of ultrasound image acquisition setups. We demonstrate the applicability of our confidence maps for ultrasound shadow detection, 3D freehand ultrasound reconstruction, and multi-modal image registration.

Ultrasound bone detection using patient-specific CT prior.

Registration of pre-operative CT datasets to intra-operative 3D freehand ultrasound has been of high interest for computer assisted orthopedic surgery. Feature-based registration relies on an accurate detection of the bone surface in the B-mode ultrasound images. In this work we present a fully automatic bone detection approach for US. The pre-operative CT is utilized to create a patient-specific bone model for our joint detection-registration framework. The model provides a geometric constraint for accurate and robust detection. Simultaneously to the detection, our method yields a close estimate of the rigid transformation from US to CT, which can be used as an initialization for further refinement through sophisticated intensity-/feature-based registration methods. We evaluated our approach on datasets of the human femur acquired in a cadaver study and demonstrate a mean bone detection error of below 0.4 mm.

Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology.

Intravascular ultrasound (IVUS) is the predominant imaging modality in the field of interventional cardiology that provides real-time cross-sectional images of coronary arteries and the extent of atherosclerosis. Due to heterogeneity of lesions and stringent spatial/spectral behavior of tissues, atherosclerotic plaque characterization has always been a challenge and still is an open problem. In this paper, we present a systematic framework from in vitro data collection, histology preparation, IVUS-histology registration along with matching procedure, and finally a robust texture-derived unsupervised atherosclerotic plaque labeling. We have performed our algorithm on in vitro and in vivo images acquired with single-element 40 MHz and 64-elements phased array 20 MHz transducers, respectively. In former case, we have quantified results by local contrasting of constructed tissue colormaps with corresponding histology images employing an independent expert and in the latter case, virtual histology images have been utilized for comparison. We tackle one of the main challenges in the field that is the reliability of tissues behind arc of calcified plaques and validate the results through a novel random walks framework by incorporating underlying physics of ultrasound imaging. We conclude that proposed framework is a formidable approach for retrieving imperative information regarding tissues and building a reliable training dataset for supervised classification and its extension for in vivo applications.

Midbrain segmentation in transcranial 3D ultrasound for Parkinson diagnosis.

Ultrasound examination of the human brain through the temporal bone window, also called transcranial ultrasound (TC-US), is a completely non-invasive and cost-efficient technique, which has established itself for differential diagnosis of Parkinson's Disease (PD) in the past decade. The method requires spatial analysis of ultrasound hyperechogenicities produced by pathological changes within the Substantia Nigra (SN), which belongs to the basal ganglia within the midbrain. Related work on computer aided PD diagnosis shows the urgent need for an accurate and robust segmentation of the midbrain from 3D TC-US, which is an extremely difficult task due to poor image quality of TC-US. In contrast to 2D segmentations within earlier approaches, we develop the first method for semi-automatic midbrain segmentation from 3D TC-US and demonstrate its potential benefit on a database of 11 diagnosed Parkinson patients and 11 healthy controls.

Fast ultrasound image simulation using the Westervelt equation.

The simulation of ultrasound wave propagation is of high interest in fields as ultrasound system development and therapeutic ultrasound. From a computational point of view the requirements for realistic simulations are immense with processing time reaching even an entire day. In this work we present a framework for fast ultrasound image simulation covering the imaging pipeline from the initial pulse transmission to the final image formation. The propagation of ultrasound waves is modeled with the Westervelt equation, which is solved explicitly with a Finite Difference scheme. Solving this scheme in parallel on the Graphics Processing Unit allows us to simulate realistic ultrasound images in a short time.