Evaluation of commercially available point-of-care ultrasound for automated optic nerve sheath measurement

BACKGROUND

Measurement of the optic nerve sheath diameter (ONSD) via ultrasonography has been proposed as a non-invasive metric of intracranial pressure that may be employed during in-field patient triage. However, first responders are not typically trained to conduct sonographic exams and/or do not have access to an expensive ultrasound device. Therefore, for successful deployment of ONSD measurement in-field, we believe that first responders must have access to low-cost, portable ultrasound and be assisted by artificial intelligence (AI) systems that can automatically interpret the optic nerve sheath ultrasound scan. We examine the suitability of five commercially available, low-cost, portable ultrasound devices that can be combined with future artificial intelligence algorithms to reduce the training required for and cost of in-field optic nerve sheath diameter measurement. This paper is focused on the quality of the images generated by these low-cost probes. We report results of a clinician preference survey and compare with a lab analysis of three quantitative image quality metrics across devices. We also examine the suitability of the devices in a hypothetical far-forward deployment using operators unskilled in ultrasound, with the assumption of a future onboard AI video interpreter.

RESULTS

We find statistically significant differences in clinician ranking of the devices in the following categories: "Image Quality", "Ease of Acquisition", "Software", and "Overall ONSD". We show differences in signal-to-noise ratio, generalized contrast-to-noise ratio, point-spread function across the devices. These differences in image quality result in a statistically significant difference in manual ONSD measurement. Finally, we show that sufficiently wide transducers can capture the optic nerve sheath during blind (no visible B-mode) scans performed by operators unskilled in sonography.

CONCLUSIONS

Ultrasound of the optic nerve sheath has the potential to be a convenient, non-invasive, point-of-injury or triage measure for elevated intracranial pressure in cases of traumatic brain injury. When transducer width is sufficient, briefly trained operators may obtain video sequences of the optic nerve sheath without guidance. This data suggest that unskilled operators are able to achieve the images needed for AI interpretation. However, we also show that image quality differences between ultrasound probes may influence manual ONSD measurements.

Optimal transport features for morphometric population analysis

Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect to a clinical variable of interest, such as disease progression. Morphometric analysis approaches capture local differences in the distribution of tissue or other quantities of interest in relation to a clinical variable. We propose to augment morphometric analysis with an additional feature extraction step based on unbalanced optimal transport. The optimal transport feature extraction step increases statistical power for pathologies that cause spatially dispersed tissue loss, minimizes sensitivity to shifts due to spatial misalignment or differences in brain topology, and separates changes due to volume differences from changes due to tissue location. We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease. The results demonstrate that the proposed approach can identify tissue changes and differences that are not otherwise measurable.

Interactive, in-browser cinematic volume rendering of medical images

The diversity and utility of cinematic volume rendering (CVR) for medical image visualization have grown rapidly in recent years. At the same time, volume rendering on augmented and virtual reality systems is attracting greater interest with the advance of the WebXR standard. This paper introduces CVR extensions to the open-source visualization toolkit (vtk.js) that supports WebXR. This paper also summarizes two studies that were conducted to evaluate the speed and quality of various CVR techniques on a variety of medical data. This work is intended to provide the first open-source solution for CVR that can be used for in-browser rendering as well as for WebXR research and applications. This paper aims to help medical imaging researchers and developers make more informed decision when selecting CVR algorithms for their applications. Our software and this paper also provide a foundation for new research and product development at the intersection of medical imaging, web visualization, XR, and CVR.

Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images

Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.

Abstract 1955: A preclinical ultrasound platform for widefield 3D imaging of rodent tumors

Background: Preclinical ultrasound (US) and contrast-enhanced ultrasound (CEUS) imaging have long been used in oncology to noninvasively measure tumor volume and vascularity. While the value of preclinical US has been repeatedly demonstrated, these modalities are not without several key limitations that make them unattractive to cancer researchers, including: high user-variability, low throughput, and limited imaging field-of-view (FOV). Herein, we present a novel robotic preclinical US/CEUS system that addresses these limitations and demonstrates its use in evaluating tumors in 3D in a rodent model.

Methods: The imaging system was designed to allow seamless whole-body 3D imaging, which requires rodents to be imaged without physical contact between the US transducer and the animal. To achieve this, a custom dual-element transducer was mounted on a robotic carriage, submerged in a hydrocarbon fluid, and the reservoir sealed with an acoustically transmissive top platform. Eight NOD/scid/gamma (NSG) female mice were injected subcutaneously in the flank with 8×109 786-O human clear-cell renal cell carcinoma (ccRCC) cells. Weekly imaging commenced after tumors reached a size of 150 mm3 and continued until tumors reached a maximum size of 1 cm3 (∼4-5 weeks). An additional six nude athymic female mice were injected subcutaneously in the flank with 7 × 105 SVR angiosarcoma cells to perform an inter-operator variability study. Imaging consisted of 3D B-mode (conventional ultrasound) of the whole abdomen (< 1 min), as well as contrast-enhanced acoustic angiography (< 10 min) to measure blood vessel density (BVD). Tumors were manually segmented in 3D (< 2 min) and inter-operator and inter-reader reliability was assessed with Krippendorff’s alpha.

Results: Wide-field US images reconstructed from 3D volumetric data showed superior FOV over conventional US. Several anatomical landmarks could be identified within each image surrounding the tumor, including the liver, small intestines, bladder, and inguinal lymph nodes. Tumor boundaries were clearly delineated in both B-mode and BVD images, with BVD images showing heterogeneous microvessel density at later timepoints suggesting tumor necrosis. Excellent agreement was measured for both inter-reader and inter-operator experiments, with alpha coefficients of 0.914 (95% CI: 0.824-0.948) and 0.959 (0.911-0.981), respectively.

Conclusion: We have demonstrated a novel preclinical US imaging system that can accurately and consistently evaluate tumors in rodent models. The system leverages cost-effective robotic technology, and a new scanning paradigm that allows for easy and reproducible data acquisition to enable wide-field, 3D, multi-parametric ultrasound imaging.

Note: This abstract was not presented at the meeting.

Citation Format: Tomasz Czernuszewicz, Virginie Papadopoulou, Juan D. Rojas, Rajalekha Rajamahendiran, Jonathan Perdomo, James Butler, Max Harlacher, Graeme O'Connell, Dzenan Zukic, Paul A. Dayton, Stephen Aylward, Ryan C. Gessner. A preclinical ultrasound platform for widefield 3D imaging of rodent tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1955.

Abstract 1958: Early treatment response detected in a murine clear cell renal cell carcinoma model in response to combination therapy with antiangiogenic and notch inhibition therapy using a non-invasive imaging tool

Background: Functional and molecular changes often precede gross anatomical changes in cancer, so early assessment of a tumor’s functional and molecular response to therapy can help reduce a patient’s exposure to the side effects of ineffective chemotherapeutics or other treatment strategies. Clear-cell renal cell carcinoma (ccRCC) is an aggressive and hyper-vascular form of renal cancer that is often treated with anti-angiogenic and Notch Inhibition therapies, which target the vasculature feeding the disease. The purpose of this work is to show that ultrasound microvascular imaging can provide indications of response to antiangiogenic and Notch Inhibition therapies prior to measurable changes in tumor size.

Methods: Mice bearing 786-O ccRCC xenograft tumors were treated with SU (Sunitnib malate, Selleckchem, TX), an antiangiogenic drug, and a combination of SU and the Notch inhibitor GSI (Gamma secretase inhibitor, PF-03084014, Pfizer, New York, NY) therapies (n=8). A 3D ultrasound system (SonoVol Inc., Research Triangle Park, NC), in addition to microbubble ultrasound contrast agents, was used to obtain a measurement of microvascular density over time and assess the response of the tumors to the therapies. CD31 immunohistochemistry was performed to serve as a gold standard for comparison against imaging results. Statistical tests included: Spearman correlation to compare imaging and histology; Kruskal-Wallis analysis with Tukey multiple comparison post-test to determine if the vessel density or tumor volume were significantly different between the treatment groups; and receiver operating characteristic (ROC) curve analysis to determine sensitivity/specificity for separating treated/untreated groups.

Results: Data indicated that ultrasound-derived microvascular density can detect response to antiangiogenic and Notch inhibition therapies a week prior to changes in tumor volume. Furthermore, the imaging measurements of vasculature are strongly correlated with physiological characteristics of the tumors as measured by histology (p=0.75). Moreover, data demonstrated that ultrasound measurements of vascular density can determine response to therapy and classify between-treatment groups 1 week after the start of treatment with a high sensitivity and specificity of 94% and 86%, respectively.

Conclusion: This work shows vascular density measurements that are strongly correlated with histology can be obtained using ultrasound, and that imaging-derived vessel density metrics may be a better tool for assessing the response of ccRCC to antiangiogenic and Notch inhibition therapies than anatomical size measurements.

Note: This abstract was not presented at the meeting.

Citation Format: Juan D. Rojas, Virginie Papadopoulou, Tomasz Czernuszewicz, Rajalekha Rajamahendiran, Anna Chytil, Yun-Chen Chiang, Diana Chong, Victoria L. Bautch, Wendy K. Rathmell, Stephen Aylward, Ryan Gessner, Paul Dayton. Early treatment response detected in a murine clear cell renal cell carcinoma model in response to combination therapy with antiangiogenic and notch inhibition therapy using a non-invasive imaging tool [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1958.

Ultrasound Measurement of Vascular Density to Evaluate Response to Anti-Angiogenic Therapy in Renal Cell Carcinoma

BACKGROUND

Functional and molecular changes often precede gross anatomical changes, so early assessment of a tumor's functional and molecular response to therapy can help reduce a patient's exposure to the side effects of ineffective chemotherapeutics or other treatment strategies.

OBJECTIVE

Our intent was to test the hypothesis that an ultrasound microvascular imaging approach might provide indications of response to therapy prior to assessment of tumor size.

METHODS

Mice bearing clear-cell renal cell carcinoma xenograft tumors were treated with antiangiogenic and Notch inhibition therapies. An ultrasound measurement of microvascular density was used to serially track the tumor response to therapy.

RESULTS

Data indicated that ultrasound-derived microvascular density can indicate response to therapy a week prior to changes in tumor volume and is strongly correlated with physiological characteristics of the tumors as measured by histology ([Formula: see text]). Furthermore, data demonstrated that ultrasound measurements of vascular density can determine response to therapy and classify between-treatment groups with high sensitivity and specificity.

CONCLUSION/SIGNIFICANCE

Results suggests that future applications utilizing ultrasound imaging to monitor tumor response to therapy may be able to provide earlier insight into tumor behavior from metrics of microvascular density rather than anatomical tumor size measurements.

SCOLIOSIS SCREENING AND MONITORING USING SELF CONTAINED ULTRASOUND AND NEURAL NETWORKS

We aim to diagnose scoliosis using a self contained ultrasound device that does not require significant training to operate. The device knows its angle relative to vertical using an embedded inertial measurement unit, and it estimates its angle relative to a vertebrae using a neural network analysis of its ultrasound images. The composition of those angles defines the angle of a vertebrae from vertical. The maximum difference between vertebrae angles collected from a scan of a spine yields the Cobb angle measure that is used to quantify scoliosis severity.

Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction for a wide variety of images.

Ultrasound Augmentation: Rapid 3-D Scanning for Tracking and On-Body Display

By using a laser projector and high speed camera, we can add three capabilities to an ultrasound system: tracking the probe, tracking the patient, and projecting information onto the probe and patient. We can use these capabilities to guide an untrained operator to take high quality, well framed ultrasound images for computer-augmented, point-of-care ultrasound applications.

EFFICIENT REGISTRATION OF PATHOLOGICAL IMAGES: A JOINT PCA/IMAGE-RECONSTRUCTION APPROACH

Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

Registration of Pathological Images

This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, for which more accurate registration is possible. Specifically, the method uses a deep variational convolutional encoder-decoder network to learn the mapping. Furthermore, the method estimates local mapping uncertainty through network inference statistics and uses those estimates to down-weight the image registration similarity measure in areas of high uncertainty. The performance of the method is quantified using synthetic brain tumor images and images from the brain tumor segmentation challenge (BRATS 2015).

Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience

The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.

Low-Rank Atlas Image Analyses in the Presence of Pathologies

We present a common framework, for registering images to an atlas and for forming an unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic brain injury lesions. This common framework is particularly useful when a sufficient number of protocol-matched scans from healthy subjects cannot be easily acquired for atlas formation and when the pathologies in a patient cause large appearance changes. Our framework combines a low-rank-plus-sparse image decomposition technique with an iterative, diffeomorphic, group-wise image registration method. At each iteration of image registration, the decomposition technique estimates a "healthy" version of each image as its low-rank component and estimates the pathologies in each image as its sparse component. The healthy version of each image is used for the next iteration of image registration. The low-rank and sparse estimates are refined as the image registrations iteratively improve. For unbiased atlas formation, at each iteration, the average of the low-rank images from the patients is used as the atlas image for the next iteration, until convergence. Since each iteration's atlas is comprised of low-rank components, it provides a population-consistent, pathology-free appearance. Evaluations of the proposed methodology are presented using synthetic data as well as simulated and clinical tumor MRI images from the brain tumor segmentation (BRATS) challenge from MICCAI 2012.

Low-rank to the rescue - atlas-based analyses in the presence of pathologies

Low-rank image decomposition has the potential to address a broad range of challenges that routinely occur in clinical practice. Its novelty and utility in the context of atlas-based analysis stems from its ability to handle images containing large pathologies and large deformations. Potential applications include atlas-based tissue segmentation and unbiased atlas building from data containing pathologies. In this paper we present atlas-based tissue segmentation of MRI from patients with large pathologies. Specifically, a healthy brain atlas is registered with the low-rank components from the input MRIs, the low-rank components are then re-computed based on those registrations, and the process is then iteratively repeated. Preliminary evaluations are conducted using the brain tumor segmentation challenge data (BRATS '12).

Localizing target structures in ultrasound video - a phantom study

The problem of localizing specific anatomic structures using ultrasound (US) video is considered. This involves automatically determining when an US probe is acquiring images of a previously defined object of interest, during the course of an US examination. Localization using US is motivated by the increased availability of portable, low-cost US probes, which inspire applications where inexperienced personnel and even first-time users acquire US data that is then sent to experts for further assessment. This process is of particular interest for routine examinations in underserved populations as well as for patient triage after natural disasters and large-scale accidents, where experts may be in short supply. The proposed localization approach is motivated by research in the area of dynamic texture analysis and leverages several recent advances in the field of activity recognition. For evaluation, we introduce an annotated and publicly available database of US video, acquired on three phantoms. Several experiments reveal the challenges of applying video analysis approaches to US images and demonstrate that good localization performance is possible with the proposed solution.

Studying cerebral vasculature using structure proximity and graph kernels

An approach to study population differences in cerebral vasculature is proposed. This is done by (1) extending the concept of encoding cerebral blood vessel networks as spatial graphs and (2) quantifying graph similarity in a kernel-based discriminant classifier setup. We argue that augmenting graph vertices with information about their proximity to selected brain structures adds discriminative information and consequently leads to a more expressive encoding. Using graph-kernels then allows us to quantify graph similarity in a principled way. To demonstrate our approach, we assess the hypothesis that gender differences manifest as variations in the architecture of cerebral blood vessels, an observation that previously had only been tested and confirmed for the Circle of Willis. Our results strongly support this hypothesis, i.e, we can demonstrate non-trivial, statistically significant deviations from random gender classification in a cross-validation setup on 40 healthy patients.

3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.

3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.