Development of a Robotic Shear Wave Elastography System for Noninvasive Staging of Liver Disease in Murine Models.

Shear wave elastography (SWE) is an ultrasound-based stiffness quantification technology that is used for noninvasive liver fibrosis assessment. However, despite widescale clinical adoption, SWE is largely unused by preclinical researchers and drug developers for studies of liver disease progression in small animal models due to significant experimental, technical, and reproducibility challenges. Therefore, the aim of this work was to develop a tool designed specifically for assessing liver stiffness and echogenicity in small animals to better enable longitudinal preclinical studies. A high-frequency linear array transducer (12-24 MHz) was integrated into a robotic small animal ultrasound system (Vega; SonoVol, Inc., Durham, NC) to perform liver stiffness and echogenicity measurements in three dimensions. The instrument was validated with tissue-mimicking phantoms and a mouse model of nonalcoholic steatohepatitis. Female C57BL/6J mice (n = 40) were placed on choline-deficient, L-amino acid-defined, high-fat diet and imaged longitudinally for 15 weeks. A subset was sacrificed after each imaging timepoint (n = 5) for histological validation, and analyses of receiver operating characteristic (ROC) curves were performed. Results demonstrated that robotic measurements of echogenicity and stiffness were most strongly correlated with macrovesicular steatosis (R2  = 0.891) and fibrosis (R2  = 0.839), respectively. For diagnostic classification of fibrosis (Ishak score), areas under ROC (AUROCs) curves were 0.969 for ≥Ishak1, 0.984 for ≥Ishak2, 0.980 for ≥Ishak3, and 0.969 for ≥Ishak4. For classification of macrovesicular steatosis (S-score), AUROCs were 1.00 for ≥S2 and 0.997 for ≥S3. Average scanning and analysis time was <5 minutes/liver. Conclusion: Robotic SWE in small animals is feasible and sensitive to small changes in liver disease state, facilitating in vivo staging of rodent liver disease with minimal sonographic expertise.

Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI.

Subject motion during an MRI sequence can cause ghosting effects or diffuse image noise in the phase-encoding direction and hence is likely to bias findings in neuroimaging studies. Detecting motion artifacts often relies on experts visually inspecting MRIs, which is subjective and expensive. To improve this detection, we develop a framework to automatically quantify the severity of motion artifact within a brain MRI. We formulate this task as a regression problem and train the regressor from a data set of MRIs with various amounts of motion artifacts. To resolve the issue of missing fine-grained ground-truth labels (level of artifacts), we propose Adversarial Bayesian Optimization (ABO) to infer the distribution of motion parameters (i.e., rotation and translation) underlying the acquired MRI data and then inject synthetic motion artifacts sampled from that estimated distribution into motion-free MRIs. After training the regressor on the synthetic data, we applied the model to quantify the motion level in 990 MRIs collected by the National Consortium on Alcohol and Neurodevelopment in Adolescence. Results show that the motion level derived by our approach is more reliable than the traditional metric based on Entropy Focus Criterion and manually defined binary labels.

An open-source solution for shape modeling and analysis of objects of challenging topologies.

Shape analysis is an important and powerful tool in a wide variety of medical applications. Many shape analysis techniques require shape representations which are in correspondence. Unfortunately, popular techniques for generating shape representations do not handle objects with complex geometry or topology well, and those that do are not typically readily available for non-expert users. We describe a method for generating correspondences across a population of objects using a given template. We also describe its implementation and distribution via SlicerSALT, an open-source platform for making powerful shape analysis techniques more widely available and usable. Finally, we show results of this implementation on mouse femur data.

PET/CT-guided biopsy with respiratory motion correction.

PURPOSE: Given the ability of positron emission tomography (PET) imaging to localize malignancies in heterogeneous tumors and tumors that lack an X-ray computed tomography (CT) correlate, combined PET/CT-guided biopsy may improve the diagnostic yield of biopsies. However, PET and CT images are naturally susceptible to problems due to respiratory motion, leading to imprecise tumor localization and shape distortion. To facilitate PET/CT-guided needle biopsy, we developed and investigated the feasibility of a workflow that allows to bring PET image guidance into interventional CT suite while accounting for respiratory motion. METHODS: The performance of PET/CT respiratory motion correction using registered and summed phases method was evaluated through computer simulations using the mathematical 4D extended cardiac-torso phantom, with motion simulated from real respiratory traces. The performance of PET/CT-guided biopsy procedure was evaluated through operation on a physical anthropomorphic phantom. Vials containing radiolabeled 18F-fluorodeoxyglucose were placed within the physical phantom thorax as biopsy targets. We measured the average distance between target center and the simulated biopsy location among multiple trials to evaluate the biopsy localization accuracy. RESULTS: The computer simulation results showed that the RASP method generated PET images with a significantly reduced noise of 0.10 ± 0.01 standardized uptake value (SUV) as compared to an end-of-expiration image noise of 0.34 ± 0.04 SUV. The respiratory motion increased the apparent liver lesion size from 5.4 ± 1.1 to 35.3 ± 3.0 cc. The RASP algorithm reduced this to 15.7 ± 3.7 cc. The distances between the centroids for the static image lesion and two moving lesions in the liver and lung, when reconstructed with the RASP algorithm, were 0.83 ± 0.72 mm and 0.42 ± 0.72 mm. For the ungated imaging, these values increased to 3.48 ± 1.45 mm and 2.5 ± 0.12 mm, respectively. For the ungated imaging, this increased to 1.99 ± 1.72 mm. In addition, the lesion activity estimation (e.g., SUV) was accurate and constant for images reconstructed using the RASP algorithm, whereas large activity bias and variations (± 50%) were observed for lesions in the ungated images. The physical phantom studies demonstrated a biopsy needle localization error of 2.9 ± 0.9 mm from CT. Combined with the localization errors due to respiration for the PET images from simulations, the overall estimated lesion localization error would be 3.08 mm for PET-guided biopsies images using RASP and 3.64 mm when using ungated PET images. In other words, RASP reduced the localization error by approximately 0.6 mm. The combined error analysis showed that replacing the standard end-of-expiration images with the proposed RASP method in PET/CT-guided biopsy workflow yields comparable lesion localization accuracy and reduced image noise. CONCLUSION: The RASP method can produce PET images with reduced noise, attenuation artifacts and respiratory motion, resulting in more accurate lesion localization. Testing the PET/CT-guided biopsy workflow using computer simulation and physical phantoms with respiratory motion, we demonstrated that guided biopsy procedure with the RASP method can benefit from improved PET image quality due to noise reduction, without compromising the accuracy of lesion localization.

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP.

ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.

Calibration Software for Quantitative PET/CT Imaging Using Pocket Phantoms.

Multicenter clinical trials that use positron emission tomography (PET) imaging frequently rely on stable bias in imaging biomarkers to assess drug effectiveness. Many well-documented factors cause variability in PET intensity values. Two of the largest scanner-dependent errors are scanner calibration and reconstructed image resolution variations. For clinical trials, an increase in measurement error significantly increases the number of patient scans needed. We aim to provide a robust quality assurance system using portable PET/computed tomography "pocket" phantoms and automated image analysis algorithms with the goal of reducing PET measurement variability. A set of the "pocket" phantoms was scanned with patients, affixed to the underside of a patient bed. Our software analyzed the obtained images and estimated the image parameters. The analysis consisted of 2 steps, automated phantom detection and estimation of PET image resolution and global bias. Performance of the algorithm was tested under variations in image bias, resolution, noise, and errors in the expected sphere size. A web-based application was implemented to deploy the image analysis pipeline in a cloud-based infrastructure to support multicenter data acquisition, under Software-as-a-Service (SaaS) model. The automated detection algorithm localized the phantom reliably. Simulation results showed stable behavior when image properties and input parameters were varied. The PET "pocket" phantom has the potential to reduce and/or check for standardized uptake value measurement errors.

A new preclinical ultrasound platform for widefield 3D imaging of rodents.

Noninvasive in vivo imaging technologies enable researchers and clinicians to detect the presence of disease and longitudinally study its progression. By revealing anatomical, functional, or molecular changes, imaging tools can provide a near real-time assessment of important biological events. At the preclinical research level, imaging plays an important role by allowing disease mechanisms and potential therapies to be evaluated noninvasively. Because functional and molecular changes often precede gross anatomical changes, there has been a significant amount of research exploring the ability of different imaging modalities to track these aspects of various diseases. Herein, we present a novel robotic preclinical contrast-enhanced ultrasound system and demonstrate its use in evaluating tumors in a rodent model. By leveraging recent advances in ultrasound, this system favorably compares with other modalities, as it can perform anatomical, functional, and molecular imaging and is cost-effective, portable, and high throughput, without using ionizing radiation. Furthermore, this system circumvents many of the limitations of conventional preclinical ultrasound systems, including a limited field-of-view, low throughput, and large user variability.

Segmentation of pituitary adenoma: a graph-based method vs. a balloon inflation method.

Among all abnormal growths inside the skull, the percentage of tumors in sellar region is approximately 10-15%, and the pituitary adenoma is the most common sellar lesion. A time-consuming process that can be shortened by using adequate algorithms is the manual segmentation of pituitary adenomas. In this contribution, two methods for pituitary adenoma segmentation in the human brain are presented and compared using magnetic resonance imaging (MRI) patient data from the clinical routine: Method A is a graph-based method that sets up a directed and weighted graph and performs a min-cut for optimal segmentation results: Method B is a balloon inflation method that uses balloon inflation forces to detect the pituitary adenoma boundaries. The ground truth of the pituitary adenoma boundaries - for the evaluation of the methods - are manually extracted by neurosurgeons. Comparison is done using the Dice Similarity Coefficient (DSC), a measure for spatial overlap of different segmentation results. The average DSC for all data sets is 77.5±4.5% for the graph-based method and 75.9±7.2% for the balloon inflation method showing no significant difference. The overall segmentation time of the implemented approaches was less than 4s - compared with a manual segmentation that took, on the average, 3.9±0.5min.

Square-cut: a segmentation algorithm on the basis of a rectangle shape.

We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed non-uniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts (neurological surgeons) with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 90.97±2.2%.