Ultrasound-Triggered Piezoelectric Catalysis of Zinc Oxide@Glucose Derived Carbon Spheres for the Treatment of MRSA Infected Osteomyelitis.

Currently, methicillin-resistant Staphylococcus aureus (MRSA)-induced osteomyelitis is a clinically life-threatening disease, however, long-term antibiotic treatment can lead to bacterial resistance, posing a huge challenge to treatment and public health. In this study, glucose-derived carbon spheres loaded with zinc oxide (ZnO@HTCS) are successfully constructed. This composite demonstrates the robust ability to generate reactive oxygen species (ROS) under ultrasound (US) irradiation, eradicating 99.788% ± 0.087% of MRSA within 15 min and effectively treating MRSA-induced osteomyelitis infection. Piezoelectric force microscopy tests and finite element method simulations reveal that the ZnO@HTCS composite exhibits superior piezoelectric catalytic performance compared to pure ZnO, making it a unique piezoelectric sonosensitizer. Density functional theory calculations reveal that the formation of a Mott-Schottky heterojunction and an internal piezoelectric field within the interface accelerates the electron transfer and the separation of electron-hole pairs. Concurrently, surface vacancies of the composite enable the adsorption of a greater amount of oxygen, enhancing the piezoelectric catalytic effect and generating a substantial quantity of ROS. This work not only presents a promising approach for augmenting piezoelectric catalysis through construction of a Schottky heterojunction interface but also provides a novel, efficient therapeutic strategy for treating osteomyelitis.

Evaluation of gadolinium deposition in cortical bone using three-dimensional ultrashort echo time quantitative susceptibility mapping: A preliminary study.

The aim of the current study was to investigate the feasibility of three-dimensional ultrashort echo time quantitative susceptibility mapping (3D UTE-QSM) for the assessment of gadolinium (Gd) deposition in cortical bone. To this end, 40 tibial bovine cortical bone specimens were divided into five groups then soaked in phosphate-buffered saline (PBS) solutions with five different Gd concentrations of 0, 0.4, 0.8, 1.2, and 1.6 mmol/L for 48 h. Additionally, eight rabbits were randomly allocated into three groups, consisting of a normal-dose macrocyclic gadolinium-based contrast agent (GBCA) group (n = 3), a high-dose macrocyclic GBCA group (n = 3), and a control group (n = 2). All bovine and rabbit tibial bone samples underwent magnetic resonance imaging (MRI) on a 3-T clinical MR system. A 3D UTE-Cones sequence was utilized to acquire images with five different echo times (i.e., 0.032, 0.2, 0.4, 0.8, and 1.2 ms). The UTE images were subsequently processed with the morphology-enabled dipole inversion algorithm to yield a susceptibility map. The average susceptibility was calculated in three regions of interest in the middle of each specimen, and the Pearson's correlation between the estimated susceptibility and Gd concentration was calculated. The bone samples soaked in PBS with higher Gd concentrations exhibited elevated susceptibility values. A mean susceptibility value of -2.47 ± 0.23 ppm was observed for bovine bone soaked in regular PBS, while the mean QSM value increased to -1.75 ± 0.24 ppm for bone soaked in PBS with the highest Gd concentration of 1.6 mmol/L. A strong positive correlation was observed between Gd concentrations and QSM values. The mean susceptibility values of rabbit tibial specimens in the control group, normal-dose GBCA group, and high-dose GBCA group were -4.11 ± 1.52, -3.85 ± 1.33, and -3.39 ± 1.35 ppm, respectively. In conclusion, a significant linear correlation between Gd in cortical bone and QSM values was observed. The preliminary results suggest that 3D UTE-QSM may provide sensitive noninvasive assessment of Gd deposition in cortical bone.

Design, synthesis, and anti-oomycete activity of 3-acyloxymaltol/ethyl maltol derivatives.

Twenty 3-acyloxymaltol/ethyl maltol derivatives (7a-j and 8a-j) were synthesized and evaluated in vitro for their anti-oomycete activity against Phytophthora capsici, respectively. Among all of twenty derivatives, more than half of the compounds 7f, 7h, 8a-h and 8j had anti-oomycete activity higher than the positive control zoxamide (EC50 = 22.23 mg/L), and the EC50 values of 18.66, 20.32, 12.80, 16.18, 10.59, 14.98, 16.80, 10.36, 15.32, 12.64, and 13.59 mg/L, respectively. Especially, compounds 8c and 8f exhibited the best anti-oomycete activity against P. capsici with EC50 values of 10.59 and 10.36 mg/L, respectively. Overall, hydroxyl group of maltol/ethyl maltol is important active modification site.

Abnormal white matter along fibers by automated fiber quantification in patients undergoing hemodialysis.

BACKGROUND: Abnormal white matter has been reported in patients with end-stage renal disease (ESRD). However, few studies have investigated the relationship between specific damage segments and cognition in ESRD. This study aimed to delineate white matter alterations in ESRD and its relationship with cognition. METHODS: A total of 36 patients undergoing hemodialysis and 25 healthy controls underwent diffusion tensor imaging (DTI) and a series of neuropsychiatric tests. Automated fiber quantification was used to extract distinct DTI indices, and the relationship between the specific segment of the white matter and clinical properties was investigated. Furthermore, a support vector machine was applied to differentiate patients with ESRD from healthy controls. RESULTS: Fractional anisotropy values decreased in multiple fiber bundles, including bilateral thalamic radiata, cingulum cingulate, inferior fronto-occipital fasciculus (IFOF), uncinate, Callosum_Forceps_Major/Callosum_Forceps_Minor (CFMaj/CFMin), and left uncinate from the tract level in patients with ESRD. Specific damaged segments were detected in 8 fiber bundles, including bilateral thalamic radiation, cingulum cingulate, IFOF, CFMin, and left corticospinal tract. Few alterations in these fiber bundles were correlated with cognition impairment and hemoglobin levels. The tract profiles of the left thalamic radiata and left cingulum cingulate could be used to differentiate hemodialysis patients from healthy controls, with an accuracy of 76.9% and 67.6%, respectively. CONCLUSIONS: This study revealed white matter damage in hemodialysis patients. This damage occurred in specific segments of the tract, especially in the left thalamic radiata and left cingulum cingulate, which might become a new biomarker for patients with ESRD and cognition impairment.

Dynamic functional connectivity changes associated with decreased memory performance in betel quid dependence.

The temporal variability of the dynamic functional connectivity (dFC) has been suggested as a useful metric for studying abnormal cognitive function. This study aimed to explore the associations between the temporal properties of dFC and memory performance in betel quid dependence (BQD). Sixty-four BQD individuals and 47 gender- and age-matched healthy controls (HCs) underwent functional magnetic resonance imaging and a series of neuropsychological assessments. The dFC was constructed by calculating the Pearson correlation coefficients within a sliding window and was clustered into three functional connectivity states using k-means clustering. The dFC temporal properties derived from the cluster results were compared between the BQD and HC groups. The results showed that States 1 and 3 featured more frequent and weak connectivity, and State 2 featured less frequent and strong connectivity. There were significant differences for mean dwell time (MDT) in State 3 (p = 0.022) and fraction of time in State 2 (p = 0.018) between the BQD and HC groups. Pearson correlation analyses showed that the MDT in State 1 was negatively correlated with long delay free recall and short delay free recall, and the MDT in State 3 was positively correlated with false positive of long delay recall. Our findings provide strong evidence that MDT match the memory performance and suggest new insights into the pathophysiological mechanism of memory disorders in BQD individuals.

Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features.

PURPOSE: To build an automatic computer-aided diagnosis (CAD) pipeline based on multiparametric magnetic resonance imaging (mpMRI) and explore the role of different imaging features in the classification of breast cancer. MATERIALS AND METHODS: A total of 222 histopathology-confirmed breast lesions, together with their BI-RADS scores, were included in the analysis. The cohort was randomly split into training (159) and test (63) cohorts, and another 50 lesions were collected as an external cohort. An nnUNet-based lesion segmentation model was trained to automatically segment lesion ROI, from which radiomics features were extracted for diffusion-weighted imaging (DWI), T2-weighted imaging (T2WI), and contrast-enhanced (DCE) pharmacokinetic parametric maps. Models based on combinations of sequences were built using support vector machine (SVM) and logistic regression (LR). Also, the performance of these sequence combinations and BI-RADS scores were compared. The Dice coefficient and AUC were calculated  to evaluate the segmentation and classification results. Decision curve analysis (DCA) was used to assess clinical utility. RESULTS: The segmentation model achieved a Dice coefficient of 0.831 in the test cohort. The radiomics model used only three features from diffusion coefficient (ADC) images, T2WI, and DCE-derived kinetic mapping, and achieved an AUC of 0.946 [0.883-0.990], AUC of 0.842 [0.6856-0.998] in the external cohort, which was higher than the BI-RADS score with an AUC of 0.872 [0.752-0.975]. The joint model using both radiomics score and BI-RADS score achieved the highest test AUC of 0.975 [0.935-1.000], with a sensitivity of 0.920 and a specificity of 0.923. CONCLUSION: Three radiomics features can be used to construct an automatic radiomics-based pipeline to improve the diagnosis of breast lesions and reduce unnecessary biopsies, especially when using jointly with BI-RADS scores.

Detection of gadolinium deposition in cortical bone with ultrashort echo time T1 mapping: an ex vivo study in a rabbit model.

OBJECTIVES: To investigate the capacity of ultrashort echo time (UTE) T1 mapping to non-invasively assess gadolinium deposition in cortical bone after gadolinium-based contrast agent (GBCA) administration. METHODS: Twenty-eight New Zealand rabbits (male, 3.0-3.5 kg) were randomly allocated into control, macrocyclic, high-dose macrocyclic, and linear GBCA groups (n = 7 for each group), and respectively given daily doses of 0.9 ml/kg bodyweight saline, 0.3 mmol/kg bodyweight gadobutrol, 0.9 mmol/kg bodyweight gadobutrol, and 0.3 mmol/kg bodyweight gadopentetate dimeglumine for five consecutive days per week over a period of 4 weeks. After a subsequent 4 weeks of recovery, the rabbits were sacrificed and their tibiae harvested. T1 value of cortical bone was measured using a combination of UTE actual flip angle imaging and variable repetition time on a 7T animal scanner. Gadolinium concentration in cortical bone was measured using inductively coupled plasma mass spectrometry (ICP-MS). Pearson's correlation between R1 value (R1 = 1/T1) and gadolinium concentration in cortical bone was assessed. RESULTS: Bone T1 values were significantly lower in the lower-dose macrocyclic (329.2 ± 21.0 ms, p < 0.05), higher-dose macrocyclic (316.8 ± 21.7 ms, p < 0.01), and linear (296.8 ± 24.1 ms, p < 0.001) GBCA groups compared with the control group (356.3 ± 19.4 ms). Gadolinium concentrations measured by ICP-MS in the control, lower-dose macrocyclic, higher-dose macrocyclic, and linear GBCA groups were 0.04 ± 0.02 µg/g, 2.60 ± 0.48 µg/g, 4.95 ± 1.17 µg/g, and 13.62 ± 1.55 µg/g, respectively. There was a strong positive correlation between R1 values and gadolinium concentrations in cortical bone (r = 0.73, p < 0.001). CONCLUSIONS: These results suggest that UTE T1 mapping has the potential to provide a non-invasive assessment of gadolinium deposition in cortical bone following GBCA administration. KEY POINTS: • Changes in T1 value related to gadolinium deposition were found in bone after both linear and macrocyclic GBCA administrations. • R1 relaxometry correlates strongly with gadolinium concentration in cortical bone. • UTE T1 mapping provides a potential tool for non-invasively monitoring gadolinium deposition in cortical bone.

Non-Gaussian Diffusion Models and T1 rho Quantification in the Assessment of Hepatic Sinusoidal Obstruction Syndrome in Rats.

BACKGROUND: Non-Gaussian diffusion models and T1 rho quantification may reflect the changes in tissue heterogeneity in hepatic sinusoidal obstruction syndrome (SOS). PURPOSE: To investigate the feasibility of diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and T1 rho quantification in detecting and staging SOS in a monocrotaline (MCT)-induced rat model. STUDY TYPE: Animal study. POPULATION: Thirty male Sprague-Dawley rats gavaged with MCT to induce hepatic SOS and six male rats without any intervention. FIELD STRENGTH/SEQUENCE: 3.0T, DWI with five b-values (0-2000 s/mm2 ) and T1 rho with five spin lock times (1-60 msec). ASSESSMENT: MRI was performed 1 day before and 1, 3, 5, 7, and 10 days after MCT administration. The corrected apparent diffusion coefficient (Dapp ), kurtosis coefficient (Kapp ), distributed diffusion coefficient (DDC), and intravoxel water molecular diffusion heterogeneity (α) were calculated from the corresponding non-Gaussian diffusion model. The T1 rho value was calculated using a monoexponential model. Specimens obtained from the six timepoints were categorized into normal liver (n = 6), early-stage (n = 16), and late-stage (n = 14) SOS in accordance with the pathological score. STATISTICAL TESTS: Parametric statistical methods and receiver operating characteristic (ROC) curves were employed to determine diagnostic accuracy. RESULTS: The Dapp , Kapp , DDC, α, and T1 rho values were correlated with pathological score with r values of -0.821, 0.726, -0.828, -0.739, and 0.714 (all P < 0.001), respectively. DKI (combined Dapp and Kapp ) and SEM (combined DDC and α) were better than T1 rho for staging SOS. The areas under the ROC curve of DKI, SEM, and T1 rho for differentiating normal liver and early-stage SOS were 0.97, 1.00, and 0.79, whereas those of DKI, SEM, and T1 rho for differentiating early-stage and late-stage SOS were 1.00, 0.97, and 0.92, respectively. DATA CONCLUSION: DKI, SEM, and T1 rho may be helpful in staging SOS. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1110-1121.

Quantitative susceptibility mapping of the spine using in-phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat-water separation.

Quantitative susceptibility mapping (QSM) of human spinal vertebrae from a multi-echo gradient-echo (GRE) sequence is challenging, because comparable amounts of fat and water in the vertebrae make it difficult to solve the nonconvex optimization problem of fat-water separation (R2*-IDEAL) for estimating the magnetic field induced by tissue susceptibility. We present an in-phase (IP) echo initialization of R2*-IDEAL for QSM in the spinal vertebrae. Ten healthy human subjects were recruited for spine MRI. A 3D multi-echo GRE sequence was implemented to acquire out-phase and IP echoes. For the IP method, the R2* and field maps estimated by separately fitting the magnitude and phase of IP echoes were used to initialize gradient search R2*-IDEAL to obtain final R2*, field, water, and fat maps, and the final field map was used to generate QSM. The IP method was compared with the existing Zero method (initializing the field to zero), VARPRO-GC (variable projection using graphcuts but still initializing the field to zero), and SPURS (simultaneous phase unwrapping and removal of chemical shift using graphcuts for initialization) on both simulation and in vivo data. The single peak fat model was also compared with the multi-peak fat model. There was no substantial difference on QSM between the single peak and multi-peak fat models, but there were marked differences among different initialization methods. The simulations demonstrated that IP provided the lowest error in the field map. Compared to Zero, VARPRO-GC and SPURS, the proposed IP method provided substantially improved spine QSM in all 10 subjects.

Magnetic Susceptibility and Fat Content in the Lumbar Spine of Postmenopausal Women With Varying Bone Mineral Density.

BACKGROUND: Osteoporosis is a systemic disease characterized by low bone mass with increased fracture risk. Quantitative imaging biomarkers are important for accurately predicting fracture risk in patients with osteoporosis. PURPOSE: To prospectively study the changes of magnetic susceptibility and fat content in the lumbar spine of postmenopausal females with varying bone mineral density (BMD), and investigate their application to osteoporosis assessment. STUDY TYPE: Cohort. POPULATION: In all, 108 postmenopausal females (58.2 ± 6.7 [range 45-79] years old). FIELD STRENGTH/SEQUENCE: Quantitative computed tomography (QCT) performed on a 64-detector CT scanner; quantitative susceptibility mapping (QSM) and mDixon quant MR imaging performed using a 3.0T imaging system with a 16-channel posterior coil. ASSESSMENT: QCT, QSM, and mDixon were performed in 108 postmenopausal females to measure vertebral BMD, susceptibility, and proton-density fat fraction (PDFF). Mean vertebral QSM and PDFF were compared among three BMD cohorts (normal, osteopenic, and osteoporotic). Receiver operating characteristic analyses were performed to evaluate the performance of QSM, PDFF, and QSM+PDFF for assessing osteoporosis. STATISTICAL TESTS: Parameters were compared using Kruskal-Wallis test and Pearson test. RESULTS: Compared with that of the normal BMD group (-17.0 ± 43.6 ppb), vertebral QSM was significantly increased in osteopenia (30.8 ± 47.0 ppb, P < 0.001), and further increased in osteoporosis (82.0 ± 39.9 ppb, P < 0.001). QSM was negatively correlated with BMD (r = -0.70, P < 0.001) and positively correlated with PDFF (r = 0.64, P < 0.001). Compared with the area under the curve (AUC) of PDFF, the AUC of QSM was higher in differentiating between normal and osteoporosis (P = 0.44), and between osteopenia and osteoporosis (P = 0.13), but without statistical significance. The AUC of QSM+PDFF was significantly higher than that of PDFF for differentiating between osteopenia and osteoporosis (0.82 vs. 0.70, P = 0.039). DATA CONCLUSION: The combination of vertebral susceptibility and fat content may be a promising marker for assessing postmenopausal osteoporosis. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1020-1028.

Rapid automated liver quantitative susceptibility mapping.

BACKGROUND: Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC. PURPOSE: To develop a rapid, robust, and automated liver QSM for clinical practice. STUDY TYPE: Prospective. POPULATION: 13 healthy subjects and 22 patients. FIELD STRENGTH/SEQUENCES: 1.5 T and 3 T/3D multiecho gradient-recalled echo (GRE) sequence. ASSESSMENT: Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T2 *-IDEAL) before performing QSM. Liver QSM was generated through an automated pipeline without manual intervention. This IP echo-based initialization method was compared with an existing graph cuts initialization method (simultaneous phase unwrapping and removal of chemical shift, SPURS) in healthy subjects (n = 5). Reproducibility was assessed over four scanners at two field strengths from two manufacturers using healthy subjects (n = 8). Clinical feasibility was evaluated in patients (n = 22). STATISTICAL TESTS: IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and region of interest (ROI) measurements. Reproducibility of QSM, R2 *, and proton density fat fraction (PDFF) among the four different scanners was assessed using linear regression, Bland-Altman analysis, and the intraclass correlation coefficient (ICC). RESULTS: Liver QSM using the IP method was found to be ~5.5 times faster than SPURS (P < 0.05) in initializing T2 *-IDEAL with similar outputs. Liver QSM using the IP method were reproducibly generated in all four scanners (average coefficient of determination 0.95, average slope 0.90, average bias 0.002 ppm, 95% limits of agreement between -0.06 to 0.07 ppm, ICC 0.97). DATA CONCLUSION: Use of IP echo-based initialization enables robust water/fat separation and field estimation for automated, rapid, and reproducible liver QSM for clinical applications. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:725-732.

Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge.

PURPOSE: The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. METHODS: Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions. RESULTS: Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. CONCLUSION: Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Bone susceptibility mapping with MRI is an alternative and reliable biomarker of osteoporosis in postmenopausal women.

OBJECTIVES: To investigate the efficacy of quantitative susceptibility mapping (QSM) in the assessment of osteoporosis for postmenopausal women. METHODS: Between May and September 2017, a total of 70 postmenopausal women who underwent MRI-based QSM and quantitative computed tomography (QCT) were consecutively enrolled in this prospective study. The measurement of QSM and QCT values was performed on the L3 vertebrae body. On the basis of QCT value, all individuals were divided into three groups (normal, osteopenia and osteoporosis). RESULTS: On the basis of QCT, 18 individuals were normal (25.7%), 26 osteopenic (37.1%) and 26 osteoporotic (37.1%). The QSM value was age-related (p = 0.04) and significantly higher in the osteoporosis group than in either the normal or osteopenia group (for all, p < 0.001). In addition, the QSM value was highly correlated with QCT value (r = - 0.720, p < 0.001). For QSM, the area under the curve (AUC), sensitivity and specificity for differentiating osteopenia from non-osteopenia were 0.88, 86.5% and 77.8%, respectively, and for differentiating osteoporosis from non-osteoporosis they were 0.86, 80.8% and 77.3%, respectively. CONCLUSIONS: MRI-based QSM could be used for quantifying susceptibility in vertebrae and has the potential to be a new biomarker in the assessment of osteoporosis for postmenopausal women. KEY POINTS: • Osteoporosis significantly increases risk of fracture for postmenopausal women. • QSM value was correlated with QCT value (r = - 0.72, p < 0.001). • QSM is feasible in the assessment of osteoporosis for postmenopausal women. • QSM offers the quantification of susceptibility within bone.

Hepatic artery diameter predicts bleeding risk after gastroesophageal varices treatment: a contrast-enhanced CT study.

OBJECTIVE: Portal hypertension leads to hepatic artery dilatation and a higher risk of bleeding. We tried to identify the bleeding risk after gastroesophageal varices (GOV) treatment using hepatic artery diameter of contrast-enhanced CT. METHODS: Retrospective retrieval of 258 patients with cirrhosis who underwent contrast-enhanced CT from January 2022 to May 2023 and endoscopy within one month thereafter at Hainan Affiliated Hospital of Hainan Medical University. Cirrhotic patients before GOV treatment were used as the test cohort (n = 199), and cirrhotic patients after GOV treatment were used as the validation cohort (n = 59). The grading and bleeding risk was classified according to the endoscopic findings. Arterial-phase images of contrast-enhanced CT were used for coronal reconstruction, and the midpoint diameter of the hepatic artery was measured on coronal images. The optimal cutoff value for identifying bleeding risk was analyzed and calculated in the test cohort, and its diagnostic performance was evaluated in the validation cohort. RESULTS: In the test cohort, hepatic artery diameters were significantly higher in high-risk GOV than in low-risk GOV [4.69 (4.31, 5.56) vs. 3.10 (2.59, 3.77), P < 0.001]. With a hepatic artery diameter cutoff value of 4.06 mm, the optimal area under the operating characteristic curve was 0.940 (95% confidence interval: 0.908-0.972), with a sensitivity of 0.887, a specificity of 0.892, a positive predictive value of 0.904, and a negative predictive value of 0.874 for identifying bleeding risk in the test cohort, while in the validation cohort, the sensitivity was 0.885, specificity was 0.939, positive predictive value was 0.920, and negative predictive value was 0.912. CONCLUSION: Hepatic artery diameter has high diagnostic performance in identifying bleeding risk after GOV treatment.

Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging.

PURPOSE: To investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI). METHODS: We included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed. RESULTS: In the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle. CONCLUSIONS: White matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients.

Achieving consistency of flexible surface acoustic wave sensors with artificial intelligence.

Flexible surface acoustic wave technology has garnered significant attention for wearable electronics and sensing applications. However, the mechanical strains induced by random deformation of these flexible SAWs during sensing often significantly alter the specific sensing signals, causing critical issues such as inconsistency of the sensing results on a curved/flexible surface. To address this challenge, we first developed high-performance AlScN piezoelectric film-based flexible SAW sensors, investigated their response characteristics both theoretically and experimentally under various bending strains and UV illumination conditions, and achieved a high UV sensitivity of 1.71 KHz/(mW/cm²). To ensure reliable and consistent UV detection and eliminate the interference of bending strain on SAW sensors, we proposed using key features within the response signals of a single flexible SAW device to establish a regression model based on machine learning algorithms for precise UV detection under dynamic strain disturbances, successfully decoupling the interference of bending strain from target UV detection. The results indicate that under strain interferences from 0 to 1160 µÎµ the model based on the extreme gradient boosting algorithm exhibits optimal UV prediction performance. As a demonstration for practical applications, flexible SAW sensors were adhered to four different locations on spacecraft model surfaces, including flat and three curved surfaces with radii of curvature of 14.5, 11.5, and 5.8 cm. These flexible SAW sensors demonstrated high reliability and consistency in terms of UV sensing performance under random bending conditions, with results consistent with those on a flat surface.

Characterizing Typhoon-related Posttraumatic Stress Disorder Based on Multimodal Fusion of Structural, Diffusion, and Functional Magnetic Resonance Imaging.

Diagnosing posttraumatic stress disorder (PTSD) using only single-modality images is controversial. We aimed to use multimodal magnetic resonance imaging (MRI) combining structural, diffusion, and functional MRI to possibly provide a more comprehensive viewpoint on the decisive characteristics of PTSD patients. Typhoon-exposed individuals with (n = 26) and without PTSD (n = 32) and healthy volunteers (n = 30) were enrolled. Five MRI features from three modalities, including two resting-state functional MRI (rs-fMRI) features (amplitude of low-frequency fluctuation, ALFF; and regional homogeneity, ReHo), one structural MRI feature (gray matter density, GM), and two diffusion tensor imaging (DTI) features (fractional anisotropy, FA; and mean diffusivity, MD) were investigated simultaneously with a multimodal canonical correlation analysis + joint independent component analysis model to identify abnormalities in the PTSD brain. We identified statistical differences between PTSD patients and healthy controls in terms of 1 rs-fMRI (ALFF, ReHo) alterations in the superior frontal gyrus, precuneus, inferior parietal lobule (IPL), anterior cingulate cortex (ACC), and posterior cingulate cortex (PCC), 2 DTI (FA, MD) changes in the pons, genu, and splenium of the corpus callosum, and 3 Structural MRI abnormalities in the precuneus, IPL, ACC, and PCC. A novel ReHo component was found to distinguish PTSD and trauma-exposed controls, including the precuneus, IPL, middle frontal gyrus, middle occipital gyrus, and cerebellum. This study reveals that PTSD individuals exhibit intertwined functional and structural anomalies within the default mode network. Some alterations within this network may serve as a potential marker to distinguish between PTSD patients and trauma-exposed controls.

Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alteration in Alzheimer's disease.

Background: Quantitative transport mapping (QTM) of blood velocity, based on the transport equation has been demonstrated higher accuracy and sensitivity of perfusion quantification than the traditional Kety's method-based blood flow (Kety flow). This study aimed to investigate the associations between QTM velocity and cognitive function in Alzheimer's disease (AD) using multiple post-labeling delay arterial spin labeling (ASL) MRI. Methods: A total of 128 subjects (21 normal controls (NC), 80 patients with mild cognitive impairment (MCI), and 27 AD) were recruited prospectively. All participants underwent MRI examination and neuropsychological evaluation. QTM velocity and traditional Kety flow maps were computed from multiple delay ASL. Regional quantitative perfusion measurements were performed and compared to study group differences. We tested the hypothesis that cognition declines with reduced cerebral blood flow with consideration of age and gender effects. Results: In cortical gray matter (GM) and the hippocampus, QTM velocity and Kety flow showed decreased values in AD group compared to NC and MCI groups; QTM velocity, but not Kety flow, showed a significant difference between MCI and NC groups. QTM velocity and Kety flow showed values decreasing with age; QTM velocity, but not Kety flow, showed a significant gender difference between male and female. QTM velocity and Kety flow in the hippocampus were positively correlated with cognition, including global cognition, memory, executive function, and language function. Conclusion: This study demonstrated an increased sensitivity of QTM velocity as compared with the traditional Kety flow. Specifically, we observed only in QTM velocity, reduced perfusion velocity in GM and the hippocampus in MCI compared with NC. Both QTM velocity and Kety flow demonstrated reduction in AD vs controls. Decreased QTM velocity and Kety flow in the hippocampus were correlated with cognitive measures. These findings suggest QTM velocity as an improved biomarker for early AD blood flow alterations.

SASAN: Spectrum-Axial Spatial Approach Networks for Medical Image Segmentation.

Ophthalmic diseases such as central serous chorioretinopathy (CSC) significantly impair the vision of millions of people globally. Precise segmentation of choroid and macular edema is critical for diagnosing and treating these conditions. However, existing 3D medical image segmentation methods often fall short due to the heterogeneous nature and blurry features of these conditions, compounded by medical image clarity issues and noise interference arising from equipment and environmental limitations. To address these challenges, we propose the Spectrum Analysis Synergy Axial-Spatial Network (SASAN), an approach that innovatively integrates spectrum features using the Fast Fourier Transform (FFT). SASAN incorporates two key modules: the Frequency Integrated Neural Enhancer (FINE), which mitigates noise interference, and the Axial-Spatial Elementum Multiplier (ASEM), which enhances feature extraction. Additionally, we introduce the Self-Adaptive Multi-Aspect Loss (LSM), which balances image regions, distribution, and boundaries, adaptively updating weights during training. We compiled and meticulously annotated the Choroid and Macular Edema OCT Mega Dataset (CMED-18k), currently the world's largest dataset of its kind. Comparative analysis against 13 baselines shows our method surpasses these benchmarks, achieving the highest Dice scores and lowest HD95 in the CMED and OIMHS datasets. Our code is publicly available at https://github.com/IMOP-lab/SASAN-Pytorch.

Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.

Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.