Systematic development of immunohistochemistry protocol for large cryosections-specific to non-perfused fetal brain

BACKGROUND

Immunohistochemistry (IHC) is an important technique in understanding the expression of neurochemical molecules in the developing human brain. Despite its routine application in the research and clinical setup, the IHC protocol specific for soft fragile fetal brains that are fixed using the non-perfusion method is still limited in studying the whole brain.

NEW METHOD

This study shows that the IHC protocols, using a chromogenic detection system, used in animals and adult humans are not optimal in the fetal brains. We have optimized key steps from Antigen retrieval (AR) to chromogen visualization for formalin-fixed whole-brain cryosections (20 µm) mounted on glass slides.

RESULTS

We show the results from six validated, commonly used antibodies to study the fetal brain. We achieved optimal antigen retrieval with 0.1 M Boric Acid, pH 9.0 at 70°C for 20 minutes. We also present the optimal incubation duration and temperature for protein blocking and the primary antibody that results in specific antigen labeling with minimal tissue damage.

COMPARISON WITH EXISTING METHODS

The IHC protocol commonly used for adult human and animal brains results in significant tissue damage in the fetal brains with little or suboptimal antigen expression. Our new method with important modifications including the temperature, duration, and choice of the alkaline buffer for AR addresses these pitfalls and provides high-quality results.

CONCLUSION

The optimized IHC protocol for the developing human brain (13-22 GW) provides a high-quality, repeatable, and reliable method for studying chemoarchitecture in neurotypical and pathological conditions across different gestational ages.

Histological characterization and development of mesial surface sulci in the human brain at 13-15 gestational weeks through high-resolution histology

Cellular-level anatomical data from early fetal brain are sparse yet critical to the understanding of neurodevelopmental disorders. We characterize the organization of the human cerebral cortex between 13 and 15 gestational weeks using high-resolution whole-brain histological data sets complimented with multimodal imaging. We observed the heretofore underrecognized, reproducible presence of infolds on the mesial surface of the cerebral hemispheres. Of note at this stage, when most of the cerebrum is occupied by lateral ventricles and the corpus callosum is incompletely developed, we postulate that these mesial infolds represent the primordial stage of cingulate, callosal, and calcarine sulci, features of mesial cortical development. Our observations are based on the multimodal approach and further include histological three-dimensional reconstruction that highlights the importance of the plane of sectioning. We describe the laminar organization of the developing cortical mantle, including these infolds from the marginal to ventricular zone, with Nissl, hematoxylin and eosin, and glial fibrillary acidic protein (GFAP) immunohistochemistry. Despite the absence of major sulci on the dorsal surface, the boundaries among the orbital, frontal, parietal, and occipital cortex were very well demarcated, primarily by the cytoarchitecture differences in the organization of the subplate (SP) and intermediate zone (IZ) in these locations. The parietal region has the thickest cortical plate (CP), SP, and IZ, whereas the orbital region shows the thinnest CP and reveals an extra cell-sparse layer above the bilaminar SP. The subcortical structures show intensely GFAP-immunolabeled soma, absent in the cerebral mantle. Our findings establish a normative neurodevelopment baseline at the early stage.

A Standardized Protocol for the Safe Retrieval of Infectious Postmortem Human Brain for Studying Whole-Brain Pathology

ABSTRACT

We describe a safe and standardized perfusion protocol for studying brain pathology in high-risk autopsies using a custom-designed low-cost infection containment chamber and high-resolution histology. The output quality was studied using the histological data from the whole cerebellum and brain stem processed using a high-resolution cryohistology pipeline at 0.5 μm per pixel, in-plane resolution with serial sections at 20-μm thickness. To understand the pathophysiology of highly infectious diseases, it is necessary to have a safe and cost-effective method of performing high-risk autopsies and a standardized perfusion protocol for preparing high-quality tissues. Using the low-cost infection containment chamber, we detail the cranial autopsy protocol and ex situ perfusion-fixation of 4 highly infectious adult human brains. The digitized high-resolution histology images of the Nissl-stained series reveal that most of the sections were free of processing artifacts, such as fixation damage, freezing artifacts, and osmotic shock, at the macrocellular and microcellular level. The quality of our protocol was also tested with the highly sensitive immunohistochemistry staining for specific protein markers. Our protocol provides a safe and effective method in high-risk autopsies that allows for the evaluation of pathogen-host interaction, the underlying pathophysiology, and the extent of the infection across the whole brain at microscopic resolutions.

A technology platform for standardized cryoprotection and freezing of large-volume brain tissues for high-resolution histology

Understanding and mapping the human connectome is a long-standing endeavor of neuroscience, yet the significant challenges associated with the large size of the human brain during cryosectioning remain unsolved. While smaller brains, such as rodents and marmosets, have been the focus of previous connectomics projects, the processing of the larger human brain requires significant technological advancements. This study addresses the problem of freezing large brains in aligned neuroanatomical coordinates with minimal tissue damage, facilitating large-scale distortion-free cryosectioning. We report the most effective and stable freezing technique utilizing an appropriate choice of cryoprotection and leveraging engineering tools such as brain master patterns, custom-designed molds, and a continuous temperature monitoring system. This standardized approach to freezing enables high-quality, distortion-free histology, allowing researchers worldwide to explore the complexities of the human brain at a cellular level. Our approach combines neuroscience and engineering technologies to address this long-standing challenge with limited resources, enhancing accessibility of large-scale scientific endeavors beyond developed countries, promoting diverse approaches, and fostering collaborations.

Wide field block face imaging using deep ultraviolet induced autofluorescence of the human brain

BACKGROUND

Imaging large volume human brains at cellular resolution involve histological methods that cause structural changes. A reference point prior to sectioning is needed to quantify these changes and is achieved by serial block face imaging (BFI) methods that have been applied to small volume tissue (∼1 cm3).

NEW METHOD

We have developed a BFI uniquely designed for large volume tissues (∼1300 cm3) with a very large field of view (20 × 20 cm) at a resolution of 70 µm/pixel under deep ultraviolet (UV-C) illumination which highlights key features.

RESULTS

The UV-C imaging ensures high contrast imaging of the brain tissue and highlights salient features of the brain. The system is designed to provide uniform and stable illumination across the entire surface area of the tissue and to work at low temperatures, which are required during cryosectioning. Most importantly, it has been designed to maintain its optical focus over the large depth of tissue and over long periods of time, without readjustments. The BFI was installed within a cryomacrotome, and was used to image a large cryoblock of an adult human cerebellum and brainstem (∼6 cm depth resulting in 2995 serial images) with precise optical focus and no loss during continuous serial acquisition.

COMPARISON WITH EXISTING METHOD(S)

The deep UV-C induced BFI highlights several large fibre tracts within the brain including the cerebellar peduncles, and the corticospinal tract providing important advantage over white light BFI.

CONCLUSIONS

The 3D reconstructed serial BFI images can assist in the registration and alignment of the microscopic high-resolution histological tissue sections.

Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning

Magnetic Resonance (MR) images suffer from various types of artifacts due to motion, spatial resolution, and under-sampling. Conventional deep learning methods deal with removing a specific type of artifact, leading to separately trained models for each artifact type that lack the shared knowledge generalizable across artifacts. Moreover, training a model for each type and amount of artifact is a tedious process that consumes more training time and storage of models. On the other hand, the shared knowledge learned by jointly training the model on multiple artifacts might be inadequate to generalize under deviations in the types and amounts of artifacts. Model-agnostic meta-learning (MAML), a nested bi-level optimization framework is a promising technique to learn common knowledge across artifacts in the outer level of optimization, and artifact-specific restoration in the inner level. We propose curriculum-MAML (CMAML), a learning process that integrates MAML with curriculum learning to impart the knowledge of variable artifact complexity to adaptively learn restoration of multiple artifacts during training. Comparative studies against Stochastic Gradient Descent and MAML, using two cardiac datasets reveal that CMAML exhibits (i) better generalization with improved PSNR for 83% of unseen types and amounts of artifacts and improved SSIM in all cases, and (ii) better artifact suppression in 4 out of 5 cases of composite artifacts (scans with multiple artifacts).Clinical relevance- Our results show that CMAML has the potential to minimize the number of artifact-specific models; which is essential to deploy deep learning models for clinical use. Furthermore, we have also taken another practical scenario of an image affected by multiple artifacts and show that our method performs better in 80% of cases.

Automated Knowledge Modeling for Cancer Clinical Practice Guidelines

Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation.

Automation of slide staining for large tissue sections

Staining is a critical step in tissue analysis as it enhances the visibility and contrast of tissue structures for microscopic examination. Large tissue sections such as the human brain, heart, and liver are becoming increasingly important in studying complex tissue structures, providing critical information about the tissue's normal or abnormal development, function, and disease processes. Manual staining methods are still widely used and are prone to inconsistencies and inaccuracies, leading to unreliable results. Commercially available automated staining systems offer a more efficient alternative, but currently, these systems are only available for smaller 1" x 3" slides which are ill-suited for examining larger tissue sections. To address this challenge, we present a custom-designed Large format Automated Slide Stainer that can handle various glass slides, from the standard 1" x 3" slides to the custom-sized 2" x 3", 5" x 7", and 6" x 8" glass slides. The system uses a Cartesian robotic arm to stain the slides and has a user-friendly and intuitive interface for creating and modifying custom staining protocols. Safety features include chemical isolation, a ventilation system, an emergency shutdown, and a protective shield to minimize hazards from handling chemicals and biological materials. The automated stainer showed little variability in positioning with a mean offset error of 1.65 ± 0.65 mm and 1.73 ± 0.76 mm in the X and Y axes, respectively. In addition, the automated staining process showed better uniformity than manual staining. A pairwise distance was used to evaluate how well image histograms matched within a batch. The automated staining had a mean pairwise distance of 0.0070 ± 0.0017 (Nissl) and 0.0060 ± 0.0003 (Hematoxylin and Eosin(H&E)), which were far superior to the manual staining distances (Nissl: 0.0173 ± 0.0107 and H&E: 0.0185 ± 0.0067). This system represents a substantial advancement in tissue staining and has the potential to improve the reliability of tissue analysis significantly.Clinical relevance - Automated system for providing accurate, reproducible, and high-throughput staining of large tissue sections for use in histopathology and research.

Multispectral Imaging for Vein Localization and Contrast Enhancement

Intravenous (IV) catheterization is a common procedure. Still, there is a 26% chance of the first attempt catheterization failure due to the changing visibility of veins because of the patient's skin tone and body fat content. Ultrasound assistive devices help locate deeper veins but are not practical in emergencies, and transillumination assistive devices have a low field of view. Commercial near-infrared (NIR) imaging devices are effective in vein localization but are expensive and are not used in low-cost clinical settings. To overcome this, NIR Multispectral Imaging (MSI) was used to find the optimal wavelength that provides the enhanced visualization of veins for all skin types and Body Mass Index (BMI). The band with the highest vein-to-skin contrast ratio was selected and contrast enhancement was done using our proposed method. The primary blocks of the proposed method are Gamma correction, Contrast Limited Adaptive Histogram Equalization (CLAHE), Adaptive Thresholding, and image Fusion. The optimal spectral range was found to be 814-876 nm and our method increased the contrast by 0.41, 0.375, and 0.39 for fair, brown, and dark brown skin types, respectively, with different BMI.Clinical relevance- From the study, we can develop a potentially low-cost vein localization assistive device for training medical and nursing students and use it in emergencies for venous access to improve confidence in IV catheterization.

Face-Free Chest Detection Using Convolutional Neural Networks for Non-Contact Respiration Monitoring

Non-contact methods for monitoring respiration face limitations when it comes to selecting the chest region of interest. The semi-automatic method, which requires the user to select the chest region in the first frame, is not suitable for real-time applications. The automatic method, which tracks the face first and then detects the chest region based on the face's position, can be inaccurate if the face is not visible or is rotated. Moreover, using the face region to track the chest region can under-utilize camera pixels since the face is not essential for monitoring respiration. This approach may adversely affect the quality of the respiration signal being measured. To address these issues, we propose a face-free chest detection model based on Convolutional Neural Networks. Our model enhances the measured non-contact respiration signal quality and utilizes more pixels for the chest region alone. In our quantitative study, we demonstrate that our method outperforms traditional methods that require the presence of the face. This approach offers potential benefits for real-time, non-contact respiration monitoring applicationsClinical relevance- This work enhances the performance of non-contact respiration monitoring techniques by precisely detecting the chest region without the need of face in it through a CNN-based model. The use of the CNN-based chest detection model also enhances the real-time monitoring capabilities of non-contact respiration monitoring techniques.

Improving Endoscopic Image Quality through the Use of High Dynamic Range Imaging-Like Method with Real-Time Performance

High Dynamic Range (HDR) imaging is a digital image processing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in real time. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.

Automation of slide coverslipping for large tissue sections

Coverslipping is the process of placing a cover glass or coverslip over a glass slide mounted with a stained tissue specimen without forming air bubbles, which can negatively impact the microscopic examination. While manual coverslipping is still widely used, automated systems have made the process easier and more consistent. Commercially available automated cover-slippers are limited to handling only slides that are 1" x 3", suitable for processing smaller tissue specimens. However, for larger tissue specimens sectioned from organs like the brain, liver, etc., slides can reach sizes up to 6" x 8", exceeding the capabilities of these systems. We present SLIDE PROTEKT, a fully automated large format coverslipping system designed to efficiently coverslip large format slides. This system has multiple zones for slide and coverslip transportation, dispensing of mounting medium, and precise placement of the coverslip without air bubbles. The ability of the system to output quality coverslipped slides was validated by processing 50 large-format brain tissue slides. The results were found to be comparable to manual coverslipping. The system achieved a coverslip placement accuracy of 80% with a mean positional offset that was within a tolerance of ±3 millimeters. Additionally, 75% of the slides had no air bubbles, while the remaining slides had air bubbles that were less than 120 micrometers in size. These results demonstrate the potential impact of SLIDE PROTEKT in the field of histology.

Arterial Wave Separation Analysis and Reflection Wave Transit Time Estimation using a Double Rayleigh Flow Rate Model

Arterial pulse wave separation analysis (WSA) requires simultaneously measured pressure and flow rate waveform from the same arterial site. Modelling approaches to flow rate waveforms offers a methodological and instrumentational advantage. However, current techniques are limited to the aortic site. For non-aortic sites such as carotid artery, modelling methods that were developed for aortic sites are not likely to capture the intrinsic differences in the carotid flow rate. In this work, a double-Rayleigh flow rate model for the carotid artery is developed to separate the forward and backward pressure waves using WSA (DRMWSA). The model parameters are optimally found based on characteristic features - obtained from the pressure waveform. The DRMWSA was validated using a database of 4374 virtual (healthy) subjects, and its performance was compared with actual flow rate based WSA (REFWSA) at the carotid artery. An RMSE < 2 mmHg were obtained for forward and backward pressure waveforms. The reflection quantification indices (ΔPF, ΔPB), (RM, RI) obtained from DRMWSA demonstrated strong and statistically significant correlation (r > 0.96, p < 0.001) and (r > 0.80, p < 0.001) respectively, with insignificant bias (p > 0.05), upon comparing with counterparts in REFWSA. A moderate correlation (r = 0.64, p < 0.001) was obtained for reflection wave transit time between both methods. The proposed method minimises the measurements required for WSA and has the potential to widen the vascular screening procedures incorporating carotid pulse wave dynamics.Clinical Relevance-This methodology quantifies arterial pressure wave reflections in terms of pressure augmentation and reflection transit time. The methodological advantage of using only a single waveform helps easy translation to technological solutions for clinical research.

Wearable Ambulatory Accelerometer System for Estimating Arterial Stiffness: A Pilot Study

Given the gap between the crucial role of measuring arterial stiffness in cardiovascular disease prevention and the lack of a technology for frequent/continuous measurement to assess it without an operator, we have developed a wearable accelerometer-based system. It estimates local stiffness metrics (Ep, β, and AC) by employing a one-point patient-specific calibration on the features of acceleration plethysmogram (APG) signal. An in-vivo study on 12 subjects was conducted (a) to select suitable ones from the host features on which the calibration could be applied and (b) to assess the feasibility of reliably estimating the stiffness metrics post-exercise when calibrated prior. The acquired APG signals were found to be reliable (SNR > 38 dB) and repeatable (CoV < 10 %). By examining a correlation matrix, it was found that (a-b)/(a"-b") is a potential feature of consideration for calibration against the stiffness. Due to exercise intervention, the local stiffness metrics have physiologically perturbed by a significant amount (p < 0.05), as observed from the reference measurements. Estimated Ep was found to have statistically significant and strong correlation (r = 0.761, p < 0.05) with actual Ep value, whereas statistically significant and moderate correlation were found with estimated β (r = 0.682, p < 0.05) and estimated AC (r = 0.615, p < 0.05) with their respective actual measures. The system demonstrated its ability to estimate post-exercise stiffness metrics using the baseline calibration, even when subject to significant physiological changes.Clinical Relevance- This study reveals the potential of the developed wearable system to be used for continuous stiffness estimation even in the presence of hemodynamic perturbations.

A Pilot Observational Cohort Study to Investigate the Effect of Valsalva Maneuver on Internal Jugular Venous Diameter

Valsalva maneuver (VM) is a technique widely used for acute elevation of blood pressure in humans. It has potential applications in cardiac health prediction and is also a diagnostic tool in cardiovascular, neurology and ENT screening. The jugular venous (JV) diameter increases during the VM procedure and hence it has been widely used to aid central venous catheterization in medical units. In this pilot study, we have quantified the variation in JV diameter response to VM across young and middle-aged populations. The study was conducted on a cohort of 16 males and 11 females, where the JV diameter in baseline, during and post VM intervention were acquired using a B-mode imaging system. The JV diameter measurements were within the ranges specified in earlier literature. The beat-to-beat variability in baseline diameter measurements was found to be between 8% to 20%. In younger population, the average maximum JV diameter during baseline was found to be 9.25 ± 2.61 mm and in middle-aged population it was 12.49 ± 2.65 mm. The average maximum JV diameter in young and middle-aged population during VM was 11.66 ± 2.74 mm and 16.73 ± 3.28 mm respectively. The study findings suggested a statistically significant variation (p < 0.05) between the JV diameter responses from young and middle-aged populations. The JV distensibility decreased significantly during VM in younger cohort (-35%) in comparison with the minimal changes observed in middle-aged population. The study demonstrates the variation in JV diameter and distensibility to VM in young and middle-aged populations.Clinical Relevance- This pilot study reveals the variations in JV diameter in response to VM intervention in young and middle-aged groups which has potential utility in assessing age dependent changes in vasculature.

Ultrasound for Venous Local Pulse Wave Velocity: Comparison of Pulse Transit Time Methods

Venous pulse wave velocity (vPWV) is a potential marker for determining the state of venous hemodynamics, venosclerosis, and vascular filling. Although there have been several studies on pulse wave velocity through blood vessels, the majority have focused on arteries, with only limited studies on veins. To our knowledge, this study is the first to compare the local vPWV estimation metrices. An in vivo study was conducted on 10 participants where the jugular venous pulses (JVP) from two proximal sites were simultaneously acquired using a dual-element high frame rate system. The local vPWV was computed using different transit time-based techniques. The study demonstrates the comparison between vPWV ranges computed using thresholding, fiduciary point (c and v) and correlation-based approaches indicated as vPWV|th, vPWV|c, vPWV|v and vPWV|Xcorr respectively. High fidelity echo frames were acquired from the jugular vein (JV) at a temporal resolution of 2 ms and an amplitude resolution of 10 µm. The study findings indicated that the vPWV computed using various transit time metrics were comparable without significant bias (p > 0.05). Among the VPWV metrices, vPWV|th had the lowest beat-to-beat variation (CoV = 18 %). The mean deviations in vPWV|c, vPWV|v and vPWV|Xcorr values from vPWV|th were 0.28, 0.17 and 0.22 m/s respectively, where the average beat-to-beat variation was minimal. The results suggested that the thresholding and cross-correlation metrices offered better performance in comparison with the fiduciary point techniques for vPWV estimation.Clinical Relevance- The study demonstrated the potential of direct transit time methods to reliably estimate the local vPWV on the internal jugular vein.

Methodological Considerations for Assessing Automatic Brightness Control in Endoscopy: Experimental Study

Endoscopy is a critical application that requires adaptable illumination to adjust to varying imaging conditions. Automatic brightness control (ABC) algorithms ensure optimal brightness throughout the image with rapid but smooth response and render the true colours of the biological tissue under examination. To achieve good image quality, high-quality ABC algorithms are necessary. In this study, we propose a three-assessment method approach for objectively evaluating ABC algorithms based on (1) image brightness and its homogeneity, (2) controller response and response time, and (3) colour rendition. We conducted an experimental study to assess the effectiveness of ABC algorithms in one commercial and two developmental endoscopy systems using the proposed methods. The results showed that the commercial system achieved good, homogeneous brightness within 0.4 s, and its damping ratio was 0.597, indicating a stable system, but its colour rendition was suboptimal. The developmental systems had control parameter values that resulted in either a sluggish response (over 1 s) or a fast (about 0.3 ms) but unstable response with damping ratios above 1, causing flickers. Our findings indicate that the interdependency among the proposed methods can establish tradeoffs in the overall ABC performance better than single-parameter approaches. The study establishes that comprehensive assessments using the proposed methods can contribute to designing new ABC algorithms and optimising already implemented ones for efficient performance in endoscopy systems.

Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models

Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes' well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model's accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors.

A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation

Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model's parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices.

Time-warping for robust automated arterial wall-recognition and tracking from single-scan-line ultrasound signals

Current ultrasound methods for recognition and motion-tracking of arterial walls are suited for image-based B-mode or M-mode scans but not adequately robust for single-line image-free scans. We introduce a time-warping-based technique to address this need. Its performance was validated through simulations and in-vivo trials on 21 subjects. The method recognized wall locations with 100 % precision for simulated frames (SNR > 10 dB). Clustering detections for multiple frames achieved sensitivity >98 %, while it was ∼90 % without clustering. The absence of arterial walls was predicted with 100 % specificity. In-vivo results corroborated the performance outcomes yielding a sensitivity ≥94 %, precision ≥98 %, and specificity ≥98 % using the clustering scheme. Further, excellent frame-to-frame tracking accuracy (absolute error <3 %, RMSE <2 μm) was demonstrated. Image-free measurements of peak arterial distension agreed with the image-based ones, within an error of 1.08 ± 3.65 % and RMSE of 38 μm. The method discerned the presence of arterial walls in A-mode frames, robustly localized, and tracked them even when they were proximal to hyperechoic regions or slow-moving tissue structures. Unification of delineation techniques with the proposed methods facilitates a complete image-free framework for measuring arterial dynamics and the development of reliable A-mode devices.

Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

Estimation of Characteristic Impedance using Multi-Gaussian Modelled Flow Velocity Waveform: A Virtual Subjects Study

Characteristic impedance (Zc) of the blood vessel relates the pulsatile pressure to pulsatile blood flow velocity devoid of any wave reflections. Estimation of Z_C is useful for indirect evaluation of local pulse wave velocity and crucial for solving wave separation analysis (WSA) which separates the forward-backward pressure and flow velocity waveforms. As opposed to conventional WSA, which requires simultaneous measurement of pressure and flow velocity waveform, simplified WSA relies on modelled flow velocity waveforms, mainly introduced for the aorta. This work uses a multi-Gaussian decomposition (MGD) modelled flow velocity waveform to estimate Z_C by employing a frequency domain analysis, which is applicable to other arteries such as carotid. Thus obtained ZC is compared with Zc estimated from true flow velocity waveform for healthy (virtual) subjects taken for the carotid artery. The MGD modelled flow velocity waveform estimated ZC for a range of 4.98 to 34.79 with a group average of 16.43±0.10. The difference between the group average values of both ZC was only 4.72%. A statistically significant and strong correlation (r = 0.708, p < 0.0001) was observed for ZC obtained from MGD modelled flow velocity waveform with ZC obtained from actual flow velocity waveform. The bias for ZC1 between the two methods was 0.74, with confidence intervals (CIs) between 7.44 and -5.96 for the Bland-Altman analysis. Therefore, ZC from MGD modelled flow velocity waveform is a potential surrogate of the flow velocity model for WSA at the carotid artery. Clinical Relevance- This study provides a new method to derive characteristic impedance without the measurement of actual flow velocity waveform. The method requires a single pulse waveform (pressure or diameter).

Evaluation of Pulse Contour Markers using an A-Mode Ultrasound: Association with Carotid Stiffness Markers and Ageing

Vascular ageing is directly associated with the blood vessel wall structural and functional abnormalities. Pulse morphology carries information on these abnormalities, and pulse contour analysis (PCA) identifies key amplitudes and timing information on the pulse waveforms that has a prognostic value towards cardiovascular risk stratification. PCA markers derived from second derivative waveforms represent the accelerative and decelerative phase of an arterial pulse. In this work, second derivative diameter waveforms of central arteries such as carotid artery are obtained using an A-mode ultrasound device. The derived PCA markers (b/a, c/a, d/a, e/a, (b-c-d-e)/a) from diameter waveform is investigated for its association with central stiffness markers and aging. An observational and cross-sectional study on 106 subjects (51 male/55 females) was conducted for this investigation. The highest correlation (r = 0.5, P < 0.001) was observed between c/a and PWV, and the lowest correlation was between c/a and AC. Group average values of PCA markers for each age decade group were correlated strongly (r > 0.9, p < 0.001) with age. A change > 19% was observed between the group average values of PCA markers of the normotensive and hypertensive population. The applicability of aforesaid PCA markers on central pulse waveforms, measured using a noninvasive device in resource-limited field settings, would accelerate such large scale vascular screening that is essential to understanding the cardiovascular risks at a population level. Clinical Relevance- This study provides an investigation into using second derivative diameter waveforms obtained from the carotid artery to find its associations with arterial stiffness and ageing.

Association of Local Arterial Stiffness and Windkessel Model Parameters with Ageing in Normotensives and Hypertensives

Computation of arterial stiffness is a well-established, widely accepted method for estimating vascular age. Although carotid-femoral pulse wave velocity is typically used for vascular age assessment, most recent studies have reported the need to consider a combination of local and regional stiffness indices possessing distinct association with the vascular structure and/or function for better prediction of early vascular ageing syndrome. In this work, we investigate the association of clinically validated local stiffness (obtained using biomechanical relations), global stiffness (obtained from 3-element Windkessel modelling), and pulse contour indices from the aorta with ageing and their distribution in normotensives and hypertensives. The analysis was performed on 420 (virtual) subjects (age: 65 ± 11 years) with an equal proportion of hypertensive (age: 65 ± 11 years) and normotensive (age: 65 ± 11 years) subjects. Multivariate linear regression analysis revealed an independent association of each of the indices with age (Adjusted r = 0.75 p < 0.01). Specific stiffness index (r = 0.67, p < 0.001), Augmentation index (r = 0.55, p< 0.001) and total arterial compliance (r = -0.50, p < 0.001) depicted highest correlation with age. There was a significant difference (> 16%, p < 0.001) in mean values of the measured indices between hypertensive and normotensive subjects. The study findings further emphasize the need to combine multiple non-invasive vascular markers to capture the unique aspects of age-induced arterial wall remodelling for reliable monitoring and management of the early vascular ageing syndrome. Clinical Relevance- This study demonstrates an independent and combined predictive role of local/global stiffness and pulse contour indices in ageing.

Enhanced Vascular Features in Porcine Gastrointestinal Endoscopy Using Multispectral Imaging

Endoscopic investigation is a predominant stan-dard while assessing the gastrointestinal tract. Even though it has been rigorously used in diagnostics for many decades, a high miss rate has been recorded. Advanced endoscopic imaging still has not found solutions to problems like early cancer detection, polyp generality, disease classification, etc. One of the less explored techniques to study early cancer detection is spectral imaging which deals with the absorption and reflection spectra of various wavelengths of light by different layers of tissue. To study tissues under various illumination, a multi-spectral light source unit that can be used along with an endoscopy system was developed with 10 different LEDs of very narrow bandwidths. Using this light source, a feasibility study was per-formed on an animal in which the upper GI tract of a porcine model was imaged and sample images were taken for processing from five different sections. Some wavelengths showed better contrast enhancements for visualization of vascular structures. Wavelength 420 nm (violet light) showed better contrast and the gradient of the line profile histogram showed the highest intensity change between the blood vessels and the surrounding mucosa. These enhancements showed that spectral imaging can potentially help in studying tissues for early cancer detection and improved visualization of the G I tract using endoscopy.

Comparative Analysis of Resting Heart Rate Measurement at Multiple Instances in a Single Day

Resting Heart Rate (RHR) is used as an indicator of cardiovascular health and overall fitness. Clinically, RHR is measured from beat-to-beat heart rate data during the day when the body is at rest (RHR_rest), typically for ≥ 5 minutes. In this paper, we have compared the RHR measurements done at multiple instances in a single day namely, [Formula: see text], RHR immediately after waking up (RHR_morning) and RHR during sleep (RHR_sleep). The significance of measuring RHR_sleep and why it can be used as a potential replacement for the conventional methods is analysed through an experimental study in this paper. The results obtained using the proposed method stands out in terms of repeatability. RHR measurements were taken for 3 instances on a single day for 9 subjects on 5 alternate workdays. A comparative analysis was performed by measuring the repeatability coefficient (RC) and Standard Deviation (SD) on the RHR measurements taken during multiple instances for each subject separately. The average RC and SD over the 5 alternate workdays was 5 bpm and SD was 2 bpm for RHR_slep. For RHR_rest and RHR_morning, the average RC was 12 bpm and 11 bpm and the average SD was 5 bpm and 4 bpm respectively, which is comparatively higher. Hence this method can be potentially adopted instead of the conventional methods as the RHRsleep parameter is more reliable and precise due to its repeatable nature.

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning-based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.

High Frame-Rate A-Mode Ultrasound System for Jugular Venous Pulse Tracking: A Feasibility Study

Jugular venous pulse (JVP) helps in the early detection of central venous pressure abnormalities and various cardiovascular diseases. Studies have been reported indicating that contour features of the JVP waveform provide crucial information regarding cardiac function. Although current ultrasound systems reliably provide the diameter measurements, they are limited by low frame rates resulting in poor resolution JVP cycles that are inadequate to yield distinguishable critical points. In this work, we propose an image-free high frame rate system for the assessment of JVP signals. The proposed A-mode ultrasound system acquires high fidelity JVP pulses with a temporal resolution of 4 ms and amplitude resolution of 10 µm. The functionality verification of the proposed system was performed by comparing it against a clinical-grade B-mode imaging system. A study was conducted on a cohort of 25 subjects in the 20-30 age group. While the system provided diameter measurements comparable to that of the imaging ones (r > 0.98, p < 0.05), it also yielded high-resolution JVP exhibiting the presence of all fiduciary points. This was a leveraging feature as opposed to the imaging system that possessed limited temporal and amplitude resolution. Clinical Relevance- The proposed system is a potential ultrasound means for measuring the diameter values from JV at the same time yielding the JVP critical points necessary for clinical analysis.

Improving Fast MRI Reconstructions with Pretext Learning in Low-Data Regime

Supervised deep learning methods have shown great promise for making magnetic resonance (MR) imaging scans faster. However, these supervised deep learning models need large volumes of labelled data to learn valuable representations and produce high-fidelity MR image reconstructions. The data used to train these models are often fully-sampled raw MR data, retrospectively under-sampled to simulate different MR acquisition acceleration factors. Obtaining high-quality, fully sampled raw MR data is costly and time-consuming. In this paper, we exploit the self supervision based learning by introducing a pretext method to boost feature learning using the more commonly available under-sampled MR data. Our experiments using different deep-learning-based reconstruction models in a low data regime demonstrate that self-supervision ensures stable training and improves MR image reconstruction.

Camera fusion for real-time temperature monitoring of neonates using deep learning

Abstract

The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera systems are increasingly investigated. The combination of deep learning–based algorithms and camera modalities such as RGB and infrared thermography can improve the development of cable-free methods for the extraction of vital parameters. In this study, a real-time approach for local extraction of temperatures on the body surface of neonates using a multi-modal clinical dataset was implemented. Therefore, a trained deep learning–based keypoint detector was used for body landmark prediction in RGB. Image registration was conducted to transfer the RGB points to the corresponding thermographic recordings. These landmarks were used to extract the body surface temperature in various regions to determine the central-peripheral temperature difference. A validation of the keypoint detector showed a mean average precision of 0.82. The registration resulted in mean absolute errors of 16.4 px (8.2 mm) for x and 22.4 px (11.2 mm) for y. The evaluation of the temperature extraction revealed a mean absolute error of 0.55 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C. A final performance of 31 fps was observed on the NVIDIA Jetson Xavier NX module, which proves real-time capability on an embedded GPU system. As a result, the approach can perform real-time temperature extraction on a low-cost GPU module.

Graphical abstract

Arterial pressure pulse wave separation analysis using a multi-gaussian decomposition model

OBJECTIVE

Methods for separating the forward-backward components from blood pulse waves rely on simultaneously measured pressure and flow velocity from a target artery site. Modelling approaches for flow velocity simplify the wave separation analysis (WSA), providing a methodological and instrumentational advantage over the former; however, current methods are limited to the aortic site. In this work, a multi-Gaussian decomposition (MGD) modelled WSA (MGDWSA) is developed for a non-aortic site asuch as the carotid artery. While the model is an adaptation of the existing wave separation theory, it does not rely on the information of measured or modelled flow velocity.

APPROACH

The proposed model decomposes the arterial pressure waveform using weighted and shifted multi-Gaussians, which are then uniquely combined to yield the forward (PF(t)) and backward (PB(t)) pressure wave. A study using the database of healthy (virtual) subjects was used to evaluate the performance of MGDWSA at the carotid artery and was compared against reference flow-based WSA methods.

MAIN RESULTS

The MGD modelled pressure waveform yielded a root-mean-square error (RMSE) < 0.35 mmHg. Reliable forward-backward components with a group average RMSE < 2.5 mmHg for PF(t) and PB(t) were obtained. When compared with the reference counterparts, the pulse pressures (ΔPF and ΔPB), as well as reflection quantification indices, showed a statistically significant strong correlation (r > 0.96, p < 0.0001) and (r > 0.83, p < 0.0001) respectively, with an insignificant (p > 0.05) bias.

SIGNIFICANCE

This study reports WSA for carotid pressure waveforms without assumptions on flow conditions. The proposed method has the potential to adapt and widen the vascular health assessment techniques incorporating pulse wave dynamics.

Cross fertilisation of Public Health and Translational Research

Public health is defined as the science of protecting the safety and improving the health of communities through education, policy-making and research for the prevention of disease (Gatseva and Argirova in J Public Health 19(3):205–6, 2011, 10.1007/s10389-011-0412-8; Winslow in Mod Med 2(1306):183–91, 1920. 10.1126/science.51.1306.23; What is public health. Centers for Disease Control Foundation. Centers for Disease Control, Atlanta, https://www.cdcfoundation.org/what-public-health; What is the WHO definition of health? from the Preamble to the Constitution of WHO as adopted by the International Health Conference, New York, On 7 April 1948. The definition has not been amended since. 22 July 1946; signed by the representatives of 61 States (Official Records of WHO, no. 2, p. 100) and entered into force, 19 June;1948. https://web.archive.org/web/20190307113324/https:/www.who.int/about/who-we-are/frequently-asked-questions). Translational research in healthcare is not only useful and satisfying for the researchers to bring their work to market but it would also support public health by bringing affordable, attainable and scalable solutions to the community at large. This is of high significance because instead of increasing the GDP spent in public health, we should focus on the increasing the translational research spending, as this would lead to improved solutions. Hence, the public health offering would reach a larger community at an improved cost. The COVID-19 pandemic and the huge number of lives it claimed exposes challenges in the public health. The pandemic has caused economic and social disruption to millions of people around the world, with many falling into extreme poverty. In early 2021, it was estimated nearly 690 million people are undernourished and by end of 2021 to increase further by 132 million (Joint statement by ILO, FAO, IFAD and WHO. Impact of COVID-19 on people's livelihoods, their health and our food systems https://www.who.int/news/item/13-10-2020-impact-of-covid-19-on-people's-livelihoods-their-health-and-our-food-systems). The spending for public health has increased many folds during the pandemic and this is where translational research in healthcare can play a transformative role to reduce the burden on government healthcare budget (Covid-19 and its impact on Indian society. https://timesofindia.indiatimes.com/readersblog/covid-19-and-its-impact-on-india/covid-19-and-its-impact-on-indian-society-27565/). Over the past decade, public health research has started playing a major role in Indian academic settings. COVID-19 pandemic has further highlighted the role of public health. However, the potential of using technological advancement has not been fully utilised. This is where translational research and public health can play a role to tap the full potential of technology. This review paper explores the public health practices to understand the different practices to examine how both public health and translational research can cross-fertilise. It concludes with a short discussion on implications on policymakers.

High-frame-rate A-mode ultrasound for calibration-free cuffless carotid pressure: feasibility study using lower body negative pressure intervention

PURPOSE

Existing technologies to measure central blood pressure (CBP) intrinsically depend on peripheral pressure or calibration models derived from it. Pharmacological or physiological interventions yielding different central and peripheral responses compromise the accuracy of such methods. We present a high-frame-rate ultrasound technology for cuffless and calibration-free evaluation of BP from the carotid artery. The system uses a pair of single-element ultrasound transducers to capture the arterial diameter and local pulse wave velocity (PWV) for the evaluation of beat-by-beat BP employing a novel biomechanical model.

MATERIALS AND METHODS

System's functionality assessment was conducted on eight male subjects (26 ± 4 years, normotensive and no history of cardiovascular risks) by perturbing pressure via short-term moderate lower body negative pressure (LBNP) intervention (-40 mmHg for 1 min). The ability of the system to capture dynamic responses of carotid pressure to LBNP was investigated and compared against the responses of peripheral pressure measured using a continuous BP monitor.

RESULTS

While the carotid pressure manifested trends similar to finger measurements during LBNP, the system also captured the differential carotid-to-peripheral pressure response, which corroborates the literature. The carotid diastolic and mean pressures agreed with the finger pressures (limits-of-agreement within ±7 mmHg) and exhibited acceptable uncertainty (mean absolute errors were 2.4 ± 3.5 and 2.6 ± 4.0 mmHg, respectively). Concurrent to the literature, the carotid systolic and pulse pressures (PPs) were significantly lower than those of the finger pressures by 11.1 ± 9.4 and 11.3 ± 8.2 mmHg, respectively (p < .0001).

CONCLUSIONS

The study demonstrated the method's potential for providing cuffless and calibration-free pressure measurements while reliably capturing the physiological aspects, such as PP amplification and dynamic pressure responses to intervention.

A Machine Learning Pipeline for Measurement of Arterial Stiffness in A-Mode Ultrasound

Arterial stiffness (AS) of the carotid artery is an early marker of stratifying cardiovascular disease risk. This article aims to improve the performance of ARTSENS, a noninvasive A-mode ultrasound-based device for measuring AS. The primary objective of ARTSENS is to enable the measurement of elastic modulus using A-Mode ultrasound and blood pressure. As this device is image-free, there is a need to automate: 1) carotid detection; 2) wall localization; and 3) inner lumen diameter measurement. This has been performed using conventional signal processing methods in some of the earlier works in this domain. In this article, deep neural network (DNN) models are employed to perform the above three tasks. The DNNs were trained over data acquired from 82 subjects at two different medical centers. Ground-truth labeling was performed by a trained operator using corresponding measurements from the state-of-the-art Aloka e-Tracking system. All three DNN models had significantly lower errors compared to earlier signal processing methods and could perform their measurements using a single A-Mode frame. Using the DNNs, two different machine learning pipelines have been proposed here to measure the elastic modulus; the best among them could achieve an error of 9.3% with the Pearson correlation coefficient of 0.94 ( ). The models were tested on Raspberry Pi and Jetson Nano single board computers to demonstrate real-time processing on low computational resources.

Community seroprevalence and risk factors for SARS CoV-2 infection in different subpopulations in Vellore, India and its implications for future prevention

Objectives

The aim of this study was to inform public health policy decisions through the assessment of IgG antibody seroprevalence in the population and the risk factors for SARS-CoV-2 infection.

Methods

The seroprevalence of IgG antibodies among different subpopulations at the end of the first and second waves of the pandemic was estimated. Various risk factors associated with seropositivity, including sociodemography, IgG antibodies against endemic human coronavirus, and vaccination status, were also assessed.

Results

For all 2433 consenting participants, the overall estimated seroprevalences at the end of first and second waves were 28.5% (95% CI 22.3–33.7%) and 71.5% (95% CI 62.8–80.5%), respectively. The accrual of IgG positivity was heterogeneous, with the highest seroprevalences found in urban slum populations (75.1%). Vaccine uptake varied among the subpopulations, with low rates (< 10%) among rural and urban slum residents. The majority of seropositive individuals (75%) were asymptomatic. Residence in urban slums (OR 2.02, 95% CI 1.57–2.6; p < 0.001), middle socioeconomic status (OR 1.77, 95% CI 1.17–2.67; p = 0.007), presence of diabetes (OR 1.721, 95% CI 1.148–2.581; p = 0.009), and hypertension (OR 1.75, 95% CI 1.16–2.64; p = 0.008) were associated with seropositivity in multivariable analyses.

Conclusion

Although considerable population immunity has been reached, with more than two-thirds seropositive, improved vaccination strategies among unreached subpopulations and high-risk individuals are suggested for better preparedness in future.

Separation of Forward-Backward Waves in the Arterial System using Multi-Gaussian Approach from Single Pulse Waveform

The arterial pulse waveform has an immense wealth of information in its morphology yet to be explored and translated to clinical practice. Wave separation analysis involves decomposing a pulse wave (pressure or diameter waveform) into a forward wave and a backward wave. The backward wave accumulates reflections due to arterial stiffness gradient, branching and geometric tapering of blood vessels across the arterial tree. The state-of-the-art wave separation analysis is based on estimating the input impedance of the target artery in the frequency/time domain, which requires simultaneously measured or modelled flow velocity and pressure waveform. We are proposing a new method of wave separation analysis using a multi-gaussian decomposition. The novelty of this approach is that it requires only a single pulse waveform at the target artery. Our method was compared against the triangular waveform-based impedance method. We successfully separated forward and backward waveform from the pressure waveform with maximum RMSE less than 5 mmHg and mean RMSE of 1.31 mmHg when compared against the triangular flow/impedance method. Results demonstrated a statistically significant correlation (r>0.66, p<0.0001) for Reflection Magnitude (RM) and Reflection Index (RI) for the multi-gaussian approach against the triangular flow method for 105 virtual subjects. The range of RM was from 0.35 to 0.97 (RI: 27.53% to 49.29%). This method proves to be a technique for evaluating reflection parameters if only a single pulse measurement is available from any artery.Clinical Relevance- This simulation study supplements the evidence for wave reflections. It provides a new method to study wave reflections using only a single pulse waveform without the need for any measured or modelled flow.

Evaluation of Vascular Pulse Contour Indices over the Physiological Blood Pressure Ranges in an Anesthetized Porcine Model

A series of physiological measures can be assessed from the arterial pulse waveform, which is beneficial for cardiovascular health diagnosis, monitoring, and decision making. In this work, we have investigated the variations in regional pulse wave velocity (PWVR) and other pulse waveform indexes such as reflected wave transit time (RWTT), augmentation index (Alx), ejection duration index (ED), and subendocardial viability ratio (SEVR) with blood pressure (BP) parameters and heartrate on a vasoconstrictor drug-induced porcine model. Two healthy female (nulliparous and non-pregnant) Sus scrofa swine (~ 80 kg) was used for the experimental study. The measurement system consists of a catheter-based system with two highly accurate pressure catheters placed via the sheath at the femoral and carotid artery for acquiring and recording the pressure waveforms. The pulse waveform indexes were extracted from these recorded waveforms. Results from the pulse contour analysis of these waveforms demonstrated that Phenylephrine, as a post-synaptic alpha-adrenergic receptor agonist that causes vasoconstriction, produced a significant increment in the carotid BP parameters and heartrate. Due to the drug's effect, the PWVR and SEVR were significantly increased, whereas the RWTT, AIx index and ED index significantly decreased.Clinical Relevance- This experimental study provides the usefulness of the pulse contour analysis and estimation of various pulse waveform indexes for cardiovascular health screening and diagnosis.

Phantom Assessment of an Image-free Ultrasound Technology for Online Local Pulse Wave Velocity Measurement

Cardiovascular community has started clinically adopting the assessment of local stiffness, contrary to the traditionally measured carotid-femoral pulse wave velocity (PWV). Though they offer higher reliability, ultrasound methods require advanced hardware and processing methods to perform real-time measurement of local PWV. This work presents a system and method to perform online PWV measurement in an automated manner. It is a fast image-free ultrasound technology that meets the methodological requirements necessary to measure small orders of local pulse transit, from which PWV is measured. The measurement accuracy and repeatability were assessed via phantom experiments, where the measured transit time-based PWV (PWVTT) was compared against the theoretically calculated PWV from Bramwell-Hill equation (PWVBH). The beat-to-beat variability in the measured PWVTT was within 3%. PWVTT values strongly correlated (r=0.98) with PWVBH, yielding a negligible bias of -0.01 m/s, mean error of 3%, and RMSE of 0.27 m/s. These pilot study results demonstrated the presented system's reliability in yielding online local PWV measurements.

Gaussian-Mixture Modelling of A-Mode Radiofrequency Scans for the Measurement of Arterial Wall Thickness

Measurement of arterial wall thickness is an integral component of vascular properties and health assessment. State-of-the-art automated or semi-automated techniques are majorly applicable to B-mode images and are not available for entry-level in-expensive devices. Considering this, we have earlier developed and validated an image-free (A-mode) ultrasound device, ARTSENS^® for the evaluation of vascular properties. In this work, we present a novel gaussian-mixture modeling-based method to measure arterial wall thickness from A-mode frames, which is readily deployable to the existing technology. The method's performance was assessed based on systematic simulations and controlled phantom experiments. Simulations revealed that the method could be confidently applied to A-mode frames with above-moderate SNR (>15 dB). When applied to A-mode frames acquired from the flow-phantom setup (SNR > 25 dB), the mean error was limited to (2 ± 1%), and RMSE was 19 μm, on comparison with B-mode measurements. The measured and reference wall thickness strongly agreed with each other (r = 0.88, insignificant mean bias = 7 μm, p = 0.16). The proposed method was capable of performing real-time measurements.

Evaluation of Nonlinear Wave Separation Method to Assess Reflection Transit Time: A Virtual Patient Study

Conventional methods to calculate reflection transit time (RTT) is based on pulse counter analysis. An alternative to this approach is separating forward and backward components from a pulse waveform to calculate the RTT. State-of-the-art in wave separation requires simultaneously measured pressure and flow velocity waveforms. Practically, getting a simultaneous measurement from a single arterial site has its limitations, and this has made the translation of wave separation methods to clinical practice difficult. We propose a new method of wave separation analysis that requires only a single pulse waveform measurement using a multi-Gaussian decomposition approach. The novelty of the method is that it does not require any measured or modelled flow velocity waveform. In this method, the pulse waveform is decomposed into the sum of Gaussians and reconstructed based on model criteria. RTT is calculated as the time difference between normalized forward and backward waveform. The method's feasibility in using RTT as a potential surrogate is demonstrated on 105 diverse selections of virtual subjects. The results were statistically significant and had a strong correlation (r>79, p<0.0001) against clinically approved artery stiffness markers such as Peterson's elastic modulus (Ep), pulse wave velocity (PWV), specific stiffness index (β), and arterial compliance (AC). Out of all the elasticity markers, a better correlation was found against AC.Clinical Relevance-This simulation study supplements the evidence for the dependence of pulse wave reflections on arterial stiffness. It provides a new method to study wave reflections using only a single pulse waveform.

High-Framerate A-Mode Ultrasound for Vascular Structural Assessments: In-Vivo Validation in a Porcine Model

Capturing vascular dynamics using ultrasound at a high framerate provided a unique way to track time-dependent and transient physiologic events non-invasively. In this work, we present an A-model high-framerate (500 frames per second) image-free ultrasound system for monitoring vascular structural and material properties. It was developed based on our clinically validated ARTSENS^® technology. Following in-vitro verification on arterial flow phantoms, its measurement accuracy and high-framerate data acquisition and processing were verified in-vivo on 2 anesthetized Sus scrofa swine. Measurements of the carotid artery (the luminal diameter, distension, and wall thickness) obtained using the high-framerate system were comparable to those provided by a clinical-grade reference ultrasound imaging device (absolute error < 4%, < 6.3%, and < 6.6%, respectively). Notably, the morphology of the arterial distension waveforms obtained at high-framerate depicted vital physiological fiduciary points compared to the low-framerate reference waveform. The compression-decompression pattern of the arterial wall was also captured with the high-framerate system, which is challenging with low-framerate ultrasound. Potential applications of these high temporal structural waveforms have also been discussed.

Association of incremental pulse wave velocity with cardiometabolic risk factors

We investigate the association of incremental pulse wave velocity (ΔC; the change in pulse wave velocity over a cardiac cycle) with cardiometabolic risk factors and report the first and (currently) the largest population-level data. In a cross-sectional study performed in a cohort of 1373 general population participants, ΔC was measured using clinically validated ARTSENS devices. There were 455 participants in the metabolic syndrome (MetS) group whose average ΔC was ~ 28.4% higher than that of the non-metabolic syndrome (Non-MetS) group. Females with MetS showed ~ 10.9% elevated average ΔC compared to males of the Non-MetS group. As the number of risk factors increased from 0 to 5, the average ΔC escalated by ~ 55% (1.50 ± 0.52 m/s to 2.33 ± 0.91 m/s). A gradual increase in average ΔC was observed across each decade from the younger (ΔC = 1.53 ± 0.54 m/s) to geriatric (ΔC = 2.34 ± 0.59 m/s) populations. There was also a significant difference in ΔC among the blood pressure categories. Most importantly, ΔC ≥ 1.81 m/s predicted a constellation of ≥ 3 risks with AUC = 0.615, OR = 2.309, and RR = 1.703. All statistical trends remained significant, even after adjusting for covariates. The study provides initial evidence for the potential use of ΔC as a tool for the early detection and screening of vascular dysfunction, which opens up avenues for active clinical and epidemiological studies. Further investigations are encouraged to confirm and establish the causative mechanism for the reported associations.

VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.

IQVision: An Image-Based Evaluation Tool for Quantitative Lateral Flow Immunoassay Kits

The development of quantitative lateral flow immunoassay test strips involves a lot of research from kit manufacturers’ standpoint. Kit providers need to evaluate multiple parameters, including the location of test regions, sample flow speed, required sample volumes, reaction stability time, etc. A practical visualization tool assisting manufacturers in this process is very much required for the design of more sensitive and reliable quantitative LFIA test strips. In this paper, we present an image-based quantitative evaluation tool determining the practical functionality of fluorescence-labelled LFIA test cartridges. Image processing-based algorithms developed and presented in this paper provide a practical analysis of sample flow rates, reaction stability times of samples under test, and detect any abnormalities in test strips. Evaluation of the algorithm is done with Glycated Hemoglobin (HbA1C) and Vitamin D test cartridges. Practical sample flow progress for HbA1C test cartridges is demonstrated. The reaction stability time of HbA1C test samples is measured to be 12 min, while that of Vitamin D test samples is 24 min. Experimental evaluation of the abnormality detection algorithm is carried out, and sample flow abnormalities are detected with 100% accuracy while membrane irregularities are detected with 96% accuracy.

Comparative study of silicone membrane simulator and animal eye models for sub-Tenon's block

To compare and assess silicone membrane-based sub-Tenon's block (STB) simulator and animal eye model (goat's eye) for practicing STB in terms of anatomical similarity and feel of texture of eye layers. The study included 34 participants (26 learners and 8 consultants) from tertiary ophthalmic centres. The participants were divided into groups A and B. Group A performed STB on the goat's eyes before using the silicone membrane simulator. Group B performed STB on the simulator and further proceeded to the goat's eye. Participants had to rate the anatomical similarity and feel of the texture for the simulator model on a scale of 0-10 and share their preference between the two models. In group A, the scores given to the simulator model and the feel of texture of layers were 8.05 ± 0.88 and 7.97 ± 1.07, respectively, and the scores given to the animal model and the feel of texture of layers were 8.11 ± 0.97 and 8.21 ± 0.88, respectively. Group B participants scored the simulator model and feel of texture of layers with 8.13 ± 0.95 and 8.25 ± 0.99, respectively. Overall, 89% participants preferred the simulator; the reasons included ease of usage, helpful warning system, absence of biological waste, and facility for repeatable training. The study validated anatomical accuracy, preference, and ability of usage of the STB simulator. For broader usage, further study involving higher number of participants is recommended.

Fast body part segmentation and tracking of neonatal video data using deep learning

Photoplethysmography imaging (PPGI) for non-contact monitoring of preterm infants in the neonatal intensive care unit (NICU) is a promising technology, as it could reduce medical adhesive-related skin injuries and associated complications. For practical implementations of PPGI, a region of interest has to be detected automatically in real time. As the neonates' body proportions differ significantly from adults, existing approaches may not be used in a straightforward way, and color-based skin detection requires RGB data, thus prohibiting the use of less-intrusive near-infrared (NIR) acquisition. In this paper, we present a deep learning-based method for segmentation of neonatal video data. We augmented an existing encoder-decoder semantic segmentation method with a modified version of the ResNet-50 encoder. This reduced the computational time by a factor of 7.5, so that 30 frames per second can be processed at 960 × 576 pixels. The method was developed and optimized on publicly available databases with segmentation data from adults. For evaluation, a comprehensive dataset consisting of RGB and NIR video recordings from 29 neonates with various skin tones recorded in two NICUs in Germany and India was used. From all recordings, 643 frames were manually segmented. After pre-training the model on the public adult data, parts of the neonatal data were used for additional learning and left-out neonates are used for cross-validated evaluation. On the RGB data, the head is segmented well (82% intersection over union, 88% accuracy), and performance is comparable with those achieved on large, public, non-neonatal datasets. On the other hand, performance on the NIR data was inferior. By employing data augmentation to generate additional virtual NIR data for training, results could be improved and the head could be segmented with 62% intersection over union and 65% accuracy. The method is in theory capable of performing segmentation in real time and thus it may provide a useful tool for future PPGI applications. Graphical Abstract This work presents the development of a customized, real-time capable Deep Learning architecture for segmenting of neonatal videos recorded in the intensive care unit. In addition to hand-annotated data, transfer learning is exploited to improve performance.

Demonstration of Pressure-Dependent Inter and Intra-Cycle Variations in Local Pulse Wave Velocity Using Excised Bovine Carotid Artery

Pulse wave velocity (PWV) is a function of the artery's material property, and its incremental nature in elastic modulus led to the concept of incremental PWV. Recent advancements in technology paved the way for reliable measurement of the variation in PWV within a cardiac cycle. This change in PWV has shown its potential as a biomarker for advanced cardiovascular diagnostics, screening, and has recently started using as a vascular screening tool and medical device development. In this work, we have demonstrated the concept of inter and intra-cycle variations of PWV with pressure using an excised bovine carotid artery. Results demonstrated that local PWV measured at the foot of the waveform followed the same trend as of the pressure. As the pressure level was increased to 68% across the cycles, resulting PWV increased up to 81%. An exponential PWV-Pressure relationship was obtained, in agreement with the widely used models. The incremental nature of PWV was recorded in a reflection-free region of the pressure pulse wave. This was further demonstrated in continuous pulse cycles with varying pressure ranges, by comparing the PWV values at two fiduciary points selected in the upstroke of the pressure wave. On average, a 48.11% increase in PWV was observed for 31.04% increase in pressure between the selected fiducial points within a pulse cycle. The article concludes, highlighting the clinical significance of incremental PWV.Clinical Relevance- This experimental study supplements the evidence for the incremental nature of PWV within a cardiac cycle, which has the potential for being a biomarker for advanced cardiovascular screening and diagnostics.

MRI Super-Resolution using Laplacian Pyramid Convolutional Neural Networks with Isotropic Undecimated Wavelet Loss

High spatial resolution of Magnetic Resonance images (MRI) provide rich structural details to facilitate accurate diagnosis and quantitative image analysis. However the long acquisition time of MRI leads to patient discomfort and possible motion artifacts in the reconstructed image. Single Image Super-Resolution (SISR) using Convolutional Neural networks (CNN) is an emerging trend in biomedical imaging especially Magnetic Resonance (MR) image analysis for image post processing. An efficient choice of SISR architecture is required to achieve better quality reconstruction. In addition, a robust choice of loss function together with the domain in which these loss functions operate play an important role in enhancing the fine structural details as well as removing the blurring effects to form a high resolution image. In this work, we propose a novel combined loss function consisting of an L1 Charbonnier loss function in the image domain and a wavelet domain loss function called the Isotropic Undecimated Wavelet loss (IUW loss) to train the existing Laplacian Pyramid Super-Resolution CNN. The proposed loss function was evaluated on three MRI datasets - privately collected Knee MRI dataset and the publicly available Kirby21 brain and iSeg infant brain datasets and on benchmark SISR datasets for natural images. Experimental analysis shows promising results with better recovery of structure and improvements in qualitative metrics.