Diffusion weighted imaging: Technique and applications

Comparison of Echo Planar and Turbo Spin Echo Diffusion-Weighted Imaging in Intraoperative MRI

BACKGROUND

Diffusion-weighted imaging (DWI) is routinely used in brain tumor surgery guided by intraoperative MRI (IoMRI). However, conventional echo planar imaging DWI (EPI-DWI) is susceptible to distortion and artifacts that affect image quality. Turbo spin echo DWI (TSE-DWI) is an alternative technique with minimal spatial distortions that has the potential to be the radiologically preferred sequence.

PURPOSE

To compare via single- and multisequence assessment EPI-DWI and TSE-DWI in the IoMRI setting to determine whether there is a radiological preference for either sequence.

STUDY TYPE

Retrospective.

POPULATION

Thirty-four patients (22 female) aged 2-61 years (24 under 18 years) undergoing IoMRI during surgical resection of intracranial tumors.

FIELD STRENGTH/SEQUENCE

3-T, EPI-DWI, and TSE-DWI.

ASSESSMENT

Patients were scanned with EPI- and TSE-DWI as part of the standard IoMRI scanning protocol. A single-sequence assessment of spatial distortion and image artifact was performed by three neuroradiologists blinded to the sequence type. Images were scored regarding distortion and artifacts, around and remote to the resection cavity. A multisequence radiological assessment was performed by three neuroradiologists in full radiological context including all other IoMRI sequences from each case. The DWI images were directly compared with scorings of the radiologists on which they preferred with respect to anatomy, abnormality, artifact, and overall preference.

STATISTICAL TESTS

Wilcoxon signed-rank tests for single-sequence assessment, weighted kappa for single and multisequence assessment. A P-value <0.001 was considered statistically significant.

RESULTS

For the blinded single-sequence assessment, the TSE-DWI sequence was scored equal to or superior to the EPI-DWI sequence for distortion and artifacts, around and remote to the resection cavity for every case. In the multisequence assessment, all radiologists independently expressed a preference for TSE-DWI over EPI-DWI sequences on viewing brain anatomy, abnormalities, and artifacts.

DATA CONCLUSION

The TSE-DWI sequences may be favored over EPI-DWI for IoMRI in patients with intracranial tumors.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 5.

Review of mammography screening guidelines of the 5 largest global economies

Breast cancer is the number one cancer among women globally. Breast imaging-based screening is important for the early detection of breast cancer and decreases mortality rates significantly. Breast cancer screening guidelines vary worldwide, and it is important to know about the variations in screening guidelines in different countries. Japan, China, and Germany are three countries with national screening programs only while, the United States and India have nationally recommended guidelines but not national screening programs. In this review, we aim to outline the screening guidelines in the 5 countries with the highest Gross Domestic Product and offer insights into relevant screening practices across different nations.

White matter changes in paediatric bipolar disorder: A systematic review of diffusion magnetic resonance imaging studiesA systematic review of diffusion magnetic resonance imaging studies

BACKGROUND

Paediatric bipolar disorder (PBD) is characterized by severe mood fluctuations that deviate from typical childhood emotional development. Despite the efforts, the pathophysiology of this disorder is not well understood yet. In this review, we aimed to synthesize existing diffusion magnetic resonance imaging (dMRI) research findings in PBD.

METHODS

A literature search was conducted using PubMed, Embase, Scopus, and Web of Science databases to identify relevant studies published before April 2024.

RESULTS

A total of 23 studies were included in the review. The findings showed variations of fractional anisotropy (FA), axial diffusivity, radial diffusivity, and apparent diffusion coefficient in PBD compared to healthy controls (HCs). Key findings included decreased FA in the anterior cingulate, anterior corona radiata, and corpus callosum, particularly the genu, which correlated with clinical symptoms. Furthermore, longitudinal studies emphasized the significance of the uncinate fasciculus as having atypical developmental trajectories in PBD compared to HCs. In addition, graph analysis revealed widespread changes in structural connectivity, especially affecting the orbitofrontal cortex, frontal gyrus, and basal ganglia. Lastly, machine learning models showed promising results in differentiating PBD from HCs.

LIMITATIONS

Cross-sectional design of the studies, small sample sizes, and different imaging protocols preclude integration of the findings.

CONCLUSION

PBD seems to be associated with widespread structural changes compared to HC. Understanding these changes, which might account for the clinical manifestations of this disorder, increase our knowledge of the neurobiological underpinnings of PBD. This, in turn, may help develop more effective treatments for this disorder.

Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysis

Background

Atrial fibrillation (AF) represents a major risk factor of ischemic stroke recurrence with serious management implications. However, it often remains undiagnosed due to lack of standard or prolonged cardiac rhythm monitoring. We aim to create a novel end-to-end artificial intelligence (AI) model that uses MRI data to rapidly identify high AF risk in patients who suffer from an acute ischemic stroke.

Methods

This study comprises an internal retrospective cohort and a prospective cohort from Shanghai sixth people's hospital to train and validate an MRI-based AI model. Between January 1, 2018 and December 31, 2021, 510 patients were retrospectively enrolled for algorithm development and performance was measured using fivefold cross-validation. Patients from this trial were registered with http://www.chictr.org.cn, ChiCTR2200056385. Between September 1, 2022 and July 31, 2023, 73 patients were prospectively enrolled for algorithm test. An external cohort of 175 patients from Huashan Hospital, Minhang Hospital, and Shanghai Tenth People's Hospital was also enrolled retrospectively for further model validation. A combined classifier leveraging pre-defined radiomics features and de novo features extracted by convolutional neural network (CNN) was proposed to identify underlying AF in acute ischemic stroke patients. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated for model evaluation.

Findings

The top-performing combined classifier achieved an AUC of 0.94 (95% CI, 0.90-0.98) in the internal retrospective validation group, 0.85 (95% CI, 0.79-0.91) in the external validation group, and 0.87 (95% CI, 0.90-0.98) in the prospective test group. Based on subgroup analysis, the AI model performed well in female patients, patients with NIHSS > 4 or CHA2DS2-VASc ≤ 3, with the AUC of 0.91, 0.94, and 0.90, respectively. More importantly, our proposed model identified all the AF patients that were diagnosed with Holter monitoring during index stroke admission.

Interpretation

Our work suggested a potential association between brain ischemic lesion pattern on MR images and underlying AF. Furthermore, with additional validation, the AI model we developed may serve as a rapid screening tool for AF in clinical practice of stroke units.

Funding

This work was supported by grants from the National Natural Science Foundation of China (NSFC, Grant Number: 81871102 and 82172068); Shanghai Jiao Tong University School of Medicine, Two-Hundred Talent Program as Research Doctor (Grant Number: SBR202204); Municipal Science and Technology Commission Medical Innovation Project of Shanghai, (Grant/Award Number: 20Y11910200); Research Physician Program of Shanghai Shen Kang Hospital Development Center (Grant Number: SHD2022CRD039) to Dr. Dong Huang and the SJTU Trans-med Awards Research (No. 20220101) to Dahong Qian.

Identifying Biomarkers for Remyelination and Recovery in Multiple Sclerosis: A Measure of Progress

Background: Multiple sclerosis (MS) pathology is characterized by acute and chronic inflammation, demyelination, axonal injury, and neurodegeneration. After decades of research into MS-related degeneration, recent efforts have shifted toward recovery and the prevention of further damage. A key area of focus is the remyelination process, where researchers are studying the effects of pharmacotherapy on myelin repair mechanisms. Multiple compounds are being tested for their potential to foster remyelination in different clinical settings through the application of less or more complex techniques to assess their efficacy. Objective: To review current methods and biomarkers to track myelin regeneration and recovery over time in people with MS (PwMS), with potential implications for promyelinating drug testing. Methods: Narrative review, based on a selection of PubMed articles discussing techniques to measure in vivo myelin repair and functional recovery in PwMS. Results: Non-invasive tools, such as structural Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), are being implemented to track myelin repair, while other techniques like evoked potentials, functional MRI, and digital markers allow the assessment of functional recovery. These methods, alone or in combination, have been employed to obtain precise biomarkers of remyelination and recovery in various clinical trials on MS. Conclusions: Combining different techniques to identify myelin restoration in MS could yield novel biomarkers, enhancing the accuracy of clinical trial outcomes for remyelinating therapies in PwMS.

The role of MRI in perianal fistulizing disease: diagnostic imaging and classification systems to monitor disease activity

Perianal fistulizing disease, commonly associated with Crohn's disease, poses significant diagnostic and therapeutic challenges due to its complex anatomy and high recurrence rates. Radiological imaging plays a pivotal role in the accurate diagnosis, classification, and management of this condition. This article reviews the current radiological modalities employed in the evaluation of perianal fistulizing disease, including magnetic resonance imaging (MRI), endoanal ultrasound, and computed tomography (CT). MRI, recognized as the gold standard, offers superior soft tissue contrast and multiplanar capabilities, facilitating detailed assessment of fistula tracts and associated abscesses. CT, although less detailed than MRI, remains valuable in acute settings for detecting abscesses and guiding drainage procedures. This article discusses the advantages and limitations of each modality, highlights the importance of standardized imaging protocols, and underscores the need for interdisciplinary collaboration in the management of perianal fistulizing disease. Future directions include advancements in imaging techniques and the integration of artificial intelligence to enhance diagnostic accuracy and treatment outcomes.

Attention-Based Q-Space Deep Learning Generalized for Accelerated Diffusion Magnetic Resonance Imaging

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusion weighted signals along multiple directions, which is widely used in the quantification of microstructures. Obtaining microscopic parameters requires dense sampling in the q space, leading to significant time consumption. The most popular approach to accelerating dMRI acquisition is to undersample the q-space data, along with applying deep learning methods to reconstruct quantitative diffusion parameters. However, the reliance on a predetermined q-space sampling strategy often constrains traditional deep learning-based reconstructions. The present study proposed a novel deep learning model, named attention-based q-space deep learning (aqDL), to implement the reconstruction with variable q-space sampling strategies. The aqDL maps dMRI data from different scanning strategies onto a common feature space by using a series of Transformer encoders. The latent features are employed to reconstruct dMRI parameters via a multilayer perceptron. The performance of the aqDL model was assessed utilizing the Human Connectome Project datasets at varying undersampling numbers. To validate its generalizability, the model was further tested on two additional independent datasets. Our results showed that aqDL consistently achieves the highest reconstruction accuracy at various undersampling numbers, regardless of whether variable or predetermined q-space scanning strategies are employed. These findings suggest that aqDL has the potential to be used on general clinical dMRI datasets.

The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review

INTRODUCTION

The prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies. Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are two prominent neurodegenerative conditions that present diagnostic challenges due to overlapping symptoms. To address these challenges, experts utilize a range of imaging techniques, including magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). These techniques facilitate a detailed examination of the manifestations of these diseases. Recent research has demonstrated the potential of artificial intelligence (AI) in automating the diagnostic process, generating significant interest in this field.

MATERIALS AND METHODS

This narrative review aims to compile and analyze articles related to the AI-assisted diagnosis of FTD and AD. We reviewed 31 articles published between 2012 and 2024, with 23 focusing on machine learning techniques and 8 on deep learning techniques. The studies utilized features extracted from both single imaging modalities and multi-modal approaches, and evaluated the performance of various classification models.

RESULTS

Among the machine learning studies, Support Vector Machines (SVM) exhibited the most favorable performance in classifying FTD and AD. In deep learning studies, the ResNet convolutional neural network outperformed other networks.

CONCLUSION

This review highlights the utility of different imaging modalities as diagnostic aids in distinguishing between FTD and AD. However, it emphasizes the importance of incorporating clinical examinations and patient symptom evaluations to ensure comprehensive and accurate diagnoses.

Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review

Background and Objective

Diagnosis of pathology in the mediastinum has proven quite challenging, given the wide variability of both benign and malignant diseases that affect a diverse array of structures. This complexity has led to the development of many different non-invasive and invasive diagnostic modalities. Historically, diagnosis of the mediastinum has relied on different imaging modalities such as chest X-ray, computed tomography (CT), magnetic resonance imaging, and positron emission topography. Once a suspicious lesion was identified with one of these techniques, the gold standard for diagnosis was mediastinoscopy for diagnosis and staging of disease. More recently, many minimally invasive techniques such as CT-guided biopsy, endobronchial ultrasound with transbronchial needle aspiration, and endoscopic ultrasound with fine needle aspiration have revolutionized the diagnosis of the mediastinum. This review provides a comprehensive analysis of all the modalities available for diagnosing mediastinal disease with an emphasis on bronchoscopic techniques.

Methods

Literature search was performed via the PubMed database. We included all types of articles and study designs, including original research, meta-analyses, reviews, and abstracts.

Key Content and Findings

Minimally invasive techniques such as endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA) and endoscopic ultrasound-fine needle aspiration (EUS-FNA) have demonstrated high diagnostic yield and low complication rate and have made a significant difference in the time to diagnosis and lives of patients. There continues to be innovation in the field of bronchoscopy with the development of new technologies such as confocal laser endomicroscopy, optical coherence tomography, and artificial intelligence.

Conclusions

Bronchoscopy is and will continue to be an integral modality in minimally invasive diagnosis of the mediastinum.

Spherical-deconvolution informed filtering of tractograms changes laterality of structural connectome

Diffusion MRI-driven tractography, a non-invasive technique that reveals how the brain is connected, is widely used in brain lateralization studies. To improve the accuracy of tractography in showing the underlying anatomy of the brain, various tractography filtering methods were applied to reduce false positives. Based on different algorithms, tractography filtering methods are able to identify the fibers most consistent with the original diffusion data while removing fibers that do not align with the original signals, ensuring the tractograms are as biologically accurate as possible. However, the impact of tractography filtering on the lateralization of the brain connectome remains unclear. This study aims to investigate the relationship between fiber filtering and laterality changes in brain structural connectivity. Three typical tracking algorithms were used to construct the raw tractography, and two popular fiber filtering methods(SIFT and SIFT2) were employed to filter the tractography across a range of parameters. Laterality indices were computed for six popular biological features, including four microstructural measures (AD, FA, RD, and T1/T2 ratio) and two structural features (fiber length and connectivity) for each brain region. The results revealed that tractography filtering may cause significant laterality changes in more than 10% of connections, up to 25% for probabilistic tracking, and deterministic tracking exhibited minimal laterality changes compared to probabilistic tracking, experiencing only about 6%. Except for tracking algorithms, different fiber filtering methods, along with the various biological features themselves, displayed more variable patterns of laterality change. In conclusion, this study provides valuable insights into the intricate relationship between fiber filtering and laterality changes in brain structural connectivity. These findings can be used to develop improved tractography filtering methods, ultimately leading to more robust and reliable measurements of brain asymmetry in lateralization studies.

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.

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.

Single-shot multi-b-value (SSMb) diffusion-weighted MRI using spin echo and stimulated echoes with variable flip angles

Conventional diffusion-weighted imaging (DWI) sequences employing a spin echo or stimulated echo sensitize diffusion with a specific b-value at a fixed diffusion direction and diffusion time (Δ). To compute apparent diffusion coefficient (ADC) and other diffusion parameters, the sequence needs to be repeated multiple times by varying the b-value and/or gradient direction. In this study, we developed a single-shot multi-b-value (SSMb) diffusion MRI technique, which combines a spin echo and a train of stimulated echoes produced with variable flip angles. The method involves a pair of 90° radio frequency (RF) pulses that straddle a diffusion gradient lobe (GD), to rephase the magnetization in the transverse plane, producing a diffusion-weighted spin echo acquired by the first echo-planar imaging (EPI) readout train. The magnetization stored along the longitudinal axis is successively re-excited by a series of n variable-flip-angle pulses, each followed by a diffusion gradient lobe GD and a subsequent EPI readout train to sample n stimulated-echo signals. As such, (n + 1) diffusion-weighted images, each with a distinct b-value, are acquired in a single shot. The SSMb sequence was demonstrated on a diffusion phantom and healthy human brain to produce diffusion-weighted images, which were quantitative analyzed using a mono-exponential model. In the phantom experiment, SSMb provided similar ADC values to those from a commercial spin-echo EPI (SE-EPI) sequence (r = 0.999). In the human brain experiment, SSMb enabled a fourfold scan time reduction and yielded slightly lower ADC values (0.83 ± 0.26 μm2/ms) than SE-EPI (0.88 ± 0.29 μm2/ms) in all voxels excluding cerebrospinal fluid, likely due to the influence of varying diffusion times. The feasibility of using SSMb to acquire multiple images in a single shot for intravoxel incoherent motion (IVIM) analysis was also demonstrated. In conclusion, despite a relatively low signal-to-noise ratio, the proposed SSMb technique can substantially increase the data acquisition efficiency in DWI studies.

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.

Characterisation of Paediatric Neuroblastic Tumours by Quantitative Structural and Diffusion-Weighted MRI

Purpose: To determine the diagnostic accuracy of quantitative diffusion-weighted (DW) MRI apparent diffusion coefficient (ADC) and tumour volumes to differentiate between malignant (neuroblastoma (NB)) and benign types of neuroblastic tumours (ganglioneuroma (GN) and ganglioneuroblastoma (GNB)) using different region-of-interest (ROI) sizes. Materials and Methods: This single-centre retrospective study included malignant and benign paediatric neuroblastic tumours that had undergone DW MRI at diagnosis. The outcome was diagnostic accuracy of the tumour volume from structural and ADC DW MRI, in comparison to histopathology (reference standard). Results: Data from 40 patients (NB, n = 24; GNB, n = 6; GN, n = 10), 18 (45%) females and 22 (55%) males, with a median age at diagnosis of 21 months (NB), 64 months (GNB), and 133 months (GN), respectively, ranging from 0 to 193 months, were evaluated. The area under the receiver operating characteristic (AUROC) curve for ADC for discriminating between neuroblastic tumours' histopathology for a small ROI was 0.86 (95% CI: 0.75-0.98), and for a large ROI, 0.83 (95% CI: 0.71-0.96). An ADC cut-off value of 1.06 × 10-3 mm2/s was able to distinguish malignant from benign tumours with 83% (68-98%) sensitivity and 75% (95% CI: 54-98%) specificity. Tumour volume was not indicative of malignant vs. benign tumour diagnosis. Conclusions: In this study, both small and large ROIs used to derive ADC DW MRI metrics demonstrated high accuracy to differentiate malignant from benign neuroblastic tumours, with the ADC AUROC for the averaged multiple small ROIs being slightly greater than that of large ROIs, but with overlapping 95% CIs. This should be taken into consideration for standardisation of ROI-related data analysis by international initiatives.

Magnetic Resonance Imaging Assessment of Morphological Changes and Molecular Behavior to Evaluate Treatment Response of Brain Metastatic Lesions After Stereotactic Radiosurgery

BACKGROUND

Brain metastases (BMs) are the most common type of intracranial tumors, frequently arising from primary cancers such as lung, breast, melanoma, and renal cell carcinoma. Magnetic resonance imaging (MRI) plays a crucial role in assessing both the morphological and molecular characteristics of BMs, particularly in evaluating treatment response following radiosurgery. However, the interpretation of these imaging changes remains complex, often influencing clinical decision-making.

OBJECTIVE

This study aims to evaluate the morphological changes and molecular behavior of BMs postradiosurgery using MRI to assess treatment response.

MATERIALS AND METHODS

A retrospective review was conducted at a high specialty medical center, including 41 patients with BMs treated with stereotactic radiosurgery (SRS) from 2018 to 2022. Patients had a baseline MRI (pre-SRS) prior to treatment and follow-ups at 2-3 months (MRI-2) and 5-6 months (MRI-3). The response assessment in neuro-oncology brain metastases (RANO-BM) criteria were used, and T1/T2 matching was analyzed for each follow-up. Logistic regression was performed relating the T1/T2 matching and susceptibility areas (susceptibility-weighted imaging (SWI)) for MRI-2 and MRI-3. Cross tables were created regarding treatment response and demographic characteristics according to Pearson's Chi-squared test.

RESULTS

The mean age was 56.7 years; 53.7% (n = 22) were female. Primary tumors included lung (29.3%, n = 12), breast (19.5%, n = 8), colon (12.2%, n = 5), and melanoma and kidney tumors (7.3%, n = 3). Post-SRS changes included transitions from solid to cystic lesions, reduced perilesional edema, size reduction, and increased areas of magnetic susceptibility. A mixed pattern (areas of T1/T2 match + mismatch) was noted at lesion margins during follow-ups (MRI-2: 70.7% (n = 29), MRI-3: 68.3% (n = 28)). Most patients exhibited a partial response at MRI-2 (43.9%, n = 18), while at MRI-3, disease progression occurred (43.9%, n = 18) due to an increase in lesion number. Logistic regression linking T1/T2 matching and SWI demonstrated a significantly central-peripheral SWI distribution for T1/T2 match during both follow-ups (MRI-2: p = 0.005, R2: 0.52; MRI-3: p = 0.002, R2: 0.56). SWI distribution was higher when a mixed T1/T2 matching was present. Significant associations were found with systemic treatment and response type at MRI-2 (p =0.001), predominantly showing a partial response for those receiving chemotherapy + targeted therapy.

CONCLUSIONS

SWI and T1/T2 mismatch are valuable tools reflecting changes in the tumor microenvironment postradiosurgery, aiding in treatment response monitoring. The appearance of susceptibility areas may precede changes in the enhancement of the lesion margin. Short-term follow-ups (2-3 months) are crucial due to prevalent progression, marked primarily by the appearance of new lesions in approximately 50% of patients.

ACR Appropriateness Criteria® Acute Onset of Scrotal Pain-Without Trauma, Without Antecedent Mass: 2024 Update

Acute scrotum is a medical emergency that requires prompt accurate diagnosis to appropriately triage potentially surgical conditions. Numerous differential diagnoses with overlapping clinical presentations make this a diagnostic challenge. Ultrasound is the established first-line imaging modality for acute scrotal disease and can be used to diagnose most scrotal disorders promptly and with high accuracy. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

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.

Insights into pathophysiology, management, and outcomes of near-hanging patients: A narrative review

Hanging is suspension of a person by noose or ligature around the neck. Hanging has been used as a judicial execution method since medieval times and is still a common method of suicide in developing countries. The term "near hanging" is used for those who survive long enough to reach the hospital, and these patients still hold mortality as high as 45%. These patients can present with a wide array of complaints involving cardiovascular, neurologic, and pulmonary systems and local injuries to the airway and vascular structures. High serum lactate and cardiac arrest at arrival are some of the prognostic factors which portend a poor prognosis. Early admission and aggressive treatment are essential to provide best neurologic outcome. Due to paucity of definitive guidelines, therapy has to be based on clinical reports and expertise of the treating physician. Hence, near-hanging patients pose a great challenge in primary emergency care and intensive care management. This review aims to outline the pathophysiology, prognostic indicators, and recent evidence in the management of near-hanging patients.

A Pilot Study of Adolescents with Psychotic Experiences: Potential Cerebellar Circuitry Disruption Early Along the Psychosis Spectrum

A berrant connectivity in the cerebellum has been found in psychotic conditions such as schizophrenia corresponding with cognitive and motor deficits found in these conditions. Diffusion differences in the superior cerebellar peduncles, the white matter connecting the cerebellar circuitry to the rest of the brain, have also been found in schizophrenia and high-risk states. However, white matter diffusivity in the peduncles in individuals with sub-threshold psychotic experiences (PEs) but not reaching the threshold for a definitive diagnosis remains unstudied. This study investigates the cerebellar peduncles in adolescents with PEs but no formal psychiatric diagnosis.Sixteen adolescents with PEs and 17 age-matched controls recruited from schools underwent High-Angular-Resolution-Diffusion neuroimaging. Following constrained spherical deconvolution whole-brain tractography, the superior, inferior and middle peduncles were isolated and virtually dissected out using ExploreDTI. Differences for macroscopic and microscopic tract metrics were calculated using one-way between-group analyses of covariance controlling for age, sex and estimated Total Intracranial Volume (eTIV). Multiple comparisons were corrected using Bonferroni correction.A decrease in fractional anisotropy was identified in the right (p = 0.045) and left (p = 0.058) superior cerebellar peduncle; however, this did not survive strict Bonferroni multiple comparison correction. There were no differences in volumes or other diffusion metrics in either the middle or inferior peduncles.Our trend level changes in the superior cerebellar peduncle in a non-clinical sample exhibiting psychotic experiences complement similar but more profound changes previously found in ultra-high-risk individuals and those with psychotic disorders. This suggests that superior cerebellar peduncle circuitry perturbations may occur early along in the psychosis spectrum.

Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging - A Symposium Review

Advances in the spatiotemporal resolution and field-of-view of neuroimaging tools are driving mesoscale studies for translational neuroscience. On October 10, 2023, the Center for Mesoscale Mapping (CMM) at the Massachusetts General Hospital (MGH) Athinoula A. Martinos Center for Biomedical Imaging and the Massachusetts Institute of Technology (MIT) Health Sciences Technology based Neuroimaging Training Program (NTP) hosted a symposium exploring the state-of-the-art in this rapidly growing area of research. "Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging" brought together researchers who use a broad range of imaging techniques to study brain structure and function at the convergence of the microscopic and macroscopic scales. The day-long event centered on areas in which the CMM has established expertise, including the development of emerging technologies and their application to clinical translational needs and basic neuroscience questions. The in-person symposium welcomed more than 150 attendees, including 57 faculty members, 61 postdoctoral fellows, 35 students, and four industry professionals, who represented institutions at the local, regional, and international levels. The symposium also served the training goals of both the CMM and the NTP. The event content, organization, and format were planned collaboratively by the faculty and trainees. Many CMM faculty presented or participated in a panel discussion, thus contributing to the dissemination of both the technologies they have developed under the auspices of the CMM and the findings they have obtained using those technologies. NTP trainees who benefited from the symposium included those who helped to organize the symposium and/or presented posters and gave "flash" oral presentations. In addition to gaining experience from presenting their work, they had opportunities throughout the day to engage in one-on-one discussions with visiting scientists and other faculty, potentially opening the door to future collaborations. The symposium presentations provided a deep exploration of the many technological advances enabling progress in structural and functional mesoscale brain imaging. Finally, students worked closely with the presenting faculty to develop this report summarizing the content of the symposium and putting it in the broader context of the current state of the field to share with the scientific community. We note that the references cited here include conference abstracts corresponding to the symposium poster presentations.

Unveiling the Exquisite Microstructural Details in Zebrafish Brain Non-Invasively Using Magnetic Resonance Imaging at 28.2 T

Zebrafish (Danio rerio) is an important animal model for a wide range of neurodegenerative diseases. However, obtaining the cellular resolution that is essential for studying the zebrafish brain remains challenging as it requires high spatial resolution and signal-to-noise ratios (SNR). In the current study, we present the first MRI results of the zebrafish brain at the state-of-the-art magnetic field strength of 28.2 T. The performance of MRI at 28.2 T was compared to 17.6 T. A 20% improvement in SNR was observed at 28.2 T as compared to 17.6 T. Excellent contrast, resolution, and SNR allowed the identification of several brain structures. The normative T1 and T2 relaxation values were established over different zebrafish brain structures at 28.2 T. To zoom into the white matter structures, we applied diffusion tensor imaging (DTI) and obtained axial, radial, and mean diffusivity, as well as fractional anisotropy, at a very high spatial resolution. Visualisation of white matter structures was achieved by short-track track-density imaging by applying the constrained spherical deconvolution method (stTDI CSD). For the first time, an algorithm for stTDI with multi-shell multi-tissue (msmt) CSD was tested on zebrafish brain data. A significant reduction in false-positive tracks from grey matter signals was observed compared to stTDI with single-shell single-tissue (ssst) CSD. This allowed the non-invasive identification of white matter structures at high resolution and contrast. Our results show that ultra-high field DTI and tractography provide reproducible and quantitative maps of fibre organisation from tiny zebrafish brains, which can be implemented in the future for a mechanistic understanding of disease-related microstructural changes in zebrafish models of various brain diseases.

Ex-Vivo Human-Sized Organ Machine Perfusion: A Systematic Review on the Added Value of Medical Imaging for Organ Condition Assessment

Machine perfused ex-vivo organs offer an excellent experimental platform, e.g., for studying organ physiology and for conducting pre-clinical trials for drug delivery. One main challenge in machine perfusion is the accurate assessment of organ condition. Assessment is often performed using viability markers, i.e., lactate concentrations and blood gas analysis. Nonetheless, existing markers for condition assessment can be inconclusive, and novel assessment methods remain of interest. Over the last decades, several imaging modalities have given unique insights into the assessment of organ condition. A systematic review was conducted according to accepted guidelines to evaluate these medical imaging methods, focussed on literature that use machine perfused human-sized organs, that determine organ condition with medical imaging. A total of 18 out of 1,465 studies were included that reported organ condition results in perfused hearts, kidneys, and livers, using both conventional viability markers and medical imaging. Laser speckle imaging, ultrasound, computed tomography, and magnetic resonance imaging were used to identify local ischemic regions and quantify intra-organ perfusion. A detailed investigation of metabolic activity was achieved using 31P magnetic resonance imaging and near-infrared spectroscopy. The current review shows that medical imaging is a powerful tool to assess organ condition.

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.

Advances in Imaging for Metastatic Epidural Spinal Cord Compression: A Comprehensive Review of Detection, Diagnosis, and Treatment Planning

Metastatic epidural spinal cord compression (MESCC) is a critical oncologic emergency caused by the invasion of metastatic tumors into the spinal epidural space, leading to compression of the spinal cord. If not promptly diagnosed and treated, MESCC can result in irreversible neurological deficits, including paralysis, significantly impacting the patient's quality of life. Early detection and timely intervention are crucial to prevent permanent damage. Imaging modalities play a pivotal role in the diagnosis, assessment of disease extent, and treatment planning for MESCC. Magnetic resonance imaging (MRI) is the current gold standard due to its superior ability to visualize the spinal cord, epidural space, and metastatic lesions. However, recent advances in imaging technologies have enhanced the detection and management of MESCC. Innovations such as functional MRI, diffusion-weighted imaging (DWI), and hybrid techniques like positron emission tomography-computed tomography (PET-CT) and PET-MRI have improved the accuracy of diagnosis, particularly in detecting early metastatic changes and guiding therapeutic interventions. This review provides a comprehensive analysis of the evolution of imaging techniques for MESCC, focusing on their roles in detection, diagnosis, and treatment planning. It also discusses the impact of these advances on clinical outcomes and future research directions in imaging modalities for MESCC. Understanding these advancements is critical for optimizing the management of MESCC and improving patient prognosis.

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.

Differential Tractography: A Biomarker for Neuronal Function in Neurodegenerative Disease

GM1 gangliosidosis is an ultra-rare inherited neurodegenerative lysosomal storage disorder caused by biallelic mutations in the GLB1 gene. GM1 is uniformly fatal and has no approved therapies, although clinical trials investigating gene therapy as a potential treatment for this condition are underway. Novel outcome measures or biomarkers demonstrating the longitudinal effects of GM1 and potential recovery due to therapeutic intervention are urgently needed to establish efficacy of potential therapeutics. One promising tool is differential tractography, a novel imaging modality utilizing serial diffusion weighted imaging (DWI) to quantify longitudinal changes in white matter microstructure. In this study, we present the novel use of differential tractography in quantifying the progression of GM1 alongside age-matched neurotypical controls. We analyzed 113 DWI scans from 16 GM1 patients and 32 age-matched neurotypical controls to investigate longitudinal changes in white matter pathology. GM1 patients showed white matter degradation evident by both the number and size of fiber tract loss. In contrast, neurotypical controls showed longitudinal white matter improvements as evident by both the number and size of fiber tract growth. We also corroborated these findings by documenting significant correlations between cognitive global impression (CGI) scores of clinical presentations and our differential tractography derived metrics in our GM1 cohort. Specifically, GM1 patients who lost more neuronal fiber tracts also had a worse clinical presentation. This result demonstrates the importance of differential tractography as an important biomarker for disease progression in GM1 patients with potential extension to other neurodegenerative diseases and therapeutic intervention.

Insight Into Uncommon Territory: Exploring Internuclear Ophthalmoplegia in Artery of Percheron Infarct

Internuclear ophthalmoplegia (INO), a neurological disorder is characterized by horizontal gaze palsy because of a lesion in the medial longitudinal fasciculus, a neural pathway that is mainly responsible for coordinating the movements of the eye. INO presents with diplopia and impaired adduction of the affected eye, accompanied by abducting eye nystagmus. The condition also arises from different etiologies which include multiple sclerosis, encephalitis, Lyme disease, HIV, and herpes zoster. Artery of Percheron (AOP) infarction is a subtype of bilateral thalamic infarction that poses a unique form of diagnostic perplexity due to its varied and often non-specific clinical manifestations such as altered responsiveness, memory disturbances, and oculomotor deficits. Here we discuss a 53-year-old female who presented with INO in the context of an AOP infarct. Under this context, the clinical finding includes some paradigms like nystagmus, anisocoria, and bilateral ptosis. Magnetic resonance imaging confirmed an acute infarct in the AOP territory, which supplies the rostral midbrain and paramedian thalami. This case emphasizes the critical importance of meticulous clinical evaluation and the utilization of advanced imaging techniques in diagnosing rare stroke syndromes like AOP infarction. Management of the patient included dual antiplatelet therapy to prevent further thromboembolic events and supportive care to address the immediate neurological deficits. Early recognition and prompt treatment are crucial for better patient outcomes. Long-term management focuses on the secondary prevention of stroke through lifestyle modifications, medical therapy, and regular monitoring. Education on uncommon stroke syndromes and continued research are essential for enhancing the accuracy of diagnosis and efficacy of treatment which ultimately leads to better patient care and prognosis.

Microstructural Changes in the Corpus Callosum in Neurodegenerative Diseases

The corpus callosum, the largest white matter structure in the brain, plays a crucial role in interhemispheric communication and cognitive function. This review examines the microstructural changes observed in the corpus callosum across various neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis (ALS). New neuroimaging studies, mainly those that use diffusion tensor imaging (DTI) and advanced tractography methods, were put together to show how changes have happened in the organization of white matter and the connections between them. Some of the most common ways the corpus callosum breaks down are discussed, including less fractional anisotropy, higher mean diffusivity, and atrophy in certain regions. The relationship between these microstructural changes and cognitive decline, motor dysfunction, and disease progression is explored. Additionally, we consider the potential of corpus callosum imaging as a biomarker for early disease detection and monitoring. Studies show that people with these disorders have lower fractional anisotropy and higher mean diffusivity in the corpus callosum, often in ways that are specific to the disease. These changes often happen before gray matter atrophy and are linked to symptoms, which suggests that the corpus callosum could be used as an early sign of neurodegeneration. The review also highlights the implications of these findings for understanding disease mechanisms and developing therapeutic strategies. Future directions, including the application of advanced imaging techniques and longitudinal studies, are discussed to elucidate the role of corpus callosum degeneration in neurodegenerative processes. This review underscores the importance of the corpus callosum in understanding the pathophysiology of neurodegenerative diseases and its potential as a target for therapeutic interventions.

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.

Harnessing technology to measure individual differences in spatial thinking in early childhood from a relational developmental systems perspective

According to the Relational Developmental Systems perspective, the development of individual differences in spatial thinking (e.g., mental rotation, spatial reorientation, and spatial language) are attributed to various psychological (e.g., children's cognitive strategies), biological (e.g., structure and function of hippocampus), and cultural systems (e.g., caregiver spatial language input). Yet, measuring the development of individual differences in spatial thinking in young children, as well as the psychological, biological, and cultural systems that influence the development of these abilities, presents unique challenges. The current paper outlines ways to harness available technology including eye-tracking, eye-blink conditioning, MRI, Zoom, and LENA technology, to study the development of individual differences in young children's spatial thinking. The technologies discussed offer ways to examine children's spatial thinking development from different levels of analyses (i.e., psychological, biological, cultural), thereby allowing us to advance the study of developmental theory. We conclude with a discussion of the use of artificial intelligence.

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.

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.

Apparent Diffusion Coefficient of the Optic Nerve Head in Idiopathic Intracranial Hypertension

Idiopathic Intracranial Hypertension (IIH) is a condition in which patients have elevated intracranial pressure which does not have an apparent cause. To diagnose IIH, evaluation excluding other causes of elevated pressure must be performed. This typically includes magnetic resonance imaging (MRI) of the brain and venous sinuses. Despite there being known radiographic signs suggestive of IIH on MRI, there currently are no established correlations between radiographic findings and visual outcomes. Previous work revealed diffusion weighted imaging (DWI), a qualitative measurement on MRI, correlated with clinical findings (i.e presence and grade of papilloedema), but not visual outcomes. We hypothesized that the apparent diffusion coefficient (ADC), a quantitative value obtained during clinical MRI, may correlate with visual outcomes. We conducted a retrospective chart review to correlate findings on the ADC sequence on routine brain MRIs in patients with papilloedema with visual outcomes. In 49 patients with IIH, this study shows the ADC in the retrobulbar optic nerve to be 1,487 ± 559 × 10-6 mm2 /s, 15% lower than reported value of 1744 ± 413 in healthy controls. This suggests that there is true restricted diffusion in patients with IIH and papilloedema, as previously reported visually by MRI. However, there was no significant correlation with clinical outcomes of papilloedema grade, mean deviation on standard perimetry, and retinal nerve fibre layer (RNFL) on optical coherence tomography (OCT). We discuss reasons why the ADC measurement may be confounded by motion and partial volume and propose methods that may reduce these confounders for future studies.

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.

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).