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Aim: To evaluate systematically the magnetic resonance imaging (MRI) features of tubo-ovarian abscess (TOA), the efficacy of diffusion-weighted imaging (DWI) in the assessment of TOA, and the differentiation of TOA from ovarian masses. Material and methods: PubMed/MEDLINE, Web of Science, and ResearchGate were searched with keywords: Tubo-ovarian abscess and Diffusion-weighted magnetic resonance imaging. No restrictions regarding the language or publication year were applied. Inclusion criteria regarded research papers evaluating the role of MRI and DWI in the assessment of TOA, and the differentiation of TOA from ovarian tumours. Studies appearing to meet inclusion criteria were reviewed in full. Results: A total of 14 studies were included. TOA is usually presented as a multilocular, cystic, pelvic mass with a heterogeneously high signal on T2- weighted (T2W) and low signal on T1-weighted (T1W) images with the “penumbra sign” on T1W images. Following contrast administration, septal and thick wall rim enhancement could be visualized. TOA usually demonstrated hyperintensity on DWI and hypointensity on ADC maps with lower ADC values of cystic, and higher ADC values of solid components compared to ovarian malignancy. TOA was smaller in size, and invasion into adjacent organs and tubal dilatation were more frequent in TOA than in ovarian neoplasms. In comparison to other methods, DWI possessed the highest sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in differentiating TOA from adnexal tumours. Conclusions: The addition of DWI with ADC values improves the detection, characterization, and overall diagnostic accuracy of TOA and its distinction from ovarian malignancy. The combination of DWI with MRI in the assessment of TOA is obligatory. [ABSTRACT FROM AUTHOR]

BACKGROUND: Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. OBJECTIVE: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. METHOD: By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. RESULT: The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). CONCLUSION: The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients. [ABSTRACT FROM AUTHOR]

Recent work has identified a critical role for the hippocampus in reward‐sensitive behaviors, including motivated memory, reinforcement learning, and decision‐making. Animal histology and human functional neuroimaging have shown that brain regions involved in reward processing and motivation are more interconnected with the ventral/anterior hippocampus. However, direct evidence examining gradients of structural connectivity between reward regions and the hippocampus in humans is lacking. The present study used diffusion MRI (dMRI) and probabilistic tractography to quantify the structural connectivity of the hippocampus with key reward processing regions in vivo. Using a large sample of subjects (N = 628) from the human connectome dMRI data release, we found that connectivity profiles with the hippocampus varied widely between different regions of the reward circuit. While the dopaminergic midbrain (ventral tegmental area) showed stronger connectivity with the anterior versus posterior hippocampus, the ventromedial prefrontal cortex showed stronger connectivity with the posterior hippocampus. The limbic (ventral) striatum demonstrated a more homogeneous connectivity profile along the hippocampal long axis. This is the first study to generate a probabilistic atlas of the hippocampal structural connectivity with reward‐related networks, which is essential to investigating how these circuits contribute to normative adaptive behavior and maladaptive behaviors in psychiatric illness. These findings describe nuanced structural connectivity that sets the foundation to better understand how the hippocampus influences reward‐guided behavior in humans. [ABSTRACT FROM AUTHOR]

Wilson's disease (WD) is a genetic disorder of copper metabolism with pathological copper accumulation in many organs, resulting in clinical symptoms, mostly hepatic and neuropsychiatric. As copper accumulates in the brain during WD, and almost 50% of WD patients at diagnosis present with neurological symptoms, neuroimaging studies (especially brain magnetic resonance imaging (MRI)) are part of WD diagnosis. The classical sequences (T1, T2, and fluid-attenuated inversion recovery) were used to describe brain MRI; however, with the development of neuroradiology, several papers proposed the use of new MRI sequences and techniques like susceptibility-weighted images, T2*, diffusion MRI, tractography, volumetric assessment and post-processing brain MRI analysis of paramagnetic accumulation—quantitative susceptibility mapping. Based on these neuroradiological data in WD, currently, brain MRI semiquantitative scale and the pathognomonic neuroradiological brain MRI signs in WD were proposed. Further, the volumetric studies and brain iron accumulation MRI analysis suggested brain atrophy and iron accumulation as biomarkers of neurological WD disease severity. All these results highlight the significance of brain MRI examinations in WD. Due to the extreme progress of these studies, based on the available literature, the authors present the current state of knowledge about the significance, practical aspects, and future directions of brain MRI in WD. [ABSTRACT FROM AUTHOR]

Determining the grade of a glioma is extremely important for treatment planning and prognosis prediction. The study aimed to evaluate the usefulness of multiparametric perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) in preoperative glioma grading. In this retrospective study, 63 individuals with brain tumors histologically confirmed, of which 23 had low-grade gliomas (LGGs) and 40 had high-grade gliomas (HGGs) were involved. We conducted this paper on apparent diffusion coefficient (ADC) maps using the entire tumor volume method, allowing us to use all ADC values of the tumor. Small-sample regions of interest (ROIs) were drawn to collect parameters of relative cerebral blood flow (rCBF), cerebral blood flow (CBF), and relative cerebral blood volume (rCBV), from both the tumor core and peritumoral edema. The PWI and DWI metrics were compared to identify the most accurate distinguishing HGGs and LGGs, analyze receiver operating characteristics (ROC), and evaluate the diagnostic performance using solitary parameters and combined. In diffusion MRI, there were significant differences in minimum ADC and mean ADC between LGGs and HGGs (p<0.05), with the larger area under the curve (AUC) of 0.898 found for mean ADC at a cut-off value of 1.275, with sensitivity of 82.6 % and specificity of 90 %. The maximum ADC value did not differ significantly (p>0.05). All perfusion parameters in both the tumor core and peritumoral edema area were significantly greater values in cases of HGG compared to LGG (p<0.001), with the highest AUC of 0.946 found for solid tumor rCBV value (rCBVt), the cut-off is 3.585, sensitivity of 85 % and specificity of 100 %. Combining mean ADC and rCBVt provided an excellent AUC of 0.975, a sensitivity of 92.5 %, and a specificity of 91.3 % for differentiating between HGGs and LGGs. Perfusion and diffusion MRI are valuable in discriminating between high-grade and low-grade gliomas, with the major criterion in the decision-making process being the combined mean ADC and rCBVt parameters. [ABSTRACT FROM AUTHOR]

Background and Purpose: We aimed to test whether synthetic T1‐weighted imaging derived from a post‐contrast Quantitative Transient‐state Imaging (QTI) acquisition enabled revealing pathological contrast enhancement in intracranial lesions. Methods: The analysis included 141 patients who underwent a 3 Tesla‐MRI brain exam with intravenous contrast media administration, with the post‐contrast acquisition protocol comprising a three‐dimensional fast spoiled gradient echo (FSPGR) sequence and a QTI acquisition. Synthetic T1‐weighted images were generated from QTI‐derived quantitative maps of relaxation times and proton density. Two neuroradiologists assessed synthetic and conventional post‐contrast T1‐weighted images for the presence and pattern of pathological contrast enhancement in intracranial lesions. Enhancement volumes were quantitatively compared. Results: Using conventional imaging as a reference, synthetic T1‐weighted imaging was 93% sensitive in revealing the presence of contrast enhancing lesions. The agreement for the presence/absence of contrast enhancement was almost perfect both between readers (k = 1 for both conventional and synthetic imaging) and between sequences (k = 0.98 for both readers). In 91% of lesions, synthetic T1‐weighted imaging showed the same pattern of contrast enhancement visible in conventional imaging. Differences in enhancement pattern in the remaining lesions can be due to the lower spatial resolution and the longer acquisition delay from contrast media administration of QTI compared to FSPGR. Overall, enhancement volumes appeared larger in synthetic imaging. Conclusions: QTI‐derived post‐contrast synthetic T1‐weighted imaging captures pathological contrast enhancement in most intracranial enhancing lesions. Further comparative studies employing quantitative imaging with higher spatial resolution is needed to support our data and explore possible future applications in clinical trials. [ABSTRACT FROM AUTHOR]

Gray matter (GM) alterations play a role in aging‐related disorders like Alzheimer's disease and related dementias, yet MRI studies mainly focus on macroscopic changes. Although reliable indicators of atrophy, morphological metrics like cortical thickness lack the sensitivity to detect early changes preceding visible atrophy. Our study aimed at exploring the potential of diffusion MRI in unveiling sensitive markers of cortical and subcortical age‐related microstructural changes and assessing their associations with cognitive and behavioral deficits. We leveraged the Human Connectome Project‐Aging cohort that included 707 participants (394 female; median age = 58, range = 36–90 years) and applied the powerful mean apparent diffusion propagator model to measure microstructural parameters, along with comprehensive behavioral and cognitive test scores. Both macro‐ and microstructural GM characteristics were strongly associated with age, with widespread significant microstructural correlations reflective of cellular morphological changes, reduced cellular density, increased extracellular volume, and increased membrane permeability. Importantly, when correlating MRI and cognitive test scores, our findings revealed no link between macrostructural volumetric changes and neurobehavioral performance. However, we found that cellular and extracellular alterations in cortical and subcortical GM regions were associated with neurobehavioral performance. Based on these findings, it is hypothesized that increased microstructural heterogeneity and decreased neurite orientation dispersion precede macrostructural changes, and that they play an important role in subsequent cognitive decline. These alterations are suggested to be early markers of neurocognitive performance that may distinctly aid in identifying the mechanisms underlying phenotypic aging and subsequent age‐related functional decline. [ABSTRACT FROM AUTHOR]

Background: Recurrence, even after years from the last treatment, characterizes lymphoproliferative disorders. Therefore, patients in complete remission from the disease should be followed up with periodic clinical checks. There is not a consensus on the role of imaging for this aim, because the radiological techniques used at the time of diagnosis expose patients to a risk of ionizing radiation damage. Whole-body magnetic resonance imaging with diffusion-weighted imaging (WB-MRI-DWI) has given similar results to gold standard techniques in detecting lymphoma in the involved sites without ionizing radiation. In this retrospective real-life study, we aimed to assess the accuracy of WB-MRI-DWI during follow-ups of lymphoma patients in terms of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Methods: Lymphoma patients who were subject to at least one WB-MRI-DWI during follow-up between February 2010 and February 2022 were enrolled. Results: Based on our investigation, the calculated sensitivity of WB-MRI-DWI was 100% (95% CI: 99.4–100.0), the specificity was 98.6% (95% CI: 97.4–99.3), PPV was 79% (95% CI: 75.9–81.9), and NPV was 100% (95% CI: 99.4–100.0). Conclusions: Despite the possibility of poor patient compliance and the identification of false positives, WB-MRI-DWI examination demonstrated an excellent sensitivity in ruling out the disease relapse. [ABSTRACT FROM AUTHOR]

Aim: Tumor Ki‐67 expression reflects prognosis and cancer grade, and biopsy‐based preoperative assessment of Ki‐67 expression is key to treatment. Apparent diffusion coefficient (ADC) values obtained with this imaging may noninvasively predict Ki‐67 by reflecting tumor cell density and limited water molecule movement from irregular alignment. This study aimed to investigate the ability of ADC values to predict Ki‐67 expression in intrahepatic cholangiocarcinoma (ICC). Method: We retrospectively analyzed 39 cases of ICC confirmed by surgical pathology. All patients had undergone magnetic resonance imaging, and ADC values (mean, minimum, and maximum) were calculated. Ki‐67 expression was assessed by immunohistochemistry, and patients were divided into groups of high (n = 18) and low (n = 21) Ki‐67 expression. To assess the diagnostic performance of the ADC values for Ki‐67 expression, we used the receiver operating characteristic curve and compared the areas under the curve (AUC). Results: The mean and minimum ADC values were significantly lower in the group with high Ki‐67 expression. For predicting high Ki‐67 expression, the AUC values were 0.701 for mean ADC, 0.818 for minimum ADC, and 0.571 for maximum ADC. The diagnostic sensitivity and specificity of the minimum ADC values were 88.9% and 76.2%, respectively. In addition, with ADC values combined, the AUC increased to 0.831. Apparent diffusion coefficient is a useful predictor of Ki‐67 expression level in ICC. Conclusion: Apparent diffusion coefficient values, especially minimum ADC values, can noninvasively predict ICC associated with high Ki‐67 expression. [ABSTRACT FROM AUTHOR]

Sports-related concussions may cause white matter injuries and persistent post-concussive symptoms (PPCS). We hypothesized that athletes with PPCS would have neurocognitive impairments and white matter abnormalities that could be revealed by advanced neuroimaging using ultra-high field strength diffusion tensor (DTI) and diffusion kurtosis (DKI) imaging metrics and cerebrospinal fluid (CSF) biomarkers. A cohort of athletes with PPCS severity limiting the ability to work/study and participate in sport school and/or social activities for ≥6 months completed 7T magnetic resonance imaging (MRI) (morphological T1-weighed volumetry, DTI and DKI), extensive neuropsychological testing, symptom rating, and CSF biomarker sampling. Twenty-two athletes with PPCS and 22 controls were included. Concussed athletes performed below norms and significantly lower than controls on all but one of the psychometric neuropsychology tests. Supratentorial white and gray matter, as well as hippocampal volumes did not differ between concussed athletes and controls. However, of the 72 examined white matter tracts, 16% of DTI and 35% of DKI metrics (in total 28%) were significantly different between concussed athletes and controls. DKI fractional anisotropy and axial kurtosis were increased, and DKI radial diffusivity and radial kurtosis decreased in concussed athletes when compared with controls. CSF neurofilament light (NfL; an axonal injury marker), although not glial fibrillary acidic protein, correlated with several diffusion metrics. In this first 7T DTI and DKI study investigating PPCS, widespread microstructural alterations were observed in the white matter, correlating with CSF markers of axonal injury. More white matter changes were observed using DKI than using DTI. These white matter alterations may indicate persistent pathophysiological processes following concussion in sport. [ABSTRACT FROM AUTHOR]

Malignant soft tissue spinal canal tumors compromise 20% of all spinal neoplasms. They may be primary or metastatic lesions, originating from a diverse range of tissues within and surrounding the spinal canal. These masses can present as diverse emergencies such as secondary cauda equina syndrome, vascular compromise, or syringomyelia. Interpretation of malignant soft tissue spinal canal tumors imaging is an essential for non-radiologists in the setting of emergencies. This task is intricate due to a great radiologic pattern overlap among entities. We present a step-by-step strategy that can guide nonradiologists identify a likely malignant soft tissue lesion in the spinal canal based on imaging features, as well as a review of the radiologic features of malignant soft tissue spinal canal tumors. Diagnosis of soft tissue spinal canal malignancies starts with the identification of the lesion's spinal level and its relationship to the dura and medulla. The second step consists of characterizing it as likely-malignant based on radiological signs like a larger size, ill-defined margins, central necrosis, and/or increased vascularity. The third step is to identify additional imaging features such as intratumoral hemorrhage or cyst formation that can suggest specific malignancies. The physician can then formulate a differential diagnosis. The most encountered malignant soft tissue tumors of the spinal canal are anaplastic ependymomas, anaplastic astrocytomas, metastatic tumors, lymphoma, peripheral nerve sheath tumors, and central nervous system melanomas. A review of the imaging features of every type/subtype of lesion is presented in this work. Although magnetic resonance imaging remains the modality of choice for spinal tumor assessment, other techniques such as dynamic contrast agent-enhanced perfusion magnetic resonance imaging or diffusion-weighted imaging could guide diagnosis in specific situations. In this review, diagnostic strategies for several spinal cord tumors were presented, including anaplastic ependymoma, metastatic spinal cord tumors, anaplastic and malignant astrocytoma, lymphoma, malignant peripheral nerve sheath tumors , and primary central nervous system melanoma. Although the characterization of spinal cord tumors can be challenging, comprehensive knowledge of imaging features can help overcome these challenges and ensure optimal management of spinal canal lesions. [ABSTRACT FROM AUTHOR]

Objective: To systematically review the literature assessing the role of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in the differentiation of soft tissue sarcomas from benign lesions. Materials and methods: A comprehensive literature search was performed with the following keywords: multiparametric magnetic resonance imaging, DCE-MR perfusion, soft tissue, sarcoma, and neoplasm. Original studies evaluating the role of DCE-MRI for differentiating benign soft-tissue lesions from soft-tissue sarcomas were included. Results: Eighteen studies with a total of 965 imaging examinations were identified. Ten of twelve studies evaluating qualitative parameters reported improvement in discriminative power. One of the evaluated qualitative parameters was time-intensity curves (TIC), and malignant curves (TIC III, IV) were found in 74% of sarcomas versus 26.5% benign lesions. Six of seven studies that used the semiquantitative approach found it relatively beneficial. Four studies assessed quantitative parameters including Ktrans (contrast transit from the vascular compartment to the interstitial compartment), Kep (contrast return to the vascular compartment), and Ve (the volume fraction of the extracellular extravascular space) in addition to other parameters. All found Ktrans, and 3 studies found Kep to be significantly different between sarcomas and benign lesions. The values for Ve were variable. Additionally, eight studies assessed diffusion-weighted imaging (DWI), and 6 of them found it useful. Conclusion: Of different DCE-MRI approaches, qualitative parameters showed the best evidence in increasing the diagnostic performance of MRI. Semiquantitative and quantitative approaches seemed to improve the discriminative power of MRI, but which parameters and to what extent is still unclear and needs further investigation. [ABSTRACT FROM AUTHOR]

Objective: To identify the region of interest (ROI) to represent injury and observe between-limb diffusion tensor imaging (DTI) microstructural differences in muscle following hamstring strain injury. Materials and methods: Participants who sustained a hamstring strain injury prospectively underwent 3T-MRI of bilateral thighs using T1, T2, and diffusion-weighted imaging at time of injury (TOI), return to sport (RTS), and 12 weeks after RTS (12wks). ROIs were using the hyperintense region on a T2-weighted sequence: edema, focused edema, and primary muscle injured excluding edema (no edema). Linear mixed-effects models were used to compare diffusion parameters between ROIs and timepoints and limbs and timepoints. Results: Twenty-four participants (29 injuries) were included. A significant ROI-by-timepoint interaction was detected for all diffusivity measures. The edema and focused edema ROIs demonstrated increased diffusion at TOI compared to RTS for all diffusivity measures (p-values < 0.006), except λ1 (p-values = 0.058–0.12), and compared to 12wks (p-values < 0.02). In the no edema ROI, differences in diffusivity measures were not observed (p-values > 0.82). At TOI, no edema ROI diffusivity measures were lower than the edema ROI (p-values < 0.001) but not at RTS or 12wks (p-values > 0.69). A significant limb-by-timepoint interaction was detected for all diffusivity measures with increased diffusion in the involved limb at TOI (p-values < 0.001) but not at RTS or 12wks (p-values > 0.42). Significant differences in fractional anisotropy over time or between limbs were not detected. Conclusion: Hyperintensity on T2-weighted imaging used to define the injured region holds promise in describing muscle microstructure following hamstring strain injury by demonstrating between-limb differences at TOI but not at follow-up timepoints. [ABSTRACT FROM AUTHOR]

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline. [ABSTRACT FROM AUTHOR]

OBJECTIVES The evaluation of Fontan-associated liver disease is often challenging. Diffusion-weighted magnetic resonance imaging can detect hepatic fibrosis from capillary perfusion and diffusion abnormalities from extracellular matrix accumulation. This study investigated its role in the evaluation of liver disease in Fontan patients and explored possible diagnostic methods for early detection of advanced liver fibrosis. METHODS Stable adult Fontan patients who could safely be examined with magnetic resonance imaging were enrolled, and blood biomarkers, transient elastography were also examined. RESULTS Forty-six patients received diffusion-weighted imaging; and 58.7% were diagnosed with advanced liver fibrosis (severe liver fibrosis, 37.0%, and cirrhosis 21.7%). Two parameters of hepatic dysfunction, platelet counts (Spearman's ρ: –0.456, P  = 0.001) and cholesterol levels (Spearman's ρ: –0.383, P  = 0.009), decreased with increasing severity of fibrosis. Using transient elastography, a cut-off value of 14.2 kPa predicted the presence of advanced liver fibrosis, but with a low positive predictive value. When we included platelet count, cholesterol, post-Fontan years and transient elastography values as a composite, the capability of predicting advanced liver fibrosis was the most satisfactory (C statistic 0.817 ± 0.071, P  < 0.001). A cut-off value of 5.0 revealed a sensitivity of 78% and a specificity of 82%. CONCLUSIONS In Fontan patients, diffusion-weighted imaging was helpful in detecting liver fibrosis that was correlated with hepatic dysfunction. A simple score was proposed for long-term surveillance and early detection of advanced liver disease in adult Fontan patients. For adult Fontan patients with a calculated score > 5.0, we may consider timely diffusion-weight imaging and early management for liver complications. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic potential of whole-tumor histogram analysis of multiple non-Gaussian diffusion models for differentiating cervical cancer (CC) aggressive status regarding of pathological types, differentiation degree, stage, and p16 expression. Methods: Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000 s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses. Results: 89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05). Conclusion: Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC. [ABSTRACT FROM AUTHOR]

Objective: We developed and validated a clinical-radiomics model for preoperative prediction of the short-term efficacy of initial drug-eluting beads transarterial chemoembolization (D-TACE) treatment in patients with hepatocellular carcinoma (HCC). Methods: In this retrospective cohort study of 113 patients with intermediate and advanced HCC, 5343 features were extracted based on three sequences of the arterial phase (AP), diffusion-weighted imaging, and T2-weighted images based on contrast-enhanced magnetic resonance imaging, and minimum redundancy maximum correlation and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and model construction. Multifactor logistic regression was used to build a clinical-imaging model based on clinical factors and a clinical-radiomics model. The area under the curve (AUC) and calibration curves were used to assess model performance, and the clinical value of the model was analyzed using decision curve analysis. The relationship between the actual and predicted short-term efficacy of the combined model and progression-free survival (PFS) was evaluated using Kaplan–Meier survival curves and log-rank tests. Results: A total of 34 radiomics features were selected by LASSO, and the clinical-radiomics model had the best predictive performance (AUC = 0.902 and AUC = 0.845 for the training and testing sets, respectively), and the model based on AP had the best predictive performance among the four radiomics models (AUC = 0.89 for the training set and AUC = 0.85 for the testing set); the multifactorial logistic regression results showed that microsphere type (p = 0.042) and AP Rad-score (p = 0.01) were associated with short-term efficacy. In addition, a difference in PFS was observed in patients with HCC with different short-term efficacies predicted by the combined model. Moreover, prognosis was better in the objective versus non-objective response group. Conclusions: The combined clinical-radiomics model is an effective predictor of the short-term efficacy of initial D-TACE in patients with HCC, contributing to clinical and economic benefits for patients. [ABSTRACT FROM AUTHOR]

Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge. The effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV ¯) and non-parametric statistical tests. The simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV ¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV ¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV ¯ ={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV ¯ ={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI). ADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction. [ABSTRACT FROM AUTHOR]

To compare the image quality of multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and single-shot echo-planar imaging (SS-EPI-DWI) techniques in uterine MRI. Eighty-eight eligible patients underwent MUSE-DWI and SS-EPI-DWI examinations simultaneously using a 3.0 T MRI system. Two radiologists independently performed quantitative and qualitative analysis of the two groups of images using a double-blind method. The weighted Kappa test was used to evaluate the interobserver agreement. Wilcoxon's rank sum test was used for qualitative parameters, and paired t -test was used for quantitative parameters. Spearman rank correlation analysis was used to obtained correlation between pathological results and mean apparent diffusion coefficient (ADC) value. The qualitative and quantitative analysis of the images by the two radiologists were in good or excellent agreement, with weighted kappa value ranging from 0.636 to 0.981. The scores of total subjective image quality (15.4 ± 0.99) and signal-to-noise ratio (158.99 ± 60.71) of MUSE-DWI were significantly higher than those of SS-EPI-DWI (12.93 ± 1.62 P < 0.001; 130.23 ± 48.29 P < 0.05). It effectively reduced image distortion and artifact, and had better lesion conspicuity. There was no significant difference in contrast-to-noise ratio score and average ADC values between the two DWI sequences. The average ADC values of the two DWI sequences were highest in the normal uterus group and lowest in the endometrial cancer group, with statistically significant differences among groups (P < 0.01). In addition, the average ADC values of the two DWI sequences were negatively correlated with the type of lesions, decreasing with the malignancy of the lesions (r = −0.805 P < 0.01, r = −0.815 P < 0.01). Compared to SS-EPI-DWI, MUSE-DWI can significantly reduce distortion, artifacts, and fuzziness in MRI of uterine lesions, which is more conducive to lesion detection. [ABSTRACT FROM AUTHOR]

Multi-Shot (MS) Echo-Planar Imaging (EPI) may improve the in-plane resolution of multi-b-value DWI, yet it also considerably increases the scan time. Here we explored the combination of EPI with Keyhole (EPIK) and a calibrationless reconstruction algorithm for acceleration of multi-b-value MS-DWI. We firstly analyzed the impact of nonuniform phase accrual in EPIK on the reconstructed image. Based on insights gained from the analysis, we developed a calibrationless reconstruction algorithm based on a Space-Contrast-Coil Locally Low-Rank Tensor (SCC-LLRT) constraint for reconstruction of EPIK-acquired data. We compared the algorithm with a modified SPatial-Angular Locally Low-Rank (SPA-LLR) algorithm through simulations, phantoms, and in vivo study. We then compared EPIK with uniformly undersampled EPI for accelerating multi-b-value DWI in 6 healthy subjects. Through theoretical derivations, we found that the reconstruction of EPIK with a SENSE-encoding-based algorithm, such as SPA-LLR, may cause additional aliasing artifacts due to the frequency-dependent distortion of the coil sensitivity. Results from simulations, phantoms, and in vivo study verified the theoretical finding by showing that the calibrationless SCC-LLRT algorithm reduced aliasing artifacts compared with SPA-LLR. Finally, EPIK with SCC-LLRT substantially reduced the ghosting artifacts compared with uniform undersampled multi-b-value DWI, decreasing the fitting errors in ADC (0.05 ± 0.01 vs 0.10 ± 0.01, P < 0.001) and IVIM mapping (0.026 ± 0.004 vs 0.06 ± 0.006, P < 0.001). The SCC-LLRT algorithm reduced the aliasing artifacts of EPIK by using a calibrationless modeling of the multi-coil data. The dense sampling of k-space center offers EPIK a potential to improve image quality for acceleration of multi-b-value MS-DWI. [ABSTRACT FROM AUTHOR]

Conventional amide proton transfer (APT)-weighted imaging requires a chemical exchange saturation transfer (CEST) sequence with multiple saturation frequency offsets and a B0 correction sequence, plus a long acquisition time that can be reduced by applying the conventional method using CEST images with seven radiation pulses (i.e., the seven-points method). For a further reduction of acquisition times, we propose fast two-dimensional (2D) APT-weighted imaging based on a self B0 correction using the turbo spin echo (TSE)-Dixon method. We conducted a phantom study to investigate the accuracy of TSE-Dixon APT-weighted imaging. We prepared two types of phantoms with six samples for a concentrationdependent evaluation and a pH-dependent evaluation. APT-weighted images were acquired by the conventional, seven-points, and TSE-Dixon methods. Linear regression analyses assessed the dependence between each method's APT signal intensities (SIs) and the concentration or pH. We performed a one-way analysis of variance with Tukey's honestly significant difference post hoc test to compare the APT SIs among the three methods. The agreement of the APT SIs between the conventional and seven-points or TSE-Dixon methods was assessed by a Bland- Altman plot analysis. The APT SIs of all three acquisition methods showed positive concentration dependence and pH dependence. No significant differences were observed in the APT SIs between the conventional and TSE-Dixon methods at each concentration. The Bland-Altman plot analyses showed that the APT SIs measured with the seven-points method resulted in 0.42% bias and narrow 95% limits of agreement (LOA) (0.93%–0.09%) compared to the conventional method. The APT SIs measured using the TSE-Dixon method showed 0.14% bias and similar 95% LOA (−0.33% to 0.61%) compared with the seven-points method. The APT SIs of all three methods showed positive pH dependence. At each pH, no significant differences in the APT SIs were observed among the methods. Bland-Altman plot analyses showed that the APT SIs measured with the seven-points method resulted in low bias (0.03%) and narrow 95% LOA (−0.30% to 0.36%) compared to the conventional method. The APT SIs measured by the TSE-Dixon method showed slightly larger bias (0.29%) and similar 95% LOA (from −0.15% to 0.72%) compared to those measured by the seven-points method. These results demonstrated that our proposed method has the same concentration dependence and pH dependence as the conventional method and the seven-points method. We thus expect that APT-weighted imaging with less influence of motion can be obtained in clinical examinations. [ABSTRACT FROM AUTHOR]

To evaluate the effect of stent implantation for vertebrobasilar artery stenosis，by using 3D arterial spin labeling (3D ASL) technique. A retrospective analysis was conducted on the clinical and 3D ASL data of 48 patients who underwent vertebral-basilar artery stenting. Post-labeling delay times (PLD) of 1.5 s and 2.5 s were chosen, and the average regional cerebral blood flow (rCBF) values were measured in nine brain regions of the posterior circulation: bilateral thalamus, bilateral occipital lobes, bilateral cerebellar hemispheres, midbrain, pons, and medulla. The 48 patients were divided into two groups based on the presence or absence of acute ischemic stroke in the posterior cerebral circulation region detected by diffusion-weighted imaging (DWI). The preoperative and postoperative rCBF results were statistically analyzed. In the infarct group, there were significant increases in rCBF values of all nine brain regions at both PLD = 1.5 s and 2.5 s postoperatively compared to preoperatively. At PLD = 1.5 s, statistically significant differences in rCBF values between the preoperative and postoperative periods were found in the right thalamus, left cerebellum, midbrain, and pons regions (P < 0.05). At PLD = 2.5 s, statistically significant differences in rCBF values between the preoperative and postoperative periods were observed in the left occipital lobe, right cerebellum, midbrain, and pons regions (P < 0.05). When analyzing the rCBF values of the brain regions with recent infarcts in the infarct group, there was a significant increase in postoperative rCBF values compared to preoperative values (P < 0.05). After excluding the data from brain regions with recent infarcts, the CBF values in the remaining brain regions were also increased postoperatively, and some brain regions showed statistically significant differences in rCBF values between the preoperative and postoperative periods (P < 0.05). In the non-infarct group, there were no statistically significant differences in the preoperative and postoperative rCBF values in all brain regions at both PLD = 1.5 s and 2.5 s (P > 0.05). The application of 3D ASL technology shows significant value in assessing the surgical efficacy of vertebral-basilar artery stenting, especially in patients with acute posterior circulation infarction. [ABSTRACT FROM AUTHOR]

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data. [ABSTRACT FROM AUTHOR]

Purpose: Posterior circulation ischemic stroke (PCIS) possesses unique features. However, previous studies have primarily or exclusively relied on anterior circulation stroke cases to build machine learning (ML) models for predicting onset time. To date, there is no research reporting the effectiveness and stability of ML in identifying PCIS onset time. We aimed to build diffusion-weighted imaging-based ML models to identify the onset time of PCIS patients. Methods: Consecutive PCIS patients within 24 h of definite symptom onset were included (112 in the training set and 49 in the independent test set). Images were processed as follows: volume of interest segmentation, image feature extraction, and feature selection. Five ML models, naïve Bayes, logistic regression, tree ensemble, k-nearest neighbor, and random forest, were built based on the training set to estimate the stroke onset time (binary classification: ≤ 4.5 h or > 4.5 h). Relative standard deviations (RSD), receiver operating characteristic (ROC) curves, and the calibration plot was performed to evaluate the stability and performance of the five models. Results: The random forest model had the best performance in the test set, with the highest area under the curve (AUC, 0.840; 95% CI: 0.706, 0.974). This model also achieved the highest accuracy, sensitivity, specificity, positive predictive value, and negative predictive value (83.7%, 64.3%, 91.4%, 75.0%, and 86.5%, respectively). Furthermore, the model had high stability (RSD = 0.0094). Conclusion: The PCIS case-based ML model was effective for estimating the symptom onset time and achieved considerably high specificity and stability. [ABSTRACT FROM AUTHOR]

Purpose: Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images. Methods: The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders. Results: The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD). Conclusion: In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Measuring non‐parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes.We developed a data‐driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB‐1 labeling index (LI) levels and molecular status of gliomas.An FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR ∈[30,300]$ \in [ {30,\ 300} ]$. Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years ±$ \pm $ 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo–echo planar imaging (SE‐EPI) diffusion‐weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C‐means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions (fn,n=1−6${{f}_n},\ n = 1 -6$) and diffusivities of the maximum peaks (Dpeak${{D}_{peak}}$). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann–Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation.The simulation results showed that the FCN‐based MDD approach (FCN‐MDD) achieved higher CSS than non‐negative least squares‐based MDD (NNLS‐MDD). For in vivo data, the spectra of ET and NET obtained by FCN‐MDD are more distinguishable than NNLS‐MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non‐enhancing tumor, edema, and necrosis). f3,f4,Dpeak${{f}_3},\ {{f}_4},{{D}_{peak}}$ showed a positive and negative correlation with MIB‐1 respectively (r=0.568,r=−0.521,r=−0.654$r = 0.568,\ r = - 0.521,\ r = - 0.654$, all p<0.001$p < 0.001$). The AUC of Dpeak${{D}_{peak}}$ for predicting MIB‐1 LI levels was 0.900 (95% CI, 0.826–0.974), versus 0.781 (0.677–0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model (f1+f3${{f}_1} + {{f}_3}$) was 0.873 (95% CI, 0.802–0.944).The proposed FCN‐MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS‐MDD method. FCN‐MDD also enabled qualitative and quantitative evaluation of the composition of gliomas. [ABSTRACT FROM AUTHOR]

Objective: The dentato‐thalamo‐cortical tract (DTT) is the main cerebellar efferent pathway. Degeneration of the DTT is a core feature of Friedreich ataxia (FRDA). However, it remains unclear whether DTT disruption is spatially specific, with some segments being more impacted than others. This study aimed to investigate microstructural integrity along the DTT in FRDA using a profilometry diffusion MRI (dMRI) approach. Methods: MRI data from 45 individuals with FRDA (mean age: 33.2 ± 13.2, Male/Female: 26/19) and 37 healthy controls (mean age: 36.5 ± 12.7, Male/Female:18/19) were included in this cross‐sectional multicenter study. A profilometry analysis was performed on dMRI data by first using tractography to define the DTT as the white matter pathway connecting the dentate nucleus to the contralateral motor cortex. The tract was then divided into 100 segments, and dMRI metrics of microstructural integrity (fractional anisotropy, mean diffusivity and radial diffusivity) at each segment were compared between groups. The process was replicated on the arcuate fasciculus for comparison. Results: Across all diffusion metrics, the region of the DTT connecting the dentate nucleus and thalamus was more impacted in FRDA than downstream cerebral sections from the thalamus to the cortex. The arcuate fasciculus was minimally impacted. Interpretation: Our study further expands the current knowledge about brain involvement in FRDA, showing that microstructural abnormalities within the DTT are weighted to early segments of the tract (i.e., the superior cerebellar peduncle). These findings are consistent with the hypothesis of DTT undergoing anterograde degeneration arising from the dentate nuclei and progressing to the primary motor cortex. [ABSTRACT FROM AUTHOR]

Background: Post-treatment evaluation of patients with rectal cancer (RC) using magnetic resonance imaging (MRI) burdens medical resources, necessitating an exploration of abbreviated protocols Purpose: To evaluate the diagnostic performance of abbreviated MRI (A-MRI) for the post-treatment evaluation of RC patients. Material and Methods: This retrospective study included RC patients who underwent non-contrast rectal MRI and standard liver MRI, as well as abdominal contrast-enhanced computed tomography (CECT) for post-treatment evaluation. A-MRI comprised diffusion-weighted imaging (DWI) and T2-weighted imaging of the upper abdomen and the pelvic cavity. Three radiologists independently reviewed A-MRI, CECT, and standard liver MRI in the detection of viable disease. The diagnostic performances were compared using a reference standard considering all available information, including pathology, FDG-PET, endoscopic results, and clinical follow-up. Results: We included 78 patients (50 men, 28 women; mean age=60.9 ± 10.2 years) and observed viable disease in 34 (43.6%). On a per-patient-basis analysis, A-MRI showed significantly higher sensitivity (95% vs. 81%, P = 0.04) and higher accuracy (93% vs. 82%, P < 0.01), compared to those of CECT, while A-MRI showed comparable sensitivity (91% vs. 91%, P = 0.42) and accuracy (97% vs. 98%, P = 0.06) to that of standard liver MRI. On a per-lesion-based analysis, A-MRI exhibited significantly superior lesion detectability than that of CECT (figure of merit 0.91 vs. 0.77, P < 0.01) and comparable to that of standard liver MRI (figure of merit 0.91 vs. 0.92, P = 0.75). Conclusion: A-MRI exhibited higher sensitivity and diagnostic accuracy than those of CECT in the post-treatment evaluation of RC, while it showed comparable performances with standard liver MRI. A-MRI provides diagnostic added value in the follow-up of RC patients. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this proof-of-principle study combining data analysis and computer simulation was to evaluate the robustness of apparent diffusion coefficient (ADC) values for lymph node classification in prostate cancer under conditions comparable to clinical practice. Materials and methods: To assess differences in ADC and inter-rater variability, ADC values of 359 lymph nodes in 101 patients undergoing simultaneous prostate-specific membrane antigen (PSMA)-PET/MRI were retrospectively measured by two blinded readers and compared in a node-by-node analysis with respect to lymph node status. In addition, a phantom and 13 patients with 86 lymph nodes were prospectively measured on two different MRI scanners to analyze inter-scanner agreement. To estimate the diagnostic quality of the ADC in real-world application, a computer simulation was used to emulate the blurring caused by scanner and reader variability. To account for intra-individual correlation, the statistical analyses and simulations were based on linear mixed models. Results: The mean ADC of lymph nodes showing PSMA signals in PET was markedly lower (0.77 × 10−3 mm2/s) compared to inconspicuous nodes (1.46 × 10−3 mm2/s, p < 0.001). High inter-reader agreement was observed for ADC measurements (ICC 0.93, 95%CI [0.92, 0.95]). Good inter-scanner agreement was observed in the phantom study and confirmed in vivo (ICC 0.89, 95%CI [0.84, 0.93]). With a median AUC of 0.95 (95%CI [0.92, 0.97]), the simulation study confirmed the diagnostic potential of ADC for lymph node classification in prostate cancer. Conclusion: Our model-based simulation approach implicates a high potential of ADC for lymph node classification in prostate cancer, even when inter-rater and inter-scanner variability are considered. Clinical relevance statement: The ADC value shows a high diagnostic potential for lymph node classification in prostate cancer. The robustness to scanner and reader variability implicates that this easy to measure and widely available method could be readily integrated into clinical routine. Key Points: • The diagnostic value of the apparent diffusion coefficient (ADC) for lymph node classification in prostate cancer is unclear in the light of inter-rater and inter-scanner variability. • Metastatic and inconspicuous lymph nodes differ significantly in ADC, resulting in a high diagnostic potential that is robust to inter-scanner and inter-rater variability. • ADC has a high potential for lymph node classification in prostate cancer that is maintained under conditions comparable to clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density. Methods: Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A–D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D). Results: Image quality of DWI was higher in the case of increased breast density, with good scores (score 3–5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3–5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively). Conclusions: Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts. Clinical relevance statement: Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts. Key Points: • Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts. [ABSTRACT FROM AUTHOR]

• Stage II Endometrial cancer occurs in only 12% of cases. • Hysteroscopy biopsy of cervical stroma and MRI have a good diagnostic power. • Hysteroscopy and MRI are helpful to rule out the unclear parametrial invasion. Stage II Endometrial cancer (EC) accounts only for 12% of cases. Recent evidences redraw the weight of radicality in this stage as it would seem to have no impact on survival outcomes claiming for radicality when free surgical margins are not ensured to be achieved by simple hysterectomy. Thus, an accurate pre-operative evaluation might be crucial. This study aims to estimate the diagnostic power of Hysteroscopic excisional biopsy (HEB) of cervical stroma alone and combined with Magnetic resonance imaging (MRI) to predict the stage and concealed parametrial invasion in patients with preoperative stage II EC. From January 2019 to November 2023, all patients evaluated at the Department of Gynaecology Oncology of Humanitas, Istituto Clinico Catanese, Catania, Italy, with a diagnosis of EC and evidence of cervical stromal diffusion on preoperative MRI and/or office hysteroscopy evaluation, considered suitable for laparoscopic modified type B hysterectomy, were consecutively included in the study. These underwent endometrial and cervical hysteroscopy excisional biopsy (HEB) for histological evaluation before definitive surgery. The data obtained were compared with the definitive histological examination (reference standard). Sixteen patients met the including/excluding criteria and were considered into the study. Stage II endometrial cancer were confirmed in 3 cases (18.7%). We reported 2 (12,5%) parametrial involvement (IIIB), 4 (25%) cases of lymph nodes metastasis (IIIc), 7 (43,7%) cases of I stage. MRI had a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (95% CIs) of 71%, 44%, 50%, 66% and 56.2 % respectively. HEB showed sensitivity, specificity, PPV, NPV and accuracy (95 % CI) of 85 %, 89 %, 85 %, 88 % and 87 % respectively. Comparing HEB + MRI to HEB alone, no statistical differences were noted in all fields. Considering parametrial invasion, MRI had better sensitivity but there were no statistical differences to HEB in other fields, showing both a worthy NPV. HEB was accurate in all fields for cervical stroma assessment and had a fine NPV to exclude massive cervical involvement up to parametrial. Considering the new FIGO staging a preoperative molecular and histological evaluation of the cervical stroma may be useful. Operative hysteroscopy seems to be a feasible and accurate method for this purpose. [ABSTRACT FROM AUTHOR]

The article discusses the importance of validating health economic models in order to make optimal decisions regarding healthcare interventions. Validation involves assessing the accuracy of the model's implementation, conceptual validity, and alignment with real-world data. Despite the recognition of validation's importance, efforts to validate health economic models remain unreported. The lack of standardization in terminology and guidelines further complicates the validation process. The article recommends the use of standardized tools and guidelines to improve the transparency and credibility of health economic model validation. [Extracted from the article]

This article discusses the challenges and complexities of visual field loss, visual neglect, and ocular dominance. It presents a case report of an 81-year-old male with severe bilateral primary open angle glaucoma who experienced functional vision deficits due to reduced visual field. The article explores the clinical presentation, differential diagnosis, ancillary testing, and proper referrals for this patient. It provides information on tests and techniques used to identify and manage visual neglect and ocular dominance, as well as optometric management strategies. Referrals for a multidisciplinary approach to care are recommended. [Extracted from the article]

Magnetic resonance elastography (MRE) and diffusion-weighted imaging (DWI) are complementary imaging techniques that detect disease based on viscoelasticity and water mobility, respectively. However, the relationship between viscoelasticity and water diffusion is still poorly understood, hindering the clinical translation of combined DWI-MRE markers. We used DWI-MRE to study 129 biomaterial samples including native and cross-linked collagen, glycosaminoglycans (GAGs) with different sulfation levels, and decellularized specimens of pancreas and liver, all with different proportions of solid tissue, or solid fractions. We developed a theoretical framework of the relationship between mechanical loss and tissue-water mobility based on two parameters, solid and fluid viscosity. These parameters revealed distinct DWI-MRE property clusters characterizing weak, moderate, and strong water-network interactions. Sparse networks interacting weakly with water, such as collagen or diluted decellularized tissue, resulted in marginal changes in water diffusion over increasing solid viscosity. In contrast, dense networks with larger solid fractions exhibited both free and hindered water diffusion depending on the polarity of the solid components. For example, polar and highly sulfated GAGs as well as native soft tissues hindered water diffusion despite relatively low solid viscosity. Our results suggest that two fundamental properties of tissue networks, solid fraction and network polarity, critically influence solid and fluid viscosity in biological tissues. Since clinical DWI and MRE are sensitive to these viscosity parameters, the framework we present here can be used to detect tissue remodeling and architectural changes in the setting of diagnostic imaging. The viscoelastic properties of biological tissues provide a wealth of information on the vital state of cells and host matrix. Combined measurement of viscoelasticity and water diffusion by medical imaging is sensitive to tissue microarchitecture. However, the relationship between viscoelasticity and water diffusion is still poorly understood, hindering full exploitation of these properties as a combined clinical biomarker. Therefore, we analyzed the parameter space accessible by diffusion-weighted imaging (DWI) and magnetic resonance elastography (MRE) and developed a theoretical framework for the relationship between water mobility and mechanical parameters in biomaterials. Our theory of solid material properties related to particle motion can be translated to clinical radiology using clinically established MRE and DWI. [Display omitted] [ABSTRACT FROM AUTHOR]

INTRODUCTION: Atrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs. METHODS AND RESULTS: We examined 1335 stroke‐free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free‐water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance. DISCUSSION: Integrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies. Highlights: We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls.Our analysis revealed AF‐associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning.Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free‐water content as well as widespread differences of white matter integrity.Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF‐related cognitive decline. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Age‐related and Alzheimer's disease (AD) dementia–related neurodegeneration impact brain health. While morphometric measures from T1‐weighted scans are established biomarkers, they may be less sensitive to earlier changes. Neurite orientation dispersion and density imaging (NODDI), offering biologically meaningful interpretation of tissue microstructure, may be an advanced brain health biomarker. METHODS: We contrasted regional gray matter NODDI and morphometric evaluations concerning their correlation with (1) age, (2) clinical diagnosis stage, and (3) tau pathology as assessed by AV1451 positron emission tomography. RESULTS: Our study hypothesizes that NODDI measures are more sensitive to aging and early AD changes than morphometric measures. One NODDI output, free water fraction (FWF), showed higher sensitivity to age‐related changes, generally better effect sizes in separating mild cognitively impaired from cognitively unimpaired participants, and stronger associations with regional tau deposition than morphometric measures. DISCUSSION: These findings underscore NODDI's utility in capturing early neurodegenerative changes and enhancing our understanding of aging and AD. Highlights: Neurite orientation dispersion and density imaging can serve as an effective brain health biomarker for aging and early Alzheimer's disease (AD).Free water fraction has higher sensitivity to normal brain aging.Free water fraction has stronger associations with early AD and regional tau deposition. [ABSTRACT FROM AUTHOR]

Background: Some voxels may alter the tractography results due to unintentional alteration of noises and other unwanted factors. Objective: This study aimed to investigate the effect of local phase features on tractography results providing data are mixed by a Gaussian or random distribution noise. Material and Methods: In this simulation study, a mask was firstly designed based on the local phase features to decrease false-negative and -positive tractography results. The local phase features are calculated according to the local structures of images, which can be zero-dimensional, meaning just one point (equivalent to noise in tractography algorithm), a line (equivalent to a simple fiber), or an edge (equivalent to structures more complex than a simple fiber). A digital phantom evaluated the feasibility current model with the maximum complexities of configurations in fibers, including crossing fibers. In this paper, the diffusion images were mixed separately by a Gaussian or random distribution noise in 2 forms: a zero-mean noise and a noise with a mean of data. Results: The local mask eliminates the pixels of unfitted values with the main structures of images, due to noise or other interferer factors. Conclusion: The local phase features of diffusion images are an innovative solution to determine principal diffusion directions. [ABSTRACT FROM AUTHOR]

In the current study, we assessed changes in interstitial fluid dynamics resulting after whole‐brain radiotherapy using the diffusion‐weighted image analysis along the perivascular space (DWI‐ALPS) method, which is a simplified variation of the diffusion tensor image ALPS (DTI‐ALPS) method using diffusion‐weighted imaging (DWI) with orthogonal motion‐probing gradients (MPGs). This retrospective study included 47 image sets from 22 patients who underwent whole‐brain radiotherapy for brain tumors. The data for the normal control group comprised 105 image sets from 105 participants with no pathological changes. DWI was performed with the three MPGs applied in an orthogonal direction to the imaging plane, and apparent diffusion coefficient images for the x‐, y‐, and z‐axes were retrospectively generated. The ALPS index was calculated to quantify interstitial fluid dynamics. The independent t‐test was used to compare the ALPS index between normal controls and patients who underwent whole‐brain radiotherapy. Patients were compared in all age groups and individual age groups (20–39, 40–59, and 60–84 years). We also examined the correlation between biologically equivalent doses (BEDs) and the ALPS index, as well as the correlation between white matter hyperintensity and the ALPS index. In the comparison of all age groups, the ALPS index was significantly lower (p < 0.001) in the postradiation group (1.32 ± 0.16) than in the control group (1.44 ± 0.17), suggesting that interstitial fluid dynamics were altered in patients following whole‐brain radiotherapy. Significant age group differences were found (40–59 years: p < 0.01; 60–84 years: p < 0.001), along with a weak negative correlation between BEDs (r = −0.19) and significant correlations between white matter hyperintensity and the ALPS index (r = −0.46 for periventricular white matter, r = −0.38 for deep white matter). It was concluded that the ALPS method using DWI with orthogonal MPGs suggest alteration in interstitial fluid dynamics in patients after whole‐brain radiotherapy. Further systematic prospective studies are required to investigate their association with cognitive symptoms. [ABSTRACT FROM AUTHOR]

Recent studies have shown significant changes to brain microstructure during sleep and anesthesia. In vivo optical microscopy and magnetic resonance imaging (MRI) studies have attributed these changes to anesthesia and sleep‐related modulation of the brain's extracellular space (ECS). Isoflurane anesthesia is widely used in preclinical diffusion MRI (dMRI) and it is therefore important to investigate if the brain's microstructure is affected by anesthesia to an extent detectable with dMRI. Here, we employ diffusion kurtosis imaging (DKI) to assess brain microstructure in the awake and anesthetized mouse brain (n = 22). We find both mean diffusivity (MD) and mean kurtosis (MK) to be significantly decreased in the anesthetized mouse brain compared with the awake state (p < 0.001 for both). This effect is observed in both gray matter and white matter. To further investigate the time course of these changes we introduce a method for time‐resolved fast DKI. With this, we show the time course of the microstructural alterations in mice (n = 5) as they transition between states in an awake‐anesthesia‐awake paradigm. We find that the decrease in MD and MK occurs rapidly after delivery of gas isoflurane anesthesia and that values normalize only slowly when the animals return to the awake state. Finally, time‐resolved fast DKI is employed in an experimental mouse model of brain edema (n = 4), where cell swelling causes the ECS volume to decrease. Our results show that isoflurane affects DKI parameters and metrics of brain microstructure and point to isoflurane causing a reduction in the ECS volume. The demonstrated DKI methods are suitable for in‐bore perturbation studies, for example, for investigating microstructural modulations related to sleep/wake‐dependent functions of the glymphatic system. Importantly, our study shows an effect of isoflurane anesthesia on rodent brain microstructure that has broad relevance to preclinical dMRI. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the quality of clinical brain imaging in healthy subjects and patients on an FDA‐approved commercial 0.55 T MRI scanner, and to provide information about the feasibility of using this scanner in a clinical workflow. In this IRB‐approved study, brain examinations on the scanner were prospectively performed in 10 healthy subjects (February–April 2022) and retrospectively derived from 44 patients (February–July 2022). Images collected using the following pulse sequences were available for assessment: axial DWI (diffusion‐weighted imaging), apparent diffusion coefficient maps, 2D axial fluid‐attenuated inversion recovery images, axial susceptibility‐weighted images (both magnitude and phase), sagittal T1‐weighted (T1w) Sampling Perfection with Application Optimized Contrast images, sagittal T1w MPRAGE (magnetization prepared rapid gradient echo) with contrast enhancement, axial T1w turbo spin echo (TSE) with and without contrast enhancement, and axial T2‐weighted TSE. Two readers retrospectively and independently evaluated image quality and specific anatomical features in a blinded fashion on a four‐point Likert scale, with a score of 1 being unacceptable and 4 being excellent, and determined the ability to answer the clinical question in patients. For each category of image sequences, the mean, standard deviation, and percentage of unacceptable quality images (<2) were calculated. Acceptable (rating ≥ 2) image quality was achieved at 0.55 T in all sequences for patients and 85% of the sequences for healthy subjects. Radiologists were able to answer the clinical question in all patients scanned. In total, 50% of the sequences used in patients and about 60% of the sequences used in healthy subjects exhibited good (rating ≥ 3) image quality. Based on these findings, we conclude that diagnostic quality clinical brain images can be successfully collected on this commercial 0.55 T scanner, indicating that the routine brain imaging protocol may be deployed on this system in the clinical workflow. [ABSTRACT FROM AUTHOR]

Carbon-ion irradiation is increasingly used at the skull base and spine near the radiation-sensitive spinal cord. To better characterize the in vivo radiation response of the cervical spinal cord, radiogenic changes in the high-dose area were measured in rats using magnetic resonance imaging (MRI) diffusion measurements in comparison to conventional photon irradiations. In this longitudinal MRI study, we examined the gray matter (GM) of the cervical spinal cord in 16 female Sprague-Dawley rats after high-dose photon (n = 8) or carbon-ion (12C) irradiation (n = 8) and in 6 sham-exposed rats until myelopathy occurred. The differences in the diffusion pattern of the GM of the cervical spinal cord were examined until the endpoint of the study, occurrence of paresis grade II of both forelimbs was reached. In both radiation techniques, the same order of the occurrence of MR-morphological pathologies was observed – from edema formation to a blood spinal cord barrier (BSCB) disruption to paresis grade II of both forelimbs. However, carbon-ion irradiation showed a significant increase of the mean apparent diffusion coefficient (ADC; P = 0.031) with development of a BSCB disruption in the GM. Animals with paresis grade II as a late radiation response had a highly significant increase in mean ADC (P = 0.0001) after carbon-ion irradiation. At this time, a tendency was observed for higher mean ADC values in the GM after 12C irradiation as compared to photon irradiation (P = 0.059). These findings demonstrated that carbon-ion irradiation leads to greater structural damage to the GM of the rat cervical spinal cord than photon irradiation due to its higher linear energy transfer (LET) value. [ABSTRACT FROM AUTHOR]

Background: Oscillating gradient diffusion‐weighted imaging (DWI) enables elucidation of microstructural characteristics in cancers; however, there are limited data to evaluate its utility in patients with endometrial cancer. Purpose: To investigate the utility of oscillating gradient DWI for risk stratification in patients with uterine endometrial cancer compared with conventional pulsed gradient DWI. Study Type: Retrospective. Subjects: Sixty‐three women (mean age: 58 [range: 32–85] years) with endometrial cancer. Field Strength/Sequence: 3 T MRI including DWI using oscillating gradient spin‐echo (OGSE) and pulsed gradient spin‐echo (PGSE) research sequences. Assessment: Mean value of the apparent diffusion coefficient (ADC) values for OGSE (ADCOGSE) and PGSE (ADCPGSE) as well as the ADC ratio (ADCOGSE/ADCPGSE) within endometrial cancer were measured using regions of interest. Prognostic factors (histological grade, deep myometrial invasion, lymphovascular invasion, International Federation of Gynecology and Obstetrics [FIGO] stage, and prognostic risk classification) were tabulated. Statistical Tests: Interobserver agreement was analyzed by calculating the intraclass correlation coefficient. The associations of ADCOGSE, ADCPGSE, and ADCOGSE/ADCPGSE with prognostic factors were examined using the Kendall rank correlation coefficient, Mann–Whitney U test, and receiver operating characteristic (ROC) curve. A P value of <0.05 was statistically significant. Results: Compared with ADCOGSE and ADCPGSE, ADCOGSE/ADCPGSE was significantly and strongly correlated with histological grade (observer 1, τ = 0.563; observer 2, τ = 0.456), FIGO stage (observer 1, τ = 0.354; observer 2, τ = 0.324), and prognostic risk classification (observer 1, τ = 0.456; observer 2, τ = 0.385). The area under the ROC curves of ADCOGSE/ADCPGSE for histological grade (observer 1, 0.92, 95% confidence intervals [CIs]: 0.83–0.98; observer 2, 0.84, 95% CI: 0.73–0.92) and prognostic risk (observer 1, 0.80, 95% CI: 0.68–0.89; observer 2, 0.76, 95% CI: 0.63–0.86) were significantly higher than that of ADCOGSE and ADCPGSE. Data Conclusion: The ADC ratio obtained via oscillating gradient and pulsed gradient DWIs might be useful imaging biomarkers for risk stratification in patients with endometrial cancer. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Background: Independent factors are needed to supplement vesical imaging‐reporting and data system (VI‐RADS) to improve its ability to identify muscle invasive bladder cancer (MIBC). Purpose: To assess the correlation between MIBC and diffusion kurtosis imaging (DKI) ratio, VI‐RADS, and other factors (such as tumor location). Study Type: Retrospective. Population: Sixty‐eight patients (50 males and 18 females; age: 70.1 ± 9.5 years) with bladder urothelial carcinoma. Field Strength/Sequence: 1.5 T, conventional diffusion‐weighted imaging (DWI), and DKI (single shot echo‐planar sequence). Assessment: Three radiologists independently measured the diffusion parameters of each bladder cancer (BCa) and obturator internus, including the mean apparent diffusion coefficient (ADCmean), mean kurtosis (MK), and mean diffusion (MD). And the ratio of diffusion parameters between BCa and obturator internus was calculated (diffusion parameter ratio = bladder cancer:obturator internus). Based on the VI‐RADS, the target lesions were independently scored. Furthermore, the actual tumor‐wall contact length (ACTCL) and absolute tumor‐wall contact length (ABTCL) were measured. Statistical Tests: Multicollinearity among independent variables was evaluated using the variance inflation factor (VIF). Multivariable logistic regression analysis was used to determine the independent risk factors of MIBC. The receiver operating characteristic curve was used to evaluate the efficacy of each variable in detecting MIBC. The DeLong test was used to compare the area under the curve (AUC). A P < 0.05 was considered statistically significant. Results: MKratio (median: 0.62) and VI‐RADS were independent risk factors for MIBC. AUCs for MKratio, VI‐RADS, and MKratio combined with VI‐RADS in assessing MIBC were 0.895, 0.871, and 0.973, respectively. MKratio combined with VI‐RADS was more effective in diagnosing MIBC than VI‐RADS alone. Data Conclusions: MKratio has potential to assist the assessment of MIBC. MKratio can be used as a supplement to VI‐RADS for detecting MIBC. Level of Evidence: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Purpose: To reduce the inter‐scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor‐neutral dMRI pulse sequence using the open‐source vendor‐agnostic Pulseq platform. Methods: We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within‐ and inter‐scanner variability across the vendors, using both the vendor‐provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). Results: Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in‐vivo, maintaining scan‐rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor‐provided sequences. Conclusion: The Pulseq diffusion sequence reduces the cross‐scanner variability for both phantom and in‐vivo data, which will benefit multi‐center neuroimaging studies and improve the reproducibility of neuroimaging studies. [ABSTRACT FROM AUTHOR]

Purpose: To introduce the diffusion signal characteristics presented by spherical harmonics (SH) basis into the q‐space imaging method based on Gaussian radial basis function (GRBF) to robustly reconstruct ensemble average diffusion propagator (EAP) in diffusion MRI (dMRI). Methods: We introduced the Laplacian regularization of the signal into the dMRI imaging method based on GRBF, and derived the relevant indicators of microstructure imaging and the orientation distribution function (ODF) providing fiber bundle direction information based on EAP. In addition, this method is combined with a multi‐compartment model to calculate the diameter of fiber bundle axons. The evaluation of the results included qualitative comparisons and quantitative assessments of the signal fitting. Results: The results show that the proposed method achieves the more significant accuracy improvement in reconstructing signal. Meanwhile, ODFs estimated by the proposed method show the sharper profiles and less spurious peaks, even under the sparse and noisy conditions. In the 36 sets of axon diameter estimation experiments, 34 and 30 sets of results showed that the proposed method reduced the mean and SD of axon diameter estimates, respectively. Moreover, compared with the current state‐of‐the‐art method, the mean and SD of axon diameter estimated by the proposed method are mostly lower, with 32 and 29 of 36 groups. Conclusion: The proposed method outperforms the GRBF regarding signal fitting and the estimation of the EAP and ODF with multi‐shell sparse samples. Moreover, it shows the potential to recover important features of microstructures with less uncertainty by using proposed method together with multi‐compartment models. [ABSTRACT FROM AUTHOR]

Purpose: This study addresses the challenge of low resolution and signal‐to‐noise ratio (SNR) in diffusion‐weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b‐values through multiple acquisitions, but this results in diminished image resolution due to motion‐induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions. Methods: We introduce a novel approach employing a "Perturbation Network" to learn subvoxel‐size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low‐resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher‐resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion‐correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans. Results: Quantitative analyses revealed significantly higher structural similarity measures of super‐resolution images to ground truth high‐resolution images compared to high‐order interpolation (p<$$ < $$ 0.005). In blind qualitative experiments, 96.1%$$ 96.1\% $$ of super‐resolution images were assessed to have superior diagnostic quality compared to interpolated images. Conclusion: High‐resolution details in DWI can be obtained without the need for high‐resolution training data. One notable advantage of the proposed method is that it does not require a super‐resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option. [ABSTRACT FROM AUTHOR]

Purpose: Joint analysis of flow‐compensated (FC) and non‐flow‐compensated (NC) diffusion MRI (dMRI) data has been suggested for increased robustness of intravoxel incoherent motion (IVIM) parameter estimation. For this purpose, a set of methods commonly used or previously found useful for IVIM analysis of dMRI data obtained with conventional diffusion encoding were evaluated in healthy human brain. Methods: Five methods for joint IVIM analysis of FC and NC dMRI data were compared: (1) direct non‐linear least squares fitting, (2) a segmented fitting algorithm with estimation of the diffusion coefficient from higher b‐values of NC data, (3) a Bayesian algorithm with uniform prior distributions, (4) a Bayesian algorithm with spatial prior distributions, and (5) a deep learning‐based algorithm. Methods were evaluated on brain dMRI data from healthy subjects and simulated data at multiple noise levels. Bipolar diffusion encoding gradients were used with b‐values 0–200 s/mm2 and corresponding flow weighting factors 0–2.35 s/mm for NC data and by design 0 for FC data. Data were acquired twice for repeatability analysis. Results: Measurement repeatability as well as estimation bias and variability were at similar levels or better with the Bayesian algorithm with spatial prior distributions and the deep learning‐based algorithm for IVIM parameters D$$ D $$ and f$$ f $$, and for the Bayesian algorithm only for vd$$ {v}_d $$, relative to the other methods. Conclusion: A Bayesian algorithm with spatial prior distributions is preferable for joint IVIM analysis of FC and NC dMRI data in the healthy human brain, but deep learning‐based algorithms appear promising. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation. Methods: Noise‐free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise‐free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal‐to‐noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI‐Watson model. Results: The parallel and perpendicular kurtosis and all of the WMTI–Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects. Conclusion: For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects. [ABSTRACT FROM AUTHOR]

The article discusses the evolving field of MR theranostics and intervention, which combines therapy and diagnostics to accurately track and assess treatment efficacy. It highlights the potential of MRI as a non-invasive and patient-friendly tool in theranostics, as well as advancements in nanotechnology. The article also explores the use of MRI in guiding interventional procedures and its potential in various diagnostic and therapeutic interventions. The upcoming Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) will feature speakers discussing MR theranostics and intervention in plenary and educational sessions, covering topics such as multimodal imaging, invasive and non-invasive interventions, and the role of MRI in drug development. [Extracted from the article]

This document discusses the potential impact of artificial intelligence (AI) and machine learning on magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). AI has the potential to improve all stages of the imaging process, from image acquisition and reconstruction to analysis, diagnosis, and prognosis. The use of deep learning algorithms in MRI and MRS has shown promising results, but challenges remain in terms of usability, reliability, and validation. The document highlights current developments and challenges in the field and provides insights into the potential future applications of AI in MRI and MRS. [Extracted from the article]