Application of Time-Fractional Order Bloch Equation in Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is one novel fast quantitative imaging framework for simultaneous quantification of multiple parameters with pseudo-randomized acquisition patterns. The accuracy of the resulting multi-parameters is very important for clinical applications. In this paper, we derived signal evolutions from the anomalous relaxation using a fractional calculus. More specifically, we utilized time-fractional order extension of the Bloch equations to generate dictionary to provide more complex system descriptions for MRF applications. The representative results of phantom experiments demonstrated the good accuracy performance when applying the time-fractional order Bloch equations to generate dictionary entries in the MRF framework. The utility of the proposed method is also validated by in-vivo study.Comment: Accepted at 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019

Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods

BACKGROUND: Ultrasonography is a convenient technique to investigate muscle properties and has been widely used to look into muscle functions since it is non-invasive and real-time. Muscle thickness, a quantification which can effectively reflect the muscle activities during muscle contraction, is an important measure for musculoskeletal studies using ultrasonography. The traditional manual operation to read muscle thickness is subjective and time-consuming, therefore a number of studies have focused on the automatic estimation of muscle fascicle orientation and muscle thickness, to which the speckle noises in ultrasound images could be the major obstacle. There have been two popular methods proposed to enhance the hyperechoic regions over the speckles in ultrasonography, namely Gabor Filtering and Multiscale Vessel Enhancement Filtering (MVEF). METHODS: A study on gastrocnemius muscle is conducted to quantitatively evaluate whether and how these two methods could help the automatic estimation of the muscle thickness based on Revoting Hough Transform (RVHT). The muscle thickness results obtained from each of the two methods are compared with the results from manual measurement, respectively. Data from an aged subject with cerebral infarction is also studied. RESULTS: It’s shown in the experiments that, Gabor Filtering and MVEF can both enable RVHT to generate comparable results of muscle thickness to those by manual drawing (mean ± SD, 1.45 ± 0.48 and 1.38 ± 0.56 mm respectively). However, the MVEF method requires much less computation than Gabor Filtering. CONCLUSIONS: Both methods, as preprocessing procedure can enable RVHT the automatic estimation of muscle thickness and MVEF is believed to be a better choice for real-time applications

A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning

Histopathological images provide rich information for disease diagnosis. Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group >= 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset.Comment: 9 pages, 6 figure

Information Bottleneck Revisited: Posterior Probability Perspective with Optimal Transport

Information bottleneck (IB) is a paradigm to extract information in one target random variable from another relevant random variable, which has aroused great interest due to its potential to explain deep neural networks in terms of information compression and prediction. Despite its great importance, finding the optimal bottleneck variable involves a difficult nonconvex optimization problem due to the nonconvexity of mutual information constraint. The Blahut-Arimoto algorithm and its variants provide an approach by considering its Lagrangian with fixed Lagrange multiplier. However, only the strictly concave IB curve can be fully obtained by the BA algorithm, which strongly limits its application in machine learning and related fields, as strict concavity cannot be guaranteed in those problems. To overcome the above difficulty, we derive an entropy regularized optimal transport (OT) model for IB problem from a posterior probability perspective. Correspondingly, we use the alternating optimization procedure and generalize the Sinkhorn algorithm to solve the above OT model. The effectiveness and efficiency of our approach are demonstrated via numerical experiments.Comment: ISIT 202

Point-of-Care Ultrasound: New Concepts and Future Trends

Ultrasound (US) technology, with major advances and new developments, has become an essential and first-line imaging modality for clinical diagnosis and interventional treatment. US imaging has evolved from one-dimensional, twodimensional to three-dimensional display, and from static to real-time imaging, as well as from structural to functional imaging. Based on its portability and advanced digital imaging technique, US was first adopted by emergency medicine in the 1980s and gradually gained popularity among other specialists for clinical diagnosis and interventional treatment. Point-of-Care Ultrasound (POCUS) was then proposed as a new concept and developed for new uses, which greatly extended clinical US applications. Nowadays, artificial intelligence (AI), cloud computing, 5G network, robotics, and remote technologies are starting to be integrated into US equipment. US systems have gradually evolved to an intelligent terminal platform with powerful imaging and communication tools. In addition, specialized US machines tend to be more suitable and important to meet increasing demands and requirements by various clinical specialties and departments. In this article, we review current US technology and POCUS as new concepts and its future trends, as well as related technological developments and clinical applications

A time-resolved fluorescence microsphere-lateral flow immunochromatographic strip for quantitative detection of Pregnanediol-3-glucuronide in urine samples

Introduction: Pregnanediol-3-glucuronide (PdG), as the main metabolite of progesterone in urine, plays a significant role in the prediction of ovulation, threatened abortion, and menstrual cycle maintenance.Methods: To achieve a rapid and sensitive assay, we have designed a competitive model-based time-resolved fluorescence microsphere-lateral flow immunochromatography (TRFM-LFIA) strip.Results: The optimized TRFM-LFIA strip exhibited a wonderful response to PdG over the range of 30–2,000 ng/mL, the corresponding limit of detection (LOD) was calculated as low as 8.39 ng/mL. More importantly, the TRFM-LFIA strip was innovatively used for the quantitative detection of PdG in urine sample, and excellent recovery results were also obtained, ranging from 97.39% to 112.64%.Discussion: The TRFMLFIA strip possessed robust sensitivity and selectivity in the determination of PdG, indicating the great potential of being powerful tools in the biomedical and diagnosis region

ErbB2 Signaling Increases Androgen Receptor Expression in Abiraterone-Resistant Prostate Cancer

Purpose: ErbB2 signaling appears to be increased and may enhance AR activity in a subset of CRPC, but agents targeting ErbB2 have not been effective. This study was undertaken to assess ErbB2 activity in abiraterone-resistant prostate cancer (PCa), and determine whether it may contribute to androgen receptor (AR) signaling in these tumors. Experimental Design: AR activity and ErbB2 signaling were examined in the radical prostatectomy specimens from a neoadjuvant clinical trial of leuprolide plus abiraterone, and in the specimens from abiraterone-resistant CRPC xenograft models. The effect of ErbB2 signaling on AR activity was determined in two CRPC cell lines. Moreover, the effect of combination treatment with abiraterone and an ErbB2 inhibitor was assessed in a CRPC xenograft model. Results: We found that ErbB2 signaling was elevated in residual tumor following abiraterone treatment in a subset of patients, and was associated with higher nuclear AR expression. In xenograft models, we similarly demonstrated that ErbB2 signaling was increased and associated with AR reactivation in abiraterone-resistant tumors, while ERBB2 message level was not changed. Mechanistically, we show that ErbB2 signaling and subsequent activation of the PI3K/AKT signaling stabilizes AR protein. Inhibitors targeting ErbB2/PI3K/AKT pathway disrupt AR transcriptional activity. Furthermore, concomitantly treating CRPC xenograft with abiraterone and an ErbB2 inhibitor, lapatinib, blocked AR reactivation and suppressed tumor progression. Conclusions: ErbB2 signaling is elevated in a subset of abiraterone-resistant prostate cancer patients and stabilizes AR protein. Combination therapy with abiraterone and ErbB2 antagonists may be effective for treating the subset of CRPC with elevated ErbB2 activity

Prognosis for patients with apical hypertrophic cardiomyopathy: A multicenter cohort study based on propensity score matching

Background: Apical hypertrophic cardiomyopathy (AHCM) is a subtype of HCM, and few studies on the prognosis in AHCM are available.Aims: This study aimed to explore the clinical prognosis for AHCM and non-AHCM patients through clinical data based on propensity score matching (PSM) in a large cohort of Chinese HCM patients.Methods: The cohort study included 2268 HCM patients, 226 AHCM and 2042 non-AHCM patients from 13 tertiary hospitals, who were treated between 1996 and 2021. Fifteen demographic and clinical variables of 226 AHCM patients and 2042 non-AHCM patients were matched using 1:2 PSM. A Cox proportional hazard regression model was constructed to assess the effect of AHCM on mortality.Results: During a median follow-up of 5.1 (2.4–8.4) years, 353 (15.6%) of the 2268 HCM patients died, of whom 205 died due to cardiovascular mortality/cardiac transplantation and 94 experienced sudden cardiac death (SCD). In the matched cohort, the ACHM patients had lower rates of all-cause mortality (P = 0.003), cardiovascular mortality/cardiac transplantation (P = 0.03), and SCD (P = 0.02) than the non-AHCM patients. Furthermore, the Cox proportional hazard regression model showed that AHCM was an independent prognostic predictor of all-cause HCM mortality (P = 0.004) and a univariable prognostic predictor of cardiovascular mortality/cardiac transplantation (P = 0.03) and for SCD (P = 0.03). However, AHCM was not significant in multivariable Cox regression models in relation to cardiovascular mortality/cardiac transplantation and SCD.Conclusion: AHCM had a favorable prognosis both before and after matching, with lower all-cause mortality, cardiovascular mortality/cardiac transplantation, and SCD than non-AHCM