Gregory, Adriana

Background In kidney transplantation, a contrast CT scan is obtained in the donor candidate to detect subclinical pathology in the kidney. Recent work from the Aging Kidney Anatomy study has characterized kidney, cortex, and medulla volumes using a manual image-processing tool. However, this technique is time consuming and impractical for clinical care, and thus, these measurements are not obtained during donor evaluations. This study proposes a fully automated segmentation approach for measuring kidney, cortex, and medulla volumes. Methods A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used to develop the algorithm. A convolutional neural network model was trained ( n =1238) and validated ( n =306), and then evaluated in a hold-out test set of reference standard segmentations ( n =386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites ( n =1226). Results The automated model was found to perform on par with manual segmentation, with errors similar to interobserver variability with manual segmentation. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets. Conclusions A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications.

Polycystic kidney disease (PKD) is an inherited disorder characterized by renal cyst formation and enlargement of the kidney. PKD severity can be staged noninvasively by measuring total kidney volume (TKV), a promising biomarker that has recently received regulatory qualification. In preclinical mouse models, where the disease is studied and potential therapeutics are evaluated, the most popular noninvasive method of measuring TKV is magnetic resonance imaging (MRI). Although MRI provides excellent 3D resolution and contrast, these systems are expensive to operate, have long acquisition times, and, consequently, are not heavily used in preclinical PKD research. In this study, a new imaging instrument, based on robotic ultrasound (US), was evaluated as a complementary approach for assessing PKD in rodent models. The objective was to determine the extent to which TKV measurements on the robotic US scanner correlated with both in vivo and ex vivo reference standards (MRI and Vernier calipers, respectively). A cross-sectional study design was implemented that included both PKD-affected mice and healthy wild types, spanning sex and age for a wide range of kidney volumes. It was found that US-derived TKV measurements and kidney lengths were strongly associated with both in vivo MRI and ex vivo Vernier caliper measurements ( R 2 =0.94 and 0.90, respectively). In addition to measuring TKV, renal vascular density was assessed using acoustic angiography (AA), a novel contrast-enhanced US methodology. AA image intensity, indicative of volumetric vascularity, was seen to have a strong negative correlation with TKV ( R 2 =0.82), suggesting impaired renal vascular function in mice with larger kidneys. These studies demonstrate that robotic US can provide a rapid and accurate approach for noninvasively evaluating PKD in rodent models.

Ultrasound measurements of detrusor muscle thickness have been proposed as a diagnostic biomarker in patients with bladder overactivity and voiding dysfunction. In this study, we present an approach based on deep learning (DL) and dynamic programming (DP) to segment the bladder sac and measure the detrusor muscle thickness from transabdominal 2D B-mode ultrasound images. To assess the performance of our method, we compared the results of automated methods to the manually obtained reference bladder segmentations and wall thickness measurements of 80 images obtained from 11 volunteers. It takes less than a second to segment the bladder from a 2D B-mode image for the DL method. The average Dice index for the bladder segmentation is 0.93 ± 0.04 mm, and the average root-mean-square-error and standard deviation for wall thickness measurement are 0.7 ± 0.2 mm, which is comparable to the manual ground truth. The proposed fully automated and fast method could be a useful tool for segmentation and wall thickness measurement of the bladder from transabdominal B-mode images. The computation speed and accuracy of the proposed method will enable adaptive adjustment of the ultrasound focus point, and continuous assessment of the bladder wall during the filling and voiding process of the bladder.

AbstractBackgroundShear wave elastography (SWE) shows promise in peripheral neuropathy evaluation but has potential limitations due to tissue size and heterogeneity. We tested SWE sensitivity to elasticity change and the effect of probe position in a median nerve cadaver model.MethodsTen specimens were used to measure median nerve elasticity under increasing loads using SWE and indentation. Measurements were compared using repeated‐measures analysis of variance.ResultsIndentation and SWE‐based longitudinal nerve elasticity increased with tensile loading (P< .01), showing a similar relationship. Acquisition in a transverse plane showed lower values compared with longitudinal measurements, mostly under higher loads (P= .03), as did postdissection elasticity (P= .02). Elasticity did not change when measured proximal to the carpal tunnel.ConclusionsLongitudinal SWE is sensitive to changes in median nerve elasticity. Measuring elasticity of peripheral nerves noninvasively could elucidate intra‐neural pathology related to compression neuropathies, and proof to be of added value as a diagnostic or prognostic tool.

Vibrational characteristics of the bone are directly dependent on bone’s physical properties. In this paper, a vibrational method for bone evaluation is introduced. We propose a new type of quantitative vibro-acoustic method based on acoustic radiation force of ultrasound for bone characterization in patients with fracture. In this method, we excite the clavicle and ulna by an ultrasound radiation force (URF) pulse. The URF pulse induces vibrations in the bone, resulting in an acoustic wave that is measured by a hydrophone placed on the skin. The resulting acoustic signals were used for wave velocity estimation based on cross-correlation technique. To further separate different vibration characteristics, we adopt a variational mode decomposition (VMD) technique to decompose the received signal into an ensemble of band-limited intrinsic mode functions, which allows analyzing the acoustic signals in terms of their constitutive components. We conducted a prospective study that included a total of 15 patients, 12 with clavicle fractures and 3 with ulna fractures. The contralateral intact bones were used as control. Statistical analysis demonstrated that fracture bones can be differentiated from intact bone with a detection probability of 80%. Also, we introduce a “healing factor” that quantifies the progress of healing in clavicle bones. Statistical analysis showed that healing factor can track the progress of healing in clavicle in 80% of fractures.

Vibrational characteristics of bone are directly dependent on its physical properties. In this study, a vibrational method for bone evaluation is introduced. We propose a new type of quantitative vibro-acoustic method based on the acoustic radiation force of ultrasound for bone characterization in persons with fracture. Using this method, we excited the clavicle or ulna by an ultrasound radiation force pulse which induces vibrations in the bone, resulting in an acoustic wave that is measured by a hydrophone placed on the skin. The acoustic signals were used for wave velocity estimation based on a cross-correlation technique. To further separate different vibration characteristics, we adopted a variational mode decomposition technique to decompose the received signal into an ensemble of band-limited intrinsic mode functions, allowing analysis of the acoustic signals by their constitutive components. This prospective study included 15 patients: 12 with clavicle fractures and three with ulna fractures. Contralateral intact bones were used as controls. Statistical analysis demonstrated that fractured bones can be differentiated from intact ones with a detection probability of 80%. Additionally, we introduce a “healing factor” to quantify the bone healing progress which successfully tracked the progress of healing in 80% of the clavicle fractures in the study.

We hypothesize that pattern of microvasculature in the thyroid nodules can be a biomarker of thyroid cancer. In this study, morphological features of microvasculature networks are assessed as potential biomarkers for differentiation of benign, suspicious and malignant thyroid nodules. This prospective study included n = 81 patients, with a total of 61 benign, 12 suspicious and 8 malignant nodules verfied by pathology and cytology findings. Thyroid nodules were first identified using conventional B-mode ultrasound. A sequence of raw ultrasound data were acquired from each nodule site in two perpendicular planes at ~600 frames/sec. Power Doppler image of the microvasculature was formed using singular value decomposition clutter removal filtering. Gross images were further processed using a combination of morphology and Hessian-based vessel enhancement filtering. Background-free images of the microvasculature networks were segmented (vessels segment length &gt;385μm and diameter &gt;90μm) for morphological analysis. Using a Bayesian framework, measures of vessels density, diameter and tortuosity were tested for significant differences in the three groups. Among all measures, the probability of vessel diameter &gt;600μm in benign cases was significantly higher than that in malignant cases with the p-value &lt;0.01 using Wilcoxon-Rank-Sum test, suggesting reliable cancer detection performance of the proposed method. [Acknowledgment: NIH Grant EB17213.]

Early detection of osteopenia and osteoporosis is critical in identifying at-risk groups and manage necessary therapy. In this paper, a new noninvasive and quantitative vibro-acoustic method is proposed for detection of osteoporosis and osteopenia. In this method, we excite the bone by an ultrasound radiation force (URF) pulse. The URF pulse induces a vibration in the bone, resulting an acoustic wave that is received by a hydrophone placed on the skin. This prospective study included n = 23 volunteers. The number of volunteers with osteopenia and osteoporosis were 7, 6, respectively, and the number of volunteers with normal bone was 10. URF excitations were applied at four equi-distance points on the bone. The resulting acoustic signals were used for wave velocity estimation based on cross-correlation technique. In general, the hydrophone signal may be modeled as a superposition of different modes of bone vibration. Based on this model, we used variational mode decomposition (VMD) as an efficient technique to decompose the received signal into an ensemble of band-limited intrinsic mode functions. Statistical analysis demonstrates that osteopenia and osteoporosis bones can be differentiated from normal bone with p&lt; 0.001 using Wilcoxon rank-sum test on estimated velocity of the modes with maximum power.

Healthy human bladder is a viscoelastic shell capable of expanding to allow storage of urine at low pressures. The ability to maintain low pressures as the urine volume increases is called bladder compliance. Compliance is a key in proper functioning of urinary system. Spinal cord injuries and other neurogenic diseases can cause deterioration in bladder expansion capability (e.g., through excessive growth of fibrotic tissue), which in turn can increase the rigidity of the wall and reduce bladder compliance. Urodynamic study (UDS) is the clinical standard for evaluation of the bladder compliance through observation of bladder pressure-volume behavior. However, this method is costly and invasive. Ultrasound bladder vibrometry (UBV) has been introduced as noninvasive technique for evaluation of bladder viscoelasticity through excitation of the bladder using ultrasound radiation force and tracking the resulting Lamb waves. Here, we present the results of UBV from 44 patients undergoing concurrent UDS and UBV studies. Our results show that elasticity parameters obtained from the UBV closely correlate with the UDS pressure-volume data with a median Pearson’s correlation value of more than 0.8. These results prove the potential utility of UBV as an alternative noninvasive method for bladder compliance assessment. [Work supported by NIH grant DK99231.]

There is an urgent need for a noninvasive but more accurate method to assess breast masses before referring the patient to biopsy. In this study, we use the slow viscoelastic creep response to evaluate breast masses at low (&lt;1 Hz) frequencies. This method uses an automated ramp-and-hold compression device and a high-frame-rate ultrasound imaging to track tissue strain. The study cohort included 30 pre-biopsy patients with suspicious masses. Sequential high-frame-rate images were recorded during the ramp-and-hold process. The data were used to calculate the creep response at each point. Then, using a classic linear model, the retardation-time map was created within the field of view. The resulting retardation-time maps exhibited clear distinction of the mass margins. The results showed that benign breast lesions appear with an increase in creep retardation-time compared to surrounding breast glandular tissue, and the opposite trend was true for the malignant lesions. Statistical analysis of the viscoelasticity features revealed that a contrast feature based on the retardation-time is an accurate classifier of malignant and benign masses (P&lt;0.0003). It is concluded that the retardation time is a promising biomarker for differentiation of breast masses. [Work supported by NIH Grant CA168575.]