Boron- and Oxygen-Doped π-Extended Helical Nanographene with Circularly Polarised Thermally Activated Delayed Fluorescence.

Helical nanographenes have garnered substantial attention owing to their finely adjustable optical and semiconducting properties. The strategic integration of both helicity and heteroatoms into the nanographene structure, facilitated by a boron-oxygen-based multiple resonance (MR) thermally activated delayed fluorescence (TADF), elevates its photophysical and chiroptical features. This signifies the introduction of an elegant category of helical nanographene that combines optical (TADF) and chiroptical (CPL) features. In this direction, we report the synthesis, optical, and chiroptical properties of boron, oxygen-doped Π-extended helical nanographene. The π-extension induces distortion in the DOBNA-incorporated nanographene, endowing a pair of helicenes, (P)-B2NG, and (M)-B2NG exhibiting circularly polarized luminescence with glum of -2.3×10-3 and +2.5×10-3, respectively. B2NG exhibited MR-TADF with a lifetime below 5 µs, and a reasonably high fluorescence quantum yield (50 %). Our molecular design enriches the optical and chiroptical properties of nanographenes and opens up new opportunities in multidisciplinary fields.

The Future Is Beyond Bright: The Evolving Role of Quantitative US for Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is a common cause of morbidity and mortality. Nonfocal liver biopsy is the historical reference standard for evaluating NAFLD, but it is limited by invasiveness, high cost, and sampling error. Imaging methods are ideally situated to provide quantifiable results and rule out other anatomic diseases of the liver. MRI and US have shown great promise for the noninvasive evaluation of NAFLD. US is particularly well suited to address the population-level problem of NAFLD because it is lower-cost, more available, and more tolerable to a broader range of patients than MRI. Noninvasive US methods to evaluate liver fibrosis are widely available, and US-based tools to evaluate steatosis and inflammation are gaining traction. US techniques including shear-wave elastography, Doppler spectral imaging, attenuation coefficient, hepatorenal index, speed of sound, and backscatter-based estimation have regulatory clearance and are in clinical use. New methods based on channel and radiofrequency data analysis approaches have shown promise but are mostly experimental. This review discusses the advantages and limitations of clinically available and experimental approaches to sonographic liver tissue characterization for NAFLD diagnosis as well as future applications and strategies to overcome current limitations.

Acoustic diffraction-resistant adaptive profile technology (ADAPT) for elasticity imaging.

Acoustic beam shaping with high degrees of freedom is critical for applications such as ultrasound imaging, acoustic manipulation, and stimulation. However, the ability to fully control the acoustic pressure profile over its propagation path has not yet been achieved. Here, we demonstrate an acoustic diffraction-resistant adaptive profile technology (ADAPT) that can generate a propagation-invariant beam with an arbitrarily desired profile. By leveraging wave number modulation and beam multiplexing, we develop a general framework for creating a highly flexible acoustic beam with a linear array ultrasonic transducer. The designed acoustic beam can also maintain the beam profile in lossy material by compensating for attenuation. We show that shear wave elasticity imaging is an important modality that can benefit from ADAPT for evaluating tissue mechanical properties. Together, ADAPT overcomes the existing limitation of acoustic beam shaping and can be applied to various fields, such as medicine, biology, and material science.

Isomer Effect on Energy Storage of π-Extended S-Shaped Double[6]Heterohelicene.

Recently, chiral and nonplanar cutouts of graphene have been the favorites due to their unique optical, electronic, and redox properties and high solubility compared with their planar counterparts. Despite the remarkable progress in helicenes, π-extended heterohelicenes have not been widely explored. As an anode in a lithium-ion battery, the racemic mixture of π-extended double heterohelical nanographene containing thienothiophene core exhibited a high lithium storage capability, attaining a specific capacity of 424 mAh g-1 at 0.1 A g-1 with excellent rate capability and superior long-term cycling performance over 6000 cycles with negligible fade. As a first report, the π-extended helicene isomer (PP and MM), with the more interlayer distance that helps faster diffusion of ions, has exhibited a high capacity of 300 mAh g-1 at 2 A g-1 with long-term cycling performance over 1500 cycles compared to the less performing MP and PM isomer and racemic mixture (150 mAh g-1 at 2 A g-1 ). As supported by single-crystal X-ray analysis, a unique molecular design of nanographenes with a fixed (helical) molecular geometry, avoiding restacking of the layers, renders better performance as an anode in lithium-ion batteries. Interestingly, the recycled nanographene anode material displayed comparable performance.

Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices.

Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter and the risk of catheter-associated urinary tract infection. Wearable ultrasound devices combined with machine-learning based bladder volume estimation algorithms reduce the burdens of nurses in hospital settings and improve outpatient care. However, existing algorithms are memory and computation intensive, thereby demanding the use of expensive GPUs. In this paper, we develop and validate a low-compute memory-efficient deep learning model for accurate bladder region segmentation and urine volume calculation. B-mode ultrasound bladder images of 360 patients were divided into training and validation sets; another 74 patients were used as the test dataset. Our 1-bit quantized models with 4-bits and 6-bits skip connections achieved an accuracy within [Formula: see text] and [Formula: see text], respectively, of a full precision state-of-the-art neural network (NN) without any floating-point operations and with an [Formula: see text] and [Formula: see text] reduction in memory requirements to fit under 150 kB. The means and standard deviations of the volume estimation errors, relative to estimates from ground-truth clinician annotations, were [Formula: see text] ml and [Formula: see text] ml, respectively. This lightweight NN can be easily integrated on the wearable ultrasound device for automated and continuous monitoring of urine volume. Our approach can potentially be extended to other clinical applications, such as monitoring blood pressure and fetal heart rate.

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration, and regeneration: part 1, neurophysiology.

Peripheral neuroregeneration research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures that can serve as biomarkers of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, such biomarkers can elucidate regeneration mechanisms and open new avenues for research. Without these measures, clinical decision-making falls short, and research becomes more costly, time-consuming, and sometimes infeasible. As a companion to Part 2, which is focused on non-invasive imaging, Part 1 of this two-part scoping review systematically identifies and critically examines many current and emerging neurophysiological techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Polymerizable Solvent-free Organic Liquids: A New Approach for Large Area Flexible and Foldable Luminescent Films.

The high demand for light-emitting and display devices made luminescent organic materials as attractive candidates. Solvent-free organic liquids are one of the promising emitters among them due to the salient features. However, the inherent limitations of forming sticky and noncurable surfaces must be addressed to become an alternate emitter for large-area device applications. Herein, we functionalized solvent-free organic liquids having monomeric emission in bulk with polymerizable groups to improve the processability. The polymerizable group on carbazole, naphthalene monoimide, and diketopyrrolopyrrole-based solvent-free liquid emitters enabled on-surface polymerization. These emitters alone and in combinations can be directly coated on a glass substrate without the help of solvents. Subsequent photo or thermal polymerization leads to stable, non-sticky, flexible, foldable, and free-standing large-area films with reasonably high quantum yield. Our demonstration of the tunable and white light-emitting films using polymerizable solvent-free liquids might be a potential candidate in flexible/foldable/stretchable electronics. The new concept of polymerizable liquid can be extended to other functional features suitable for futuristic applications.

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration and regeneration: part 2, non-invasive imaging.

Peripheral neuroregenerative research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, novel biomarkers can elucidate regenerative mechanisms and open new avenues for research. Without such measures, clinical decision-making is impaired, and research becomes more costly, time-consuming, and sometimes infeasible. Part 1 of this two-part scoping review focused on neurophysiology. In part 2, we identify and critically examine many current and emerging non-invasive imaging techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Liver Fat Quantification With Ultrasound: Depth Dependence of Attenuation Coefficient.

OBJECTIVES: The primary aim was to estimate the influence of various depths on ultrasound attenuation coefficient (AC) of multiple vendors in the liver. The secondary aim was to evaluate the impact of region of interest (ROI) size on AC measurements in a subset of participants. METHODS: This Institutional Review Board (IRB)-approved Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study was carried out in two centers using AC-Canon and AC-Philips algorithms and extracting AC-Siemens values from ultrasound-derived fat fraction algorithm. Measurements were performed positioning ROI upper edge (3 cm size) at 2, 3, 4, 5 cm from the liver capsule with AC-Canon and AC-Philips and at 1.5, 2, 3 cm with Siemens algorithm. In a subset of participants, measurements were obtained with 1 and 3 cm ROI size. Univariate and multivariate linear regression models and Lin's concordance correlation coefficient (CCC) were used for statistical analysis as appropriate. RESULTS: Three different cohorts were studied. Sixty-three participants (34 females; mean age: 51 ± 14 years) were studied with AC-Canon, 60 (46 females; mean age: 57 ± 11 years) with AC-Philips, and 50 (25 females; 61 ± 13 years) with AC-Siemens. There was a decrease in AC values per 1 cm increase in depth in all. In multivariable analysis, the coefficient was -0.049 (-0.060; -0.038 P < .001) with AC-Canon, -0.058 (-0.066; -0.049 P < .001) with AC-Philips and -0.081 (-0.112; -0.050 P < .001) with AC-Siemens. AC values with 1 cm ROI were significantly higher than those obtained with 3 cm ROI at all depths (P < .001) but the agreement between AC values obtained with different ROI size was excellent (CCC 0.82 [0.77-0.88]). CONCLUSIONS: There is depth dependence in AC measurement that affects results. A standardized protocol with fixed ROI's depth and size is needed.

HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy.

Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles' diameter is usually in the range of 2-10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of 90% for ovaries and 81% for antral follicles, an improvement of 2% and 10%, respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of 91.01% and 76.49%, respectively.

Stacked dilated convolutions and asymmetric architecture for U-Net-based medical image segmentation.

Deep learning has been widely utilized for medical image segmentation. The most commonly used U-Net and its variants often share two common characteristics but lack solid evidence for the effectiveness. First, each block (i.e., consecutive convolutions of feature maps of the same resolution) outputs feature maps from the last convolution, limiting the variety of the receptive fields. Second, the network has a symmetric structure where the encoder and the decoder paths have similar numbers of channels. We explored two novel revisions: a stacked dilated operation that outputs feature maps from multi-scale receptive fields to replace the consecutive convolutions; an asymmetric architecture with fewer channels in the decoder path. Two novel models were developed: U-Net using the stacked dilated operation (SDU-Net) and asymmetric SDU-Net (ASDU-Net). We used both publicly available and private datasets to assess the efficacy of the proposed models. Extensive experiments confirmed SDU-Net outperformed or achieved performance similar to the state-of-the-art while using fewer parameters (40% of U-Net). ASDU-Net further reduced the model parameters to 20% of U-Net with performance comparable to SDU-Net. In conclusion, the stacked dilated operation and the asymmetric structure are promising for improving the performance of U-Net and its variants.

Machine Learning-Assisted Diagnostic System for Indeterminate Thyroid Nodules.

To develop an ultrasound-based machine learning classifier to diagnose benignity within indeterminate thyroid nodules (ITNs) by fine-needle aspiration, 180 patients with 194 ITNs (Bethesda classes III, IV and V) undergoing surgery over a 5-y study period were analyzed. The data set was randomly divided into training and testing data sets with 155 and 39 ITNs, respectively. All nodules were evaluated by ultrasound using the American College of Radiology Thyroid Imaging Reporting and Data System by manually scoring composition, echogenicity, shape, margin and echogenic foci. Nodule size, participant age and patient sex were recorded. A support vector machine (SVM) model with a cost-sensitive approach was developed using the aforementioned eight parameters with surgical histopathology as the reference standard. Surgical pathology determined 90 (46.4%) ITNs were malignant and 104 (53.6%) were benign. The SVM model classified 14 nodules as benign in the testing data set, of which 13 were correct (sensitivity = 93.8%, specificity = 56.5%). Considering malignancy prevalence by Bethesda group, the negative predictive values of this model for Bethesda III and IV categories were 93.9% and 93. 8%, respectively. The high negative predictive value of the SVM ultrasound-based model suggests a pathway by which surgical excision of Bethesda III and IV ITNs classified as benign may be avoided.

US Attenuation for Liver Fat Quantification: An AIUM-RSNA QIBA Pulse-Echo Quantitative Ultrasound Initiative.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an estimated prevalence of up to 30% in the general population and higher in people with type 2 diabetes. The assessment of liver fat content is essential to help identify patients with or who are at risk for NAFLD and to follow their disease over time. The American Institute of Ultrasound in Medicine-RSNA Quantitative Imaging Biomarkers Alliance Pulse-Echo Quantitative Ultrasound Initiative was formed to help develop and standardize acquisition protocols and to better understand confounding factors of US-based fat quantification. The three quantitative US parameters explored by the initiative are attenuation, backscatter coefficient, and speed of sound. The purpose of this review is to present the current state of attenuation imaging for fat quantification and to provide expert opinion on examination performance and interpretation. US attenuation methods that need further study are outlined.

Automated Segmentation of Thyroid Nodule, Gland, and Cystic Components From Ultrasound Images Using Deep Learning.

Sonographic features associated with margins, shape, size, and volume of thyroid nodules are used to assess their risk of malignancy. Automatically segmenting nodules from normal thyroid gland would enable an automated estimation of these features. A novel multi-output convolutional neural network algorithm with dilated convolutional layers is presented to segment thyroid nodules, cystic components inside the nodules, and normal thyroid gland from clinical ultrasound B-mode scans. A prospective study was conducted, collecting data from 234 patients undergoing a thyroid ultrasound exam before biopsy. The training and validation sets encompassed 188 patients total; the testing set consisted of 48 patients. The algorithm effectively segmented thyroid anatomy into nodules, normal gland, and cystic components. The algorithm achieved a mean Dice coefficient of 0.76, a mean true positive fraction of 0.90, and a mean false positive fraction of 1.61×10-6. The values are on par with a conventional seeded algorithm. The proposed algorithm eliminates the need for a seed in the segmentation process, thus automatically detecting and segmenting the thyroid nodules and cystic components. The detection rate for thyroid nodules and cystic components was 82% and 44%, respectively. The inference time per image, per fold was 107ms. The mean error in volume estimation of thyroid nodules for five select cases was 7.47%. The algorithm can be used for detection, segmentation, size estimation, volume estimation, and generating thyroid maps for thyroid nodules. The algorithm has applications in point of care, mobile health monitoring, improving workflow, reducing localization time, and assisting sonographers with limited expertise.

Gap-filling method for suppressing grating lobes in ultrasound imaging: Experimental study with deep-learning approach.

Sparse arrays reduce the number of active channels that effectively increases the inter-element spacing. Large inter-element spacing results in grating lobe artifacts degrading the ultrasound image quality and reducing the contrast-to-noise ratio. A deep learning-based custom algorithm is proposed to estimate inactive channel data in periodic sparse arrays. The algorithm uses data from multiple active channels to estimate inactive channels. The estimated inactive channel data effectively reduces the inter-element spacing for beamforming, thus suppressing the grating lobes. Estimated inactive element channel data was combined with active element channel data resulting in a pseudo fully sampled array. The channel data was beamformed using a simple delay-and-sum method and compared with the sparse array and fully sampled array. The performance of the algorithm was validated using a wire target in a water tank, multi-purpose tissue-mimicking phantom, and in-vivo carotid data. Grating lobes suppression up to 15.25 dB was observed with an increase in contrast-to-noise (CNR) for the pseudo fully sampled array. Hypoechoic regions showed more improvement in CNR than hyperechoic regions. Root-mean-square error for unwrapped phase between fully sampled array and the pseudo fully sampled array was low, making the estimated data suitable for Doppler and elastography applications. Speckle pattern was also preserved; thus, the estimated data can also be used for quantitative ultrasound applications. The algorithm can improve the quality of sparse array images and has applications in small scale ultrasound devices and 2D arrays.

C-Elastography: In Vitro Feasibility Phantom Study.

C-Elastography (CE) is a new ultrasound technique that locally maps the non-linear elasticity of soft tissue using low-frequency (150-250 Hz) shear waves generated by the acoustic radiation force (ARF). CE is based on a recent finding that the magnitude of the ARF in an isotropic tissue-like solid is related linearly to a third-order modulus of elasticity, C, which is responsible for the coupling between deviatoric and volumetric constitutive behaviors. The main objective of the work described here was to examine the feasibility of using and performance of C-elastography in differentiating and characterizing soft tissue via a pilot study on ex vivo tissue and tissue-mimicking inclusions cast in a gelatin block. In this vein, the CE technique deploys a combination of ultrasound motion sensing and 3-D visco-elastodynamic simulation to estimate the non-linear modulus C. As ultrasound focusing inherently confines the ARF to a small region, CE provides the means for measuring C within O(mm3) volumes. Equipped with such data analysis, we performed in vitro CE experiments on agar-based, xenograft and normal breast tissue samples embedded in a gelatin matrix. The compound C-elastograms indicate marked (and sharp) C-contrast, with average values of 1.9 and 5.6 at push points inside the featured soft and hard inclusions, respectively.

[18F]-Alfatide PET imaging of integrin αvß3 for the non-invasive quantification of liver fibrosis.

BACKGROUND & AIMS: The vitronectin receptor integrin αvß3 drives fibrogenic activation of hepatic stellate cells (HSCs). Molecular imaging targeting the integrin αvß3 could provide a non-invasive method for evaluating the expression and the function of the integrin αvß3 on activated HSCs (aHSCs) in the injured liver. In this study, we sought to compare differences in the uptake of [18F]-Alfatide between normal and injured liver to evaluate its utility for assessment of hepatic fibrogenesis. METHODS: PET with [18F]-Alfatide, non-enhanced CT, histopathology, immunofluorescence staining, immunoblotting and gene analysis were performed to evaluate and quantify hepatic integrin αvß3 levels and liver fibrosis progression in mouse models of fibrosis (carbon tetrachloride [CCl4] and bile duct ligation [BDL]). The liver AUC divided by the blood AUC over 30 min was used as an integrin αvß3-PET index to quantify fibrosis progression. Ex vivo analysis of frozen liver tissue from patients with fibrosis and cirrhosis verified the animal findings. RESULTS: Fibrotic mouse livers showed enhanced [18F]-Alfatide uptake and retention compared to control livers. The radiotracer was demonstrated to bind specifically with integrin αvß3, which is mainly expressed on aHSCs. Autoradiography and histopathology confirmed the PET imaging results. Further, the mRNA and protein level of integrin αvß3 and its signaling complex were higher in CCl4 and BDL models than controls. The results obtained from analyses on human fibrotic liver sections supported the animal findings. CONCLUSIONS: Imaging hepatic integrin αvß3 with PET and [18F]-Alfatide offers a potential non-invasive method for monitoring the progression of liver fibrosis. LAY SUMMARY: Integrin αvß3 expression on activated hepatic stellate cells (aHSCs) is associated with HSC proliferation during hepatic fibrogenesis. Herein, we show that a radioactive tracer, [18F]-Alfatide, binds to integrin αvß3 with high affinity and specificity. [18F]-Alfatide could thus be used as a non-invasive imaging biomarker to track hepatic fibrosis progression.

Predictive value of comb-push ultrasound shear elastography for the differentiation of reactive and metastatic axillary lymph nodes: A preliminary investigation.

OBJECTIVES: To evaluate the predictive performance of comb-push ultrasound shear elastography for the differentiation of reactive and metastatic axillary lymph nodes. METHODS: From June 2014 through September 2018, 114 female volunteers (mean age 58.1±13.3 years; range 28-88 years) with enlarged axillary lymph nodes identified by palpation or clinical imaging were prospectively enrolled in the study. Mean, standard deviation and maximum shear wave elastography parameters from 117 lymph nodes were obtained and compared to fine needle aspiration biopsy results. Mann-Whitney U test and ROC curve analysis were performed. RESULTS: The axillary lymph nodes were classified as reactive or metastatic based on the fine needle aspiration outcomes. A statistically significant difference between reactive and metastatic axillary lymph nodes was observed based on comb-push ultrasound shear elastography (CUSE) results (p<0.0001) from mean and maximum elasticity values. Mean elasticity showed the best separation with a ROC analysis resulting in 90.5% sensitivity, 94.4% specificity, 0.97 area under the curve, 95% positive predictive value, and 89.5% negative predictive value with a 30.2-kPa threshold. CONCLUSIONS: CUSE provided a quantifiable parameter that can be used for the assessment of enlarged axillary lymph nodes to differentiate between reactive and metastatic processes.

Pulsed vibro-acoustic method for assessment of osteoporosis & osteopenia: A feasibility study on human subjects.

In this paper we propose a new non-invasive ultrasound method, pulsed vibro-acoustic, for evaluating osteoporotic and osteopenic bone in humans. Vibro-acoustic method uses acoustic radiation force (ARF) to stimulate bone and the resulting acoustic signal can be used to characterize bone. The resulting acoustic signal is collected by a hydrophone at the skin surface. Wave velocity and numbers of intrinsic modes are used for analysis. Wave velocity is estimated using the received signal and maximum power mode of the decomposed signal is estimated using variational mode composition from different push points of ARF based on the cross-correlation method. A total of 27 adult volunteers, including healthy and those diagnosed with osteopenia and osteoporosis, were tested. Results of pulsed vibro-acoustic test on tibia of volunteers showed that healthy group could be differentiated from osteoporosis or osteopenia (p < 2 × 10-5). The results of our study support the feasibility of pulsed vibro-acoustic method for measuring mechanical properties of bone and the potential clinical utility of the proposed method for assessment of bone health.