Imaging Mapping Research Articles

Preoperative prediction of lymph node metastasis in endometrial cancer patients via an intratumoral and peritumoral multiparameter MRI radiomics nomogram.

While lymph node metastasis (LNM) plays a critical role in determining treatment options for endometrial cancer (EC) patients, the existing preoperative methods for evaluating the lymph node state are not always satisfactory. This study aimed to develop and validate a nomogram based on intra- and peritumoral radiomics features and multiparameter magnetic resonance imaging (MRI) features to preoperatively predict LNM in EC patients. Three hundred and seventy-four women with histologically confirmed EC were divided into training (n = 220), test (n = 94), and independent validation (n = 60) cohorts. Radiomic features were extracted from intra- and peritumoral regions via axial T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) mapping. A radiomics model (annotated as the Radscore) was established using the selected features from different regions. The clinical parameters were statistically analyzed. A nomogram was developed by combining the Radscore and the most predictive clinical parameters. Decision curve analysis (DCA) and the net reclassification index (NRI) were used to assess the clinical benefit of using the nomogram. Nine radiomics features were ultimately selected from the intra- and peritumoral regions via ADC mapping and T2WI. The nomogram combining the Radscore, serum CA125 level, and tumor area ratio achieved the highest AUCs in the training, test and independent validation sets (nomogram vs. Radscore vs. clinical model: 0.878 vs. 0.850 vs. 0.674 (training), 0.877 vs. 0.838 vs. 0.668 (test), and 0.864 vs. 0.836 vs. 0.618 (independent validation)). The DCA and NRI results revealed the nomogram had greater diagnostic performance and net clinical benefits than the Radscore alone. The combined intra- and peritumoral region multiparameter MRI radiomics nomogram showed good diagnostic performance and could be used to preoperatively predict LNM in patients with EC.

Predictive Value of T2*-Weighted Perfusion and T1-Weighted Permeability MRI Parameters in Determining IDH Mutation Status and Grade of Gliomas.

In glioma classification, the isocitrate dehydrogenase (IDH) mutation status is crucial, as it significantly influences treatment approaches and prognosis. This study assessed the performance metrics of perfusion and permeability magnetic resonance imaging (MRI) parameters with optimal cut-offs in differentiating IDH genotype and tumor grade in patients with grade 2-4 gliomas. This retrospective study included 36 patients surgically diagnosed with grade 2-4 glioma (six grade 2, seven grade 3, and 23 grade 4) with known IDH genotypes (23 IDH wild-type, 13 IDH mutant) between November 2021 and August 2023. All patients underwent preoperative perfusion and permeability MRI examinations with a 3.0 Tesla scanner. Parameters were calculated on colored map images. Using the intraclass correlation coefficient, intra- and inter-observer agreement was assessed. Following multiple testing correction, the perfusion parameters with statistically significant differences were subjected to receiver operating characteristic (ROC) analysis. Five MRI parameters (rCBV and rCBF from perfusion; Ktrans, Ve, and Vp from permeability) showed a significant difference between groups in terms of IDH genotype (p 0.001). In ROC analysis, the best parameters in differentiating IDH genotype included rCBV and Ktrans; rCBV with a cut-off of 5.58 achieved an area under the ROC curve (AUC), sensitivity, specificity, and accuracy of 0.883, 95.7%, 76.9%, and 88.8%, respectively. For Ktrans, with a cut-off of 0.0727 min-1, these values were 0.893, 100%, 69.2%, and 88.8%, respectively. In ROC analysis, these two parameters with rCBF and Ve also showed good performance in differentiating low- and high-grade gliomas with an AUC, sensitivity, and accuracy exceeding 0.940, 86%, and 88%, respectively. Perfusion and permeability MRI may provide useful parameters in differentiating the IDH genotype and grade of gliomas.

Characterization of (Zr,Ti)B2-SiC composites obtained by hot press sintering of ZrB2-SiC-TiO2 powder mixtures

The ability to enhance mechanical and oxidation properties for severe environmental applications has led to substantial academic interest in multiphase ultra-high temperature ceramics (UHTC). The purpose of this work is to study the in-situ solid solution formation of (Zr,Ti)B2 from ZrB2 and TiO2 in a ZrB2-SiC composite using hot pressing reaction sintering. For this, a mixture of 10, 20, and 30 % vol% SiC with ZrB2 was mixed with 2.0 wt% TiO2. Hot pressing sintering was performed with a load of 20 MPa at a final temperature of 1850 °C/30 min in an argon atmosphere. The microstructures, crystalline phases, densities, mechanical properties, and oxidation resistance of the composites were examined and compared with ZrB2-SiC samples lacking TiO2. In samples where TiO2 was added, the matrix grain size slightly decreased, the fracture mode was mainly intergranular, and the SiC grain morphology changed the aspect ratio to be more equiaxed. The solid solution (Zr,Ti)B2 was produced, and it was demonstrated by EDS elemental map images and the XRD analysis that Ti atoms incorporate into the ZrB2 crystalline structure. The development of solid solutions showed no impact on relative densities or Vickers hardness. However, the solid solution formation favored an improvement in fracture toughness, probably owing to the smaller matrix grain size and intergranular fracture mode. Samples exhibiting (Zr,Ti)B2 formation presented lower oxidation resistance than undoped samples in the same oxidizing condition.

Evaluation of Inflammatory Disease Activity in the Sacroiliac Joints Using Magnetic Resonance Imaging: A Comparative Analysis of Short-Tau Inversion Recovery, Post-Contrast, and Diffusion-Weighted Imaging –Preliminary Study

Introduction: It is essential to detect sacroiliitis earlier to decrease morbidity and unwanted complications such as ankylosing of sacroiliac joints. In recent studies, magnetic resonance imaging (MRI) serves as a key diagnostic tool for identifying sacroiliitis and is now included in the diagnostic criteria. We aim to detect the utility of diffusion-weighted imaging on the MRI in diagnosing sacroiliitis and measure ADC values for the response to treatment in future studies. Materials -methods: There were 39 patients (16 male, 20 female). A 1.5 Tesla MR device with a pelvic coil was used. The sequences were T1 (before and after intravenous contrast administration of gadolinium), T2-weighted fast field echo, short tau inversion recovery (STIR), diffusion-weighted (DW) images, and apparent diffusion coefficient (ADC) mapping. Sacroiliac joints were divided into four quadrants, and two radiologists interpreted all the sequences by giving scores from 0 to 3 depending on bone marrow edema from none to severe. The concordance of scores assigned to STIR, T1-weighted gadolinium-enhanced images, ADC mapping, and diffusion-weighted MR images was evaluated by calculating intraclass correlation coefficients. Results: A statistically significant positive correlation was observed between the activity scores derived from short tau inversion recovery (STIR) and other magnetic resonance (MR) sequences, namely T1-weighted gadolinium-enhanced images, apparent diffusion coefficient (ADC) mapping, and diffusion-weighted MR images. Conclusion: Active bone marrow abnormalities were identified with comparable efficacy using STIR imaging and other MR sequences (T1W-Gad, ADC mapping, DW-MR). In our study, we tried to emphasize the role of the diffusion-weighted images near the other sequences. Diffusion is a fast, easy-applied sequence. After increased experience in the diffusion-weighted images, routine application of this sequence will increase, and even becoming one of the diagnostic criteria will be inevitable. The difference in DWI from the other sequences is that it is measurable. The response to treatment may be followed up by ADC values.

Added Advantages of DWI MR Imaging and ADC Mapping in the Diagnosis of Active Sacroiliitis of Axial Seronegative SpA

Aim: To evaluate the effectiveness of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping in diagnosing active sacroiliitis in patients with axial seronegative spondyloarthropathy (SpA). Material and Methods: This prospective study comprised 26 patients with MR imaging detected active sacroiliitis categorised as a case group, and compared them with healthy controls. ADC value measurements were obtained from the sacral and iliac sides of the bilateral S.I. joints in each case and control group. Independent samples t-test, Chi-square test, and ROC curve analysis were used. Result: Twenty-six patients had active sacroiliitis in 45 S.I. joints, unilateral active sacroiliitis was observed in seven patients (26.9%), bilateral symmetrical in three patients (11.5%) and bilateral asymmetrical in 16 patients (61.5%). The mean ADC value was 1.447 ± 0.528 [SD] × 10–3 mm 2 /s in the right S.I. joint and 1.630 ± 0.441 [SD] × 10–3 mm 2 /s in the left S.I. joint in active sacroiliitis (case group). The mean ADC value of the right S.I. joint was 0.965 ± 0.123 [SD] × 10–3 mm 2 /s and the left S.I. joint was 0.967 ± 0.96 [SD] × 10–3 mm 2 /s in inactive sacroiliitis (control group). A significant difference was observed statistically between the active sacroiliitis and the inactive sacroiliitis with a P < .0005. A cut-off mean ADC value of 1.034 ×10–3 mm 2 /s was obtained for differentiating active sacroiliitis from inactive sacroiliitis with a sensitivity of 84.6% and specificity of 84.6% from the ROC curve analysis. Conclusion: Diffusion-weighted MRI imaging with ADC value mapping acts as a sensitive diagnostic technique in the detection of active sacroiliitis in axial seronegative spondyloarthropathies and also helps in assessing the treatment response.

Reconstructing hyperspectral images of textiles from a single RGB image utilizing the multihead self-attention mechanism

Hyperspectral images possess abundant information and play a pivotal role in enhancing the accuracy of color difference detection in textiles. However, traditional hyperspectral imaging methods necessitate costly equipment and intricate operational procedures. A novel deep learning model based on a multihead attention mechanism was proposed in this article to facilitate the extensive application of hyperspectral imaging technology in textile quality inspection. This model enabled the reconstruction of the hyperspectral information of plain weave textiles from a single RGB image. In this model, encoder-decoder architecture and pyramid pooling convolutional operations were employed to integrate multiscale features of plain weave cotton-linen textiles. This could capture details and contextual information in textile images more precisely, enhancing the accuracy of hyperspectral image reconstruction. Simultaneously, an attention mechanism was introduced to increase the model’s receptive field and improve its focus on key regions in the input image and feature maps. This resulted in a reduced weighting of redundant information during network learning, leading to an improved feature extraction capability of the network. Through these methods, successful reconstructions of plain weave textiles hyperspectral information from a single RGB image was achieved. Quantitative and qualitative tests were conducted on two datasets, namely, the NTIRE 2020 dataset and a self-made textile dataset, to evaluate the performance of the proposed method. The approach proposed in this article exhibited promising results on both datasets. Specifically, the reconstructed textile hyperspectral images achieved a root mean square error of 0.0344, a peak signal-to-noise ratio of 29.945, a spectral angle mapper of 3.753, and a structural similarity index measure of 0.955 on the textile dataset. In the reconstructed hyperspectral colorimetric experiment, the maximum value of average color difference was 2.641. These results demonstrate that the method can meet the requirements for textile color measurement applications.

Detection of sex-specific glutamate changes in subregions of hippocampus in an early-stage Alzheimer's disease mouse model using GluCEST MRI.

Regional glucose hypometabolism resulting in glutamate loss has been shown as one of the characteristics of Alzheimer's disease (AD). Because the impact of AD varies between the sexes, we utilized glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) for high-resolution spatial mapping of cerebral glutamate and investigated subregional changes in a sex-specific manner. Eight-month-old male and female AD mice harboring mutant amyloid precursor protein (APPNL-F/NL-F: n = 36) and wild-type (WT: n = 39) mice underwent GluCEST MRI, followed by proton magnetic resonance spectroscopy (1H-MRS) in hippocampus and thalamus/hypothalamus using 9.4T preclinical MR scanner. GluCEST measurements revealed significant (p≤0.02) glutamate loss in the entorhinal cortex (% change ± standard error: 8.73±2.12%), hippocampus (11.29±2.41%), and hippocampal fimbriae (19.15±2.95%) of male AD mice. A similar loss of hippocampal glutamate in male AD mice (11.22±2.33%; p = 0.01) was also observed in 1H-MRS. GluCEST MRI detected glutamate reductions in the fimbria and entorhinal cortex of male AD mice, which was not reported previously. Resilience in female AD mice against these changes indicates an intact status of cerebral energy metabolism. Glutamate levels were monitored in different brain regions of early-stage Alzheimer's disease (AD) and wild-type male and female mice using glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI). Male AD mice exhibited significant glutamate loss in the hippocampus, entorhinal cortex, and the fimbriae of the hippocampus. Interestingly, female AD mice did not have any glutamate loss in any brain region and should be investigated further to find the probable cause. These findings demonstrate previously unreported sex-specific glutamate changes in hippocampal sub-regions using high-resolution GluCEST MRI.

Cobalt-Doped ZnO Nanocomposits for Efficient Dye Degradation: Charge Transfer.

Doping enhances the optical properties of high-band gap zinc oxide nanoparticles (ZnO NPs), essential for their photocatalytic activity. We used the combustion approach to synthesize cobalt-doped ZnO heterostructure (CDZO). By creating a mid-edge level, it was possible to tune the indirect band gap of the ZnO NPs from 3.1 eV to 1.8 eV. The red shift and reduction in the intensity of the photoluminescence (PL) spectra resulted from hindrances in electron-hole recombination and sp-d exchange interactions. These improved optical properties expanded the absorption of solar light and enhanced charge transfer. The field emission scanning electron microscopy (FESEM) image and elemental mapping analysis confirmed the CDZO's porous nature and the dopant's uniform distribution. The porosity, nanoscale size (25-55 nm), and crystallinity of the CDZO were further verified by high-resolution transmission electron microscopy (HRTEM) and selected area electron image analysis. The photocatalytic activity of the CDZO exhibited much greater efficiency (k=0.131 min-1) than that of ZnO NPs (k=0.017 min-1). Therefore, doped heterostructures show great promise for industrial-scale environmental remediation applications.

Convolutional Neural Network-Based Drone Detection and Classification Using Overlaid Frequency-Modulated Continuous-Wave (FMCW) Range-Doppler Images.

This paper proposes a novel drone detection method based on a convolutional neural network (CNN) utilizing range-Doppler map images from a frequency-modulated continuous-wave (FMCW) radar. The existing drone detection and identification techniques, which rely on the micro-Doppler signature (MDS), face challenges when a drone is small or located far away, leading to performance degradation due to signal attenuation and faint (MDS). In order to address these issues, this paper suggests a method where multiple time-series range-Doppler images from an FMCW radar are overlaid onto a single image and fed to a CNN. The experimental results, using actual data for three different drone sizes, show significant performance improvements in drone detection accuracy compared to conventional methods.

Correlative Imaging for Comprehensive Molecular Mapping of Individual Cell Types in Biological Tissues.

Mass spectrometry imaging (MSI) is a powerful technique for label-free spatial mapping of multiple classes of biomolecules in tissue sections. However, differences in desorption and ionization efficiency of different classes of molecules make it challenging to simultaneously map biomolecules at each omics layer in the same tissue sample. Herein, we present a correlative imaging method using nanospray desorption electrospray ionization (nano-DESI) MSI, which enables the spatial mapping of lipids, metabolites, peptides, and proteins with cellular-level spatial resolution in a single tissue section. We demonstrate the molecular profiling of specific cell types and identify truncated peptides in mouse pancreatic tissue. Distinct chemical gradients of peptides and lipids extending from endocrine cells to exocrine cells indicate their different roles in endocrine-exocrine crosstalk and intracellular signaling. The results underscore the power of the developed imaging approach for spatial multi-omics analysis that provides deep insights into cellular diversity and the intricate molecular interactions that occur within heterogenous biological tissues.

Quantitative muscle MRI in sporadic inclusion body myositis (sIBM): A prospective cohort study.

Sporadic inclusion body myositis (sIBM) is the predominant idiopathic inflammatory myopathy (IIM) in older people. Limitations of classical clinical assessments have been discussed as possible explanations for failed clinical trials, underlining the need for more sensitive outcome measures. Quantitative muscle MRI (qMRI) is a promising candidate for evaluating and monitoring sIBM. Longitudinal assessment of qMRI in sIBM patients. We evaluated fifteen lower extremity muscles of 12 sIBM patients (5 females, mean age 69.6, BMI 27.8) and 12 healthy age- and gender-matched controls. Seven patients and matched controls underwent a follow-up evaluation after one year. Clinical assessment included testing for muscle strength with Quick Motor Function Measure (QMFM), IBM functional rating scale (IBM-FRS), and gait analysis (6-minute walking distance). 3T-MRI scans of the lower extremities were performed, including a Dixon-based sequence, T2 mapping and Diffusion Tensor Imaging. The qMRI-values fat-fraction (FF), water T2 relaxation time (wT2), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ1), and radial diffusivity (RD) were analysed. Compared to healthy controls, significant differences for all qMRI parameters averaged over all muscles were found in sIBM using a MANOVA (p < 0.001). In low-fat muscles (FF < 10%), a significant increase of wT2 and FA with an accompanying decrease of MD, λ1, and RD was observed (p≤0.020). The highest correlation with clinical assessments was found for wT2 values in thigh muscles (r≤-0.634). Significant changes of FF (+3.0%), wT2 (+0.6 ms), MD (-0.04 10-3mm2/s), λ1 (-0.05 10-3mm2/s), and RD (-0.03 10-3mm2/s) were observed in the longitudinal evaluation of sIBM patients (p≤0.001). FA showed no significant change (p = 0.242). qMRI metrics correlate with clinical findings and can reflect different ongoing pathophysiological mechanisms. While wT2 is an emerging marker of disease activity, the role of diffusion metrics, possibly reflecting changes in fibre size and intracellular deposits, remains subject to further investigations.

A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.

Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early diagnosis is crucial for reducing morbidity and mortality. Artificial intelligence (AI) diagnosis support tools, such as Chat Generative Pre-trained Transformer (ChatGPT), offer rapid diagnostic advantages. This study assesses ChatGPT's accuracy in interpreting diffusion-weighted imaging (DWI) for acute stroke diagnosis. A retrospective analysis was conducted to identify the presence of stroke using DWI and apparent diffusion coefficient (ADC) map images. Patients aged >18 years who exhibited diffusion restriction and had a clinically explainable condition were included in the study. Patients with artifacts that affected image homogeneity, accuracy, and clarity, as well as those who had undergone previous surgery or had a history of stroke, were excluded from the study. ChatGPT was asked four consecutive questions regarding the identification of the magnetic resonance imaging (MRI) sequence, the demonstration of diffusion restriction on the ADC map after sequence recognition, and the identification of hemispheres and specific lobes. Each question was repeated 10 times to ensure consistency. Senior radiologists subsequently verified the accuracy of ChatGPT's responses, classifying them as either correct or incorrect. We assumed a response to be incorrect if it was partially correct or suggested multiple answers. These responses were systematically recorded. We also recorded non-responses from ChatGPT-4V when it failed to provide an answer to a query. We assessed ChatGPT-4V's performance by calculating the number and percentage of correct responses, incorrect responses, and non-responses across all images and questions, a metric known as "accuracy." ChatGPT-4V was considered successful if it answered ≥80% of the examples correctly. A total of 530 diffusion MRI, of which 266 were stroke images and 264 were normal, were evaluated in the study. For the initial query identifying MRI sequence type, ChatGPT-4V's accuracy was 88.3% for stroke and 90.1% for normal images. For detecting diffusion restriction, ChatGPT-4V had an accuracy of 79.5% for stroke images, with a 15% false positive rate for normal images. Regarding identifying the brain or cerebellar hemisphere involved, ChatGPT-4V correctly identified the hemisphere in 26.2% of stroke images. For identifying the specific brain lobe or cerebellar area affected, ChatGPT-4V had a 20.4% accuracy for stroke images. The diagnostic sensitivity of ChatGPT-4V in acute stroke was found to be 79.57%, with a specificity of 84.87%, a positive predictive value of 83.86%, a negative predictive value of 80.80%, and a diagnostic odds ratio of 21.86. Despite limitations, ChatGPT shows potential as a supportive tool for healthcare professionals in interpreting diffusion examinations in stroke cases, where timely diagnosis is critical. ChatGPT can play an important role in various aspects of stroke cases, such as risk assessment, early diagnosis, and treatment planning.

Magnetic Resonance Imaging Biomarkers of Muscle.

This review is focused on the current status of quantitative MRI (qMRI) of skeletal muscle. The first section covers the techniques of qMRI in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured parameter to underlying physiology/pathophysiology, the image processing and analysis approaches, and studies on normal subjects. We cover the more established parametric mapping from T1-weighted imaging for morphometrics including image segmentation, proton density fat fraction, T2 mapping, and diffusion tensor imaging to emerging qMRI features such as magnetization transfer including ultralow TE imaging for macromolecular fraction, and strain mapping. The second section is a summary of current clinical applications of qMRI of muscle; the intent is to demonstrate the utility of qMRI in different disease states of the muscle rather than a complete comprehensive survey.

Evaluating the Impact of Digital Mapping and 3D Imaging Technologies on the Restoration of Historical Sites in Southeast Asia

The preservation of cultural heritage sites is undergoing a transformation, propelled by advancements in digital and 3D technologies. This paper explores the integration and impact of such technologies, including digital mapping, 3D imaging, Artificial Intelligence (AI), and the Internet of Things (IoT), on the conservation of historical sites. By providing detailed case studies from Southeast Asia, this study examines the accuracy improvements, enhanced visualization capabilities, and increased public engagement facilitated by these technologies. It also addresses the technical, financial, and training challenges associated with their implementation. Furthermore, the paper discusses the potential of emerging technologies to revolutionize heritage conservation and predicts future trends in the field. The findings suggest that while there are significant benefits to the adoption of these technologies, careful consideration is needed to overcome the associated challenges and ensure sustainable integration into conservation practices.

Quantitative assessment of gadolinium deposition in dentate nuclei with MR fingerprinting

Gadolinium deposition in the dentate nucleus (DN) has been evaluated by T1-weighted imaging (T1WI) and T1 (R1) mapping, but not MR fingerprinting (MRF). This study investigated associations between T1 and T2 values of DN and gadolinium-based contrast agents (GBCAs) using 2-dimensional MRF. This study included 101 patients. Region of interest analysis was performed for T1 and T2 values of DN on MRF (T1-MRF, T2-MRF) and T1-weighted images (T1WI ratio). T1 and T2 ratios compared to normal cerebellar white matter (T1-MRF ratio, T2-MRF ratio) were calculated. The type of previous GBCA was confirmed in 79 patients, and linear regressions were performed between T1, T2 values and number of GBCAs. Good correlations were observed between T1-MRF and T1WI ratio (ρ=-0.69, P<0.001) and between T1-MRF ratio and T1WI ratio (ρ=-0.76, P<0.001). Mild correlations were observed between T2-MRF and T1WI ratio (ρ=-0.32, P<0.001) and between T2-MRF ratio and T1WI ratio (ρ=-0.44, P<0.001). The number of linear-type GBCAs was associated with T1-MRF (β=-0.62, P<0.001) and T1-MRF ratio (β=-0.54, P<0.001) in univariate linear regression analyses, and with T1-MRF (β=-0.61, P<0.001) and T1-MRF ratio (β=-0.53, P<0.001) in multivariate analysis. The number of linear-type GBCAs was associated with T2-MRF (β=-0.30, P<0.001) and T2-MRF ratio (β=-0.29, P<0.001) in univariate analyses, and with T2-MRF (β=-0.31, P<0.001) and T2-MRF ratio (β=-0.32, P<0.001) in multivariate analyses. No associations were observed between number of macrocyclic GBCAs and T1-MRF (ratio) or T2-MRF (ratio). The number of linear-type GBCA administrations was associated with lower T1 and T2 values (ratios) in DN.

Augmented Reality-Based Chronic Wound Healing Assessment Using 3D Stereoscopic Imaging and Self-Organizing Maps

Background: Chronic wounds (CW) present significant public health challenges, impacting individuals' well-being and daily activities. Traditional wound measurement methods, such as the ruler technique, are time-consuming, imprecise, and pose infection risks. Advances in wound care technology, including artificial intelligence (AI) and augmented reality (AR), have improved wound healing (WH) assessment accuracy and enhanced clinician-patient communication. Methods: This study developed a novel CW evaluation device combining AR and a stereoscopic camera system. The device captures images using two Leopard Imaging LI-OV580 stereo cameras and a pico-projector to create a 3D wound model. The system calculates the wound area by processing stereoscopic images, utilizing structured-from-motion techniques and convolutional neural networks (CNNs). Additionally, the device measures epipolar and projection errors (PE) during AR-based wound evaluation. A Self-Organizing Map (SOM) was employed to refine the 3D reconstruction of the wound. Results: The AR-PE analysis demonstrated the device's accuracy, with the lowest PE recorded at 20 cm from the wound area. The projection error increased as the measurement distance moved toward the edges of the wound. The average epipolar error (EE) was 0.46 pixels, indicating high precision in stereoscopic measurements. Conclusion: The study introduced a non-invasive CW evaluation device that improves wound assessment accuracy and clinician-patient interaction. By utilizing 3D wound modeling and AR, the system enables more effective communication and progress tracking during WH. The results suggest this technology could enhance CW management, offering a reliable alternative to traditional methods.

Facile synthesis, multimode and tunable luminescence, and multifunctional applications of rare earth ions activated lead-free double perovskite crystals

Lead-free double perovskites have gained recognition as top luminescent materials due to their environmental friendliness, high chemical stability, structural adjustability, and excellent photoelectric properties. However, the poor modulation of emission restricts their applications, and it is highly desirable to explore stable and efficient double perovskites with multimode luminescence and adjustable spectra for multifunctional photoelectric applications. Herein, the rare earth ions Ln3+ (Er3+ and Ho3+) doped Cs2NaYCl6:Sb3+ crystals were synthesized by a simple solvothermal route. The X-ray diffraction pattern (XRD), energy-dispersive spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and elemental mapping images demonstrate that the Sb3+, Er3+, and Ho3+ ions have been homogeneously incorporated into the Cs2NaYCl6 crystals. As anticipated, the emission spectra of Cs2NaYCl6:Sb3+/Ln3+ are composed of two bands. One broad blue band derives from self-trapped exciton (STE) in [SbCl6]3− octahedra while another group of emission peaks stems from the f-f transitions of Ln3+ ions. The emission colors of Cs2NaYCl6:Sb3+/Ln3+ phosphors can be tuned in a wide range by modulating the doping concentrations of Ln3+ ions. The efficient energy transfer from STE to Ln3+ is the key point to achieving the efficient and tunable emissions Cs2NaYCl6:Sb3+/Ln3+ samples. Interestingly, Cs2NaYCl6:Sb3+/Ln3+ can also exhibit characteristic up-conversion luminescence of Ln3+ under near-infrared (NIR) excitation besides the down-conversion luminescence, revealing that the materials may have potential applicability in multimode anti-counterfeiting and information encryption applications. Furthermore, the light emitting diodes (LEDs) assembled by Cs2NaYCl6:Sb3+ and Cs2NaYCl6:Sb3+/Ln3+ phosphors display dazzling blue, green, and red emissions under a forward bias current, which indicates that the as-obtained double perovskites materials may have great potential in solid-state lighting and optoelectronic devices.

XUV-beamline for photoelectron imaging spectroscopy with shaped pulses.

We introduce an extreme ultraviolet (XUV)-beamline designed for the time-resolved investigation and coherent control of attosecond (as) electron dynamics in atoms and molecules by polarization-shaped as-laser pulses. Shaped as-pulses are generated through high-harmonic generation (HHG) of tailored white-light supercontinua (WLS) in noble gases. The interaction of shaped as-pulses with the sample is studied using velocity map imaging (VMI) techniques to achieve the differential detection of photoelectron wave packets. The instrument consists of the WLS-beamline, which includes a hollow-core fiber compressor and a home-built 4f polarization pulse shaper, and the high-vacuum XUV-beamline, which combines an HHG-stage and a versatile multi-experiment vacuum chamber equipped with a home-built VMI spectrometer. The VMI spectrometer allows the detection of photoelectron wave packets from both the multiphoton ionization (MPI) of atomic or molecular samples by the tailored WLS-pulses and the single-photon ionization (SPI) by the shaped XUV-pulses. To characterize the VMI spectrometer, we studied the MPI of xenon atoms by linearly polarized WLS pulses. To validate the interplay of these components, we conducted experiments on the SPI of xenon atoms with linearly polarized XUV-pulses. Our results include the reconstruction of the 3D photoelectron momentum distribution (PMD) and initial findings on the coherent control of the PMD by tuning the spectrum of the XUV-pulses with the spectral phase of the WLS. Our results demonstrate the performance of the entire instrument for HHG-based photoelectron imaging spectroscopy with prototypical shaped pulses. Perspectively, we will employ polarization-tailored WLS-pulses to generate polarization-shaped as-pulses.

Evaluation of Preoperative Low-flow Areas in STA-MCA Bypass Surgery Using Vascular Fusion Map Image.

The superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery requires an anastomosis of the STA to an MCA with diminished blood flow. However, identifying the precise location of the MCA with reduced flow preoperatively is challenging as it often remains nonvisualized. To address this issue, we developed a novel technique, the area target bypass (ATB) method, to infer the location of the responsible vessel for low-flow areas. The cornerstone of the ATB method lies in the utilization of the vascular fusion map (VFM). The VFM integrates 3D perfusion and 3D vascular images, enabling simultaneous evaluation of cerebral surface vessels and regions with reduced blood flow. This study aimed to assess the efficacy of the STA-MCA bypass surgery adopting the ATB method. Between August 2022 and March 2023, we conducted eight STA-MCA bypass surgeries using the ATB method. For each case, the VFM was generated using the MTT and DLY parameters, and blood flow improvement was evaluated based on the VFM score, determined by an average score from seven experts. In all cases, the target vessel was identified either preoperatively or during craniotomy, with postoperative patency of the STA-MCA bypass confirmed. Out of the eight cases, seven demonstrated improved blood flow with a VFM score exceeding 1. No complications were reported. The introduction of the ATB method has proven its potential in accurately pinpointing optimal anastomosis sites.

Reliability of quantitative magnetic susceptibility imaging metrics for cerebral cortex and major subcortical structures.

Susceptibility estimates derived from quantitative susceptibility mapping (QSM) images for the cerebral cortex and major subcortical structures are variably reported in brain magnetic resonance imaging (MRI) studies, as average of all ( ), absolute ( ), or positive- ( ) and negative-only ( ) susceptibility values using a region of interest (ROI) approach. This pilot study presents a reliability analysis of currently used ROI-QSM metrics and an alternative ROI-based approach to obtain voxel-weighted ROI-QSM metrics ( and ). Ten healthy subjects underwent repeated (test-retest) 3-dimensional multi-echo gradient-echo (3DMEGE) 3 Tesla MRI measurements. Complex-valued 3DMEGE images were acquired and reconstructed with slice thicknesses of 1 and 2mm (3DMEGE1, 3DMEGE2) along with 3DT1-weighted isometric (voxel 1 mm3) images for independent registration and ROI segmentation. Agreement, consistency, and reproducibility of ROI-QSM metrics were assessed through Bland-Altman analysis, intraclass correlation coefficient, and interscan and intersubject coefficient of variation (CoV). All ROI-QSM metrics exhibited good to excellent consistency and test-retest agreement with no proportional bias. Interscan CoV was higher for in comparison to the other metrics where it was below 15%, in both 3DMEGE1 and 3DMEGE2 datasets. Intersubject CoV for and exceeded 50% in all ROIs. Among the evaluated ROI-QSM metrics, and estimates were less reliable, whereas separating positive and negative values (using ) improved the reproducibility within, and the comparability between, subjects, even when reducing the slice thickness. These preliminary findings may offer valuable insights toward standardizing ROI-QSM metrics across different patient cohorts and imaging settings in future clinical MRI studies.