Diffusion weighted imaging: Technique and applications

Impact of diffusion-weighted imaging on agreement between radiologists and non-radiologist in musculoskeletal tumor imaging using magnetic resonance

Background

Diffusion-weighted imaging (DWI) is widely used in neuroradiology or abdominal imaging but not yet implemented in the diagnosis of musculoskeletal tumors.

Aim

This study aimed to evaluate how including diffusion imaging in the MRI protocol for patients with musculoskeletal tumors affects the agreement between radiologists and non-radiologist.

Methods

Thirty-nine patients with musculoskeletal tumors (Ewing sarcoma, osteosarcoma, and benign tumors) consulted at our institution were included. Three raters with different experience levels evaluated examinations blinded to all clinical data. The final diagnosis was determined by consensus. MRI examinations were split into 1) conventional sequences and 2) conventional sequences combined with DWI. We evaluated the presence or absence of diffusion restriction, solid nature, necrosis, deep localization, and diameter >4 cm as known radiological markers of malignancy. Agreement between raters was evaluated using Gwet's AC1 coefficients and interpreted according to Landis and Koch.

Results

The lowest agreement was for diffusion restriction in both groups of raters. Agreement among all raters ranged from 0.51 to 0.945, indicating moderate to almost perfect agreement, and 0.772-0.965 among only radiologists indicating substantial to almost perfect agreement.

Conclusion

The agreement in evaluating diffusion-weighted MRI sequences was lower than that for conventional MRI sequences, both among radiologists and non-radiologist and among radiologists alone. This indicates that assessing diffusion imaging is more challenging, and experience may impact the agreement.

Spatiotemporal encoding MRI in a portable low-field system

PURPOSE

Demonstrate the potential of spatiotemporal encoding (SPEN) MRI to deliver largely undistorted 2D, 3D, and diffusion weighted images on a 110 mT portable system.

METHODS

SPEN's quadratic phase modulation was used to subsample the low-bandwidth dimension of echo planar acquisitions, delivering alias-free images with an enhanced immunity to image distortions in a laboratory-built, low-field, portable MRI system lacking multiple receivers.

RESULTS

Healthy brain images with different SPEN time-bandwidth products and subsampling factors were collected. These compared favorably to EPI acquisitions including topup corrections. Robust 3D and diffusion weighted SPEN images of diagnostic value were demonstrated, with 2.5 mm isotropic resolutions achieved in 3 min scans. This performance took advantage of the low specific absorption rate and relative long TEs associated with low-field MRI.

CONCLUSION

SPEN MRI provides a robust and advantageous fast acquisition approach to obtain faithful 3D images and DWI data in low-cost, portable, low-field systems without parallel acceleration.

Obesity and diffusion-weighted imaging of subcortical grey matter in young and older adults

Obesity and hypothalamic inflammation are causally related. It is unclear whether this neuroinflammation precedes or results from obesity. Animal studies show that an increase in food intake can lead to hypothalamic inflammation, but hypothalamic inflammation can create a feedback loop that further increases food intake. Internal and external factors mediate patterns of food intake and how it can affect the hypothalamus. Measures of water diffusivity in magnetic resonance imaging of the brain such as fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) are associated with grey matter inflammation. Here, we investigated how those measures are associated with obesity-related variables in groups of young and older adults. We found relationships between decreased diffusivity and obesity markers in young adults. In older adults, obesity and comorbidities were also related to significant changes in diffusivity. Here, diffusivity was strongly associated with body mass index (BMI) and blood levels of C-reactive protein (CRP) in multiple subcortical regions, rather than only the hypothalamus. Our results suggest that diffusivity measures can be used to investigate obesity-associated changes in the brain that can potentially reflect neuroinflammation. The connection seen between subcortical inflammation and obesity opens the conversation on preventative interventions needed to reduce the effects of obesity at all stages in life.

Imaging Response to Treatment in Rheumatology

This article highlights the crucial role of various imaging techniques in the diagnosis and monitoring of rheumatologic diseases. It provides an overview of the different modalities available for imaging rheumatic diseases, the disease processes they are able to demonstrate, and their utility in the monitoring response to therapy. It emphasizes the need for a multifaceted approach that combines radiography, ultrasound, MR imaging, and PET imaging to gain a comprehensive understanding of disease progression and treatment response. Standardized grading systems along with quantitative imaging techniques are playing an increasing role in monitoring disease activity and assessing response to therapy.

The clinical utility of diffusion-weighted imaging in diagnosing and predicting treatment response of laryngeal and hypopharyngeal carcinoma: A systematic review and meta-analysis

PURPOSE

Laryngeal and Hypopharyngeal Carcinomas (LC/HPC) constitute about 24 % of head and neck cancers, causing more than 90,000 annual deaths worldwide. Diffusion-Weighted Imaging (DWI), is currently widely studied in oncologic imaging and can aid in distinguishing cellular tumors from other tissues. Our objective was to review the effectiveness of DWI in three areas: diagnosing, predicting prognosis, and predicting treatment response in patients with LC/HPC.

METHODS

A systematic search was conducted in PubMed, Web of Science, and Embase. A meta-analysis by calculating Standardized Mean Difference (SMD) and 95 % Confidence Interval (CI) was conducted on diagnostic studies.

RESULTS

A total of 16 studies were included. All diagnostic studies (n = 9) were able to differentiate between the LC/HPC and other benign laryngeal/hypopharyngeal lesions. These studies found that LC/HPC had lower Apparent Diffusion Coefficient (ADC) values than non-cancerous lesions. Our meta-analysis of 7 diagnostic studies, that provided ADC values of malignant and non-malignant tissues, demonstrated significantly lower ADC values in LC/HPC compared to non-malignant lesions (SMD = -1.71, 95 %CI: [-2.00, -1.42], ADC cut-off = 1.2 × 103 mm2/s). Furthermore, among the studies predicting prognosis, 67 % (4/6) accurately predicted outcomes based on pretreatment ADC values. Similarly, among studies predicting treatment response, 50 % (2/4) successfully predicted outcomes based on pretreatment ADC values. Overall, the studies that looked at prognosis or treatment response in LC/HPC found a positive correlation between pretreatment ADC values in larynx/hypopharynx and favorable outcomes.

CONCLUSION

DWI aids significantly in the LC/HPC diagnosis. However, further research is needed to establish DWI's reliability in predicting prognosis and treatment response in patients with LC/HPC.

Extraocular muscle Diffusion Weighted Imaging as a quantitative metric of posterior orbital involvement in thyroid associated orbitopathy

OBJECTIVES

The clinical activity score (CAS) and European severity scale (ESS) are established clinical tools to assess thyroid eye disease (TED) but are limited in terms of subjectivity and their reliability in non-Caucasian individuals, and can underestimate significant disease in the posterior orbit. Preliminary data from pilot studies have shown that diffusion-weighted imaging (DWI) using extraocular muscle (EOM) apparent diffusion coefficient (ADC) measurements may provide complementary information in TED. This study expands on previous research to assess for correlations between clinical scores and EOM-ADCs in stratifying disease activity and severity in a large patient cohort from an ethnically diverse population.

METHODS

A retrospective review of TED clinics between 2011 and 2021 identified 96 patients with a documented CAS and ESS and an orbital MRI that included DWI. From regions of interest manually placed on EOM bellies, the highest ADC was computed for each patient and analysed for correlations and associations with CAS and ESS using Spearman Rank correlation and Mann-Whitney U tests, and any potential discriminatory cut-offs using Receiver Operator Curve analyses. A p-value < 0.05 indicated statistical significance.

RESULTS

EOM-ADCs showed a positive association with CAS (p ≤ 0.001). EOM-ADCs were higher in sight-threatening compared to mild disease (p ≤ 0.01). A cut-off of 995 mm2/s achieved AUC = 0.7744, equating to 77% sensitivity and 67% specificity for discrimination between mild-moderate and sight-threatening disease.

CONCLUSION

EOM-ADCs correlate with higher scores of disease severity and activity in TED. Besides providing quantitative data to support clinical tools, EOM-ADC cut-offs may identify patients at risk of developing sight-threatening diseases.

CRITICAL RELEVANCE STATEMENT

This study critically evaluates the limitations of conventional clinical assessment tools for TED and demonstrates the utility of DWI scans with ADC measurements in identifying active disease, offering valuable insights to advance clinical radiology practice.

KEY POINTS

Conventional tools for TED assessment have subjective limitations. ADCs from non-echoplanar diffusion-weighted imaging correlate with clinical activity. Non-echoplanar diffusion-weighted imaging offers quantitative assessment to aid clinical practice reliability.

Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection

PURPOSE

Diffusion-weighted (DW) imaging provides a useful clinical contrast, but is susceptible to motion-induced dephasing caused by the application of strong diffusion gradients. Phase navigators are commonly used to resolve shot-to-shot motion-induced phase in multishot reconstructions, but poor phase estimates result in signal dropout and Apparent Diffusion Coefficient (ADC) overestimation. These artifacts are prominent in the abdomen, a region prone to involuntary cardiac and respiratory motion. To improve the robustness of DW imaging in the abdomen, region-based shot rejection schemes that selectively weight regions where the shot-to-shot phase is poorly estimated were evaluated.

METHODS

Spatially varying weights for each shot, reflecting both the accuracy of the estimated phase and the degree of subvoxel dephasing, were estimated from the phase navigator magnitude images. The weighting was integrated into a multishot reconstruction using different formulations and phase navigator resolutions and tested with different phase navigator resolutions in multishot DW-echo Planar Imaging acquisitions of the liver and pancreas, using conventional monopolar and velocity-compensated diffusion encoding. Reconstructed images and ADC estimates were compared qualitatively.

RESULTS

The proposed region-based shot rejection reduces banding and signal dropout artifacts caused by physiological motion in the liver and pancreas. Shot rejection allows conventional monopolar diffusion encoding to achieve median ADCs in the pancreas comparable to motion-compensated diffusion encoding, albeit with a greater spread of ADCs.

CONCLUSION

Region-based shot rejection is a linear reconstruction that improves the motion robustness of multi-shot DWI and requires no sequence modifications.

Distortion-free water-fat separated diffusion-weighted imaging using spatiotemporal joint reconstruction

PURPOSE

Diffusion-weighted imaging (DWI) suffers from geometric distortion and chemical shift artifacts due to the commonly used Echo Planar Imaging (EPI) trajectory. Even with fat suppression in DWI, severe B0 and B1 variations can result in residual fat, which becomes both a source of image artifacts and a confounding factor in diffusion-weighted contrast in distinguishing benign and malignant tissues. This work presents a method for acquiring distortion-free diffusion-weighted images using spatiotemporal acquisition and joint reconstruction. Water-fat separation is performed by chemical-shift encoding.

METHODS

Spatiotemporal acquisition is employed to obtain distortion-free images at a series of echo times. Chemical-shift encoding is used for water-fat separation. Reconstruction and separation are performed jointly in the spat-spectral domain. To address the shot-to-shot motion-induced phase in DWI, an Fast Spin Echo (FSE)-based phase navigator is incorporated into the sequence to obtain distortion-free phase information. The proposed method was validated in phantoms and in vivo for the brain, head and neck, and breast.

RESULTS

The proposed method enables the acquisition of distortion-free diffusion-weighted images in the presence of B0 field inhomogenieties commonly observed in the body. Water and fat components are separated with no obvious spectral leakage artifacts. The estimated Apparent Diffusion Coefficient (ADC) is comparable to that of multishot DW-EPI.

CONCLUSION

Distortion-free, water-fat separated diffusion-weighted images in body can be obtained through the utilization of spatiotemporal acquisition and joint reconstruction methods.

Ischemic perfusion radiomics: assessing neurological impairment in acute ischemic stroke

Introduction

Accurate neurological impairment assessment is crucial for the clinical treatment and prognosis of patients with acute ischemic stroke (AIS). However, the original perfusion parameters lack the deep information for characterizing neurological impairment, leading to difficulty in accurate assessment. Given the advantages of radiomics technology in feature representation, this technology should provide more information for characterizing neurological impairment. Therefore, with its rigorous methodology, this study offers practical implications for clinical diagnosis by exploring the role of ischemic perfusion radiomics features in assessing the degree of neurological impairment.

Methods

This study employs a meticulous methodology, starting with generating perfusion parameter maps through Dynamic Susceptibility Contrast-Perfusion Weighted Imaging (DSC-PWI) and determining ischemic regions based on these maps and a set threshold. Radiomics features are then extracted from the ischemic regions, and the t-test and least absolute shrinkage and selection operator (Lasso) algorithms are used to select the relevant features. Finally, the selected radiomics features and machine learning techniques are used to assess the degree of neurological impairment in AIS patients.

Results

The results show that the proposed method outperforms the original perfusion parameters, radiomics features of the infarct and hypoxic regions, and their combinations, achieving an accuracy of 0.926, sensitivity of 0.923, specificity of 0.929, PPV of 0.923, NPV of 0.929, and AUC of 0.923, respectively.

Conclusion

The proposed method effectively assesses the degree of neurological impairment in AIS patients, providing an objective auxiliary assessment tool for clinical diagnosis.

Greybox: A hybrid algorithm for direct estimation of tracer kinetic parameters from undersampled DCE-MRI data

BACKGROUND

A variety of deep learning-based and iterative approaches are available to predict Tracer Kinetic (TK) parameters from fully sampled or undersampled dynamic contrast-enhanced (DCE) MRI data. However, both the methods offer distinct benefits and drawbacks.

PURPOSE

To propose a hybrid algorithm (named as 'Greybox'), using both model- as well as DL-based, for solving a multi-parametric non-linear inverse problem of directly estimating TK parameters from undersampled DCE MRI data, which is invariant to undersampling rate.

METHODS

The proposed algorithm was inspired by plug-and-play algorithms used for solving linear inverse imaging problems. This technique was tested for its effectiveness in solving the nonlinear ill-posed inverse problem of generating 3D TK parameter maps from four-dimensional (4D; Spatial + Temporal) retrospectively undersampled k-space data. The algorithm learns a deep learning-based prior using UNET to estimate theK trans $\mathbf {K_{trans}}$ andV p $\mathbf {V_{p}}$ parameters based on the Patlak pharmacokinetic model, and this trained prior was utilized to estimate the TK parameter maps using an iterative gradient-based optimization scheme. Unlike the existing DL models, this network is invariant to the undersampling rate of the input data. The proposed method was compared with the total variation-based direct reconstruction technique on brain, breast, and prostate DCE-MRI datasets for various undersampling rates using the Radial Golden Angle (RGA) scheme. For the breast dataset, an indirect estimation using the Fast Composite Splitting algorithm was utilized for comparison. Undersampling rates of 8 × $\times$ , 12 × $\times$ and 20 × $\times$ were used for the experiments, and the results were compared using the PSNR and SSIM as metrics. For the breast dataset of 10 patients, data from four patients were utilized for training (1032 samples), two for validation (752 samples), and the entire volume of four patients for testing. Similarly, for the prostate dataset of 18 patients, 10 patients were utilized for training (720 samples), five for validation (216 samples), and the whole volume of three patients for testing. For the brain dataset of nineteen patients, ten patients were used for training (3152 samples), five for validation (1168 samples), and the whole volume of four patients for testing. Statistical tests were also conducted to assess the significance of the improvement in performance.

RESULTS

The experiments showed that the proposed Greybox performs significantly better than other direct reconstruction methods. The proposed algorithm improved the estimatedK trans $\mathbf {K_{trans}}$ andV p $\mathbf {V_{p}}$ in terms of the peak signal-to-noise ratio by up to 3 dB compared to other standard reconstruction methods.

CONCLUSION

The proposed hybrid reconstruction algorithm, Greybox, can provide state-of-the-art performance in solving the nonlinear inverse problem of DCE-MRI. This is also the first of its kind to utilize convolutional neural network-based encodings as part of the plug-and-play priors to improve the performance of the reconstruction algorithm.

Robustness of apparent diffusion coefficient-based lymph node classification for diagnosis of prostate cancer metastasis

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.

Beyond the surface: how ex-vivo diffusion-weighted imaging reveals large animal brain microstructure and connectivity

Diffusion-weighted Imaging (DWI) is an effective and state-of-the-art neuroimaging method that non-invasively reveals the microstructure and connectivity of tissues. Recently, novel applications of the DWI technique in studying large brains through ex-vivo imaging enabled researchers to gain insights into the complex neural architecture in different species such as those of Perissodactyla (e.g., horses and rhinos), Artiodactyla (e.g., bovids, swines, and cetaceans), and Carnivora (e.g., felids, canids, and pinnipeds). Classical in-vivo tract-tracing methods are usually considered unsuitable for ethical and practical reasons, in large animals or protected species. Ex-vivo DWI-based tractography offers the chance to examine the microstructure and connectivity of formalin-fixed tissues with scan times and precision that is not feasible in-vivo. This paper explores DWI's application to ex-vivo brains of large animals, highlighting the unique insights it offers into the structure of sometimes phylogenetically different neural networks, the connectivity of white matter tracts, and comparative evolutionary adaptations. Here, we also summarize the challenges, concerns, and perspectives of ex-vivo DWI that will shape the future of the field in large brains.

Neuroimaging of Vermiform Giant Arachnoid Granulations in Children

Arachnoid granulations (AGs) are generally benign structures within the subarachnoid space that extend into the dural sinuses and calvarial bone. They can present in a variety of sizes but are termed 'giant' arachnoid granulations (GAGs) when they are larger than 1 cm in diameter or take up a significant portion of the dural sinus' lumen. Vermiform giant arachnoid granulations are a specific type of GAG that are known for their worm-like appearance. Specifically, these vermiform GAGs can be challenging to diagnose as they can mimic other pathologies like dural sinus thrombosis, sinus cavernomas, or brain tumors. In this case series, we present two cases of vermiform giant arachnoid granulations, discuss their imaging characteristics and highlight the diagnostic challenges to improve identification and prevent misdiagnoses.

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project

This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a noninvasive and reliable way to improve the diagnosis and prognosis in pediatric oncology. The PRIMAGE project is a European multi-center research initiative that focuses on developing medical imaging-derived artificial intelligence (AI) solutions designed to enhance overall management and decision-making for two types of pediatric cancer: neuroblastoma and diffuse intrinsic pontine glioma. To allow this, the PRIMAGE project has created an open-cloud platform that combines imaging, clinical, and molecular data together with AI models developed from this data, creating a comprehensive decision support environment for clinicians managing patients with these two cancers. In order to achieve this, a standardized data processing and analysis workflow was implemented to generate robust and reliable predictions for different clinical endpoints. Magnetic resonance (MR) image harmonization and registration was performed as part of the workflow. Subsequently, an automated tool for the detection and segmentation of tumors was trained and internally validated. The Dice similarity coefficient obtained for the independent validation dataset was 0.997, indicating compatibility with the manual segmentation variability. Following this, radiomics and deep features were extracted and correlated with clinical endpoints. Finally, reproducible and relevant imaging quantitative features were integrated with clinical and molecular data to enrich both the predictive models and a set of visual analytics tools, making the PRIMAGE platform a complete clinical decision aid system. In order to ensure the advancement of research in this field and to foster engagement with the wider research community, the PRIMAGE data repository and platform are currently being integrated into the European Federation for Cancer Images (EUCAIM), which is the largest European cancer imaging research infrastructure created to date.

Influence of Diffusion Time and Temperature on Restricted Diffusion Signal: A Phantom Study

PURPOSE

Diffusion MRI is a physical measurement method that quantitatively indicates the displacement of water molecules diffusing in voxels. However, there are insufficient data to characterize the diffusion process physically in a uniform structure such as a phantom. This study investigated the transitional relationship between structure scale, temperature, and diffusion time for simple restricted diffusion using a capillary phantom.

METHODS

We performed diffusion-weighted pulsed-gradient stimulated-echo acquisition mode (STEAM) MRI with a 9.4 Tesla MRI system (Bruker BioSpin, Ettlingen, Germany) and a quadrature coil with an inner diameter of 86 mm (Bruker BioSpin). We measured the diffusion coefficients (radial diffusivity [RD]) of capillary plates (pore sizes 6, 12, 25, 50, and 100 μm) with uniformly restricted structures at various temperatures (10ºC, 20ºC, 30ºC, and 40ºC) and multiple diffusion times (12-800 ms). We evaluated the characteristics of scale, temperature, and diffusion time for restricted diffusion.

RESULTS

The RD decayed and became constant depending on the structural scale. Diffusion coefficient fluctuations with temperature occurred mostly under conditions of a large structural scale and short diffusion time. We obtained data suggesting that temperature-dependent changes in the diffusion coefficients follow physical laws.

CONCLUSION

No water molecules were observed outside the glass tubes in the capillary plates, and the capillary plates only reflected a restricted diffusion process within the structure.We experimentally evaluated the characteristics of simple restricted diffusion to reveal the transitional relationship of the diffusion coefficient with diffusion time, structure scale, and temperature through composite measurement.

Microstructural white matter alterations associated with social anxiety disorders: A systematic review

BACKGROUND

Social anxiety disorder (SAD) is a psychiatric condition characterized by impaired social functioning that negatively impacts individuals' quality of life. Previous neuroimaging studies have revealed morphological and functional changes in various brain regions associated with SAD. Recent advances in diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) have enabled the investigation of microstructural white matter (WM) alterations in SAD. This study aims to provide an overview of DTI/DWI studies exploring WM microstructure changes in SAD.

METHODS

A systematic search on PubMed, Scopus, Web of Science, and PsycINFO was conducted for relevant records on July 8, 2023. An exploratory meta-analysis was also performed.

RESULTS

Eight studies were reviewed. Consistent findings indicated reduced fractional anisotropy and increased diffusivity measures in different WM tracts in SAD patients compared to healthy controls. These alterations were mostly observed within regions of the fronto-limbic network, like uncinate fasciculus (UF) and superior and inferior longitudinal fasciculi (SLF and ILF). Finally, our exploratory meta-analysis on four studies utilizing a voxel-wise analytic approach yielded no significant differences between SAD patients and controls.

LIMITATIONS

Limited number of studies, small sample sizes, and heterogeneity in analysis methods.

CONCLUSIONS

Patients with SAD exhibited altered WM integrity, particularly in the UF, SLF, and ILF, compared to healthy controls. However, due to the limited number of included studies, our meta-analysis yielded no significant differences between SAD patients and controls. Therefore, future research is crucial to unravel the link between altered WM structure and the involvement of other limbic and cortical structures in SAD pathogenesis.

Cannabinoids in the treatment of glioblastoma

Glioblastoma (GBM) is the most prevalent primary malignant tumor of the nervous system. While the treatment of other neoplasms is increasingly more efficacious the median survival rate of GBM patients remains low and equals about 14 months. Due to this fact, there are intensive efforts to find drugs that would help combat GBM. Nowadays cannabinoids are becoming more and more important in the field of cancer and not only because of their properties of antiemetic drugs during chemotherapy. These compounds may have a direct cytotoxic effect on cancer cells. Studies indicate GBM has disturbances in the endocannabinoid system-changes in cannabinoid metabolism as well as in the cannabinoid receptor expression. The GBM cells show expression of cannabinoid receptors 1 and 2 (CB1R and CB2R), which mediate various actions of cannabinoids. Through these receptors, cannabinoids inhibit the proliferation and invasion of GBM cells, along with changing their morphology. Cannabinoids also induce an intrinsic pathway of apoptosis in the tumor. Hence the use of cannabinoids in the treatment of GBM may be beneficial to the patients. So far, studies focusing on using cannabinoids in GBM therapy are mainly preclinical and involve cell lines and mice. The results are promising and show cannabinoids inhibit GBM growth. Several clinical studies are also being carried out. The preliminary results show good tolerance of cannabinoids and prolonged survival after administration of these drugs. In this review, we describe the impact of cannabinoids on GBM and glioma cells in vitro and in animal studies. We also provide overview of clinical trials on using cannabinoids in the treatment of GBM.

Fibrosis in Chronic Kidney Disease: Pathophysiology and Therapeutic Targets

Chronic kidney disease (CKD) is a slowly progressive condition characterized by decreased kidney function, tubular injury, oxidative stress, and inflammation. CKD is a leading global health burden that is asymptomatic in early stages but can ultimately cause kidney failure. Its etiology is complex and involves dysregulated signaling pathways that lead to fibrosis. Transforming growth factor (TGF)-β is a central mediator in promoting transdifferentiation of polarized renal tubular epithelial cells into mesenchymal cells, resulting in irreversible kidney injury. While current therapies are limited, the search for more effective diagnostic and treatment modalities is intensive. Although biopsy with histology is the most accurate method of diagnosis and staging, imaging techniques such as diffusion-weighted magnetic resonance imaging and shear wave elastography ultrasound are less invasive ways to stage fibrosis. Current therapies such as renin-angiotensin blockers, mineralocorticoid receptor antagonists, and sodium/glucose cotransporter 2 inhibitors aim to delay progression. Newer antifibrotic agents that suppress the downstream inflammatory mediators involved in the fibrotic process are in clinical trials, and potential therapeutic targets that interfere with TGF-β signaling are being explored. Small interfering RNAs and stem cell-based therapeutics are also being evaluated. Further research and clinical studies are necessary in order to avoid dialysis and kidney transplantation.

Deep Learning Accelerated Brain Diffusion-Weighted MRI with Super Resolution Processing

OBJECTIVES

To investigate the clinical feasibility and image quality of accelerated brain diffusion-weighted imaging (DWI) with deep learning image reconstruction and super resolution.

METHODS

85 consecutive patients with clinically indicated MRI at a 3 T scanner were prospectively included. Conventional diffusion-weighted data (c-DWI) with four averages were obtained. Reconstructions of one and two averages, as well as deep learning diffusion-weighted imaging (DL-DWI), were accomplished. Three experienced readers evaluated the acquired data using a 5-point Likert scale regarding overall image quality, overall contrast, diagnostic confidence, occurrence of artefacts and evaluation of the central region, basal ganglia, brainstem, and cerebellum. To assess interrater agreement, Fleiss' kappa (ϰ) was determined. Signal intensity (SI) levels for basal ganglia and the central region were estimated via automated segmentation, and SI values of detected pathologies were measured.

RESULTS

Intracranial pathologies were identified in 35 patients. DL-DWI was significantly superior for all defined parameters, independently from applied averages (p-value <0.001). Optimum image quality was achieved with DL-DWI by utilizing a single average (p-value <0.001), demonstrating very good (80.9%) to excellent image quality (14.5%) in nearly all cases, compared to 12.5% with very good and 0% with excellent image quality for c-MRI (p-value <0.001). Comparable results could be shown for diagnostic confidence. Inter-rater Fleiss' Kappa demonstrated moderate to substantial agreement for virtually all defined parameters, with good accordance, particularly for the assessment of pathologies (p = 0.74). Regarding SI values, no significant difference was found.

CONCLUSION

Ultra-fast diffusion-weighted imaging with super resolution is feasible, resulting in highly accelerated brain imaging while increasing diagnostic image quality.

Advanced magnetic resonance neuroimaging techniques: feasibility and applications in long or post-COVID-19 syndrome - a review

Long-term or post-COVID-19 syndrome (PCS) is a condition that affects people infected with SARS‑CoV‑2, the virus that causes COVID-19. PCS is characterized by a wide range of persistent or new symptoms that last months after the initial infection, such as fatigue, shortness of breath, cognitive dysfunction, and pain. Advanced magnetic resonance (MR) neuroimaging techniques can provide valuable information on the structural and functional changes in the brain associated with PCS as well as potential biomarkers for diagnosis and prognosis. In this review, we discuss the feasibility and applications of various advanced MR neuroimaging techniques in PCS, including perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), functional MR imaging (fMRI), diffusion tensor imaging (DTI), and tractography. We summarize the current evidence on neuroimaging findings in PCS, the challenges and limitations of these techniques, and the future directions for research and clinical practice. Although still uncertain, advanced MRI techniques show promise for gaining insight into the pathophysiology and guiding the management of COVID-19 syndrome, pending larger validation studies.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

Prognostic Value of Apparent Diffusion Coefficient (ADC) in Patients with Diffuse Gliomas

This study aimed to evaluate potential posttreatment changes in ADC values within the tissue surrounding the enhancing lesion, particularly in areas not exhibiting MRI characteristics of involvement. Additionally, the objective was to investigate the correlations among ADC values, treatment response, and survival outcomes in individuals diagnosed with gliomas. This retrospective study included a total of 49 patients that underwent either stereotactic biopsy or maximal surgical resection. Histologically confirmed as Grade III or IV gliomas, all cases adhered to the 2016 and 2021 WHO classifications, with subsequent radio-chemotherapy administered post-surgery. Patients were divided into two groups: short and long survival groups. Baseline and follow-up MRI scans were obtained on a 1.5 T MRI scanner. Two ROI circles were positioned near the enhancing area, one ROI in the NAWM ipsilateral to the neoplasm and another symmetrically in the contralateral hemisphere on ADC maps. At follow-up there was a significant difference in both ipsilateral and contralateral NAWM between the two groups, -0.0857 (p = 0.004) and -0.0607 (p = 0.037), respectively. There was a weak negative correlation between survival and ADC values in ipsilateral and contralateral NAWM at the baseline with the correlation coefficient -0.328 (p = 0.02) and -0.302 (p = 0.04), respectively. The correlation was stronger at the follow-up. The findings indicate that ADC values in normal-appearing white matter (NAWM) may function as a prognostic biomarker in patients with diffuse gliomas.

Clinical and radiological correlates of apathy in multiple sclerosis

BACKGROUND

Although apathy has been associated with fronto-striatal dysfunction in several neurological disorders, its clinical and magnetic resonance imaging (MRI) correlates have been poorly investigated in people with multiple sclerosis (PwMS).

OBJECTIVES

To evaluate clinical variables and investigate microstructural integrity of fronto-striatal grey matter (GM) and white matter (WM) structures using diffusion tensor imaging (DTI).

METHODS

A total of 123 PwMS (age: 40.25 ± 11.5; female: 60.9%; relapsing-remitting multiple sclerosis: 75.6%) were prospectively enrolled and underwent neurological and neuropsychological evaluation, including Expanded Disability Status Scale (EDSS), Apathy Evaluation Scale (AES-S), Hospital Anxiety and Depression Scale (HADS), Modified Fatigue Impact Scale (MFIS) and brain 3T-MRI volumes of whole brain, frontal/prefrontal cortex (PFC) and subcortical regions were calculated. DTI-derived metrics were evaluated in the same GM regions and in connecting WM tracts.

RESULTS

Apathetic PwMS (32.5%) showed lower education levels, higher HADS, MFIS scores and WM lesions volume than nonapathetic PwMS. Significant differences in DTI metrics were found in middle frontal, anterior cingulate and superior frontal PFC subregions and in caudate nuclei. Significant alterations were found in the right cingulum and left striatal-frontorbital tracts.

CONCLUSIONS

Apathy in PwMS is associated with higher levels of physical disability, depression, anxiety and fatigue together with lower educational backgrounds. Microstructural damage within frontal cortex, caudate and fronto-striatal WM bundles is a significant pathological substrate of apathy in multiple sclerosis (MS).

Unveiling the Intricacies: A Comprehensive Review of Magnetic Resonance Imaging (MRI) Assessment of T2-Weighted Hyperintensities in the Neuroimaging Landscape

T2-weighted hyperintensities in neuroimaging represent areas of heightened signal intensity on magnetic resonance imaging (MRI) scans, holding crucial importance in neuroimaging. This comprehensive review explores the T2-weighted hyperintensities, providing insights into their definition, characteristics, clinical relevance, and underlying causes. It highlights the significance of these hyperintensities as sensitive markers for neurological disorders, including multiple sclerosis, vascular dementia, and brain tumors. The review also delves into advanced neuroimaging techniques, such as susceptibility-weighted and diffusion tensor imaging, and the application of artificial intelligence and machine learning in hyperintensities analysis. Furthermore, it outlines the challenges and pitfalls associated with their assessment and emphasizes the importance of standardized protocols and a multidisciplinary approach. The review discusses future directions for research and clinical practice, including the development of biomarkers, personalized medicine, and enhanced imaging techniques. Ultimately, the review underscores the profound impact of T2-weighted hyperintensities in shaping the landscape of neurological diagnosis, prognosis, and treatment, contributing to a deeper understanding of complex neurological conditions and guiding more informed and effective patient care.

The Role of Optic Nerve Diffusion Restriction on Visual Prognosis in Methanol Poisoning

The aim of this study was to investigate the role of optic nerve diffusion status on cranio-orbital magnetic resonance imaging (MRI) in predicting visual prognosis in cases of methanol intoxication. Diffusion-weighted imaging (DWI) from 16 eyes of eight patients who were admitted to our clinic due to methanol intoxication was analysed retrospectively. The relationship between clinical and laboratory findings, treatment regimen, visual prognosis, and imaging findings was investigated. Diffusion restriction (DR) of the optic nerve on DWI was observed in seven (43%) eyes. Regardless of the clinical and laboratory characteristics and treatment regimen, visual acuity (VA) improved in eyes in which restricted diffusion regressed over the follow-up period. DWI of the optic nerve during the acute phase of methanol poisoning may provide prognostically important data. Improvement of DR during follow-up may be an indicator of an increase in VA.

Motor Unit Magnetic Resonance Imaging (MUMRI) In Skeletal Muscle

Magnetic resonance imaging (MRI) is routinely used in the musculoskeletal system to measure skeletal muscle structure and pathology in health and disease. Recently, it has been shown that MRI also has promise for detecting the functional changes, which occur in muscles, commonly associated with a range of neuromuscular disorders. This review focuses on novel adaptations of MRI, which can detect the activity of the functional sub-units of skeletal muscle, the motor units, referred to as "motor unit MRI (MUMRI)." MUMRI utilizes pulsed gradient spin echo, pulsed gradient stimulated echo and phase contrast MRI sequences and has, so far, been used to investigate spontaneous motor unit activity (fasciculation) and used in combination with electrical nerve stimulation to study motor unit morphology and muscle twitch dynamics. Through detection of disease driven changes in motor unit activity, MUMRI shows promise as a tool to aid in both earlier diagnosis of neuromuscular disorders and to help in furthering our understanding of the underlying mechanisms, which proceed gross structural and anatomical changes within diseased muscle. Here, we summarize evidence for the use of MUMRI in neuromuscular disorders and discuss what future research is required to translate MUMRI toward clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Diffusion-Weighted Imaging and Chemical Shift Imaging to Differentiate Benign and Malignant Vertebral Lesion: A Hospital-Based Cross-Sectional Study

Objective  The aim of this study was to evaluate the role of diffusion-weighted imaging (DWI) and chemical shift imaging (CSI) for the differentiation of benign and malignant vertebral lesions. Methods  Patients with vertebral lesions underwent routine magnetic resonance imaging (MRI) along with DWI and CSI. Qualitative analysis of the morphological features was done by routine MRI. Quantitative analysis of apparent diffusion coefficient (ADC) from DWI and fat fraction (FF) from CSI was done and compared between benign and malignant vertebral lesions. Results  Seventy-two patients were included. No significant difference was noted in signal intensities of benign and malignant lesions on conventional MRI sequences. Posterior element involvement, paravertebral soft-tissue lesion, and posterior vertebral bulge were common in malignant lesion, whereas epidural/paravertebral collection, absence of posterior vertebral bulge, and multiple compression fractures were common in benign vertebral lesion ( p  < 0.001). The mean ADC value was 1.25 ± 0.27 mm 2 /s for benign lesions and 0.9 ± 0.19 mm 2 /s for malignant vertebral lesions ( p ≤ 0.001). The mean value of FF was 12.7 ± 7.49 for the benign group and 4.04 ± 2.6 for the malignant group ( p  < 0.001). A receiver operating characteristic (ROC) curve analysis showed that an ADC cutoff of 1.05 × 10 -3 mm 2 /s and an FF cutoff of 6.9 can differentiate benign from malignant vertebral lesions, with the former having 86% sensitivity and 82.8% specificity and the latter having 93% sensitivity and 96.6% specificity. Conclusion  The addition of DWI and CSI to routine MRI protocol in patients with vertebral lesions promises to be very helpful in differentiating benign from malignant vertebral lesions when difficulty in qualitative interpretation of conventional MR images arises.

Quantification of perineural satellitosis in pretreatment glioblastoma with structural MRI and a diffusion tensor imaging template

Background

Survival outcomes for glioblastoma (GBM) patients remain unfavorable, and tumor recurrence is often observed. Understanding the radiological growth patterns of GBM could aid in improving outcomes. This study aimed to examine the relationship between contrast-enhancing tumor growth direction and white matter, using an image registration and deformation strategy.

Methods

In GBM patients 2 pretreatment scans (diagnostic and neuronavigation) were gathered retrospectively, and coregistered to a template and diffusion tensor imaging (DTI) atlas. The GBM lesions were segmented and coregistered to the same space. Growth vectors were derived and divided into vector populations parallel (Φ = 0-20°) and perpendicular (Φ = 70-90°) to white matter. To test for statistical significance between parallel and perpendicular groups, a paired samples Student's t-test was performed. O6-methylguanine-DNA methyltransferase (MGMT) methylation status and its correlation to growth rate were also tested using a one-way ANOVA test.

Results

For 78 GBM patients (mean age 61 years ± 13 SD, 32 men), the included GBM lesions showed a predominant preference for perineural satellitosis (P < .001), with a mean percentile growth of 30.8% (95% CI: 29.6-32.0%) parallel (0° < |Φ| < 20°) to white matter. Perpendicular tumor growth with respect to white matter microstructure (70° < |Φ| < 90°) showed to be 22.7% (95% CI: 21.3-24.1%) of total tumor growth direction.

Conclusions

The presented strategy showed that tumor growth direction in pretreatment GBM patients correlated with white matter architecture. Future studies with patient-specific DTI data are required to verify the accuracy of this method prospectively to identify its usefulness as a clinical metric in pre and posttreatment settings.

Paediatric supratentorial tumours do not cause microstructural alterations in contralateral white matter: a preliminary study

BACKGROUND AND PURPOSE

Intracranial tumours in children can exhibit different characteristics compared to those in adults. Understanding the microstructural changes in the contralateral normal-appearing white matter (NAWM) in children with primary intracranial masses is essential for optimizing treatment strategies. This study aimed to investigate the apparent diffusion coefficient (ADC) changes in contralateral NAWM using fully automated methods and deep learning algorithms.

METHODS

We included 22 paediatric patients with primary supratentorial intracranial masses (23% high-grade) in the study. ADC values of the contralateral NAWM in the patient group were compared to those of a control group. Deep learning algorithms were utilized to analyse diffusion changes in NAWM.

RESULTS

The mean ADC values of contralateral NAWM in the patient group were 0.80 ± 0.03 × 10-3 mm2/s, while the control group had a mean ADC value of 0.81 ± 0.03 × 10-3 mm2/s. There was no statistically significant difference between the groups (p = 0.39). Our findings indicate that there are no significant diffusion changes in the contralateral white matter of children with supratentorial intracranial masses.

CONCLUSION

Primary supratentorial intracranial masses in children do not cause microstructural changes in contralateral normal-appearing white matter. This could be attributed to the less infiltrative nature and different biochemical profile of these tumour groups in the paediatric population. Further studies using advanced imaging techniques could provide additional insights into the distinct characteristics of paediatric intracranial tumours and improve patient management.

Discrimination of benign, atypical, and malignant peripheral nerve sheath tumors in neurofibromatosis type 1 using diffusion-weighted MRI

Background

Neurofibromatosis type 1 (NF1) is associated with the development of benign (BPNST) and malignant (MPNST) peripheral nerve sheath tumors. Recently described atypical neurofibromas (ANF) are considered pre-malignant precursor lesions to MPNSTs. Previous studies indicate that diffusion-weighted magnetic resonance imaging (DW-MRI) can reliably discriminate MPNSTs from BPNSTs. We therefore investigated the diagnostic accuracy of DW-MRI for the discrimination of benign, atypical, and malignant peripheral nerve sheath tumors.

Methods

In this prospective explorative single-center phase II diagnostic study, 44 NF1 patients (23 male; 30.1 ± 11.8 years) underwent DW-MRI (b-values 0-800 s/mm²) at 3T. Two radiologists independently assessed mean and minimum apparent diffusion coefficients (ADCmean/min) in areas of largest tumor diameters and ADCdark in areas of lowest signal intensity by manual contouring of the tumor margins of 60 BPNSTs, 13 ANFs, and 21 MPNSTs. Follow-up of ≥ 24 months (BPNSTs) or histopathological evaluation (ANFs + MPNSTs) served as diagnostic reference standard. Diagnostic ADC-based cut-off values for discrimination of the three tumor groups were chosen to yield the highest possible specificity while maintaining a clinically acceptable sensitivity.

Results

ADC values of pre-malignant ANFs clustered between BPNSTs and MPNSTs. Best BPNST vs. ANF + MPNST discrimination was obtained using ADCdark at a cut-off value of 1.6 × 10-3 mm2/s (85.3% sensitivity, 93.3% specificity), corresponding to an AUC of 94.3% (95% confidence interval: 85.2-98.0). Regarding BPNST + ANF vs. MPNST, best discrimination was obtained using an ADCdark cut-off value of 1.4 × 10-3 mm2/s (83.3% sensitivity, 94.5% specificity).

Conclusions

DW-MRI using ADCdark allows specific and noninvasive discrimination of benign, atypical, and malignant nerve sheath tumors in NF1.

Functional MRI Techniques Suggesting that the Stress System Interacts with Three Large Scale Core Brain Networks to Help Coordinate the Adaptive Response: A Systematic Review

OBJECTIVE

Synthesis of functional MRI (fMRI) and functional connectivity (FC) analysis data on human stress system (SS) function, as it relates to the dynamic function of the Salience (SN), Default Mode (DMN) and Central Executive (CEN) networks.

METHODS

Systematic search of Medline, Scopus, Clinical Trials.gov, and Google Scholar databases of studies published prior to September 2022 resulted in 28 full-text articles included for qualitative synthesis.

RESULTS

Acute stress changes the states of intra-/inter- neural network FCs and activities from those of resting, low arousal state in the SN, DMN and CEN, during which intra- and inter-network FCs and activities of all three networks are low. SS activation is positively linked to the activity of the SN and negatively to that of the DMN, while, in parallel, it is associated with an initial decrease and a subsequent increase of the intra- network FC and activity of the CEN. The FC between the DMN and the CEN increases, while those between the SN and the CEN decrease, allowing time for frontal lobe strategy input and "proper" CEN activity and task decision. SN activation is linked to sensory hypersensitivity, "impaired" memory, and a switch from serial to parallel processing, while trait mindfulness is associated with FC changes promoting CEN activity and producing a "task-ready state".

CONCLUSION

SS activation is tightly connected to that of the SN, with stress hormones likely potentiating the intra-network FC of the latter, attenuating that of the DMN, and causing a biphasic suppression- to-activation response of the CEN, all adaptive changes favoring proper decisions and survival.

Comparison Between EPI DWI and PROPELLER DWI in Brain MR Imaging

OBJECTIVE

EPI DWI is a routinely used sequence in brain imaging but it has limitations when it comes to SNR and artifact reduction. PROPELLER DWI has the benefit of improving image quality compared to EPI DWI. The aim of this study is to compare the EPI DWI sequence in brain MR imaging with the PROPELLER DWI sequence. The objective is to identify which sequence is more beneficial in brain imaging by evaluating image quality and the depiction of pathologies.

MATERIALS AND METHODS

A total of 101 patients (55 females and 46 males, mean age 56 years) underwent brain MRI examination on a 1.5 T scanner. EPI DWI and PROPELLER DWI sequences were acquired in every exam and were reviewed by 2 radiologists. The images were evaluated by performing a quantitative analysis based on Relative Contrast and a qualitative analysis (overall image quality, conspicuousness of lesions, artifact reduction, etc.).

RESULTS

In both the qualitative and quantitative analysis PROPELLER DWI achieved better results than EPI DWI. PROPELLER DWI showed statistical significance in the overall image quality (P < 0.001), the elimination of susceptibility (P < 0.001) and flow pulsation artifacts (P < 0.001), as well as in the contrast between CSF with white (P < 0.001) and grey matter (P < 0.001). Also, PROPELLER DWI presented better delineation of pathologies like ischemic strokes, metastasis, tumors and vasogenic edemas than conventional EPI DWI.

CONCLUSION

PROPELLER DWI was the preferred sequence during the image evaluation. Compared to EPI DWI, PROPELLER DWI managed to reduce susceptibility and flow pulsation whilst achieving higher image quality and lesion delineation and earlier depiction of ischemic strokes than the conventional EPI DWI. PROPELLER DWI may be incorporated in brain MR imaging replacing EPI DWI.

Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI

PURPOSE

To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI).

METHOD

This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI.

RESULTS

Ultimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55-0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P ≤ 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P ≤ 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16-0.89]).

CONCLUSIONS

DL reconstruction can improve the image quality of whole-spine diffusion imaging.

Diffusion-Weighted MRI as a Quantitative Imaging Biomarker in Colon Tumors

PURPOSE

To assess the use of quantitative diffusion-weighted MRI (DW-MRI) as a diagnostic imaging biomarker in differentiating between benign colon adenoma, early, and advanced cancer of the colon, as well as predicting lymph node involvement, and finally comparing mucinous-producing colon cancer with adenomas and non-mucinous colon cancer.

METHOD

Patients with a confirmed tumor on colonoscopy were eligible for inclusion in this study. Using a 3.0 Tesla MRI machine, the main tumor mean apparent diffusion coefficient (mADC) was obtained. Surgically resected tumor specimens served as an endpoint, except in mucinous colon cancers, which were classified based on T2 images.

RESULTS

A total of 152 patients were included in the study population. The mean age was 71 years. A statistically significant mADC mean difference of -282 × 10-6 mm2/s [-419--144 95% CI, p < 0.001] was found between colon adenomas and early colon cancer, with an AUC of 0.80 [0.68-0.93 95% CI] and an optimal cut off value of 1018 × 10-6 mm2/s. Only a small statistically significant difference (p = 0.039) in mADC was found between benign tumors and mucinous colon cancer. We found no statistical difference in mADC mean values between early and advanced colon cancer, and between colon cancer with and without lymph node involvement.

CONCLUSION

Quantitative DW-MRI is potentially useful for determining whether a colonic tumor is benign or malignant. Mucinous colon cancer shows less diffusion restriction when compared to non-mucinous colon cancer, a potential pitfall.

Towards detection of early response in neoadjuvant chemotherapy of breast cancer using Bayesian intravoxel incoherent motion

Introduction

The early identification of good responders to neoadjuvant chemotherapy (NACT) holds a significant potential in the optimal treatment of breast cancer. A recent Bayesian approach has been postulated to improve the accuracy of the intravoxel incoherent motion (IVIM) model for clinical translation. This study examined the prediction and early sensitivity of Bayesian IVIM to NACT response.

Materials and methods

Seventeen female patients with breast cancer were scanned at baseline and 16 patients were scanned after Cycle 1. Tissue diffusion and perfusion from Bayesian IVIM were calculated at baseline with percentage change at Cycle 1 computed with reference to baseline. Cellular proliferative activity marker Ki-67 was obtained semi-quantitatively with percentage change at excision computed with reference to core biopsy.

Results

The perfusion fraction showed a significant difference (p = 0.042) in percentage change between responder groups at Cycle 1, with a decrease in good responders [-7.98% (-19.47-1.73), n = 7] and an increase in poor responders [10.04% (5.09-28.93), n = 9]. There was a significant correlation between percentage change in perfusion fraction and percentage change in Ki-67 (p = 0.042). Tissue diffusion and pseudodiffusion showed no significant difference in percentage change between groups at Cycle 1, nor was there a significant correlation against percentage change in Ki-67. Perfusion fraction, tissue diffusion, and pseudodiffusion showed no significant difference between groups at baseline, nor was there a significant correlation against Ki-67 from core biopsy.

Conclusion

The alteration in tumour perfusion fraction from the Bayesian IVIM model, in association with cellular proliferation, showed early sensitivity to good responders in NACT.

Clinical trial registration

https://clinicaltrials.gov/ct2/show/NCT03501394, identifier NCT03501394.

Review and Perspective on Brain Bases of Tinnitus

In advancing our understanding of tinnitus, some of the more impactful contributions in the past two decades have come from human brain imaging studies, specifically the idea of both auditory and extra-auditory neural networks that mediate tinnitus. These networks subserve both the perception of tinnitus and the psychological reaction to chronic, continuous tinnitus. In this article, we review particular studies that report on the nodes and links of such neural networks and their inter-network connections. Innovative neuroimaging tools have contributed significantly to the increased understanding of anatomical and functional connections of attention, emotion-processing, and default mode networks in adults with tinnitus. We differentiate between the neural correlates of tinnitus and those of comorbid hearing loss; surprisingly, tinnitus and hearing loss when they co-occur are not necessarily additive in their impact and, in rare cases, additional tinnitus may act to mitigate the consequences of hearing loss alone on the brain. The scale of tinnitus severity also appears to have an impact on brain networks, with some of the alterations typically attributed to tinnitus reaching significance only in the case of bothersome tinnitus. As we learn more about comorbid conditions of tinnitus, such as depression, anxiety, hyperacusis, or even aging, their contributions to the network-level changes observed in tinnitus will need to be parsed out in a manner similar to what is currently being done for hearing loss or severity. Together, such studies advance our understanding of the heterogeneity of tinnitus and will lead to individualized treatment plans.

Repeatability and reproducibility of apparent exchange rate measurements in yeast cell phantoms using filter-exchange imaging

OBJECTIVES

Development of a protocol for validation and quality assurance of filter-exchange imaging (FEXI) pulse sequences with well-defined and reproducible phantoms.

MATERIALS AND METHODS

A FEXI pulse sequence was implemented on a 7 T preclinical MRI scanner. Six experiments in three different test categories were established for sequence validation, demonstration of the reproducibility of phantoms and the measurement of induced changes in the apparent exchange rate (AXR). First, an ice-water phantom was used to investigate the consistency of apparent diffusion coefficient (ADC) measurements with different diffusion filters. Second, yeast cell phantoms were utilized to validate the determination of the AXR in terms of repeatability (same phantom and session), reproducibility (separate but comparable phantoms in different sessions) and directionality of diffusion encodings. Third, the yeast cell phantoms were, furthermore, used to assess potential AXR bias because of altered cell density and temperature. In addition, a treatment experiment with aquaporin inhibitors was performed to evaluate the influence of these compounds on the cell membrane permeability in yeast cells.

RESULTS

FEXI-based ADC measurements of an ice-water phantom were performed for three different filter strengths, showed good agreement with the literature value of 1.099 × 10-3 mm2/s and had a maximum coefficient of variation (CV) of 0.55% within the individual filter strengths. AXR estimation in a single yeast cell phantom and imaging session with five repetitions resulted in an overall mean value of (1.49 ± 0.05) s-1 and a CV of 3.4% between the chosen regions of interest. For three separately prepared phantoms, AXR measurements resulted in a mean value of (1.50 ± 0.04) s-1 and a CV of 2.7% across the three phantoms, demonstrating high reproducibility. Across three orthogonal diffusion directions, a mean value of (1.57 ± 0.03) s-1 with a CV of 1.9% was detected, consistent with isotropy of AXR in yeast cells. Temperature and AXR were linearly correlated (R2 = 0.99) and an activation energy EA of 37.7 kJ/mol was determined by Arrhenius plot. Furthermore, a negative correlation was found between cell density (as determined by the reference ADC/fe) and AXR (R2 = 0.95). The treatment experiment resulted in significantly decreased AXR values at different temperatures in the treated sample compared to the untreated control indicating an inhibiting effect.

CONCLUSIONS

Using ice-water and yeast cell-based phantoms, a protocol for the validation of FEXI pulse sequences was established for the assessment of stability, repeatability, reproducibility and directionality. In addition, a strong dependence of AXR on cell density and temperature was shown. As AXR is an emerging novel imaging biomarker, the suggested protocol will be useful for quality assurance of AXR measurements within a study and potentially across multiple sites.

Can machine learning models improve early detection of brain metastases using diffusion weighted imaging-based radiomics?

Background

Metastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics.

Methods

Longitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance.

Results

The radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1.

Conclusions

The proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.

Prediction of survival and analysis of prognostic factors for hepatocellular carcinoma: a 20-year of imaging diagnosis in Upper Northern Thailand

BACKGROUND

To evaluate survival rates of hepatocellular carcinoma (HCC), the Chiang Mai Cancer Registry provided characteristics data of 6276 HCC patients diagnosed between 1998-2020 based on evolution of imaging diagnosis. Evolution can be separated into four cohorts, namely, cohort 1 (1990-2005) when we had ultrasound (US) and single-phase computed tomography (CT), cohort 2 (2006-2009) when one multi-phase CT and one magnetic resonance imaging (MRI) were added, cohort 3 (2010-2015) when MRI with LI-RADS was added, and finally, cohort 4 (2016-2020) when two upgraded MRIs with LI-RADS were added.

METHODS

Cox proportional hazard models were used to determine the relation between death and risk factors including methods of imagining diagnosis, gender, age of diagnosis, tumor stages, history of smoking and alcohol-use, while Kaplan-Meier curves were used to calculate survival rates.

RESULTS

The median age of diagnosis was 57.0 years (IQR: 50.0-65.0) and the median survival time was 5.8 months (IQR: 1.9-26.8) during the follow-up period. In the univariable analysis, all factors were all associated with a higher risk of death in HCC patients except age of diagnosis. In a multivariable analysis, elderly age at diagnosis, regional and metastatic stages and advanced methods of imagining diagnosis during cohorts 2 and 3 were independently associated with the risk of death in HCC patients. The survival rate of patients diagnosed during cohort 4 was significantly higher than the other cohorts.

CONCLUSION

As a significantly increasing survival rate of HCC patients in cohort 4, advanced methods of diagnostic imaging can be a part of the recommendation to diagnose HCC.

Diffusion-weighted imaging in Crohn's disease

BACKGROUND

Diffusion-weighted MRI (DWI) is routinely used in abdominal imaging. In addition to neoplastic diseases, inflammatory changes can be delineated and diagnosed based on diffusion restriction in DWI. DWI is also increasingly used in the context of MRI of the small and large intestine.

OBJECTIVE

This article focuses on the technical aspects of DWI and its role in the diagnosis of Crohn's disease (CD) as well as in the grading of disease severity and in treatment monitoring.

MATERIALS AND METHODS

Guidelines, basic research papers, and review articles were analyzed.

RESULTS

Diffusion-weighted MRI is a specialized MRI technique that visualizes the diffusion of water molecules in biological tissues. In the context of MRI of the small and large intestine, DWI facilitates the diagnosis of inflammatory bowel disease and assessment of treatment response. DWI enables detection of not only intra- and transmural changes, but also extramural pathologies and complications. However, DWI also has its limitations and challenges.

CONCLUSION

This article provides a comprehensive overview of the use of DWI for diagnostic evaluation of bowel wall changes and extramural complications in the setting of CD. It also summarizes the relevant evidence available in the literature.

An Update on the Role of MRI in Treatment Stratification of Patients with Cervical Cancer

Cervical cancer is the fourth most common cancer in women worldwide and the most common gynaecological malignancy. The FIGO staging system is the most commonly utilised classification system for cervical cancer worldwide. Prior to the most recent update in the FIGO staging in 2018, the staging was dependent upon clinical assessment alone. Concordance between the surgical and clinical FIGO staging decreases rapidly as the tumour becomes more advanced. MRI now plays a central role in patients diagnosed with cervical cancer and enables accurate staging, which is essential to determining the most appropriate treatment. MRI is the best imaging option for the assessment of tumour size, location, and parametrial and sidewall invasion. Notably, the presence of parametrial invasion precludes surgical options, and the patient will be triaged to chemoradiotherapy. As imaging is intrinsic to the new 2018 FIGO staging system, nodal metastases have been included within the classification as stage IIIC disease. The presence of lymph node metastases within the pelvis or abdomen is associated with a poorer prognosis, which previously could not be included in the staging classification as these could not be reliably detected on clinical examination. MRI findings corresponding to the 2018 revised FIGO staging of cervical cancers and their impact on treatment selection will be described.

Liver metastases: The role of magnetic resonance imaging

The liver is one of the organs most commonly involved in metastatic disease, especially due to its unique vascularization. It's well known that liver metastases represent the most common hepatic malignant tumors. From a practical point of view, it's of utmost importance to evaluate the presence of liver metastases when staging oncologic patients, to select the best treatment possible, and finally to predict the overall prognosis. In the past few years, imaging techniques have gained a central role in identifying liver metastases, thanks to ultrasonography, contrast-enhanced computed tomography (CT), and magnetic resonance imaging (MRI). All these techniques, especially CT and MRI, can be considered the non-invasive reference standard techniques for the assessment of liver involvement by metastases. On the other hand, the liver can be affected by different focal lesions, sometimes benign, and sometimes malignant. On these bases, radiologists should face the differential diagnosis between benign and secondary lesions to correctly allocate patients to the best management. Considering the above-mentioned principles, it's extremely important to underline and refresh the broad spectrum of liver metastases features that can occur in everyday clinical practice. This review aims to summarize the most common imaging features of liver metastases, with a special focus on typical and atypical appearance, by using MRI.

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/.

Cerebellar growth, volume and diffusivity in children cooled for neonatal encephalopathy without cerebral palsy

Children cooled for HIE and who did not develop cerebral palsy (CP) still underperform at early school age in motor and cognitive domains and have altered supra-tentorial brain volumes and white matter connectivity. We obtained T1-weighted and diffusion-weighted MRI, motor (MABC-2) and cognitive (WISC-IV) scores from children aged 6-8 years who were cooled for HIE secondary to perinatal asphyxia without CP (cases), and controls matched for age, sex, and socioeconomic status. In 35 case children, we measured cerebellar growth from infancy (age 4-15 days after birth) to childhood. In childhood, cerebellar volumes were measured in 26 cases and 23 controls. Diffusion properties (mean diffusivity, MD and fractional anisotropy, FA) were calculated in 24 cases and 19 controls, in 9 cerebellar regions. Cases with FSIQ ≤ 85 had reduced growth of cerebellar width compared to those with FSIQ > 85 (p = 0.0005). Regional cerebellar volumes were smaller in cases compared to controls (p < 0.05); these differences were not significant when normalised to total brain volume. There were no case-control differences in MD or FA. Interposed nucleus volume was more strongly associated with IQ in cases than in controls (p = 0.0196). Other associations with developmental outcome did not differ between cases and controls.

Incremental value of diffusion weighted imaging over conventional MRI for the diagnosis of osteomyelitis of extremities

OBJECTIVE

To determine the incremental value of diffusion weighted imaging (DWI) over conventional MR imaging in diagnosing extremity osteomyelitis (OM).

MATERIALS AND METHODS

In this cross-sectional study, three experienced musculoskeletal radiologists evaluated clinically suspected cases of extremity OM in two rounds-first on conventional MR imaging, and then conventional MR imaging combined with DWI 4-6 weeks later. The readers recorded a result of the presence or absence of OM and their diagnostic confidence on a 1-5 scale. Mean and minimum apparent diffusion coefficient (ADC) were measured. Pathology diagnosis served as the reference standard. Statistical analysis utilized intraclass correlation (ICC) and Conger's kappa.

RESULTS

A total of 213 scans of suspected OM were reviewed by three musculoskeletal radiologists with no significant changes in sensitivity (0.97, 0.97), specificity (0.97, 0.94), positive predictive value (0.91, 0.87), or negative predictive value (0.98, 0.98) between conventional MR imaging and MR imaging combined with DWI, respectively. Reader confidence did not significantly change with the addition of DWI (4.55 and 4.70, respectively). A high inter-reader agreement was observed for the diagnosis of OM, soft tissue abscess, and intraosseous abscess in both rounds. A higher mean (1.46+/-0.43 × 10-3 mm2/s > 0.64+/-0.47 × 10-3mm2/s) and minimum (1.18+/-0.45 × 10-3mm2/s > 0.37+/-0.44 × 10-3mm2/s) ADC value was associated with OM (p-value < 0.0001) with odds ratios of 1.34 and 1.31, respectively, for mean and minimum ADC of the involved bone.

CONCLUSION

DWI-derived ADC increase is associated with OM. The use of DWI slightly increases reader confidence in the diagnosis of OM; however, no significant incremental value over conventional MR imaging is seen for the final diagnosis of OM.

Lumbosacral zone features in individuals with nonspecific chronic low back pain are unique compared to controls and correlate with pain and dysfunction

OBJECTIVES

To compare the lumbosacral nerve distances (LNDs) and sacroiliac joint (SIJ) morphology in individuals with nonspecific chronic low back pain (NSCLBP) and control and examine their correlations with pain and dysfunction in the former.

MATERIALS AND METHODS

The sample includes 200 adult patients (ranging from 20 to 50 years old) referred for computerized abdominal tomography (CT): 100 individuals with NSCLBP (50 males and 50 females) and 100 individuals without NSCLBP (50 males and 50 females). CT scans were assessed for LNDs, degenerative sacroiliac changes, and joint bridging. Those factors were correlated to the outcomes of three self-reported questionnaires about pain and function (Oswestry, Fear-Avoidance, and Numerical Pain Rating Scale) in the NSCLBP group.

RESULTS

Individuals with NSCLBP tend to have reduced LNDs from the sacral part of the SIJ compared to controls (males: right Δ = 5.8 mm, left Δ = 6.03 mm; females: right Δ = 7.9 mm, left Δ = 7.73 mm, two-way ANOVA, p < 0.01), with moderate significant negative correlations with all three questionnaires (-0.38 < Pearson's r < - 0.57, p < 0.02, i.e., reduced LNDs with greater disability and pain). The NSCLBP group had more significant SIJ degeneration severity that moderately correlated with two questionnaires (0.39 < Pearson's r < 0.66, p < 0.04, i.e., greater SIJ degeneration with greater disability and pain). In males, the existence of SIJ bridging strongly correlated with all three questionnaires (0.38 < Pearson's r < 0.78, p < 0.03), and in females, only the Fear-Avoidance Questionnaire and Numerical Pain Scale (0.29 < Pearson's r < 0.41, p < 0.04).

CONCLUSION

Compared to controls, individuals with NSCLBP have reduced LNDs and worse SIJ degenerative changes that correlate with function and pain.

KEY POINTS

• Individuals with nonspecific low back pain tend to have reduced lumbosacral nerve distances than healthy controls. This may be due to entrapments or inflammation of the nerves or surrounding tissues. • Individuals with nonspecific low back pain tend to have more severe degeneration of their sacroiliac joint than healthy controls. • The above findings significantly correlated with the scores of three self-reported questionnaires about pain and function, implicating that they may be of clinical significance.

Imaging Techniques to Differentiate Benign Testicular Masses from Germ Cell Tumors

PURPOSE OF REVIEW

To discuss role of different diagnostic imaging modalities in differentiation of benign testicular masses from seminomatous germ cell tumors (SGCTs) and non-seminomatous GCTs (NSGCTs).

RECENT FINDINGS

New modalities of ultrasonography, including contrast enhancement and shear wave elastography, may help differentiate between benign and malignant intratesticular lesions. Ultrasonography remains the recommended imaging modality for initial evaluation of testicular masses. However, MRI can be used to better define equivocal testicular lesions on US.

Neuroimaging in acute infection-triggered encephalopathy syndromes

Acute encephalopathy associated with infectious diseases occurs frequently in Japanese children (400-700 children/year) and is the most common in infants aged 0-3 years. Acute encephalopathy is classified into several clinicoradiological syndromes; acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is the most common subtype, followed by clinically mild encephalitis/encephalopathy with a reversible splenial lesion (MERS) and acute necrotizing encephalopathy (ANE). Neuroimaging, especially magnetic resonance imaging (MRI), is useful for the diagnosis, assessment of treatment efficacy, and evaluation of the pathophysiology of encephalopathy syndromes. MRI findings essential for diagnosis include delayed subcortical reduced diffusion (bright tree appearance) for AESD, reversible splenial lesions with homogeneously reduced diffusion for MERS, and symmetric hemorrhagic thalamic lesions for ANE. We reviewed several MRI techniques that have been applied in recent years, including diffusion-weighted imaging for the characterization of cerebral edema, arterial spin labeling for evaluating cerebral perfusion, and magnetic resonance spectroscopy for evaluating metabolic abnormality.