Transfer Imaging Research Articles

Specific and rapid guanidinium CEST imaging using double saturation power and QUASS analysis in a rodent model of global ischemia.

Guanidinium CEST is sensitive to metabolic changes and pH variation in ischemia, and it can offer advantages over conventional pH-sensitive amide proton transfer (APT) imaging by providing hyperintense contrast in stroke lesions. However, quantifying guanidinium CEST is challenging due to multiple overlapping components and a close frequency offset from water. This study aims to evaluate the applicability of a new rapid and model-free CEST quantification method using double saturation power, termed DSP-CEST, for isolating the guanidinium CEST effect from confounding factors in ischemia. To further reduce acquisition time, the DSP-CEST was combined with a quasi-steady state (QUASS) CEST technique to process non-steady-state CEST signals. The specificity and accuracy of the DSP-CEST method in quantifying the guanidinium CEST effect were assessed by comparing simulated CEST signals with/without the contribution from confounding factors. The feasibility of this method for quantifying guanidinium CEST was evaluated in a rat model of global ischemia induced by cardiac arrest and compared to a conventional multiple-pool Lorentzian fit method. The DSP-CEST method was successful in removing all confounding components and quantifying the guanidinium CEST signal increase in ischemia. This suggests that the DSP-CEST has the potential to provide hyperintense contrast in stroke lesions. Additionally, the DSP-CEST was shown to be a rapid method that does not require the acquisition of the entire or a portion of the CEST Z-spectrum that is required in conventional model-based fitting approaches. This study highlights the potential of DSP-CEST as a valuable tool for rapid and specific detection of viable tissues.

Machine learning-based amide proton transfer imaging using partially synthetic training data.

Machine learning (ML) has been increasingly used to quantify CEST effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, whereas training with fully simulated data may introduce bias because of limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect. Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9 L tumors. Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data. Partially synthetic CEST data can address the challenges in conventional ML methods.

Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study

BackgroundCognitive impairment is a hallmark of multiple sclerosis (MS) but is usually an under-recorded symptom of disease progression. Identifying the predictive signatures of cognitive decline in people with MS (pwMS) over time is important to ensure effective preventative treatment strategies. Structural and functional brain characteristics as measured by various magnetic resonance (MR) methods have been correlated with variation in cognitive function in MS, but typically these studies are limited to a single MR modality and/or are cross-sectional designs. Here we assess the predictive value of multiple different MR modalities in relation to cognitive decline in pwMS over 5 years. MethodsA cohort of 43 pwMS was assessed at baseline and 5 years follow-up. Baseline (input) data consisted of 70 multi-modal MRI measures for different brain regions including magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and standard volumetrics. Age, sex, disease duration and treatment were included as clinical inputs. Cognitive function was assessed using the Audio Recorded Cognitive Screen (ARCS) and the Symbol Digit Modalities Test (SDMT). Prediction modelling was performed using the machine learning package - GLMnet, where a penalised regression was applied to identify multi-modal signatures with the most predictive value (and the least error) for each outcome. ResultsThe multi-modal approach to neuroimaging was able to accurately predict cognitive decline in pwMS. The best performing model for change in total ARCS (tARCS) included 16 features from across the various MR modalities and explained 54 % of the variation in change over time (R2=0.54, 95 % CI=0.48–0.51). The features included nine MRS, four volumetric and two DTI parameters. The model also selected disease duration, but not treatment, as a predictive feature. By comparison, the best model for SDMT included several of the same above features and explained 39 % of the change over time (R2=0.39, 95 % CI=0.48–0.51). Conventional volumetric measures were about half as good at predicting change in tARCS score compared to the best multi-modal model (R2=0.26 95 % CI:0.22–0.29). The clinical interpretation of the best predictive model for change in tARCS showed that cognitive decline could be predicted with >90 % accuracy in this cohort (AUC=0.92, SE=0.86 - 0.94). ConclusionMulti-modal MRI signatures can predict cognitive decline in a cohort of pwMS over 5 years with high accuracy. Future studies will benefit from the inclusion of even more MR modalities e.g., functional MRI, quantitative susceptibility mapping, magnetisation transfer imaging, as well as other potential predictors e.g., genetic and environmental factors. With further validation, this signature could be used in future trials with high-risk patients to personalise the management of cognitive decline in pwMS, even in the absence of relapses.

Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model.

To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters. We combine two recently proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi-solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two-pool model. We sparsely sample each time frame along this spin dynamics with a three-dimensional radial koosh-ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency. We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6-min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and longer relaxation times, consistent with previous reports. The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.

Arterial Spin Labeling and Amide Proton Transfer Imaging can Differentiate Glioblastoma from Brain Metastasis: A Systematic Review and Meta-Analysis

Currently, arterial spin labeling (ASL) and amide proton transfer (APT) imaging have shown potential for distinguishing glioblastoma from brain metastases. Thus, a meta-analysis was conducted to investigate this further. An extensive and comprehensive search was conducted in 6 English and Chinese databases according to predefined inclusion and exclusion criteria, encompassing data up to July 2023. Data from eligible literature were extracted, and bivariate models were employed to calculate pooled sensitivities, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic curve. The meta-analysis included 11 articles. For ASL, the pooled sensitivity was 0.77 (95% confidence interval [CI], 0.63-0.87), and the pooled specificity was 0.87 (95% CI, 0.77-0.93). The pooled PLR was 5.89 (95% CI, 2.97-11.69), the pooled NLR was 0.26 (95% CI, 0.15-0.47), the pooled DOR was 22.33 (95% CI, 6.89-72.34), and AUC was 0.90 (95% CI, 0.87-0.92). For APT imaging, the pooled sensitivity was 0.78 (95% CI, 0.70-0.85), and the pooled specificity was 0.86 (95% CI, 0.77-0.92). The pooled PLR was 5.51 (95% CI, 3.24-9.37), the pooled NLR was 0.25 (95% CI, 0.17-0.37), the pooled DOR was 21.99 (95% CI, 10.28-47.03), and the AUC was 0.90 (95% CI, 0.87-0.92). This meta-analysis suggest that both ASL and APT imaging exhibit high accuracy in distinguishing between glioblastoma and brain metastasis.

Research on molten pool flow characteristic in all-position LP-LMH welding

ABSTRACTThe flow characteristics of keyhole and molten pool impact the real-time process stability and seam formation severely in all-position low-power laser MAG hybrid (LP-LMH) welding. To probe into the molten pool’s real-time dynamics at various spatial welding positions, the high-speed camera images of metal transfer and molten pool were sensed. Meanwhile, a novel spatial flow model of molten pool was developed, and the functions of spatial position and gravity on the molten pool flow were investigated. The results proved that as the spatial position increases, the location of where droplet striking molten pool changes accordingly, further disturbing the keyhole’s stability. Meanwhile, the spatial position seriously affects the molten pool’s flow. The driving forces of molten pool flow at various spatial welding positions are basically the same, and gravity affects the overall movement of molten pool, thereby affecting the vortexes and heat transmission inside the molten pool, as reflected in weld penetration.

Investigation of nanotopography on SOCE mediated cell migration via live-cell

The exploration of cell response to nanotopography has attracted considerable attentions for years. This article focuses on the influence of nanotopography on the intracellular Ca2+ dynamics, the most ubiquitous but ignored second messenger. The classic titanium nanotubes (NT) were fabricated by anodization to formulate nanoporous surfaces. Firstly, the store operative calcium entry (SOCE) in endoplasmic reticulum (ER) and functional Ca2+ release-activated Ca2+ (CRAC) channels were significantly enhanced on NT surfaces that revealed by live-cell Ca2+ imaging and fluorescence resonance energy transfer (FRET) identification of orai1-stim1 connection. To investigate the potential implication of Ca2+ elevation, the dynamic cell migration trajectory was monitored by a self-made holder, which could not only be suitable for the opaque implant surface but also guarantee the focus fields identical during samples shifting. The cell migration on NT surface was more vigorous and rapid, which was correlated with higher focal adhesion proteins expression, Ca2+-dependent calpain activity and stim1 level. In conclusion, this study has confirmed the novel ER Ca2+ hemostasis pathway on nanosurfaces and its crucial role in cell migration regulation, which may help for more biofavorable implant surface design.Graphical

Regulation of Ebola GP conformation and membrane binding by the chemical environment of the late endosome.

Interaction between the Ebola virus envelope glycoprotein (GP) and the endosomal membrane is an essential step during virus entry into the cell. Acidic pH and Ca2+ have been implicated in mediating the GP-membrane interaction. However, the molecular mechanism by which these environmental factors regulate the conformational changes that enable engagement of GP with the target membrane is unknown. Here, we apply fluorescence correlation spectroscopy (FCS) and single-molecule Förster resonance energy transfer (smFRET) imaging to elucidate how the acidic pH, Ca2+ and anionic phospholipids in the late endosome promote GP-membrane interaction, thereby facilitating virus entry. We find that bis(monoacylglycero)phosphate (BMP), which is specific to the late endosome, is especially critical in determining the Ca2+-dependence of the GP-membrane interaction. Molecular dynamics (MD) simulations suggested residues in GP that sense pH and induce conformational changes that make the fusion loop available for insertion into the membrane. We similarly confirm residues in the fusion loop that mediate GP's interaction with Ca2+, which likely promotes local conformational changes in the fusion loop and mediates electrostatic interactions with the anionic phospholipids. Collectively, our results provide a mechanistic understanding of how the environment of the late endosome regulates the timing and efficiency of virus entry.

Quantifying myelin in neonates using magnetic resonance imaging: a systematic literature review.

This review aimed to assess the usefulness of various magnetic resonance imaging (MRI) techniques for the quantification of neonatal white matter myelination. The Scopus, PubMed, and Web of Science databases were searched to identify studies following the PRISMA (preferred reporting items for systematic reviews and meta-analyses) statement using quantitative MRI techniques to examine samples collected from neonates to quantify myelin. Twelve studies were ultimately included. The results demonstrated that in validation studies, relaxometry is the most frequently explored approach (83.33%), followed by magnetization transfer imaging (8.33%) and a new automatic segmentation technique (8.33%). Synthetic MRI is recommended for quantifying myelin in neonates because of several advantages that outweigh a few negligible limitations.

Investigation of relayed nuclear Overhauser enhancement effect at -1.6 ppm in an ischemic stroke model.

When an ischemic stroke happens, it triggers a complex signalling cascade that may eventually lead to neuronal cell death if no reperfusion. Recently, the relayed nuclear Overhauser enhancement effect at -1.6 ppm [NOE(-1.6 ppm)] has been postulated may allow for a more in-depth analysis of the ischemic injury. This study assessed the potential utility of NOE(-1.6 ppm) in an ischemic stroke model. Diffusion-weighted imaging, perfusion-weighted imaging, and chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) data were acquired from five rats that underwent scans at 9.4 T after middle cerebral artery occlusion. The apparent diffusion coefficient (ADC), cerebral blood flow (CBF), and apparent exchange-dependent relaxations (AREX) at 3.5 ppm and NOE(-1.6 ppm) were quantified. AREX(3.5 ppm) and NOE(-1.6 ppm) were found to be hypointense and exhibited different signal patterns within the ischemic tissue. The NOE(-1.6 ppm) deficit areas were equal to or larger than the ADC deficit areas, but smaller than the AREX(3.5 ppm) deficit areas. This suggested that NOE(-1.6 ppm) might further delineate the acidotic tissue estimated using AREX(3.5 ppm). Since NOE(-1.6 ppm) is closely related to membrane phospholipids, NOE(-1.6 ppm) potentially highlighted at-risk tissue affected by lipid peroxidation and membrane damage. Altogether, the ADC/NOE(-1.6 ppm)/AREX(3.5 ppm)/CBF mismatches revealed four zones of increasing sizes within the ischemic tissue, potentially reflecting different pathophysiological information. Using CEST coupled with ADC and CBF, the ischemic tissue may thus potentially be separated into four zones to better understand the pathophysiology after stroke and improve ischemic tissue fate definition. Further verification of the potential utility of NOE(-1.6 ppm) may therefore lead to a more precise diagnosis.

Added-value of 3D amide proton transfer MRI in assessing prognostic factors of cervical cancer: a comparative study with multiple model diffusion-weighted imaging.

Amide proton transfer (APT) imaging has been gradually applied to cervical cancer, yet the relationships between APT and multiple model diffusion-weighted imaging (DWI) have yet to be investigated. This study attempted to evaluate the added value of 3-dimensional (3D) APT imaging to multiple model DWI for assessing prognostic factors of cervical cancer. This prospective diagnostic study was conducted in The First Affiliated Hospital of Zhengzhou University. A total of 88 consecutive patients with cervical cancer underwent APT imaging and DWI with 11 b-values (0-2,000 s/mm2). The apparent diffusion coefficient (ADC), pure molecular diffusion (D), perfusion fraction (f), pseudo-diffusion (D*), mean kurtosis (MK), and mean diffusivity (MD) were calculated based on mono-exponential, bi-exponential, and kurtosis models. The mean, minimum, and maximum values of APT signal intensity (APT SI) and DWI-derived metrics were compared based on tumor stages, subtypes, grades, and lymphovascular space invasion status by Student's t-test or Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the parameters. APT SImax, APT SImin, MKmean, and MKmax showed significant differences between adenocarcinoma (AC) and squamous cell carcinoma (SCC) (all P<0.05). APT SImean, APT SImax, and MKmax were higher and ADCmin, Dmean, Dmin, and MDmin were lower in the high-grade tumor than in low-grade tumor (all P<0.05). For distinguishing lymphovascular space invasion, only MKmean showed significant difference (P=0.010). APT SImax [odds ratio (OR) =2.347, P=0.029], APT SImin (OR =0.352; P=0.024), and MKmean (OR =6.523; P=0.001) were the independent predictors for tumor subtype, and APT SImax (OR =2.885; P=0.044), MDmin (OR =0.155, P=0.012) were the independent predictors for histological grade of cervical cancer. When APT SImin and APT SImax was combined with MKmean and MKmax, the diagnostic performance was significantly improved for differentiating AC and AC [area under the curve (AUC): 0.908, sensitivity: 87.5%; specificity: 83.3%; P<0.001]. The combination of APT SImean, APT SImax, ADCmin, MKmax, and MDmin demonstrated the highest diagnostic performance for predicting tumor grade (AUC: 0.903, sensitivity: 78.6%; specificity: 88.9%; P<0.001). Addition of APT to DWI may improve the ability to noninvasively predict poor prognostic factors of cervical cancer.

A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System

This study introduces an innovative all-in-one malware identification model that significantly enhances convenience and resource efficiency in classifying malware across diverse file types. Traditional malware identification methods involve the extraction of static and dynamic features, followed by comparisons with signature-based databases or machine learning-based classifiers. However, many malware detection applications that rely on transfer learning and image transformation suffer from excessive resource consumption. In recent years, transfer learning has emerged as a powerful tool for developing effective classifiers, leveraging pre-trained neural network models. In this research, we comprehensively explore various pre-trained network architectures, including compact and conventional networks, as well as series and directed acyclic graph configurations for malware classification. Our approach utilizes grayscale transform-based features as a standardized set of characteristics, streamlining malware classification across various file types. To ensure the robustness and generalization of our classification models, we integrate multiple datasets into the training process. Remarkably, we achieve an optimal model with 96% accuracy, while maintaining a modest 5 MB size using the SqueezeNet classifier. Overall, our model efficiently classifies malware across file types, reducing the computational load, which can be useful for cybersecurity professionals and organizations.

Imaging assessment of photosensitizer emission induced by radionuclide-derived Cherenkov radiation using charge-coupled device optical imaging and long-pass filters.

Radionuclides produce Cherenkov radiation (CR), which can potentially activate photosensitizers (PSs) in phototherapy. Several groups have studied Cherenkov energy transfer to PSs using optical imaging; however, cost-effectively identifying whether PSs are excited by radionuclide-derived CR and detecting fluorescence emission from excited PSs remain a challenge. Many laboratories face the need for expensive dedicated equipment. To cost-effectively confirm whether PSs are excited by radionuclide-derived CR and distinguish fluorescence emission from excited PSs. The absorbance and fluorescence spectra of PSs were measured using a microplate reader and fluorescence spectrometer to examine the photo-physical properties of PSs. To mitigate the need for expensive dedicated equipment and achieve the aim of the study, we developed a method that utilizes a charge-coupled device optical imaging system and appropriate long-pass filters of different wavelengths (manual sequential application of long-pass filters of 515, 580, 645, 700, 750, and 800 nm). Tetrakis (4-carboxyphenyl) porphyrin (TCPP) was utilized as a model PS. Different doses of copper-64 (64CuCl2) (4, 2, and 1 mCi) were used as CR-producing radionuclides. Imaging and data acquisition were performed 0.5 h after sample preparation. Differential image analysis was conducted by using ImageJ software (National Institutes of Health) to visually evaluate TCPP fluorescence. The maximum absorbance of TCPP was at 390-430 nm, and the emission peak was at 670 nm. The CR and CR-induced TCPP emissions were observed using the optical imaging system and the high-transmittance long-pass filters described above. The emission spectra of TCPP with a peak in the 645-700 nm window were obtained by calculation and subtraction based on the serial signal intensity (total flux) difference between 64CuCl2 + TCPP and 64CuCl2. Moreover, the differential fluorescence images of TCPP were obtained by subtracting the 64CuCl2 image from the 64CuCl2 + TCPP image. The experimental results considering different 64CuCl2 doses showed a dose-dependent trend. These results demonstrate that a bioluminescence imaging device coupled with different long-pass filters and subtraction image processing can confirm the emission spectra and differential fluorescence images of CR-induced TCPP. This simple method identifies the PS fluorescence emission generated by radionuclide-derived CR and can contribute to accelerating the development of Cherenkov energy transfer imaging and the discovery of new PSs.

CEST and nuclear Overhauser enhancement imaging withdeep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility andreliability studies.

To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR. A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics. The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 μT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema. The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.

HPB P10 The impact of an electronic referral system for tertiary pancreatitis referrals

Abstract Background In the Leeds catchment, pancreatitis referrals were made ad hoc via telephone or email. This was frequently by a junior team member and essential information was frequently absent. Frustratingly the referring teams did not know who to contact and often had to make multiple calls. There was no record of these referrals and the advice provided. As a Quality Improvement we instituted an electronic referral system. Methods An Electronic referral system (Patient Pass, already within the Trust) was utilised. We developed a simple questionnaire for the referrer to complete. This was accessed via a hyperlink on the Leeds Pancreas Unit website. Images could be reviewed using a regional El Xero server without the need for image transfer This was implemented in September 2021. The system is monitored Monday to Friday by either an Advanced Clinical Practitioner or by the senior registrar/fellow. Information and scans are reviewed, and all cases discussed with the on call pancreas consultant. A survey was sent to all users in secondary care. Results 166 referrals in 21 months , Average response time from initial referral was under 3 hours. 46/166 transferred to LTHT. Average number of responses per individual referral. Overall satisfaction was over 80% from secondary care. The Pancreas team has found it easier to provide timely accurate advice on a complete set of data, especially the immediate access to images. Conclusions An electronic referral system has led to increased satisfaction for referrers. The system has led to an open, auditable, visible, referral and advice process. This has allowed patients to remain in the local hospital closer to home and visitors until the time intervention is needed or appropriate.

Amide proton transfer imaging in rats after heatstroke.

Metabolic acidosis is the most common acid-base change following heatstroke. This study aimed to evaluate the internal environment changes caused by heatstroke using amide proton transfer (APT) imaging. Nineteen male Sprague-Dawley rats were randomly divided into the control group (CTRL, n = 7) and the heatstroke group (HS, n = 12). All the rats underwent a 7.0-T MRI, which included T2-weighted imaging (T2WI) and APT imaging. Subsequently, the surviving HS group rats repeated the same magnetic resonance scanning after 25 days and were designated as the follow-up group (FU, n = 7). APT values were measured in the hippocampus, thalamus, and corpus callosum. The APT values of the three groups were statistically analyzed and found in the hippocampus (CTRL vs. HS, P = 0.011; CTRL vs. FU, P = 0.078; HS vs. FU, P = 0.484; η ² = 0.276), left thalamus (CTRL vs. HS, P = 0.004; CTRL vs. FU, P = 0.014; HS vs. FU, P = 0.822; η ² = 0.331), right thalamus (CTRL vs. HS, P = 0.003; CTRL vs. FU, P = 0.015; HS vs. FU P = 0.769; η ² = 0.336), and corpus callosum (CTRL vs. HS, P < 0.001; CTRL vs. FU, P = 0.005; HS vs. FU, P = 0.523; η ² = 0.437). APT imaging can be a viable and practical tool for diagnosing heatstroke and monitoring its progression.

SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network

Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T1) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging based on least square fitting without any extra acquisition, and thus has high potential for simplifying the qMT procedure. However, the quantification of qMT parameters with this method was suboptimal, limiting its potential for clinical application despite its simpler protocol and higher spatial resolution. To improve the fitting of qMT data obtained with multiple phase-cycled bSSFP, we propose SIMulation-based Physics-guided Learning of neural network for qMT parameters EXtraction, or SIMPLEX. In contrast to previous deep learning supervised approaches for quantitative MR that require the acquisition of input data and corresponding ground truth for training, we leveraged the MR signal model to generate training samples without expensive data curation. The network was trained exclusively with simulation data by predicting the simulation parameters. The same network was applied directly to in-vivo data without additional training. The approach was verified with both simulation and in-vivo data. SIMPLEX showed a decrease in fitting mean squared error for all simulation data compared to the existing least-square fitting method. The in-vivo experiment revealed that the network performed well with the real in vivo data unseen during training. For all experiments, we observed that SIMPLEX consistently improved the quantification quality of the qMT parameters whilst being more robust to noise compared to the prior technique. The proposed SIMPLEX will expedite the routine clinical application of qMT by providing qMT parameters (exchange rate, pool fraction) as well as T1, T2, and ΔB0 maps simultaneously with high spatial resolution, better reliability, and reduced processing time.

Microstructural changes of peripheral nerves in early multiple sclerosis: A prospective magnetic resonance neurography study.

Multiple sclerosis (MS) is a demyelinating disorder of the central nervous system (CNS). However, there is increasing evidence of peripheral nerve involvement. This study aims to characterize the pattern of peripheral nerve changes in patients with newly diagnosed MS using quantitative magnetic resonance (MR) neurography. In this prospective study, 25 patients first diagnosed with MS according to the revised McDonald criteria (16 female, mean age = 32.8 ± 10.6 years) and 14 healthy controls were examined with high-resolution 3-T MR neurography of the sciatic nerve using diffusion kurtosis imaging (DKI; 20 diffusional directions, b = 0, 700, 1200 s/mm2 ) and magnetization transfer imaging (MTI). In total, 15 quantitative MR biomarkers were analyzed and correlated with clinical symptoms, intrathecal immunoglobulin synthesis, electrophysiology, and lesion load on brain and spine MR imaging. Patients showed decreased fractional anisotropy (mean = 0.51 ± 0.04 vs. 0.56 ± 0.03, p < 0.001), extra-axonal tortuosity (mean = 2.32 ± 0.17 vs. 2.49 ± 0.17, p = 0.008), and radial kurtosis (mean = 1.40 ± 0.23 vs. 1.62 ± 0.23, p = 0.014) and higher radial diffusivity (mean = 1.09 ∙ 10-3 mm2 /s ± 0.16 vs. 0.98 ± 0.11 ∙ 10-3 mm2 /s, p = 0.036) than controls. Groups did not differ in MTI. No significant association was found between MR neurography markers and clinical/laboratory parameters or CNS lesion load. This study provides further evidence of peripheral nerve involvement in MS already at initial diagnosis. The characteristic pattern of DKI parameters indicates predominant demyelination and suggests a primary coaffection of the peripheral nervous system in MS. This first human study using DKI for peripheral nerves shows its potential and clinical feasibility in providing novel biomarkers.

A Secure IoT-Enabled Machine Learning Framework for Brain Tumor Classification and Prediction Using MR Image Data

Brain tumor identification and classification have improved due to the quick development of medical imaging and machine learning technology. This paper presents two approaches to secure image transmission in the Internet of Things (IoT): a comprehensive approach for brain tumor prediction and classification using a strong IoT infrastructure with cutting-edge machine learning models and a security approach with the implementation of the AES-ECC hybrid model in the MQTT communication protocol for image data encryption and decryption. We make use of a heterogeneous dataset that we sourced from the Kaggle Dataset platform, which includes four different types of MRI scans of brain tumors from 2870 patients. Our proposed methodology starts with the safe acquisition and transfer of MRI images through an IoT protocol infrastructure to a cloud-based platform. CNN, DenseNet, ResNet and G-Net are some of the sophisticated machine learning models that are used to interpret and analyse these pictures. The computer is trained to identify photos of brain tumors into the appropriate groups using all above four models. According to the data, our suggested CNN model performs better than the others, obtaining an amazing 89% accuracy rate. Nonetheless, we want to achieve even greater improvement in forecast precision by utilising ensemble boosting methodologies. Boosting the CNN model with Ada-Boost, Gradient Boost, XG Boost and Cat Boost algorithms aims to maximize prediction performance. We find that the CNN algorithm combined with XG Boost outperforms all other ensemble methods with an amazing accuracy rate of 97%. This encouraging result highlights how combining cutting-edge machine learning algorithms with IoT infrastructure can lead to better brain tumor classification and prognosis. The creation of more precise and effective diagnostic instruments for the identification of brain tumors is one of our study's many implications, one that will ultimately improve patient outcomes and the healthcare industry.

Role of amide proton transfer imaging in maximizing tumor resection in malignant glioma: a possibility to take the place of 11C-methionine positron emission tomography.

Amide proton transfer (APT) imaging has been proposed as a technique to assess tumor metabolism. However, the relationship between APT imaging and other quantitative modalities including positron emission tomography (PET) has not been investigated in detail. This study aimed to evaluate the clinical usefulness of APT imaging in determining the metabolic status of malignant glioma and to compare findings with those from 11C-methionine (Met)-PET. This research analyzed APT imaging data from 20 consecutive patients with malignant glioma treated between January 2022 and July 2023. Patients underwent tumor resection and correlations between tumor activity and intensity of APT signal were investigated. We also compared 11C-Met-PET and APT imaging for the same regions of the perifocal tumor invasion area. Clear, diagnostic APT images were obtained from all 20 cases. Mean APT intensity (APTmean) was significantly higher in the glioblastoma (GBM), IDH wild type group (27.2 ± 12.8%) than in other gliomas (6.0 ± 4.7%; p < 0.001). The cut-off APTmean to optimally distinguish between GBM and other malignant gliomas was 12.8%, offering 100% sensitivity and 83.3% specificity. These values for APTmean broadly matched the tumor-to-contralateral normal brain tissue ratio from 11C-Met-PET analysis (r = 0.66). The APT signal was also observed in the gadolinium non-contrast region on T1-weighted imaging, appearing to reflect the surrounding tumor-infiltrated area. APT imaging can be used to evaluate the area of tumor invasion, similar to 11C-Met-PET. APT imaging revealed low invasiveness in patients and was useful in preoperative planning for tumor resection, facilitating maximum tumor resection including the tumor invasive area.