Transfer Imaging Research Articles

Controlled Antenatal Thyroid Screening Study III: Effects of gestational thyroid status on brain microstructure.

Children born to mothers with gestational thyroid dysfunction may have an increased risk of adverse neurodevelopmental outcomes but the effects of maternal thyroid function on brain microstructure are unknown. To establish whether adolescent white matter microstructure is affected by suboptimal gestational thyroid function (SGTF). The Controlled Antenatal Thyroid Screening (CATS) study randomized mothers with SGTF to levothyroxine or no supplementation from 12 weeks' gestation. For the current study, CATS children underwent microstructural magnetic resonance imaging (MRI), including diffusion MRI to explore white matter microstructure and quantitative magnetization transfer (qMT) imaging to investigate myelin. Seventy-five children aged 11-16 years had usable diffusion and/or qMT data (untreated SGTF (n=19), normal GTF (n=21), or treated SGTF (optimally-treated (n=18), over-treated (n=17)). Primary outcome: to examine the effects of SGTF and its treatment on white matter microstructure. Secondary and exploratory outcomes: to investigate the association of (a) maternal TSH and free T4 levels with white matter microstructure, and (b) white matter microstructure with attention deficit hyperactivity disorder (ADHD) symptom scores. Untreated SGTF was associated with higher mean diffusivity (indicating reduced axonal integrity) compared with normal GTF (p=0.007) within the inferior longitudinal fasciculus, a major white matter tract connecting the occipital and temporal lobes and involved in several cognitive functions. Secondary and exploratory outcomes did not survive corrections for multiple comparisons. Untreated SGTF is associated with altered tract-specific microstructural morphology in adolescence, which may be reversible with levothyroxine administration in pregnancy.

Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review

Medical images occupy the largest part of the existing medical information and dealing with them is challenging not only in terms of management but also in terms of interpretation and analysis. Hence, analyzing, understanding, and classifying them, becomes a very expensive and time-consuming task, especially if performed manually. Deep learning is considered a good solution for image classification, segmentation, and transfer learning tasks since it offers a large number of algorithms to solve such complex problems. PRISMA-ScR guidelines have been followed to conduct the scoping review with the aim of exploring how deep learning is being used to classify a broad spectrum of diseases diagnosed using an X-ray, MRI, or Ultrasound image modality.Findings contribute to the existing research by outlining the characteristics of the adopted datasets and the preprocessing or augmentation techniques applied to them. The authors summarized all relevant studies based on the deep learning models used and the accuracy achieved for classification. Whenever possible, they included details about the hardware and software configurations, as well as the architectural components of the models employed. Moreover, the models that achieved the highest accuracy in disease classification were highlighted, along with their strengths. The authors also discussed the limitations of the current approaches and proposed future directions for medical image classification.

ALMASOP: Detection of Turbulence-induced Mass Assembly Shocks in Starless Cores

Abstract Star formation is a series of mass assembly processes and starless cores; those cold and dense condensations in molecular clouds play a pivotal role as initial seeds of stars. With only a limited sample of known starless cores, however, the origin and growth of such stellar precursors had not been well characterized previously. Meanwhile, the recent discovery of CH3OH emission, which is generally associated with the desorbed icy mantle in warm regions, particularly at the periphery of starless cores, also remains puzzling. We present sensitive Atacama Large Millimeter/submillimeter Array (Band 3) observations (at 3 mm) toward a sample of newly identified starless cores in the Orion molecular cloud. The spatially resolved images distinctly indicate that the observed CH3OH and N2H+ emission associated with these cores are morphologically anticorrelated and kinematically offset from each other. We postulate that the CH3OH emission highlights the desorption of icy mantle by shocks resulting from gas piling onto dense cores in the filaments traced by N2H+. Our magnetohydrodynamic simulations of star formation in turbulent clouds combined with radiative transfer calculations and imaging simulations successfully reproduced the observed signatures and reaffirmed the above scenario at work. Our result serves as an intriguing and exemplary illustration, a snapshot in time, of the dynamic star-forming processes in turbulent clouds. The results offer compelling insights into the mechanisms governing the growth of starless cores and the presence of gas-phase complex organic molecules associated with these cores.

Diagnosis and risk stratification of PI-RADS v2.1 category 3–5 lesions using amide proton transfer imaging

ObjectivesTo explore the value of amide proton transfer-weighted imaging (APTWI) combined with apparent diffusion coefficient (ADC) and prostate-specific antigen density (PSAD) in evaluating the risk of aggressive PI-RADS v2.1 category 3–5 lesions.Materials and methodsWe retrospectively analyzed clinical and MRI features of 69 patients with prostate cancer (PCa) and 32 patients with benign prostatic lesion (BL). The PCa group was classified into Gleason Grade Groups (GG) 1 to 4 based on Gleason Score (GS). APTWI parameters, ADC, and PSAD were compared among the different groups. The receiver operating characteristic (ROC) curve and precision recall (PR) curve were used to assess the diagnostic accuracy of each parameter, and Spearman rank correlation was used to analyze the relationship between APTWI parameters and GS.ResultsAPTWI parameters and PSAD were significantly higher in the PCa group than that in the BL group, whereas ADC was significantly lower in the PCa group than in the BL group. ADC showed the highest AUC in the diagnosis of PCa, followed by PSAD and APTmin. Combined analysis showed that APTmin+ADC + PSAD exhibited the highest AUC (0.997). In the PCa group, significant differences in APTWI parameters were found among GG1 to GG4 (P < 0.001), with intra-group comparisons showing significant differences between GG1 and GG3, GG1 and GG4, GG2 and GG3, and GG2 and GG4 subgroups. The AUC of APTmean was greatest in evaluating the risk of aggressive PCa (0.948), which further increased when APTmean was combined with ADC and PSAD (0.956).ConclusionIn PI-RADS v2.1 category 3–5 lesions, APT can serve as an important biomarker for the risk stratification of PCa, and combining APT with PSAD and ADC achieves the highest diagnostic efficacy.

Assessment of urgent investigative images transfer effectiveness from trauma and orthopaedic to neurosurgery department: a quality improvement project.

Neurosurgical emergencies, including cauda equina syndrome, traumatic spinal cord injuries and traumatic brain injuries, require timely diagnosis and intervention to prevent severe morbidity and mortality. Delays in these cases often stem from inefficiencies in transferring investigational images from district hospitals to external neurosurgery units. This quality improvement project aimed to reduce image transfer time at Sandwell and West Birmingham Hospitals Trust (SWBH) to the Queen Elizabeth Hospital Birmingham neurosurgery unit. It sought to empower Trauma and Orthopaedics (T&O) doctors to independently transfer images using an image exchange portal (IEP) software, bypassing reliance on traditional picture archiving and communication teams. Over three Plan-Do-Study-Act cycles (February and September 2024), T&O registrars and junior doctors were trained to use the IEP. Additional instructional resources were provided following low compliance. Image transfer times and neurosurgical response times were compared preintervention and postintervention. The intervention reduced the average time from referral to image transfer from 18 hours to 3.6 hours across the cycles. The proportion of images transferred within 1 hour increased from 36% to 59%, and 87% of images were reviewed by neurosurgery within 6 hours by the final cycle. Operational cost savings were achieved by reducing reliance on IT services, radiographers and length of stay. This project improved the timeliness of image transfer and neurosurgical care at SWBH. Although IEP software proved effective locally, its broader applicability across UK hospitals remains uncertain. The findings highlight the need for a national solution to streamline image transfers and further research to enhance technologies supporting rapid investigative imaging. Empowering front-line clinicians with appropriate tools provides a replicable model for addressing similar challenges in other institutions.

Accelerated ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging of macromolecular fraction (MMF) in cortical bone based on a self-attention convolutional neural network.

Accelerated ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging of macromolecular fraction (MMF) in cortical bone based on a self-attention convolutional neural network.

Specific APT and NOE Imaging Using DSP-CEST in Humans at 3 T.

Specific chemical exchange saturation transfer (CEST) imaging, especially at low-field clinical 3-T scanners where multiple pools overlap, is challenging. Recently, a double saturation power (DSP)-CEST method has been developed to improve the specificity in quantifying two major CEST variations: amide proton transfer (APT) and nuclear Overhauser enhancement (NOE). This method avoids the mutual interference between APT and NOE, unlike the conventional asymmetry analysis method. It is also model-free, making it more robust compared to various Lorentzian fitting methods. Initially, this method was developed for continuous-wave saturation and tested in animals on preclinical MRI. This paper aims to further develop the method for pulsed-CEST saturation and apply it to human imaging at 3 T. Simulations and phantom experiments were first conducted to validate its specificity. Subsequently, invivo experiments were performed on six healthy human brains to demonstrate its applications. Two CEST quantification metrics, including the magnetization transfer ratio (MTR) and apparent exchange-dependent relaxation (AREX), were evaluated. The DSP-CEST successfully eliminated all confounding components and specifically quantified the APT and NOE effects. It demonstrated significantly lower MTR-quantified APT effects and higher AREX-quantified APT effects in white matter (WM) than in gray matter (GM). Moreover, it revealed no significant difference in the MTR-quantified NOE effect between WM and GM, but a significantly higher AREX-quantified NOE effect in WM than in GM. These results, differing from those obtained using the asymmetry analysis, confirmed their different origins. In contrast, they align with those obtained using a Lorentzian difference (LD) analysis, suggesting both methods have improved specificity. Yet, without the need to assume the fitting models and set fitting parameters, the DSP-CEST method proves to be more robust than the LD method.

Detecting multiple rice diseases using transfer learning CNN method

Detecting multiple rice diseases is critical for sustaining agricultural productivity and food security, particularly in rice- dependent nations like the Philippines. Traditional manual disease detection methods are time-consuming and prone to errors due to overlapping symptoms across diseases. This study leverages the ResNet50 convolutional neural network (CNN) architecture, known for its deep learning capabilities and efficient residual connections, to classify 14 rice diseases with remarkable accuracy. By incorporating transfer learning and image augmentation techniques, the model achieved a classification accuracy of 99%, outperforming other architectures like MobileNet and EfficientNet, which attained accuracies of 87% and 91%, respectively. The results highlight the efficacy of ResNet50 in handling complex datasets, particularly in distinguishing diseases with overlapping symptoms. This automated approach offers significant potential to improve disease management, reduce crop losses, and enhance agricultural sustainability in the Philippines and other rice-producing regions.

Value of amide proton transfer imaging in neonatal hypoxic-ischemic encephalopathy.

Neonatal hypoxic-ischemic encephalopathy (HIE) is a critical condition leading to significant neonatal mortality and long-term disability. Timely and accurate diagnosis is vital for effective treatment and improved outcomes. Magnetic resonance imaging (MRI) has emerged as a key tool for assessing brain injury in neonates, with advanced techniques such as amide proton transfer imaging providing insights into tissue metabolism and pH. Amide proton transfer imaging detects changes in protein content and pH values, offering a sensitive reflection of metabolic alterations in the brain environment. Studies have shown that amide proton transfer imaging can identify differences in brain regions associated with HIE, even in the absence of conventional MRI abnormalities. Compared to other imaging modalities, amide proton transfer imaging exhibits high sensitivity in detecting protein content changes and pH, providing molecular-level information that aids in early diagnosis and treatment monitoring. Research also suggests that amide proton transfer imaging can predict long-term neurodevelopmental outcomes, supporting the formulation of personalized rehabilitation plans. Despite its advantages, amide proton transfer imaging has limitations and future research should focus on technical optimization, integration with other imaging techniques, and the discovery of new biomarkers. The integration of amide proton transfer imaging with clinical practice has the potential to significantly improve the management of neonatal HIE, leading to better treatment outcomes and quality of life for affected infants.

Preoperative prediction of p16 expression in cervical cancer by amide proton transfer imaging combined with diffusion kurtosis imaging: a retrospective study.

Immunohistochemical p16 expression is important for the assessment of risk factors and prognosis of cervical cancer. This study evaluated the correlation between preoperative amide proton transfer imaging (APT), diffusion kurtosis imaging (DKI), and p16 expression in cervical cancer. Fifty-five records of patients with cervical cancer with preoperative magnetic resonance imaging (MRI) and complete postoperative pathology were retrospectively analyzed. Uterine MRI scans were examined, and the corresponding APT values, mean kurtosis (MK), and mean diffusivity (MD) were obtained. Pathology was used to determine p16 positivity. The predictive effects of the APT- and DKI-derived parameters applied alone or in combination with p16 expression were compared. Among the 55 cases of cervical cancer, 35 were p16 positive and 20 were p16 negative. The receiver operating characteristic curve area (AUC) of p16 was 0.809, 0.801, 0.790, 0.845, 0.866, and 0.871 when APT, MK, and MD were used individually and then jointly. The effect of APT combined with the DKI derivative parameters was more pronounced than when they were used separately. Preoperative APT and DKI imaging are valuable in predicting the expression of p16 in cervical cancer, and the combination of the two can improve the prediction efficacy. These findings could help to guide treatment decisions and determine prognosis in cervical cancer.

An enhanced security framework for secured transfer of DICOM images by ensuring the CIA triad

An enhanced security framework for secured transfer of DICOM images by ensuring the CIA triad

Resolving Conformational Plasticity in Mammalian Cells with High-Resolution Fluorescence Tools

Investigating protein dynamic structural changes is fundamental for understanding protein function, drug discovery, and disease mechanisms. Traditional studies of protein dynamics often rely on investigations of purified systems, which fail to capture the complexity of the cellular environment. The intracellular milieu imposes distinct physicochemical constraints that affect macromolecular interactions and dynamics in ways not easily replicated in isolated experimental setups. We discuss the use of fluorescence resonance energy transfer, fluorescence anisotropy, and minimal photon flux imaging technologies to address these challenges and directly investigate protein conformational dynamics in mammalian cells. Key findings from the application of these techniques demonstrate their potential to reveal intricate details of protein conformational plasticity. By overcoming the limitations of traditional in vitro methods, these approaches offer a more accurate and comprehensive understanding of protein function and behavior within the complex environment of mammalian cells.

The Application of Quasi-Steady-State Chemical Exchange Saturation Transfer Imaging in the Visualization of Glioma Infiltration and the Optimal Extent of Resection Establishment.

The quasi-steady-state (QUASS) algorithm improves chemical exchange saturation transfer (CEST) reliability, but its efficacy in detecting glioma infiltration is unclear. To assess apparent and QUASS CEST in visualizing glioma infiltration and predicting optimal extent-of-resection (EOR). Prospective. 72 adult-type diffuse glioma patients (37 males, 49.57 ± 14.76 years) and 24 healthy volunteers (12 males, 48.71 ± 14.23 years). 3 T, fast spin-echo CEST. Apparent and QUASS CEST effects (amide proton transfer [APT], combined magnetization transfer and nuclear overhauser enhancement and the 2-ppm chemical exchange saturation transfer peak) were calculated in solid tumor, edema, contralateral normal apparent white matter (CNAWM) in glioma patients, and white matter in healthy individuals (WMH). Comparisons were made between these four regions, high-/low-grade gliomas (HGGs/LGGs), and isocitrate dehydrogenase (IDH)-mutant/wild-type gliomas. Twenty-seven biopsy samples from glioblastomas and peritumoral regions were selected and traced back to the original images. Then, correlations between CEST effects, cellularity, and Ki-67 labeling index (LI) were assessed. An optimal cutoff value for the normalized ratios of QUASS APT (QUASS_rAPT) was generated. Linear mixed models, t-test, receiver-operating characteristic analysis, and Pearson's correlation tests were used. The statistical significance was set at p ≤ 0.05. QUASS_APT value decreased significantly from tumor solid area, edema, and CNAWM to WMH (3.887 ± 1.489, 2.556 ± 0.985, and 1.584 ± 0.462, respectively). QUASS_rAPT of solid tumor area differentiated LGGs from HGGs (1.878 ± 0.515 vs. 2.857 ± 1.026) and IDH-mutant gliomas from IDH wild-type gliomas (2.195 ± 0.769 vs. 2.875 ± 1.092). QUASS_rAPT strongly correlated with cell density and Ki-67 LI (r = 0.801 and 0.776). The optimal cutoff value of QUASS_rAPT was 1.300. Compared to other CEST effects and apparent methods, QUASS_rAPT enhances glioma stratification and better reflects cell density and proliferative potential. A QUASS_rAPT > 1.30 optimized EOR prediction in glioblastomas. 2 TECHNICAL EFFICACY: Stage 2.

When CEST meets diffusion: Multi-echo diffusion-encoded CEST (dCEST) MRI to measure intracellular and extracellular CEST signal distributions.

To develop a multi-echo, diffusion-encoded chemical exchange saturation transfer (dCEST) imaging technique for estimating the intracellular and extracellular/intravascular contributions to the conventional CEST signal. A dCEST pulse sequence was developed to quantify the signal fractions, transverse relaxation times (T2), and apparent diffusion coefficient (ADC) of the intracellular and extracellular/intravascular water compartments. dCEST images were acquired across a wide range of TE, b-values, RF saturation strengths, and frequency offsets. The data were analyzed using a two-compartment model with distinct diffusivities and T2 values. Intracellular and extracellular fractions of conventional water-saturation spectra (Z-spectra) and corresponding amide proton transfer (APT) signals were estimated from human brain scans of healthy volunteers at 3 T. The multi-echo diffusion results showed that the intracellular water fractions were significantly higher than the extracellular water fractions, whereas the intracellular T2 values were shorter than those of the extracellular/intravascular compartments. The ADC for the intracellular compartment was significantly lower than that of the extracellular compartment. The dCEST analysis showed that the average intracellular and extracellular fractions of the Z-spectra were 85 ± 7% and 15 ± 4%, respectively. The overall intracellular APT-weighted values were higher than the total (i.e., intracellular + extracellular) APT-weighted values. The dCEST imaging technique provides valuable insight into the source of signals in conventional CEST MRI, offering potential utility for clinical applications.

Optimizing Diabetic Retinopathy Screening at Primary Health Centres in India: A Cost-Effectiveness Analysis.

The eye care package under the Ayushman Bharat comprehensive primary healthcare programme includes annual population-based screening for diabetic retinopathy (DR) using non-mydriatic fundus cameras at the primary health centres (PHCs) in India. However, there can be several implementation models for introduction of a systematic screening programme for DR. This study aims to assess the cost effectiveness of screening for DR in comparison with the usual-care scenario without a DR screening programme, and to determine cost-effective approaches for implementation of annual population-based screening for DR by optometrists at PHCs in India in terms of screening modalities (face-to-face vs tele-supported screening [screening followed by transfer and remote grading of images by ophthalmologists] vs artificial intelligence [AI]-supported screening) and target population groups for screening. A mathematical model comprising a decision tree and Markov model was developed. An extensive review of published literature was undertaken to obtain model parameters. Primary data collection was done to derive quality-of-life values. We used a lifetime horizon, abridged societal perspective, and discounted future costs and consequences at an annual rate of 3%. The incremental cost-effectiveness ratio (ICER) was computed for alternative screening strategies. A willingness-to-pay equal to gross domestic product per capita equal to ₹171,498 (US$2182) was used to determine the cost-effective choice. Sensitivity analyses were performed to assess the impact of variation in input parameters on the ICER values. All the annual screening strategies were found to have lower ICERs relative to usual care. Among the screening strategies, annual tele-supported screening in the population with diabetes duration ≥5 years was the most cost-effective strategy with an ICER value of ₹57,408 (US$730) per quality-adjusted life year (QALY) gained. At the national level, this strategy is likely to reduce the annual incidence of vision-threatening DR and blindness by 17.3%, and 38.5%, respectively, and would result in higher benefits in Indian states with higher epidemiological transition. Sensitivity analyses showed that if adequate glycaemic control is achieved in 79% of the diabetic population, annual AI-supported screening in individuals with a diabetes' duration of 10 years or more becomes the most cost-effective strategy. The results of the study suggest the prioritization of an annual tele-supported DR screening programme in India. They also highlight the importance of the adoption of an integrated approach and functional linkage between eye care and diabetes care, to intensify efforts directed at improving glycaemic control, and to facilitate early DR detection and management.

A fine-tuned convolutional neural network model for accurate Alzheimer’s disease classification

Alzheimer’s disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to accomplish simple activities gradually. Timely intervention and efficient control of the disease prove to be possible through early diagnosis. The conventional machine learning models designed for AD detection work well only up to a certain point. They usually require a lot of labeled data and do not transfer well to new datasets. Additionally, they incur long periods of retraining. Relatively powerful models of deep learning, however, also are very demanding in computational resources and data. In light of these, we put forward a new way of diagnosing AD using magnetic resonance imaging (MRI) scans and transfer learned convolutional neural networks (CNN). Transfer learning makes it easier to reduce the costs involved in training and improves performance because it allows the use of models which have been trained previously and which generalize very well even when there is very little training data available. In this research, we used three different pre-trained CNN based architectures (AlexNet, GoogleNet, and MobileNetV2) each implemented with several solvers (e.g. Adam, Stochastic Gradient Descent or SGD, and Root Mean Square Propagation or RMSprop). Our model achieved impressive classification results of 99.4% on the Kaggle MRI dataset as well as 98.2% on the Open Access Series of Imaging Studies (OASIS) database. Such results serve to demonstrate how transfer learning is an effective solution to the issues related to conventional models that limits the accuracy of diagnosis of AD, thus enabling their earlier and more accurate diagnosis. This would in turn benefit the patients by improving the treatment management and providing insights on the disease progression.

UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene.

Photorealistic stylization of 3D scenes aims to generate photorealistic images from arbitrary novel views according to a given style image, while ensuring consistency when rendering video from different viewpoints. Some existing stylization methods using neural radiance fields can effectively predict stylized scenes by combining the features of the style image with multi-view images to train 3D scenes. However, these methods generate novel view images that contain undesirable artifacts. In addition, they cannot achieve universal photorealistic stylization for a 3D scene. Therefore, a stylization image needs to retrain a 3D scene representation network based on a neural radiation field. We propose a novel photorealistic 3D scene stylization transfer framework to address these issues. It can realize photorealistic 3D scene style transfer with a 2D style image for novel view video rendering. We first pre-trained a 2D photorealistic style transfer network, which can satisfy the photorealistic style transfer between any content image and style image. Then, we use voxel features to optimize a 3D scene and obtain the geometric representation of the scene. Finally, we jointly optimize a hypernetwork to realize the photorealistic style transfer of arbitrary style images. In the transfer stage, we use a pre-trained 2D photorealistic network to constrain the photorealistic style of different views and different style images in the 3D scene. The experimental results show that our method not only realizes the 3D photorealistic style transfer of arbitrary style images, but also outperforms the existing methods in terms of visual quality and consistency.

A survey on the application of deep learning in knee joint cartilage ultrasound image segmentation

Objectives: The femoral cartilage in the knee joint is prone to degenerative changes and injuries, often requir-ing Magnetic Resonance Imaging as the diagnostic gold standard. However, due to the high cost and limited availability of Magnetic Resonance Imaging, ultrasound is explored as a viable alternative. This paper presents a comprehensive review of current deep learning (DL) strategies for knee femoral cartilage segmentation in ultrasound images, focusing on commonly used datasets, data preprocessing techniques, and state-of-the-art DL models. Methods: We systematically reviewed the literature from major medical and engineering databases, sum-marizing key contributions to knee femoral cartilage segmentation. We focused on (1) the scarcity of large-scale public ultrasound datasets and its impact on model training, (2) popular DL architectures (e.g., U-Net variants, Mask Region-based Convolutional Neural Network), and (3) evaluation techniques, particularly the Dice Similarity Coefficient. We also examined image preprocessing and data augmentation strategies aimed at mitigating data insufficiency. Results: Our review shows that U-Net and its variants (e.g., Siam U-Net, U-Net++) commonly achieve competitive Dice Similarity Coefficient values (around 0.70–0.80) for knee cartilage segmentation, despite the limitations in training data. Advanced networks like Mask Region-based Convolutional Neural Network, when com-bined with robust image preprocessing and transfer learning (e.g., using COCO/ImageNet pretrained weights), can improve Dice Similarity Coefficient by over 20%. However, the absence of standardized public datasets limits direct comparisons between studies and affects reproducibility. Conclusion: DL holds significant potential for accurate and cost-effective femoral cartilage segmentation in knee joint ultrasound images, offering a feasible alternative or complement to Magnetic Resonance Imaging-based assessments. However, challenges remain due to the lack of large-scale, open-access ultrasound datasets and inconsistent evaluation protocols. Future work should focus on establishing public benchmarks, refining novel network architectures, and enhancing real-time clinical deploy-ment to foster wider adoption and greater clinical impact.

Development and validation of a diagnostic nomogram model for osteoporosis in the elderly using 3D multi-echo Dixon sequence combined with magnetization transfer imaging.

Development and validation of a diagnostic nomogram model for osteoporosis in the elderly using 3D multi-echo Dixon sequence combined with magnetization transfer imaging.

Multimodal precision MRI of the individual human brain at ultra-high fields

Multimodal neuroimaging, in particular magnetic resonance imaging (MRI), allows for non-invasive examination of human brain structure and function across multiple scales. Precision neuroimaging builds upon this foundation, enabling the mapping of brain structure, function, and connectivity patterns with high fidelity in single individuals. Highfield MRI, operating at magnetic field strengths of 7 Tesla (T) or higher, increases signal-to-noise ratio and opens up possibilities for gains spatial resolution. Here, we share a multimodal Precision Neuroimaging and Connectomics (PNI) 7 T MRI dataset. Ten healthy individuals underwent a comprehensive MRI protocol, including T1 relaxometry, magnetization transfer imaging, T2*-weighted imaging, diffusion MRI, and multi-state functional MRI paradigms, aggregated across three imaging sessions. Alongside anonymized raw MRI data, we release cortex-wide connectomes from different modalities across multiple parcellation scales, and supply “gradients” that compactly characterize spatial patterning of cortical organization. Our precision MRI dataset will advance our understanding of structure-function relationships in the individual human brain and is publicly available via the Open Science Framework.