OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

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.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

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.

Dental microfracture detection using wavelet features and machine learning.

Microfractures (cracks) are the third most common cause of tooth loss in industrialized countries. If they are not detected early, they continue to progress until the tooth is lost. Cone beam computed tomography (CBCT) has been used to detect microfractures, but has had very limited success. We propose an algorithm to detect cracked teeth that pairs high resolution (hr) CBCT scans with advanced image analysis and machine learning. First, microfractures were simulated in extracted human teeth (n=22). hr-CBCT and microCT scans of the fractured and control teeth (n=14) were obtained. Wavelet pyramid construction was used to generate a phase image of the Fourier transformed scan which were fed to a U-Net deep learning architecture that localizes the orientation and extent of the crack which yields slice-wise probability maps that indicate the presence of microfractures. We then examine the ratio of high-probability voxels to total tooth volume to determine the likelihood of cracks per tooth. In microCT and hr-CBCT scans, fractured teeth have higher numbers of such voxels compared to control teeth. The proposed analytical framework provides a novel way to quantify the structural breakdown of teeth, that was not possible before. Future work will expand our machine learning framework to 3D volumes, improve our feature extraction in hr-CBCT and clinically validate this model. Early detection of microfractures will lead to more appropriate treatment and longer tooth retention.

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.

Methods for quantitative characterization of bone injury from computed-tomography images.

Computed tomography (CT) images can potentially provide insights into bone structure for diagnosis of disorders and diseases. However, evaluation of trabecular bone structure and whole bone shape is often qualitative or semi-quantitative. This limits inter-study comparisons and the ability to detect subtle bone quality variations during early disease onset or in response to new treatments. In this work, we enable quantitative characterization of bone diseases through bone morphometry, texture analysis, and shape analysis methods. The potential of our analysis methods to identify the impact of hemophilia is validated in a mouse femur wound model. In our results, shape localizes and characterizes the formation of spurious bone, and our texture and bone morphometry analysis results provide extra information about the composition of that bone. Some of our one-dimensional (1D) textural features were able to significantly differentiate our injured femurs from our healthy femurs, even with this small sample size demonstrating the potential of the proposed analysis framework. While trabecular bone morphometrics have been a pillar in 3D microCT bone research for decades, the proposed analysis framework augments how we define and understand phenotypical presentation of bone disease. The contributed open source software is exposed to the medical image analysis community through 3D Slicer extensions to ensure both robustness and reproducibility.

Image-Based Methods for Phase Estimation, Gating, and Temporal Superresolution of Cardiac Ultrasound.

OBJECTIVE: Ultrasound is an effective tool for rapid noninvasive assessment of cardiac structure and function. Determining the cardiorespiratory phases of each frame in the ultrasound video and capturing the cardiac function at a much higher temporal resolution are essential in many applications. Fulfilling these requirements is particularly challenging in preclinical studies involving small animals with high cardiorespiratory rates, requiring cumbersome and expensive specialized hardware. METHODS: We present a novel method for the retrospective estimation of cardiorespiratory phases directly from the ultrasound videos. It transforms the videos into a univariate time series preserving the evidence of periodic cardiorespiratory motion, decouples the signatures of cardiorespiratory motion with a trend extraction technique, and estimates the cardiorespiratory phases using a Hilbert transform approach. We also present a robust nonparametric regression technique for respiratory gating and a novel kernel-regression model for reconstructing images at any cardiac phase facilitating temporal superresolution. RESULTS: We validated our methods using two-dimensional echocardiography videos and electrocardiogram (ECG) recordings of six mice. Our cardiac phase estimation method provides accurate phase estimates with a mean-phase-error range of 3%-6% against ECG derived phase and outperforms three previously published methods in locating ECGs R-wave peak frames with a mean-frame-error range of 0.73-1.36. Our kernel-regression model accurately reconstructs images at any cardiac phase with a mean-normalized-correlation range of 0.81-0.85 over 50 leave-one-out-cross-validation rounds. CONCLUSION AND SIGNIFICANCE: Our methods can enable tracking of cardiorespiratory phases without additional hardware and reconstruction of respiration-free single cardiac-cycle videos at a much higher temporal resolution.

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.

Detection of bone loss via subchondral bone analysis.

To date, there is no single sign, symptom, or test that can clearly diagnose early stages of Temporomandibular Joint Osteoarthritis (TMJ OA). However, it has been observed that changes in the bone occur in early stages of this disease, involving structural changes both in the texture and morphometry of the bone marrow and the subchondral cortical plate. In this paper we present a tool to detect and highlight subtle variations in subchondral bone structure obtained from high resolution Cone Beam Computed Tomography (hr-CBCT) in order to help with detecting early TMJ OA. The proposed tool was developed in ITK and 3DSlicer and it has been disseminated as open-source software tools. We have validated both our texture analysis and morphometry analysis biomarkers for detection of TMJ OA comparing hr-CBCT to µCT. Our initial statistical results using the multidimensional features computed with our tool indicate that it is possible to classify areas of demonstrated loss of trabecular bone in both µCT and hr-CBCT. This paper describes the first steps to alleviate the current inability of radiological changes to diagnose TMJ OA before morphological changes are too advanced by quantifying subchondral bone biomarkers. This paper indicates that texture based and morphometry based biomarkers have the potential to identify OA patients at risk for further bone destruction.

Building bridges between cellular and molecular structural biology.

The integration of cellular and molecular structural data is key to understanding the function of macromolecular assemblies and complexes in their in vivo context. Here we report on the outcomes of a workshop that discussed how to integrate structural data from a range of public archives. The workshop identified two main priorities: the development of tools and file formats to support segmentation (that is, the decomposition of a three-dimensional volume into regions that can be associated with defined objects), and the development of tools to support the annotation of biological structures.

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).

Computational analysis of the importance of flow synchrony for cardiac ventricular assist devices.

This paper presents a patient customised fluid-solid mechanics model of the left ventricle (LV) supported by a left ventricular assist device (LVAD). Six simulations were conducted across a range of LVAD flow protocols (constant flow, sinusoidal in-sync and sinusoidal counter-sync with respect to the cardiac cycle) at two different LVAD flow rates selected so that the aortic valve would either open (60mLs(-1)) or remain shut (80mLs(-1)). The simulation results indicate that varying LVAD flow in-sync with the cardiac cycle improves both myocardial unloading and the residence times of blood in the left ventricle. In the simulations, increasing LVAD flow during myocardial contraction and decreasing it during diastole improved the mixing of blood in the LV cavity. Additionally, this flow protocol had the effect of partly homogenising work across the myocardium when the aortic valve did not open, reducing myocardial stress and thereby improving unloading.

ITK: enabling reproducible research and open science.

Reproducibility verification is essential to the practice of the scientific method. Researchers report their findings, which are strengthened as other independent groups in the scientific community share similar outcomes. In the many scientific fields where software has become a fundamental tool for capturing and analyzing data, this requirement of reproducibility implies that reliable and comprehensive software platforms and tools should be made available to the scientific community. The tools will empower them and the public to verify, through practice, the reproducibility of observations that are reported in the scientific literature. Medical image analysis is one of the fields in which the use of computational resources, both software and hardware, are an essential platform for performing experimental work. In this arena, the introduction of the Insight Toolkit (ITK) in 1999 has transformed the field and facilitates its progress by accelerating the rate at which algorithmic implementations are developed, tested, disseminated and improved. By building on the efficiency and quality of open source methodologies, ITK has provided the medical image community with an effective platform on which to build a daily workflow that incorporates the true scientific practices of reproducibility verification. This article describes the multiple tools, methodologies, and practices that the ITK community has adopted, refined, and followed during the past decade, in order to become one of the research communities with the most modern reproducibility verification infrastructure. For example, 207 contributors have created over 2400 unit tests that provide over 84% code line test coverage. The Insight Journal, an open publication journal associated with the toolkit, has seen over 360,000 publication downloads. The median normalized closeness centrality, a measure of knowledge flow, resulting from the distributed peer code review system was high, 0.46.

Gene expression profile regulated by the HPV16 E7 oncoprotein and estradiol in cervical tissue.

The HPV16 E7 oncoprotein and 17ß-estradiol are important factors for the induction of premalignant lesions and cervical cancer. The study of these factors is crucial for a better understanding of cervical tumorigenesis. Here, we assessed the global gene expression profiles induced by the HPV16 E7 oncoprotein and/or 17ß-estradiol in cervical tissue of FvB and K14E7 transgenic mice. We found that the most dramatic changes in gene expression occurred in K14E7 and FvB groups treated with 17ß-estradiol. A large number of differentially expressed genes involved in the immune response were observed in 17ß-estradiol treated groups. The E7 oncoprotein mainly affected the expression of genes involved in cellular metabolism. Our microarray data also identified differentially expressed genes that have not previously been reported in cervical cancer. The identification of genes regulated by E7 and 17ß-estradiol, provides the basis for further studies on their role in cervical carcinogenesis.

An approach to unbiased subsample interpolation for motion tracking.

Accurate subsample displacement estimation is necessary for ultrasound elastography because of the small deformations that occur and the subsequent application of a derivative operation on local displacements. Many of the commonly used subsample estimation techniques introduce significant bias errors. This article addresses a reduced bias approach to subsample displacement estimations that consists of a two-dimensional windowed-sinc interpolation with numerical optimization. It is shown that a Welch or Lanczos window with a Nelder-Mead simplex or regular-step gradient-descent optimization is well suited for this purpose. Little improvement results from a sinc window radius greater than four data samples. The strain signal-to-noise ratio (SNR) obtained in a uniformly elastic phantom is compared with other parabolic and cosine interpolation methods; it is found that the strain SNR ratio is improved over parabolic interpolation from 11.0 to 13.6 in the axial direction and 0.7 to 1.1 in the lateral direction for an applied 1% axial deformation. The improvement was most significant for small strains and displacement tracking in the lateral direction. This approach does not rely on special properties of the image or similarity function, which is demonstrated by its effectiveness with the application of a previously described regularization technique.

Impaired cognitive function in patients with atherosclerotic carotid stenosis and correlation with ultrasound strain measurements.

INTRODUCTION: It has been postulated that up to 11 million "silent" strokes occur annually. While these patients are without classic neurologic deficits, they may exhibit cognitive decline. In this study, we examine the cognitive function of patients with carotid stenosis. Additionally, we evaluate a noninvasive measure of strain in pulsating carotid artery plaques to determine its ability to predict cognitive decline. METHODS: We administered the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) to 44 patients with carotid stenosis. All patients had stenosis meeting NASCET or ACAS criteria for endarterectomy, and were classified as symptomatic or asymptomatic as defined by these publications. Age-adjusted scores for each of the 5 RBANS domains (immediate memory, visuospatial ability, language, attention, and delayed memory) were compared between symptomatic and asymptomatic patients. Mean score for each of the 5 domains was then compared to all other domains, regardless of symptom status. From this cohort, 23 patients underwent assessment of carotid plaque strain by tracking displacements in ultrasound radiofrequency data to estimate axial and principal strains over the cardiac cycle. RESULTS: Thirty symptomatic and 14 asymptomatic patients were studied. Visuospatial scores were significantly lower than any other domain regardless of symptoms (p<0.05 for all pairwise comparisons). No other domain score was significantly different from any other. In the language domain, asymptomatic patients scored significantly higher than symptomatic patients (p<0.05. For all other domains, no difference was found. Asymptomatic patients showed a relationship between plaque strain and immediate memory (r=-.61, p=ns). Left carotid disease was associated with poorer performance across multiple cognitive domains with increasing accumulated strain. This was not seen in right carotid disease. CONCLUSION: Patients with large carotid plaques (>70% stenosis) exhibit significant difficulties in mental status whether classically symptomatic or asymptomatic. While language deficits may be a non-specific marker for stroke symptoms, visuospatial deficits are seen before classic symptoms, suggesting that carotid disease may become symptomatic earlier and more subtly than previously suspected. Abnormal strain distribution with pulsation may be related to cognition.

Absolute backscatter coefficient estimates of tissue-mimicking phantoms in the 5-50 MHz frequency range.

Absolute backscatter coefficients in tissue-mimicking phantoms were experimentally determined in the 5-50 MHz frequency range using a broadband technique. A focused broadband transducer from a commercial research system, the VisualSonics Vevo 770, was used with two tissue-mimicking phantoms. The phantoms differed regarding the thin layers covering their surfaces to prevent desiccation and regarding glass bead concentrations and diameter distributions. Ultrasound scanning of these phantoms was performed through the thin layer. To avoid signal saturation, the power spectra obtained from the backscattered radio frequency signals were calibrated by using the signal from a liquid planar reflector, a water-brominated hydrocarbon interface with acoustic impedance close to that of water. Experimental values of absolute backscatter coefficients were compared with those predicted by the Faran scattering model over the frequency range 5-50 MHz. The mean percent difference and standard deviation was 54% ± 45% for the phantom with a mean glass bead diameter of 5.40 µm and was 47% ± 28% for the phantom with 5.16 µm mean diameter beads.

Bayesian regularization applied to ultrasound strain imaging.

Noise artifacts due to signal decorrelation and reverberation are a considerable problem in ultrasound strain imaging. For block-matching methods, information from neighboring matching blocks has been utilized to regularize the estimated displacements. We apply a recursive Bayesian regularization algorithm developed by Hayton et al. [Artif. Intell., vol. 114, pp. 125-156, 1999] to phase-sensitive ultrasound RF signals to improve displacement estimation. The parameter of regularization is reformulated, and its meaning examined in the context of strain imaging. Tissue-mimicking experimental phantoms and RF data incorporating finite-element models for the tissue deformation and frequency-domain ultrasound simulations are used to compute the optimal parameter with respect to nominal strain and algorithmic iterations. The optimal strain regularization parameter was found to be twice the nominal strain and did not vary significantly with algorithmic iterations. The technique demonstrates superior performance over median filtering in noise reduction at strains 5% and higher for all quantitative experiments performed. For example, the strain SNR was 11 dB higher than that obtained using a median filter at 7% strain. It has to be noted that for applied deformations lower than 1%, since signal decorrelation errors are minimal, using this approach may degrade the displacement image.

Inhibition of immune activation by a novel nuclear factor-kappa B inhibitor in HTLV-I-associated neurologic disease.

The human T-lymphotropic virus type I (HTLV-I) causes a chronic inflammatory disorder of the central nervous system termed HTLV-I-associated myelopathy/tropical spastic paraparesis (HAM/TSP). HTLV-I encodes a protein known to activate several host-signaling pathways involved in inflammation, such as the nuclear factor-κB (NF-κB). The contribution of the NF-κB pathway to the pathogenesis of HAM/TSP, however, has not been fully defined. We show evidence of canonical NF-κB activation in short-term cultures of peripheral blood mononuclear cells (PBMCs) from subjects with HAM/TSP. NF-κB activation was closely linked to HTLV-I viral protein expression. The NF-κB activation in HAM/TSP PBMCs was reversed by a novel small-molecule inhibitor that demonstrates potent and selective NF-κB antagonist activity. Inhibition of NF-κB activation led to a reduction in the expression of lymphocyte activation markers and resulted in reduced cytokine signaling in HAM/TSP PBMCs. Furthermore, NF-κB inhibition led to a reduction in spontaneous lymphoproliferation, a key ex vivo correlate of the immune activation associated with HAM/TSP. These results indicate that NF-κB activation plays a critical upstream role in the immune activation of HAM/TSP, and identify the NF-κB pathway as a potential target for immunomodulation in HAM/TSP.