Hyperpolarization stands out as a technique capable of significantly enhancing the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI). Dynamic nuclear polarization (DNP), among various hyperpolarization methods, has gained prominence for its efficacy in real-time monitoring of metabolism and physiology. By administering a hyperpolarized substrate through dissolution DNP (dDNP), the biodistribution and metabolic changes of the DNP agent can be visualized spatiotemporally. This approach proves to be a distinctive and invaluable tool for non-invasively studying cellular metabolism in vivo, particularly in animal models. Biomarkers play a pivotal role in influencing the growth and metastasis of tumor cells by closely interacting with them, and accordingly detecting pathological alterations of these biomarkers is crucial for disease diagnosis and therapy. In recent years, a range of hyperpolarized DNP molecular bioresponsive agents utilizing various nuclei, such as 13C, 15N, 31P, 89Y, etc., have been developed. In this context, we explore how these magnetic resonance signals of nuclear spins enhanced by DNP respond to biomarkers, including pH, metal ions, enzymes, or redox processes. This review aims to offer insights into the design principles of responsive DNP agents, target selection, and the mechanisms of action for imaging. Such discussions aim to propel the future development and application of DNP-based biomedical imaging agents.

UNASSIGNED: To assess the diagnostic accuracy of machine learning (ML)-based radiomics for predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma.
UNASSIGNED: A systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from inception to 1 September 2023, was conducted to collect all articles investigating the diagnostic performance of ML for the prediction of IDH mutations in gliomas. Two reviewers independently screened all papers for eligibility. Methodological quality and risk of bias were assessed using the METhodological RadiomICs Score and Quality Assessment of Diagnostic Accuracy Studies-2, respectively. The pooled sensitivity, specificity, and 95% confidence intervals were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained.
UNASSIGNED: In total, 14 original articles assessing 1740 patients with gliomas were included. The AUC of ML for predicting IDH mutation was 0.90 (0.87-0.92). The pooled sensitivity, specificity, and diagnostic odds ratio were 0.83 (0.71-0.90), 0.84 (0.74-0.90), and 25 (12,50) respectively. In subgroup analyses, modeling methods, glioma grade, and the combination of magnetic resonance imaging and clinical features affected the diagnostic performance in predicting IDH mutations in gliomas.
UNASSIGNED: ML-based radiomics demonstrated excellent diagnostic performance in predicting IDH mutations in gliomas. Factors influencing the diagnosis included the modeling methods employed, glioma grade, and whether the model incorporated clinical features.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/#myprospero, PROSPERO registry (CRD 42023395444).

Progressive inflammation of one hemisphere characterises Rasmussen\'s encephalitis (RE), but contralesional epileptiform activity has been repeatedly reported. We aimed to quantify contralesional epileptiform activity in RE and uncover its functional and structural underpinnings. We retrospectively ascertained people with RE treated between 2000 and 2018 at a tertiary centre (Centre 1) and reviewed all available EEG datasets. The temporal occurrence of preoperative contralesional epileptiform activity (interictal/ictal) was evaluated using mixed-effects logistic regression. Cases with/without contralesional epileptiform activity were compared for cognition, inflammation (ipsilesional brain biopsies), and MRI (cortical and fixel-based morphometry). EEG findings were validated in a second cohort treated at another tertiary centre (Centre 2) between 1995 and 2020. We included 127 people with RE and 687 EEG samples. Preoperatively, contralesional epileptiform activity was seen in 30/68 (44%, Centre 1) and 8/59 (14%, Centre 2). In both cohorts, this activity was associated with younger onset age (OR = 0.9; 95% CI 0.83-0.97; P = 0.006). At centre 1, contralesional epileptiform activity was associated with contralesional MRI alterations, lower intelligence (OR = 5.19; 95% CI 1.28-21.08; P = 0.021), and impaired verbal memory (OR = 10.29; 95% CI 1.97-53.85; P = 0.006). After hemispherotomy, 11/17 (65%, Centre 1) and 28/37 (76%, Centre 2) were seizure-free. Contralesional epileptiform activity was persistent postoperatively in 6/12 (50%, Centre 1) and 2/34 (6%, Centre 2). Preoperative contralesional epileptiform activity reduced the chance of postoperative seizure freedom in both cohorts (OR = 0.69; 95% CI 0.50-0.95; P = 0.029). Our findings question the concept of strict unilaterality of RE and provide the evidence of contralesional epileptiform activity as a possible EEG predictor for persisting postoperative seizures.

BACKGROUND: Frontotemporal dementia (FTD) can be phenotypically divided into behavioral variant FTD (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA). However, the neural underpinnings of this phenotypic heterogeneity remain elusive.
METHODS: Cortical morphology, white matter hyperintensities (WMH), diffusion tensor image analysis along the perivascular space (DTI-ALPS), and their interrelationships were assessed in subtypes of FTD. Neuroimaging-transcriptional analyses on the regional cortical morphological deviances among subtypes were also performed.
RESULTS: Changes in cortical thickness, surface area, gyrification, WMH, and DTI-ALPS were subtype-specific in FTD. The three morphologic indices are related to whole-brain WMH volume and cognitive performance, while cortical thickness is related to DTI-ALPS. Neuroimaging-transcriptional analyses identified key biological pathways linked to the formation and/or spread of TDP-43/tau pathologies.
CONCLUSIONS: We found subtype-specific changes in cortical morphology, WMH, and glymphatic function in FTD. Our findings have the potential to contribute to the development of personalized predictions and treatment strategies for this disorder.
CONCLUSIONS: Cortical morphologic changes, white matter hyperintensities (WMH), and glymphatic dysfunction are subtype-specific. Cortical morphologic changes, WMH, and glymphatic dysfunction are inter-correlated. Cortical morphologic changes and WMH burden contribute to cognitive impairments.

暂无摘要。

This study aimed to evaluate the correlation between measuring proton-density fat fraction (PDFF) in bone marrow using multi-echo chemical shift-encoded MRI and osteoporosis, assessing its effectiveness as a biomarker for osteoporosis. A systematic review was conducted by two independent researchers using Cochrane, PubMed, EMBASE, and Web of Science databases up to December 2023. Quality assessments were evaluated using the Cochrane risk of bias tool and the Agency for Healthcare Research and Quality (AHRQ) checklist. Fourteen studies involving 1495 patients were analyzed. The meta-analysis revealed a significant difference in PDFF values between the osteoporosis/osteopenia group and the normal control group, with a mean difference of 11.04 (95% CI: 9.17 to 12.92, Z=11.52, P < 0.00001). Measuring PDFF via MRI shows potential as an osteoporosis biomarker and may serve as a risk factor for osteoporosis. This insight opens new avenues for future diagnostic and therapeutic strategies, potentially improving osteoporosis management and patient care.
OBJECTIVE: This study aims to assess the correlation between measuring proton-density fat fraction (PDFF) in bone marrow using multi-echo chemical shift-encoded MRI and osteoporosis, evaluating its effectiveness as a biomarker for osteoporosis.
METHODS: This systematic review was carried out by two independent researchers using Cochrane, PubMed, EMBASE, and Web of Science databases up to December 2023. Quality assessments were evaluated using the Cochrane risk of bias tool and the Agency for Healthcare Research and Quality (AHRQ) checklist.
RESULTS: Fourteen studies involving 1495 patients were analyzed. The meta-analysis revealed a significant difference in PDFF values between the osteoporosis/osteopenia group and the normal control group, with a (MD = 11.04, 95% CI: 9.17 to 12.92, Z = 11.52, P < 0.00001). Subgroup analyses indicated that diagnostic methods, gender, and echo length did not significantly impact the PDFF-osteoporosis association.
CONCLUSIONS: PDFF measurement via MRI shows potential as an osteoporosis biomarker and may serve as a risk factor for osteoporosis. This insight opens new avenues for future diagnostic and therapeutic strategies, potentially improving osteoporosis management and patient care.

BACKGROUND: Segmentation of white matter hyperintensities (WMH) in CADASIL, one of the most severe cerebral small vessel disease of genetic origin, is challenging.
METHODS: We adapted and validated an automatic method based on a convolutional neural network (CNN) algorithm and using a large dataset of 2D and/or 3D FLAIR and T1-weighted images acquired in 132 patients, to measure the progression of WMH in this condition.
RESULTS: The volume of WMH measured using this method correlated strongly with reference data validated by experts. WMH segmentation was also clearly improved compared to the BIANCA segmentation method. Combining two successive learning models was found to be of particular interest, reducing the number of false-positive voxels and the extent of under-segmentation detected after a single-stage process. With the two-stage approach, WMH progression correlated with measures derived from the reference masks for lesions increasing with age, and with the variable WMH progression trajectories at individual level. We also confirmed the expected effect of the initial load of WMH and the influence of the type of MRI acquisition on measures of this progression.
CONCLUSIONS: Altogether, our findings suggest that WMH progression in CADASIL can be measured automatically with adequate confidence by a CNN segmentation algorithm.

BACKGROUND: Since the 2016 WHO guidelines, glioma diagnosis has entered an era of integrated diagnosis, combining tissue pathology and molecular pathology. The WHO has focused on promoting the application of molecular diagnosis in the classification of central nervous system tumors. Genetic information such as IDH1 and 1p/19q are important molecular markers, and pathological grading is also a key clinical indicator. However, obtaining genetic pathology labels is more costly than conventional MRI images, resulting in a large number of missing labels in realistic modeling.
METHODS: We propose a training strategy based on label encoding and a corresponding loss function to enable the model to effectively utilize data with missing labels. Additionally, we integrate a graph model with genes and pathology-related clinical prior knowledge into the ResNet backbone to further improve the efficacy of diagnosis. Ten-fold cross-validation experiments were conducted on a large dataset of 1072 patients.
RESULTS: The classification area under the curve (AUC) values are 0.93, 0.91, and 0.90 for IDH1, 1p/19q status, and grade (LGG/HGG), respectively. When the label miss rate reached 59.3 %, the method improved the AUC by 0.09, 0.10, and 0.04 for IDH1, 1p/19q, and pathological grade, respectively, compared to the same backbone without the missing label strategy.
CONCLUSIONS: Our method effectively utilizes data with missing labels and integrates clinical prior knowledge, resulting in improved diagnostic performance for glioma genetic and pathological markers, even with high rates of missing labels.

OBJECTIVE: This study aimed to assess the practical application of conventional two-dimensional (2D) pelvic ultrasound in conjunction with three-dimensional (3D) ultrasound for evaluating obstructive Müllerian abnormalities.
METHODS: Respective study in tertiary referral hospital METHOD: Computerized stored data was used to collect surgical confirmed obstructive Müllerian anomalies cases between December 2022 and October 2023 with presurgical imagings being evaluated. Acute presentation with abdominal pain and clinical suspicion of obstructive Müllerian abnormality were required for inclusion. All study participants underwent pelvic ultrasound prior to the definitive surgery, with or without a repeat MRI if one was performed previous to admission. Those situations where both MRI and ultrasound were not conducted were excluded, such as the transverse vaginal septum, imperforate hymen, iatrogenic cervical injury or Müllerian malformation alone without obstructive outflow anomalies like didelphys, bicornuate, or septate uterus.
METHODS: The concordance between the surgically confirmed diagnosis and the pelvic ultrasound was reported in 27 of 29 women (93.1%). In contrast, only 24 of 29 cases were correctly diagnosed with MRI in this study (82.8%). This pilot study presents a comparison of two techniques, with a specific focus on obstructive Müllerian anomalies. The use of pelvic ultrasound not only assisted in our surgical practice but also significantly improved patient-doctor counseling.
CONCLUSIONS: In managing obstructive Müllerian abnormalities, 3D-enhanced conventional pelvic ultrasound was found effective in diagnosis and was comparable to MRI.

OBJECTIVE: As a widely used technique for Magnetic Resonance Image (MRI) acceleration, compressed sensing MRI involves two main issues: designing an effective sampling strategy and reconstructing the image from significantly under-sampled K-space data. In this paper, an innovative approach is proposed to address these two challenges simultaneously.
METHODS: A novel MRI reconstruction method, termed as LUCMT, is implemented by integrating a learnable under-sampling strategy with a reconstruction network based on the Cross Multi-head Attention Transformer. In contrast to conventional static sampling methods, the proposed adaptive sampling scheme is processed optimally by learning the optimal sampling technique, which involves binarizing the sampling pattern by a sigmoid function and computing gradients by backpropagation. And the reconstruction network is designed by using CS-MRI depth unfolding network that incorporates a Cross Multi-head Attention (CMA) module with inertial and gradient descent terms.
RESULTS: T1 brain MR images from the FastMRI dataset are used to validate the performance of the proposed method. A series of experiments are conducted to validate the superior performance of our proposed network in terms of quantitative metrics and visual quality. Compared with other state-of-the-art reconstruction methods, LUCMT achieves better reconstruction performances with more accurate details. Specifically, LUCMT achieves PSNR and SSIM results of 41.87/0.9749, 46.64/0.9868, 50.41/0.9924, and 53.51/0.9955 at sampling rates of 10 %, 20 %, 30 %, and 40 %, respectively.
CONCLUSIONS: The proposed LUCMT method can provide a promising way for generating optimal under-sampling mask and accelerating MRI reconstruction accurately.