Multiparametric Ultrasound Imaging of Prostate Cancer Using Deep Neural Networks.

Objective: A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities (acoustic radiation force impulse [ARFI] imaging, shear wave elasticity imaging [SWEI], quantitative ultrasound-midband fit [QUS-MF], and B-mode) for the detection of prostate cancer.

Methods: A DNN was trained using co-registered ARFI, SWEI, MF, and B-mode data obtained in men with biopsy-confirmed prostate cancer prior to radical prostatectomy (15 subjects, comprising 980,620 voxels). Data were obtained using a commercial scanner that was modified to allow user control of the acoustic beam sequences and provide access to the raw image data.

Lung quantitative ultrasound to stage and monitor interstitial lung diseases.

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation.

Lung Quantitative Ultrasound to Stage and Monitor Interstitial Lung Diseases.

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation.

Improved cancer risk stratification of isoechoic thyroid nodules to reduce unnecessary biopsies using quantitative ultrasound.

Objective: Gray-scale ultrasound (US) is the standard-of-care for evaluating thyroid nodules (TNs). However, the performance is better for the identification of hypoechoic malignant TNs (such as classic papillary thyroid cancer) than isoechoic malignant TNs. Quantitative ultrasound (QUS) utilizes information from raw ultrasonic radiofrequency (RF) echo signal to assess properties of tissue microarchitecture.

Cardiac Adipose Tissue Segmentation via Image-Level Annotations.

Automatically identifying the structural substrates underlying cardiac abnormalities can potentially provide real-time guidance for interventional procedures. With the knowledge of cardiac tissue substrates, the treatment of complex arrhythmias such as atrial fibrillation and ventricular tachycardia can be further optimized by detecting arrhythmia substrates to target for treatment (i.e.

Quantitative Ultrasound Assessment of Early Osteoarthritis in Human Articular Cartilage Using a High-Frequency Linear Array Transducer.

Quantitative ultrasound (QUS) assessment of osteoarthritis (OA) using high-frequency, research-grade single-element ultrasound systems has been reported. The objective of this ex vivo study was to assess the performance of QUS in detecting early OA using a high-frequency linear array transducer. Osteochondral plugs (n = 26) of human articular cartilage were scanned with ExactVu Micro-Ultrasound using an EV29L side-fire transducer.

In vivo assessment of pulmonary fibrosis and edema in rodents using the backscatter coefficient and envelope statistics.

Quantitative ultrasound methods based on the backscatter coefficient (BSC) and envelope statistics have been used to quantify disease in a wide variety of tissues, such as prostate, lymph nodes, breast, and thyroid. However, to date, these methods have not been investigated in the lung. In this study, lung properties were quantified by BSC and envelope statistical parameters in normal, fibrotic, and edematous rat lungs in vivo.

Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation.

Identifying arrhythmia substrates and quantifying their heterogeneity has great potential to provide critical guidance for radio frequency ablation. However, quantitative analysis of heterogeneity on cardiac optical coherence tomography (OCT) images is lacking. In this paper, we conduct the first study on quantifying cardiac tissue heterogeneity from human OCT images.

Multiparametric Ultrasound for Targeting Prostate Cancer: Combining ARFI, SWEI, QUS and B-Mode.

Diagnosing prostate cancer through standard transrectal ultrasound (TRUS)-guided biopsy is challenging because of the sensitivity and specificity limitations of B-mode imaging. We used a linear support vector machine (SVM) to combine standard TRUS imaging data with acoustic radiation force impulse (ARFI) imaging data, shear wave elasticity imaging (SWEI) data and quantitative ultrasound (QUS) midband fit data to enhance lesion contrast into a synthesized multiparametric ultrasound volume. This SVM was trained and validated using a subset of 20 patients and tested on a second subset of 10 patients.

Imaging of subendocardial adipose tissue and fiber orientation distributions in the human left atrium using optical coherence tomography.

Background: Optical coherence tomography (OCT) has the potential to provide real-time imaging guidance for atrial fibrillation ablation, with promising results for lesion monitoring. OCT can also offer high-resolution imaging of tissue composition, but there is insufficient cardiac OCT data to inform the use of OCT to reveal important tissue architecture of the human left atrium. Thus, the objective of this study was to define OCT imaging data throughout the human left atrium, focusing on the distribution of adipose tissue and fiber orientation as seen from the endocardium.

Characterization of the human myocardium by optical coherence tomography.

Imaging of cardiac tissue structure plays a critical role in the treatment and understanding of cardiovascular disease. Optical coherence tomography (OCT) offers the potential to provide valuable, high-resolution imaging of cardiac tissue. However, there is a lack of comprehensive OCT imaging data of the human heart, which could improve identification of structural substrates underlying cardiac abnormalities.

Optical coherence tomography imaging of cardiac substrates.

Cardiovascular disease is the leading cause of morbidity and mortality in the United States. Knowledge of a patient's heart structure will help to plan procedures, potentially identifying arrhythmia substrates, critical structures to avoid, detect transplant rejection, and reduce ambiguity when interpreting electrograms and functional measurements. Similarly, basic research of numerous cardiac diseases would greatly benefit from structural imaging at cellular scale.

High-speed collagen fiber modeling and orientation quantification for optical coherence tomography imaging.

Quantifying collagen fiber architecture has clinical and scientific relevance across a variety of tissue types and adds functionality to otherwise largely qualitative imaging modalities. Optical coherence tomography (OCT) is uniquely suited for this task due to its ability to capture the collagen microstructure over larger fields of view than traditional microscopy. Existing image processing techniques for quantifying fiber architecture, while accurate and effective, are very slow for processing large datasets and tend to lack structural specificity.

Mapping the human pulmonary venoatrial junction with optical coherence tomography.

Imaging guidance provided by optical coherence tomography (OCT) could improve the outcomes of atrial fibrillation (AF) ablation by providing detailed structural information of the pulmonary veins, which are critical targets during ablation. In this study, stitched volumetric OCT images of venoatrial junctions from post-mortem human hearts were acquired and compared to histology. Image features corresponding to venous media and myocardial sleeves, as well as fiber orientation and fibrosis, were identified and found to vary between veins.

Tissue-Specific Optical Mapping Models of Swine Atria Informed by Optical Coherence Tomography.

Computational models and experimental optical mapping of cardiac electrophysiology serve as powerful tools to investigate the underlying mechanisms of arrhythmias. Modeling can also aid the interpretation of optical mapping signals, which may have different characteristics with respect to the underlying electrophysiological signals they represent. However, despite the prevalence of atrial arrhythmias such as atrial fibrillation, models of optical electrical mapping incorporating realistic structure of the atria are lacking.