Comparison of lung lesion assessment using free-breathing dynamic contrast-enhanced 1.5-T MRI with a golden-angle radial stack-of-stars VIBE sequence and CT.

BACKGROUND: Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung. PURPOSE: To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference. MATERIAL AND METHODS: In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis. RESULTS: The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower Ktrans, Ve, and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group (P < 0.05). CONCLUSION: Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions.

Quantification of ectopic fat storage in the liver and pancreas using six-point Dixon MRI and its association with insulin sensitivity and ß-cell function in patients with central obesity.

OBJECTIVES: To assess the association of ectopic fat deposition in the liver and pancreas quantified by Dixon magnetic resonance imaging (MRI) with insulin sensitivity and ß-cell function in patients with central obesity. MATERIALS AND METHODS: A cross-sectional study of 143 patients with central obesity with normal glucose tolerance (NGT), prediabetes (PreD), and untreated type 2 diabetes mellitus (T2DM) was conducted between December 2019 and March 2022. All participants underwent routine medical history taking, anthropometric measurements, and laboratory tests, including a standard glucose tolerance test to quantify insulin sensitivity and ß-cell function. The fat content in the liver and pancreas was measured with MRI using the six-point Dixon technique. RESULTS: Patients with T2DM and PreD had a higher liver fat fraction (LFF) than those with NGT, while those with T2DM had a higher pancreatic fat fraction (PFF) than those with PreD and NGT. LFF was positively correlated with homeostatic model assessment of insulin resistance (HOMA-IR), while PFF was negatively correlated with homeostatic model assessment of insulin secretion (HOMA-ß). Furthermore, using a structured equation model, we found LFF and PFF to be positively associated with glycosylated hemoglobin via HOMA-IR and HOMA-ß, respectively. CONCLUSIONS: In patients with central obesity, the effects of LFF and PFF on glucose metabolism. were associated with HOMA-IR and HOMA-ß, respectively. Ectopic fat storage in the liver and pancreas quantified by MR Dixon imaging potentially plays a notable role in the onset ofT2DM. CLINICAL RELEVANCE STATEMENT: We highlight the potential role of ectopic fat deposition in the liver and pancreas in the development of type 2 diabetes in patients with central obesity, providing valuable insights into the pathogenesis of the disease and potential targets for intervention. KEY POINTS: • Ectopic fat deposition in the liver and pancreas is associated with T2DM. • T2DM and prediabetes patients had higher liver and pancreatic fat fractions than normal individuals. • The results provide valuable insights into pathogenesis of T2DM and potential targets for intervention.

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform.

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was -0.04 L/min with a -1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between -20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals.

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

Differentiation between intraspinal schwannoma and meningioma by MR characteristics and clinic features.

OBJECTIVES: To retrospectively review the MRI characteristics and clinic features and evaluate the effectiveness of MR imaging in differentiating intraspinal schwannomas and meningiomas, with the excised histopathologic findings as the reference standard. MATERIALS AND METHODS: One hundred and four schwannomas (M/F, 57:47) and 53 meningiomas (M/F, 13:40) underwent MR examinations before surgical treatment. Simple clinic data and imaging findings were considered:(a) location (craniocaudal and axial), (b) size, (c) morphology, (d) dural contact, (e) signal characteristics, (f) enhancement degree and patterns. The usefulness of the algorithm for differential diagnosis was examined between the two tumors. RESULTS: Interobserver agreement was good (κ = 0.7-0.9). Ten cases meningiomas demonstrated multiple lesions. There was a female predominance in the meningiomas (P < 0.001). Meningiomas predominantly were located in the ventral or anterolateral areas of thoracic regions, while schwannomas in the posterolateral areas of the thoracic and the lumbar regions (P < 0.001). Mean size of the lesions was 1.47 ± 0.36 cm for meningioma, and 2.02 ± 1.13 cm for schwannoma (P < 0.001). A dumbbell shape with intervertebral foramen widening could detect schwannomas, while the "dural tail sign" did meningiomas (P < 0.001). Hypointense and miscellaneous signal implied meningioma on T1WIs (P < 0.001). Isointense was more frequently observed in the meningiomas, while the fluid signal intensity and miscellaneous signal in the schwannomas on T2WIs (P < 0.001). Schwannomas usually manifested rim enhancement, while meningiomas diffuse enhancement (P = 0.005). There were six variables including the logistic equation (age, size, dural tail sign, morphology, T2WI, and axial location). The accuracy of the algorithm in diagnosis of schwannomas was 87.1%. CONCLUSIONS: Combination of clinic data and MRI performs significantly for differentiating between intraspinal meningiomas and schwannomas.

Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network.

ECG helps in diagnosing heart disease by recording heart activity. During long-term measurements, data loss occurs due to sensor detachment. Therefore, research into the reconstruction of missing ECG data is essential. However, ECG requires user participation and cannot be used for continuous heart monitoring. Continuous monitoring of PPG signals is conversely low-cost and easy to carry out. In this study, a deep neural network model is proposed for the reconstruction of missing ECG signals using PPG data. This model is an end-to-end deep learning neural network utilizing WNet architecture as a basis, on which a bidirectional long short-term memory network is added in establishing a second model. The performance of both models is verified using 146 records from the MIMIC III matched subset. Compared with the reference, the ECG reconstructed using the proposed model has a Pearson's correlation coefficient of 0.851, root mean square error (RMSE) of 0.075, percentage root mean square difference (PRD) of 5.452, and a Fréchet distance (FD) of 0.302. The experimental results demonstrate that it is feasible to reconstruct missing ECG signals from PPG.

MRI-Measured Pancreatic Fat Correlates with Increased Arterial Stiffness in Patients Who are Overweight and Obese.

Purpose: Arterial stiffness is often increased in overweight or obese individuals before the development of hypertension (HT). This study aimed to determine the connection between pancreatic fat and atherosclerosis in overweight and obese people without HT. Patients and methods: We included 128 patients who were non-hypertensive and overweight or obese in a study between December 2019 and November 2022. Medical history was collected, and all participants underwent a physical examination and blood tests. Pancreatic fat content was measured by magnetic resonance imaging (MRI) and was grouped into quartiles based on pancreatic fat fraction (PFF). The upper three quartiles (PFF≥10.33%) were defined as non-alcoholic fatty pancreas disease (NAFPD) and the first quartile (PFF<10.33%) as non-NAFPD. High baPWV (H-baPWV) and low baPWV (L-baPWV) were classified according to the median baPWV (1159 cm/s). The effect of NAFPD on baPWV was examined using binary logistic regression. The study population consisted of 96 NAFPD and 32 non-NAFPD cases. Results: Participants with NAFPD had significantly higher levels of baPWV than people without. The rates of NAFPD and the PFF values varied significantly in the L-baPWV and H-baPWV groups. Logistic regression analysis suggested that the presence of NAFPD was independently correlated with increased baPWV after adjusting for age, smoking, body mass index, blood pressure, lipid profiles, and glycemic index. Conclusion: NAFPD is an independent risk factor for increased baPWV in individuals with overweight and obesity but no HT, suggesting that the presence of NAFPD may be a warning signal of early atherosclerosis.

PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments.

Electrocardiograms (ECGs) provide crucial information for evaluating a patient's cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG reconstruction using PPG signals, but some require signal alignment during the training phase, which is not feasible in real-life settings where ECG signals are not collected at the same time as PPG signals. To address this challenge, we introduce PPG2ECGps, an end-to-end, patient-specific deep-learning neural network utilizing the W-Net architecture. This novel approach enables direct ECG signal reconstruction from PPG signals, eliminating the need for signal alignment. Our experiments show that the proposed model achieves mean values of 0.977 mV for Pearson's correlation coefficient, 0.037 mV for the root mean square error, and 0.010 mV for the normalized dynamic time-warped distance when comparing reconstructed ECGs to reference ECGs from a dataset of 500 records. As PPG signals are more accessible than ECG signals, our proposed model has significant potential to improve patient monitoring and diagnosis in healthcare settings via wearable devices.

Altered functional connectivity within the brain fear circuit in Parkinson's disease with anxiety: A seed-based functional connectivity study.

Objectives: Aimed to investigate whether there are abnormal changes in the functional connectivity (FC) between the amygdala with other brain areas, in Parkinson's disease (PD) patients with anxiety. Methods: Participants were enrolled prospectively, and the Hamilton Anxiety Rating (HAMA) Scale was used to quantify anxiety disorder. Rest-state functional MRI (rs-fMRI) was applied to analyze the amygdala FC patterns among anxious PD patients, non-anxious PD patients, and healthy controls. Results: Thirty-three PD patients were recruited, 13 with anxiety, 20 without anxiety, and 19 non-anxious healthy controls. In anxious PD patients, FC between the amygdala with the hippocampus, putamen, intraparietal sulcus, and precuneus showed abnormal alterations compared with non-anxious PD patients and healthy controls. In particular, FC between the amygdala and hippocampus negatively correlated with the HAMA score (r = -0.459, p = 0.007). Conclusion: Our results support the role of the fear circuit in emotional regulation in PD with anxiety. Also, the abnormal FC patterns of the amygdala could preliminarily explain the neural mechanisms of anxiety in PD.

Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram.

The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal similarity. The existing methods of reconstructing ABP signals from PPG only focus on the similarities between systolic, diastolic, and mean arterial pressures without evaluating their global similarity. This paper proposes a deep learning model with a W-Net architecture to reconstruct ABP signals from PPG. The W-Net consists of two concatenated U-Net architectures, the first acting as an encoder and the second as a decoder to reconstruct ABP from PPG. Five hundred records of different lengths were used for training and testing. The experimental results yielded high values for the similarity measures between the reconstructed ABP signals and their reference ABP signals: the Pearson correlation, root mean square error, and normalized dynamic time warping distance were 0.995, 2.236 mmHg, and 0.612 mmHg on average, respectively. The mean absolute errors of the SBP and DBP were 2.602 mmHg and 1.450 mmHg on average, respectively. Therefore, the model can reconstruct ABP signals that are highly similar to the reference ABP signals.

Robust Reconstruction of Electrocardiogram Using Photoplethysmography: A Subject-Based Model.

Electrocardiography and photoplethysmography are non-invasive techniques that measure signals from the cardiovascular system. While the cycles of the two measurements are highly correlated, the correlation between the waveforms has rarely been studied. Measuring the photoplethysmogram (PPG) is much easier and more convenient than the electrocardiogram (ECG). Recent research has shown that PPG can be used to reconstruct the ECG, indicating that practitioners can gain a deep understanding of the patients' cardiovascular health using two physiological signals (PPG and ECG) while measuring only PPG. This study proposes a subject-based deep learning model that reconstructs an ECG using a PPG and is based on the bidirectional long short-term memory model. Because the ECG waveform may vary from subject to subject, this model is subject-specific. The model was tested using 100 records from the MIMIC III database. Of these records, 50 had a circulatory disease. The results show that a long ECG signal could be effectively reconstructed from PPG, which is, to our knowledge, the first attempt in this field. A length of 228 s of ECG was constructed by the model, which was trained and validated using 60 s of PPG and ECG signals. To segment the data, a different approach that segments the data into short time segments of equal length (and that do not rely on beats and beat detection) was investigated. Segmenting the PPG and ECG time series data into equal segments of 1-min width gave the optimal results. This resulted in a high Pearson's correlation coefficient between the reconstructed 228 s of ECG and referenced ECG of 0.818, while the root mean square error was only 0.083 mV, and the dynamic time warping distance was 2.12 mV per second on average.

The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective.

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χ McNema r ' s statistic 2 = 163 . 2 and a p-value of 2.23 × 10-37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.

Atrial Fibrillation Ablation Using Robotic Magnetic Navigation Reduces the Incidence of Silent Cerebral Embolism.

Background: The incidence of silent cerebral embolisms (SCEs) has been documented after pulmonary vein isolation using different ablation technologies; however, it is unreported in patients undergoing with atrial fibrillation (AF) ablation using Robotic Magnetic Navigation (RMN). The purpose of this prospective study was to investigate the incidence, risk predictors and probable mechanisms of SCEs in patients with AF ablation and the potential impact of RMN on SCE rates. Methods and Results: We performed a prospective study of 166 patients with paroxysmal or persistent AF who underwent pulmonary vein isolation. Patients were divided into RMN group (n = 104) and manual control (MC) group (n = 62), and analyzed for their demographic, medical, echocardiographic, and risk predictors of SCEs. All patients underwent cerebral magnetic resonance imaging within 48 h before and after the ablation procedure to assess cerebral embolism. The incidence and potential risk factors of SCEs were compared between the two groups. There were 26 total cases of SCEs in this study, including 6 cases in the RMN group and 20 cases in the MC group. The incidences of SCEs in the RMN group and the MC group were 5.77 and 32.26%, respectively (X2 = 20.63 P < 0.05). Univariate logistic regression analysis demonstrated that ablation technology, CHA2DS2-VASc score, history of cerebrovascular accident/transient ischemic attack, and low ejection fraction were significantly associated with SCEs, and multivariate logistic regression analysis showed that MC ablation was the only independent risk factor of SCEs after an AF ablation procedure. Conclusions: Ablation technology, CHA2DS2-VASc score, history of cerebrovascular accident/transient ischemic attack, and low ejection fraction are associated with SCEs. However, ablation technology is the only independent risk factor of SCEs and RMN can significantly reduce the incidence of SCEs resulting from AF ablation. Clinical Trial Registration: ChiCTR2100046505.

Synthetic photoplethysmogram generation using two Gaussian functions.

Evaluating the performance of photoplethysmogram (PPG) event detection algorithms requires a large number of PPG signals with different noise levels and sampling frequencies. As publicly available PPG databases provide few options, artificially constructed PPG signals can also be used to facilitate this evaluation. Here, we propose a dynamic model to synthesize PPG over specified time durations and sampling frequencies. In this model, a single pulse was simulated by two Gaussian functions. Additionally, the beat-to-beat intervals were simulated using a normal distribution with a specific mean value and a specific standard deviation value. To add periodicity and to generate a complete signal, the circular motion principle was used. We synthesized three classes of pulses by emulating three different templates: excellent (systolic and diastolic waves are salient), acceptable (systolic and diastolic waves are not salient), and unfit (systolic and diastolic waves are noisy). The optimized model fitting of the Gaussian functions to the templates yielded 0.99, 0.98, and 0.85 correlations between the template and synthetic pulses for the excellent, acceptable, and unfit classes, respectively, with mean square errors of 0.001, 0.003, and 0.017, respectively. By comparing the heart rate variability of real PPG and randomly synthesized PPG for 5 min in 116 records from the MIMIC III database, strong correlations were found in SDNN, RMSSD, LF, HF, SD1, and SD2 (0.99, 0.89, 0.84, 0.89, 0.90 and 0.95, respectively).

PPGSynth: An Innovative Toolbox for Synthesizing Regular and Irregular Photoplethysmography Waveforms.

Photoplethysmography (PPG) is increasingly used in digital health, exceptionally in smartwatches. The PPG signal contains valuable information about heart activity, and there is lots of research interest in its means and analysis for cardiovascular diseases. Unfortunately, to our knowledge, there is no arrhythmic PPG dataset publicly available-this paper attempt to provide a toolbox that can generate synthesized arrhythmic PPG signals. The model of a single PPG pulse in this toolbox utilizes two combined Gaussian functions. This toolbox supports synthesizing PPG waveform with regular heartbeats and three irregular heartbeats: compensation, interpolation, and reset. The user can generate a large amount of PPG data with a certain irregularity, with different sampling frequency, time length, and a range of noise types (Gaussian noise and multi-frequency noise) can be added to the synthesized PPG which can all be modified from the interface, and different types of arrhythmic PPGs (as calculated by the model) generated. The generation for large PPG datasets that simulate PPG collected from real humans could be used for testing the robustness of developed algorithms that are targeting arrhythmic PPG signals. Our PPG synthesis tool is publicly available.

Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals.

Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.

The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias.

Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.

Correlation of apparent diffusion coefficient and intravoxel incoherent motion imaging parameters with Ki-67 expression in extrahepatic cholangiocarcinoma.

PURPOSE: To investigate the correlation of magnetic resonance imaging (MRI), apparent diffusion coefficient (ADC) and intravoxel incoherent motion imaging parameters with Ki-67 expression in cholangiocarcinoma. METHODS: A total of 42 extrahepatic cholangiocarcinoma (EHCC) cases confirmed by surgical pathology were analyzed retrospectively. Subjects underwent MRI at 3.0 T and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) sequential scanning prior to surgery, and postoperative Ki-67 expression was recorded by immunohistochemistry (IHC). The patients were divided into 4 groups (I-IV) based on increasing Ki-67 expression from - to +++. ADC values and IVIM-DWI parameters were calculated, including true diffusion coefficient (D), perfusion fraction (f), and pseudo-diffusion coefficient (D*). The comparison among groups was analyzed by univariate ANOVA (normal distribution) or Kruskal-Wallis H (non-normal distribution). Spearman correlation analysis was used to analyze the correlation of each parameter with Ki-67 expression. The diagnostic efficiency of each parameter was compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: Except for D*, other values had statistically significant differences between groups (P < 0.05). ADC, D and f values had negative correlations with Ki-67 expression (r values were -0.607, -0.795, -0.531, respectively, P < 0.05). The AUCs were 0.701, 0.880, 0.623, respectively (P < 0.0001). CONCLUSION: IVIM-DWI technology can reflect the proliferative activity of EHCC cells to a certain extent, and has clinical value for predicting the degree of malignancy of a tumor.

Diagnostic evaluation of diffusion kurtosis imaging for prostate cancer: Detection in a biopsy population.

PURPOSE: To prospectively assess the feasibility of diffusional kurtosis (DK) imaging for distinguishing prostate cancer(PCa) from benign prostate hyperplasia (BPH) in comparison with standard diffusion-weighted (DW) imaging, as well as low-from high-grade malignant regions. MATERIALS AND METHODS: 147 consecutive patients with suspected PCa underwent multi-parametric 1.5-TMR. Diffusion kurtosis imaging was acquired with with 5 b values (0,600,800,1600,and 2400sec/mm2).Region of interest (ROI)-based measurements were performed on ADC, D, and K map by two radiologists. Data were analyzed by using mixed-model analysis of variance and receiver operating characteristic curves. Correlations among the three parameters (ADC,D and K) in all patients, and correlations between three parameters with the tumor Gleason score (GS) in PCa group were analyzed using Pearson's correlation coefficient in peripheral zone(PZ) and transiton zone(TZ). RESULTS: 58 patients were proved with PCa (9 GS 3 + 3[PZ/TZ = 4/5], 49 GS ≥ 7 [PZ/TZ = 26/23]), and 89 patients were with BPH. ADC,D and K were able to distinguish benignance from tumor tissue both in PZ and TZ(P＜0.01), but performed poorly in neither differentiating low-(GS 3 + 3) from high-grade (GS≥3 + 4) disease, nor GS(3 + 4) from GS(4 + 3).There was a weak correlation between the GS and ADC, D (PZ:ADC r=-0.113, D r=-0.139; TZ:ADC r=-0.104,D r=-0.103), while a moderate correlation between the GS and K(PZ:K r = 0.492; TZ:K r = 0.433, P＜0.01).K had significantly greater area under the curve for differentiating PCa from BHP than ADC both in PZ and TZ. CONCLUSION: DK model may add value in PCa detection and diagnosis, but none can differentiate low-from high-grade PCas (including GS=3+4 from GS=4+3).

Increased Insular Cortical Thickness Associated With Symptom Severity in Male Youths With Internet Gaming Disorder: A Surface-Based Morphometric Study.

With the rising increase in Internet-usage, Internet gaming disorder (IGD) has gained massive attention worldwide. However, detailed cerebral morphological changes remain unclear in youths with IGD. In the current study, our aim was to investigate cortical morphology and further explore the relationship between the cortical morphology and symptom severity in male youths with IGD. Forty-eight male youths with IGD and 32 age- and education-matched normal controls received magnetic resonance imaging scans. We employed a recently proposed surface-based morphometric approach for the measurement of cortical thickness (CT). We found that youths with IGD showed increased CT in the bilateral insulae and the right inferior temporal gyrus. Moreover, significantly decreased CT were found in several brain areas in youths with IGD, including the bilateral banks of the superior temporal sulci, the right inferior parietal cortex, the right precuneus, the right precentral gyrus, and the left middle temporal gyrus. Additionally, youths with IGD demonstrated a significantly positive correlation between the left insular CT and symptom severity. Our data provide evidence for the finding of abnormal CT in distributed cerebral areas and support the notion that altered structural abnormalities observed in substance addiction are also manifested in IGD. Such information extends current knowledge about IGD-related brain reorganization and could help future efforts in identifying the role of insula in the disorder.