Clinical applications of diffusion weighted imaging in neuroradiology

Impact of spectrum bias on deep learning-based stroke MRI analysis

PURPOSE

To evaluate spectrum bias in stroke MRI analysis by excluding cases with uncertain acute ischemic lesions (AIL) and examining patient, imaging, and lesion factors associated with these cases.

MATERIALS AND METHODS

This single-center retrospective observational study included adults with brain MRIs for suspected stroke between January 2020 and April 2022. Diagnostic uncertain AIL were identified through reader disagreement or low certainty grading by a radiology resident, a neuroradiologist, and the original radiology report consisting of various neuroradiologists. A commercially available deep learning tool analyzing brain MRIs for AIL was evaluated to assess the impact of excluding uncertain cases on diagnostic odds ratios. Patient-related, MRI acquisition-related, and lesion-related factors were analyzed using the Wilcoxon rank sum test, χ2 test, and multiple logistic regression. The study was approved by the National Committee on Health Research Ethics.

RESULTS

In 989 patients (median age 73 (IQR: 59-80), 53% female), certain AIL were found in 374 (38%), uncertain AIL in 63 (6%), and no AIL in 552 (56%). Excluding uncertain cases led to a four-fold increase in the diagnostic odds ratio (from 68 to 278), while a simulated case-control design resulted in a six-fold increase compared to the full disease spectrum (from 68 to 431). Independent factors associated with uncertain AIL were MRI artifacts, smaller lesion size, older lesion age, and infratentorial location.

CONCLUSION

Excluding uncertain cases leads to a four-fold overestimation of the diagnostic odds ratio. MRI artifacts, smaller lesion size, infratentorial location, and older lesion age are associated with uncertain AIL and should be accounted for in validation studies.

Diffusion MRI with double diffusion encoding and variable mixing times disentangles water exchange from transient kurtosis

Double diffusion encoding (DDE) makes diffusion MRI sensitive to a wide range of microstructural features, and the acquired data can be analysed using different approaches. Correlation tensor imaging (CTI) uses DDE to resolve three components of the diffusional kurtosis: isotropic, anisotropic, and microscopic kurtosis. The microscopic kurtosis is estimated from the contrast between single diffusion encoding (SDE) and parallel DDE signals at the same b-value. Another approach is multi-Gaussian exchange (MGE), which employs DDE to measure exchange. Sensitivity to exchange is obtained by contrasting SDE and DDE signals at the same b-value. CTI and MGE exploit the same signal contrast to quantify microscopic kurtosis and exchange, and this study investigates the interplay between these two quantities. We perform Monte Carlo simulations in different geometries with varying levels of exchange and study the behaviour of the parameters from CTI and MGE. We conclude that microscopic kurtosis from CTI is sensitive to the exchange rate and that intercompartmental exchange and the transient kurtosis of individual compartments are distinct sources of microscopic kurtosis. In an attempt to disentangle these two sources, we propose a heuristic signal representation referred to as tMGE (MGE incorporating transient kurtosis) that accounts for both effects by exploiting the distinct signatures of exchange and transient kurtosis with varying mixing time: exchange causes a slow dependence of the signal on mixing time while transient kurtosis arguably has a much faster dependence. We find that applying tMGE to data acquired with multiple mixing times for both parallel and orthogonal DDE may enable estimation of the exchange rate as well as isotropic, anisotropic, and transient kurtosis.

Diffusion-Weighted Imaging Does Not Differentiate Between Bacterial and Fungal Skull Base Osteomyelitis

OBJECTIVE

Apparent diffusion coefficient (ADC) value helps in differentiating infections from neoplasms on magnetic resonance imaging (MRI). We investigate the diffusion-weighted images in skull base osteomyelitis (SBO) to evaluate if ADC values can differentiate fungal and bacterial SBO and to analyse the microbiology of all SBO patients.

DESIGN

Retrospective observational study.

SETTING

Quaternary care referral centre.

PARTICIPANTS

A retrospective review of 142 patients diagnosed and treated for SBO patients from January 2010 to May 2023 was done.

MAIN OUTCOME MEASURE

Chi-square or Fisher's exact test was used to compare ADC values of bacterial and fungal SBO.

RESULTS

The most common pathogens isolated were Pseudomonas (42.2%), Aspergillus (30.98%), and S. aureus (23.94%). The average ADC value of affected soft tissues among patients was 1.13 ± 0.26 × 10-3 mm2/s compared to the average ADC value of normal soft tissue, 1.34 ± 0.31 × 10-3 mm2/s. There was no statistical significance when comparing the average ADC values of bacterial and fungal SBO patients (p value = 0.142).

CONCLUSION

This study suggests that though infection due to Pseudomonas was the commonest, it was detected only in 42.2% of patients. More than half of the cases had organisms other than Pseudomonas, demanding the clinician to obtain deeper biopsies early in the course of the disease for microbiological analysis. DWI does not help differentiate bacterial and fungal SBO, again emphasising the need for deeper tissue biopsies in all these patients to assist in the early identification of the pathogen.

Comparison of SS-EPI DWI and one-minute TGSE-BLADE DWI for diagnosis of acute infarction

The efficacy of 2D turbo gradient- and spin-echo diffusion-weighted imaging with non-Cartesian BLADE trajectory (TGSE-BLADE DWI) has not been well studied for acute stroke due to its long acquisition time. This study was performed to compare distortion, artifacts and image quality between single-shot echo planar imaging (SS-EPI) DWI and TGSE-BLADE DWI with acquisition time reduced to 1 min by simultaneous multi-slice (SMS) imaging, and to evaluate the diagnostic performance of TGSE-BLADE DWI for acute infarctions. Total 104 patients with a past history of stroke or symptoms suspicious for acute infarction or who had undergone surgery for brain tumor within two days were prospectively enrolled. Ten lesions in 9 patients were diagnosed as acute or subacute infarction and were detectable only in TGSE-BLADE DWI but not in SS-EPI DWI. Scores for geometric distortion, susceptibility artifacts, overall image quality, lesion conspicuity and diagnostic confidence were lower for SS-EPI DWI than TGSE-BLADE DWI (p ≤ .001). Distortion was significantly worse in SS-EPI DWI than TGSE-BLADE DWI (p < .001). SNR of centrum semiovale was significantly higher in SS-EPI DWI than TGSE-BLADE DWI (p < .001). One-minute TGSE-BLADE DWI showed better image quality than SS-EPI DWI in terms of distortion and artifacts, and higher diagnostic performance for acute infarctions.

Neuroimaging findings and balance problems after mild traumatic brain injury: A systematic review protocol

OBJECTIVE

To systematically review studies relating neuroimaging findings to balance problems resulting from a history of mTBI.

INTRODUCTION

Mild traumatic brain injury affects 55.9 million people worldwide every year. These injuries can have persistent symptoms such as maintaining balance which can be life-altering. Difficulties maintaining balance persist months or years after a mild traumatic brain injury in >30% of patients. Neuroimaging modalities, including magnetic resonance imaging, diffusion-weighted imaging, functional magnetic resonance imaging, electroencephalography, and magnetoencephalography, have been associated with presentation or persistence of balance difficulties, but no clinical guidelines are currently in place.

INCLUSION CRITERIA

Studies will include participants of any age or sex who were diagnosed as having mild traumatic brain injury by a medical professional, excluding studies which by design included patients with other conditions diagnosed using neuroimaging findings. There must be at least one post-injury scan from at one or more of the included neuroimaging modalities, and assessment of balance problems. A comparator must be present in the form of either a control group or longitudinal design.

METHODS

A search will be conducted in Elsevier (Embase), MEDLINE (PubMed), Google Scholar, SportDiscus (EBSCOhost) and ProQuest for studies meeting the inclusion criteria, published 2013-2024, and available in English. Reviews will not be included. The process of study selection, critical assessment, data extraction, and summarizing findings will be conducted by two independent reviewers, with disagreements resolved by a third. The meta-analysis will summarize the strength of association between specific findings related to brain regions using various neuroimaging modalities and the presentation or persistence of balance difficulties. Evidence related to each neuroimaging modality will summarized using the GRADE approach.

TRIAL REGISTRATION

Systematic review registration number: CRD42024476988.

What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review

ABSTRACT

Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.

Simultaneous multi-slice technique for reducing acquisition times in diffusion tensor imaging of the knee: a feasibility study

OBJECTIVES

To explore the feasibility of simultaneous multi-slice (SMS) technique for reducing acquisition times in readout-segmented echo planar imaging (RESOLVE) for diffusion tensor imaging (DTI) of the knee.

MATERIALS AND METHODS

A total of 30 healthy volunteers and 23 patients with knee acute injury (12 cases with anterior ligament (ACL) tears and 16 cases with patellar cartilage (PC) injury) were enrolled in this prospective study. Three DTI protocols were used: conventional RESOLVE-DTI with 12 directions (protocol 1), SMS-RESOLVE-DTI with 12 directions (protocol 2) and 20 directions (protocol 3). DTI parameters of gastrocnemius, ACL and posterior cruciate ligament (PCL), and PC from three protocols were quantitatively assessed.

RESULTS

For volunteers, protocol 2 significantly reduced acquisition time by 38.6% and 34.2% compared to protocols 1 and 3 while maintaining similar high-quality images and similar diffusive parameters, except for the fractional anisotropy (FA) and axial diffusivity (AD) of the PC between protocols 2 and 1 (P < 0.05). For injured ACL and PC, protocols 1 and 2 showed similar accurate diffusive parameters (except for AD, P = 0.025) and similar diagnostic efficacy, which demonstrated significantly lower FA and higher radial diffusivity (RD) in protocols 1 and 2 compared to volunteers (P < 0.05).

CONCLUSIONS

The 12-direction SMS-RESOLVE-DTI demonstrated a favorable balance between acquisition time and image quality, making it a promising alternative to conventional DTI for evaluating ligament and cartilage injuries.

ADVANCES IN KNOWLEDGE

The SMS technique greatly reduces acquisition time while maintaining image quality, which signified the possibility of DTI's clinical application.

Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review

Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS. Published MS MRI-based computational studies can be divided into five categories: automated diagnosis; differentiation between lesion types and/or MS stages; differential diagnosis; monitoring and predicting disease progression; and synthetic MRI dataset generation. Collectively, these approaches show promise in assisting with MS diagnosis, monitoring of disease activity and prediction of future progression, all potentially contributing to disease management. Analysis quality using ML is highly dependent on the dataset size and variability used for training. Wider public access would mean larger datasets for experimentation, resulting in higher-quality analysis, permitting for more conclusive research. This narrative review provides an outline of the fundamentals of MS pathology and pathogenesis, diagnostic techniques and data types in computational analysis, as well as collating literature pertaining to the application of computational techniques to MRI towards developing a better understanding of MS.

Image quality comparison of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients: multi-rater assessments including PI-QUAL version 2

OBJECTIVES

To compare the image quality of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients, with a specific focus on the degree of susceptibility artifacts.

METHODS

This single-center retrospective study included post-hip arthroplasty patients who underwent 1.5T prostate MRIs between 2021 and 2023, as well as comparative 3T prostate MRIs. Three blinded abdominal radiologists retrospectively reviewed their diffusion-weighted imaging (DWI, 50 s/mm2), T2-weighted imaging (T2WI), and dynamic contrast-enhanced imaging (DCE) to evaluate the image quality. The degree of susceptibility artifacts was categorized using a three-point scale, with 3 indicating the least artifact and 1 indicating the most. Image quality was also evaluated using Prostate Imaging Quality (PI-QUAL) version 2. The median of the three raters' scores was compared between 1.5T and 3T prostate MRIs using the Wilcoxon signed-rank test. The inter-rater agreement was evaluated using the multi-rater generalized kappa.

RESULTS

Twenty pairs of 1.5T and 3T prostate MRI examinations from 20 unique patients were included. The DWI susceptibility artifact score at 1.5T was significantly higher than at 3T (mean score ± standard deviation, 2.80 ± 0.41 vs. 2.35 ± 0.93, p = 0.014). In contrast, no significant differences were observed in the susceptibility artifact scores in T2WI and DCE, or in the PI-QUAL score. The inter-reader agreement in the susceptibility artifact score was moderate (multi-rater generalized kappa: 0.60) in DWI, perfect in T2WI (not applicable), and substantial (0.65) in DCE. The inter-reader agreement was fair (0.27) in the PI-QUAL score.

CONCLUSION

Using 1.5T scanners may be preferable to reduce susceptibility artifacts from hip prostheses in DWI.

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.

Normal Brain and Brain Tumor ADC: Changes Resulting From Variation of Diffusion Time and/or Echo Time in Pulsed-Gradient Spin Echo Diffusion Imaging

OBJECTIVES

Increasing gradient performance on modern magnetic resonance imaging scanners has profoundly reduced the attainable diffusion and echo times for clinically available pulsed-gradient spin echo (PGSE) sequences. This study investigated how this may impact the measured apparent diffusion coefficient (ADC), which is considered an important diagnostic marker for differentiation between normal and abnormal brain tissue and for therapeutic follow-up.

MATERIALS AND METHODS

Diffusion time and echo time dependence of the ADC were evaluated on a high-performance 3 T magnetic resonance imaging scanner. Diffusion PGSE brain scans were performed in 10 healthy volunteers and in 10 brain tumor patients using diffusion times of 16, 40, and 70 ms, echo times of 60, 75, and 104 ms at 3 b-values (0, 100, and 1000 s/mm 2 ), and a maximum gradient amplitude of 68 mT/m. A low gradient performance system was also emulated by reducing the diffusion encoding gradient amplitude to 19 mT/m. In healthy subjects, the ADC was measured in 6 deep gray matter regions and in 6 white matter regions. In patients, the ADC was measured in the solid part of the tumor.

RESULTS

With increasing diffusion time, a small but significant ADC increase of up to 2.5% was observed for 6 aggregate deep gray matter structures. With increasing echo time or reduced gradient performance, a small but significant ADC decrease of up to 2.6% was observed for 6 aggregate white matter structures. In tumors, diffusion time-related ADC changes were inconsistent without clear trend. For tumors with diffusivity above 1.0 μm 2 /ms, with prolonged echo time, there was a pronounced ADC increase of up to 12%. Meanwhile, for tumors with diffusivity at or below 1.0 μm 2 /ms, no change or a reduction was observed. Similar results were observed for gradient performance reduction, with an increase of up to 21%. The coefficient of variation determined in repeat experiments was 2.4%.

CONCLUSIONS

For PGSE and the explored parameter range, normal tissue ADC changes seem negligible. Meanwhile, observed tumor ADC changes can be relevant if ADC is used as a quantitative biomarker and not merely assessed by visual inspection. This highlights the importance of reporting all pertinent timing parameters in ADC studies and of considering these effects when building scan protocols for use in multicenter investigations.

Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types

The controversially discussed taxonomy of the Cryptococcus neoformans/Cryptococcus gattii species complex encompasses at least eight major molecular types. Cerebral cryptococcomas are a common manifestation of cryptococcal neurological disease. In this study, we compared neurotypical symptoms and differential neurovirulence induced by one representative isolate for each of the eight molecular types studied. We compared single focal lesions caused by the different isolates and evaluated the potential relationships between the fungal burden and properties obtained with quantitative magnetic resonance imaging (qMRI) techniques such as diffusion MRI, T2 relaxometry and magnetic resonance spectroscopy (MRS). We observed an inverse correlation between parametric data and lesion density, and we were able to monitor longitudinally biophysical properties of cryptococcomas induced by different molecular types. Because the MRI/MRS techniques are also clinically available, the same approach could be used to assess image-based biophysical properties that correlate with fungal cell density in lesions in patients to determine personalized treatments.

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.

Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis

OBJECT

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).

MATERIALS AND METHODS

The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.

RESULTS

The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.

DISCUSSION

In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

Evaluation of the contralateral hemisphere with DWI in pediatric patients with Dyke-Davidoff-Masson syndrome

INTRODUCTION

Dyke-Davidoff-Masson Syndrome (DDMS) is a clinical syndrome that causes different clinical symptoms and is defined by volume decrement in one cerebral hemisphere. In this study, we aimed to evaluate the involvement of the normal-appearing contralateral hemisphere in 16 pediatric patients with DDMS using diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Brain MRIs were retrospectively reviewed between January 2014 and January 2023. Sixteen pediatric patients radiologically compatible with DDMS were included in the study. Sixteen children who had undergone brain MRI, most commonly for headaches and whose MRI findings had been completely normal, were included as the control group. Apparent diffusion coefficient (ADC) values of the deep gray and white matter of the normal-appearing hemisphere in the patient group were calculated and compared with that of the control group.

RESULTS

The ADC values of the gray and white matters of the patient and control groups were not statistically different. However, in the patient group, the ADC values of the gray and white matters in males were remarkably lower than in females (p = 0.038, p = 0.037, respectively).

CONCLUSION

The difference in the ADC values of the contralateral hemisphere between females and males in the patient group suggests that the normal-appearing hemisphere may have been affected by DDMS. Although, the exact mechanism of this effect is not known. Therefore, in patients with DDMS, contralateral hemisphere involvement in cerebral hemiatrophy and hemispherectomy should be evaluated clinically and radiologically.

Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy

Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.

MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma

Purpose

To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).

Materials and methods

In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.

Results

Three features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.

Conclusion

Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.

Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences

Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.

Diffusion-weighted imaging does not seem to be a predictor of consistency in pituitary adenomas

PURPOSE

To prospectively evaluate the usefulness of T1-weighted imaging (T1WI) and diffusion-weighted imaging (DWI) sequences in predicting the consistency of macroadenomas. In addition, to determine their values ​​as prognostic factors of surgical outcomes.

METHODS

Patients with pituitary macroadenoma and surgical indication were included. All patients underwent pre-surgical magnetic resonance imaging (MRI) that included the sequences T1WI before and after contrast administration and DWI with the apparent diffusion coefficient (ADC) map. Post-surgical MRI was performed at least 3 months after surgery. The consistency of the macroadenomas was evaluated at surgery, and they were grouped into soft and intermediate/hard adenomas. Mean ADC values, signal on T1WI and the ratio of tumor ADC values ​​to pons (ADCR) were compared with tumor consistency and grade of surgical resection.

RESULTS

A total of 80 patients were included. A softened consistency was found at surgery in 53 patients and hardened in 27 patients. The median ADC in the soft consistency group was 0.532 × 10-3 mm2/sec (0.306 - 1.096 × 10-3 mm2/sec), and in the intermediate/hard consistency group was 0.509 × 10-3 mm2/sec (0.308 - 0.818 × 10-3 mm2/sec). There was no significant difference between the median values ​​of ADC, ADCR and signal on T1W between the soft and hard tumor groups, or between patients with and without tumor residue.

CONCLUSION

Our results did not show usefulness of the DWI and T1WI for assessing the consistency of pituitary macroadenomas, nor as a predictor of the degree of surgical resection.

Potential Approach to Quantifying the Volume of the Ischemic Core in Truncated Computed Tomography Perfusion Scans: A Preliminary Study

OBJECTIVE

This study aimed to provide an alternative approach for quantifying the volume of the ischemic core (IC) if truncation of computed tomography perfusion (CTP) occurs in clinical practice.

METHODS

Baseline CTP and follow-up diffusion-weighted imaging (DWI) data from 88 patients with stroke were retrospectively collected. CTP source images (CTPSI) from the unenhanced phase to the peak arterial phase (CTPSI-A) or the peak venous phase (CTPSI-V) were collected to simulate the truncation of CTP in the arterial or venous phases, respectively. The volume of IC on CTPSI-A (V CTPSI-A ) or CTPSI-V (V CTPSI-V ) was defined as the volume of the brain tissue with >65% reduction in attenuation compared with that of the normal tissue. The volume of IC on the baseline CTP (V CTP ) was defined as a relative cerebral blood flow of <30% of that in the normal tissue. The volume of the posttreatment infarct on the follow-up DWI (V DWI ) image was manually delineated and calculated. One-way analysis of variance, Bland-Altman plots, and Spearman correlation analyses were used for the statistical analysis.

RESULTS

V CTPSI-A was significantly higher than V DWI ( P < 0.001); however, no significant difference was observed between V CTP and V DWI ( P = 0.073) or between V CTPSI-V and V DWI ( P > 0.999). The mean differences between V DWI and V CTPSI-V , V DWI and V CTP , and V DWI and V CTPSI-A were 1.70 mL (limits of agreement [LoA], -56.40 to 59.70), 8.30 mL (LoA, -40.70 to 57.30), and -68.10 mL (LoA, -180.90 to 44.70), respectively. Significant correlations were observed between V DWI and V CTP ( r = 0.68, P < 0.001) and between V DWI and V CTPSI-V ( r = 0.39, P < 0.001); however, no significant correlation was observed between V DWI and V CTPSI-A ( r = 0.20, P = 0.068).

CONCLUSIONS

V CTPSI-V may be a promising method for quantifying the volume of the IC if truncation of CTP occurs.

Simultaneous perfusion, diffusion, T2 *, and T1 mapping with MR fingerprinting

PURPOSE

Quantitative mapping of brain perfusion, diffusion, T2 *, and T1 has important applications in cerebrovascular diseases. At present, these sequences are performed separately. This study aims to develop a novel MRI technique to simultaneously estimate these parameters.

METHODS

This sequence to measure perfusion, diffusion, T2 *, and T1 mapping with magnetic resonance fingerprinting (MRF) was based on a previously reported MRF-arterial spin labeling (ASL) sequence, but the acquisition module was modified to include different TEs and presence/absence of bipolar diffusion-weighting gradients. We compared parameters derived from the proposed method to those derived from reference methods (i.e., separate sequences of MRF-ASL, conventional spin-echo DWI, and T2 * mapping). Test-retest repeatability and initial clinical application in two patients with stroke were evaluated.

RESULTS

The scan time of our proposed method was 24% shorter than the sum of the reference methods. Parametric maps obtained from the proposed method revealed excellent image quality. Their quantitative values were strongly correlated with those from reference methods and were generally in agreement with values reported in the literature. Repeatability assessment revealed that ADC, T2 *, T1 , and B1 + estimation was highly reliable, with voxelwise coefficient of variation (CoV) <5%. The CoV for arterial transit time and cerebral blood flow was 16% ± 3% and 25% ± 9%, respectively. The results from the two patients with stroke demonstrated that parametric maps derived from the proposed method can detect both ischemic and hemorrhagic stroke.

CONCLUSION

The proposed method is a promising technique for multi-parametric mapping and has potential use in patients with stroke.

Guess What Is in My Brain

Magnetic resonance imaging (MRI) of the brain is an important diagnostic tool used by neurologists. This article explores the workup and management for a patient with a brain lesion and highlights the importance of neuroimaging. Similarities and differences in MRI findings for meningioma, central nervous system lymphoma, and glioblastomas are discussed, along with common MRI sequences and their utility.

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

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

Diffusion-weighted imaging of the orbit

Orbital lesions compose a heterogeneous group of pathologies that often present with non-specific imaging findings on conventional magnetic resonance imaging (MRI) sequences (T1-and T2-weighted). Accordingly, the application of diffusion MRI offers an opportunity to further distinguish between lesions along this spectrum. Diffusion-weighted imaging (DWI) represents the simplest and most frequent clinically utilised diffusion imaging technique. Recent advances in DWI techniques have extended its application to the evaluation of a wider spectrum of neurological pathology, including orbital lesions. This review details the manifestations of select orbital pathology on DWI and underscores specific situations where diffusion imaging allows for increased diagnostic sensitivity compared to more conventional MRI techniques. These examples also describe preferred management for orbital lesions identified by DWI.

Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network

Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.

MRI and Clinical Biomarkers Overlap between Glaucoma and Alzheimer's Disease

Glaucoma is the leading cause of blindness worldwide. It is classically associated with structural and functional changes in the optic nerve head and retinal nerve fiber layer, but the damage is not limited to the eye. The involvement of the central visual pathways and disruption of brain network organization have been reported using advanced neuroimaging techniques. The brain structural changes at the level of the areas implied in processing visual information could justify the discrepancy between signs and symptoms and underlie the analogy of this disease with neurodegenerative dementias, such as Alzheimer's disease, and with the complex group of pathologies commonly referred to as "disconnection syndromes." This review aims to summarize the current state of the art on the use of advanced neuroimaging techniques in glaucoma and Alzheimer's disease, highlighting the emerging biomarkers shared by both diseases.

Utility of Diffusion Tensor Imaging in Assessing Corticospinal Tracts for the Management of Brain Tumors: A Cross-Sectional Observational Study

Introduction Intra-axial brain tumors are a significant health problem and present several diagnostic and treatment challenges. Conventional magnetic resonance imaging (MRI) has posed several limitations, such as the inability to delineate the detailed anatomy of fibers in structures like the brainstem and the inability to accurately judge the extent of tumor infiltration. Diffusion tensor imaging (DTI), based on the concepts of isotropic and anisotropic diffusion, is capable of visualizing and segmenting white fiber bundles in high detail and providing crucial information about tumor boundaries, extent, neighboring tracts, and more. This information can be very useful in initial non-invasive diagnosis, preoperative tumor grading, biopsy planning, surgical planning, and prognosis. Methods and materials This is a cross-sectional observational study in a tertiary care setup, conducted over a one-year period. The study was performed in Seth Gordhandas Sunderdas Medical College (Seth G.S. Medical College) and King Edward VII Memorial Hospital (K.E.M. Hospital), a tertiary care hospital located in Mumbai, India. Fiber tractography was performed and was used to visualize the corticospinal tracts passing through the length of the brainstem. Changes in the degree of infiltration, destruction, and displacement of the corticospinal tracts were observed carefully. Adult patients who were diagnosed with brain tumors, willing to participate in the study, and capable of providing written informed consent prior to study registration were included. The DTI findings along with information from other investigations were used to decide the best course of management for each case. Results The study included 30 participants with a mean age of 46.0 ± 17.1 years, 63.3% and 37.7% being male and female, respectively. According to the lesion's location, the pons was found to be the most often affected area in 23.33% of cases, followed by the temporo-parietal region (13.3%) and the frontal region (13.3%). These lesions had heterogenous enhancement in 63.3% of the instances and homogeneous enhancement in 36.7% of the cases, according to a contrast study. According to their consistency, the lesions were further divided into two categories: solid lesions, which were present in 66.7% of instances, and cystic lesions, which were present in 90% of cases. Results from the diffusion tensor technique revealed that infiltration accounted for 40.0% of cases, displacement for 76.7%, and loss of white fiber tracts for 20.0%. DTI findings were significantly associated with the type of planned management and with the presence of post-management neurological deficit. Conclusion DTI played a complementary role in the assessment of tumors and can be used to improve surgical planning and therapeutic decision making. Preservation of corticospinal tracts is vital to prevent motor impairment. Availability of qualitative data with the depiction of corticospinal tracts in a three-dimensional projection and their relation with the brain tumors by DTI greatly helps in preoperative decision making and surgical approach.

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

BACKGROUND

To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion.

METHODS

This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant.

RESULTS

The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion.

CONCLUSIONS

The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC.

KEY POINTS

• Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.

The Role of Novel Imaging and Biofluid Biomarkers in Traumatic Axonal Injury: An Updated Review

Traumatic brain injury (TBI) is a leading cause of disability worldwide. Traumatic axonal injury (TAI) is a subtype of TBI resulting from high-impact forces that cause shearing and/or stretching of the axonal fibers in white matter tracts. It is present in almost half of cases of severe TBI and frequently associated with poor functional outcomes. Axonal injury results from axonotomy due to mechanical forces and the activation of a biochemical cascade that induces the activation of proteases. It occurs at a cellular level; hence, conventional imaging modalities often fail to display TAI lesions. However, the advent of novel imaging modalities, such as functional magnetic resonance imaging and fiber tractography, has significantly improved the detection and characteristics of TAI. Furthermore, the significance of several fluid and structural biomarkers has also been researched, while the contribution of omics in the detection of novel biomarkers is currently under investigation. In the present review, we discuss the role of imaging modalities and potential biomarkers in diagnosing, classifying, and predicting the outcome in patients with TAI.

Extra-axonal contribution to double diffusion encoding-based pore size estimates in the corticospinal tract

OBJECTIVE

To study the origin of compartment size overestimation in double diffusion encoding MRI (DDE) in vivo experiments in the human corticospinal tract. Here, the extracellular space is hypothesized to be the origin of the DDE signal. By exploiting the DDE sensitivity to pore shape, it could be possible to identify the origin of the measured signal. The signal difference between parallel and perpendicular diffusion gradient orientation can indicate if a compartment is regular or eccentric in shape. As extracellular space can be considered an eccentric compartment, a positive difference would mean a high contribution to the compartment size estimates.

MATERIALS AND METHODS

Computer simulations using MISST and in vivo experiments in eight healthy volunteers were performed. DDE experiments using a double spin-echo preparation with eight perpendicular directions were measured in vivo. The difference between parallel and perpendicular gradient orientations was analyzed using a Wilcoxon signed-rank test and a Mann-Whitney U test.

RESULTS

Simulations and MR experiments showed a statistically significant difference between parallel and perpendicular diffusion gradient orientation signals ([Formula: see text]).

CONCLUSION

The results suggest that the DDE-based size estimate may be considerably influenced by the extra-axonal compartment. However, the experimental results are also consistent with purely intra-axonal contributions in combination with a large fiber orientation dispersion.

Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities

In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.

In vivo assessment of differences in fungal cell density in cerebral cryptococcomas of mice infected with Cryptococcus neoformans or Cryptococcus gattii

In cerebral cryptococcomas caused by Cryptococcus neoformans or Cryptococcus gattii, the density of fungal cells within lesions can contribute to the overall brain fungal burden. In cultures, cell density is inversely related to the size of the cryptococcal capsule, a dynamic polysaccharide layer surrounding the cell. Methods to investigate cell density or related capsule size within fungal lesions of a living host are currently unavailable, precluding in vivo studies on longitudinal changes. Here, we assessed whether intravital microscopy and quantitative magnetic resonance imaging techniques (diffusion MRI and MR relaxometry) would enable non-invasive investigation of fungal cell density in cerebral cryptococcomas in mice. We compared lesions caused by type strains C. neoformans H99 and C. gattii R265 and evaluated potential relations between observed imaging properties, fungal cell density, total cell and capsule size. The observed inverse correlation between apparent diffusion coefficient and cell density permitted longitudinal investigation of cell density changes. Using these imaging methods, we were able to study the multicellular organization and cell density within brain cryptococcomas in the intact host environment of living mice. Since the MRI techniques are also clinically available, the same approach could be used to assess fungal cell density in brain lesions of patients.

Association of Bun/Cr ratio-based dehydration status with infarct volumes and stroke severity in acute ischemic stroke

BACKGROUND

Only a few clinical research had previously investigated the dehydration status to predict the evolution of the ischemic core. The aim of this study is to clarify the association between blood urea nitrogen (BUN)/creatinine (Cr)ratio-based dehydration and infarct volume measured using DWI (Diffusion-weighted imaging) at admission in patients with AIS (Acute Ischemic Stroke).

METHODS

We retrospectively recruited a total of 203 consecutive patients who were hospitalized through emergency or outpatient services within 72 h of acute ischemic stroke onset between October 2015 and September 2019. Stroke severity was measured by assessing the National Institutes of Health Stroke Scale (NIHSS) on admission. Infarct volume was measured using DWI with MATLAB software.

RESULTS

In this study, 203 patients who met the study criteria were enrolled. Patients in the dehydration group (Bun/Cr ratio>15) had a higher median NIHSS score (6(IQR:4-10) VS. 5(3-7); P = 0.0015)and larger DWI infarct volume (1.55 ml (IQR:0.51-6.79) VS. (0.37 ml (0.05-1.22); P < 0.001) on admission compared with patients in normal group. Further, a statistically significant correlation was found between DWI infarct volumes and NIHSS score with nonparametric Spearman rank correlation (r = 0.77; P < 0.001). The median NIHSS scores for the DWI infarct volumes quartiles were 3 ml (IQR, 2-4), 5 ml (4-7), 6 ml (5-8), and12 ml (8-17) from lowest to highest. However, the second quartile group did not show any significant correlation with the third quartile group (P = 0.4268). Multivariable linear and logistic regression analyses were used to test dehydration (Bun/Cr ratio>15), representing a predictor of infarct volume and stroke severity.

CONCLUSION

Bun/Cr ratio-based dehydration is associated with larger volumes of ischemic tissue measured using DWI and worse neurological deficit assessed by the NIHSS score in acute ischemic stroke.

Magnetic resonance imaging to assess the brain response to fasting in glioblastoma-bearing rats as a model of cancer anorexia

BACKGROUND

Global energy balance is a vital process tightly regulated by the brain that frequently becomes dysregulated during the development of cancer. Glioblastoma (GBM) is one of the most investigated malignancies, but its appetite-related disorders, like anorexia/cachexia symptoms, remain poorly understood.

METHODS

We performed manganese enhanced magnetic resonance imaging (MEMRI) and subsequent diffusion tensor imaging (DTI), in adult male GBM-bearing (n = 13) or control Wistar rats (n = 12). A generalized linear model approach was used to assess the effects of fasting in different brain regions involved in the regulation of the global energy metabolism: cortex, hippocampus, hypothalamus and thalamus. The regions were selected on the contralateral side in tumor-bearing animals, and on the left hemisphere in control rats. An additional DTI-only experiment was completed in two additional GBM (n = 5) or healthy cohorts (n = 6) to assess the effects of manganese infusion on diffusion measurements.

RESULTS

MEMRI results showed lower T₁ values in the cortex (p-value < 0.001) and thalamus (p-value < 0.05) of the fed ad libitum GBM animals, as compared to the control cohort, consistent with increased Mn²⁺ accumulation. No MEMRI-detectable differences were reported between fed or fasting rats, either in control or in the GBM group. In the MnCl₂-infused cohorts, DTI studies showed no mean diffusivity (MD) variations from the fed to the fasted state in any animal cohort. However, the DTI-only set of acquisitions yielded remarkably decreased MD values after fasting only in the healthy control rats (p-value < 0.001), and in all regions, but thalamus, of GBM compared to control animals in the fed state (p-value < 0.01). Fractional anisotropy (FA) decreased in tumor-bearing rats due to the infiltrate nature of the tumor, which was detected in both diffusion sets, with (p-value < 0.01) and without Mn²⁺ administration (p-value < 0.001).

CONCLUSIONS

Our results revealed that an altered physiological brain response to fasting occurred in hunger related regions in GBM animals, detectable with DTI, but not with MEMRI acquisitions. Furthermore, the present results showed that Mn²⁺ induces neurotoxic inflammation, which interferes with diffusion MRI to detect appetite-induced responses through MD changes.

dtiRIM: A generalisable deep learning method for diffusion tensor imaging

Diffusion weighted MRI is an indispensable tool for routine patient screening and diagnostics of pathology. Recently, several deep learning methods have been proposed to quantify diffusion parameters, but poor generalisation to new data prevents broader use of these methods, as they require retraining of the neural network for each new scan protocol. In this work, we present the dtiRIM, a new deep learning method for Diffusion Tensor Imaging (DTI) based on the Recurrent Inference Machines. Thanks to its ability to learn how to solve inverse problems and to use the diffusion tensor model to promote data consistency, the dtiRIM can generalise to variations in the acquisition settings. This enables a single trained network to produce high quality tensor estimates for a variety of cases. We performed extensive validation of our method using simulation and in vivo data, and compared it to the Iterated Weighted Linear Least Squares (IWLLS), the approach of the state-of-the-art MRTrix3 software, and to an implementation of the Maximum Likelihood Estimator (MLE). Our results show that dtiRIM predictions present low dependency on tissue properties, anatomy and scanning parameters, with results comparable to or better than both IWLLS and MLE. Further, we demonstrate that a single dtiRIM model can be used for a diversity of data sets without significant loss in quality, representing, to our knowledge, the first generalisable deep learning based solver for DTI.

Neurocysticercosis-related Seizures: Imaging Biomarkers

Neurocysticercosis (NCC)-a parasitic CNS infection endemic to developing nations-has been called the leading global cause of acquired epilepsy yet remains understudied. It is currently unknown why a large proportion of patients develop recurrent seizures, often following the presentation of acute seizures. Furthermore, the presentation of NCC is heterogenous and the features that predispose to the development of an epileptogenic state remain uncertain. Perilesional factors (such as oedema and gliosis) have been implicated in NCC-related ictogenesis, but the effects of cystic factors, including lesion load and location, seem not to play a role in the development of habitual epilepsy. In addition, the cytotoxic consequences of the cyst's degenerative stages are varied and the majority of research, relying on retrospective data, lacks the necessary specificity to distinguish between acute symptomatic and unprovoked seizures. Previous research has established that epileptogenesis can be the consequence of abnormal network connectivity, and some imaging studies have suggested that a causative link may exist between NCC and aberrant network organisation. In wider epilepsy research, network approaches have been widely adopted; studies benefiting predominantly from the rich, multimodal data provided by advanced MRI methods are at the forefront of the field. Quantitative MRI approaches have the potential to elucidate the lesser-understood epileptogenic mechanisms of NCC. This review will summarise the current understanding of the relationship between NCC and epilepsy, with a focus on MRI methodologies. In addition, network neuroscience approaches with putative value will be highlighted, drawing from current imaging trends in epilepsy research.

Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.

Imaging Methods Applicable in the Diagnostics of Alzheimer's Disease, Considering the Involvement of Insulin Resistance

Alzheimer's disease (AD) is an incurable neurodegenerative disease and the most frequently diagnosed type of dementia, characterized by (1) perturbed cerebral perfusion, vasculature, and cortical metabolism; (2) induced proinflammatory processes; and (3) the aggregation of amyloid beta and hyperphosphorylated Tau proteins. Subclinical AD changes are commonly detectable by using radiological and nuclear neuroimaging methods such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). Furthermore, other valuable modalities exist (in particular, structural volumetric, diffusion, perfusion, functional, and metabolic magnetic resonance methods) that can advance the diagnostic algorithm of AD and our understanding of its pathogenesis. Recently, new insights into AD pathoetiology revealed that deranged insulin homeostasis in the brain may play a role in the onset and progression of the disease. AD-related brain insulin resistance is closely linked to systemic insulin homeostasis disorders caused by pancreas and/or liver dysfunction. Indeed, in recent studies, linkages between the development and onset of AD and the liver and/or pancreas have been established. Aside from standard radiological and nuclear neuroimaging methods and clinically fewer common methods of magnetic resonance, this article also discusses the use of new suggestive non-neuronal imaging modalities to assess AD-associated structural changes in the liver and pancreas. Studying these changes might be of great clinical importance because of their possible involvement in AD pathogenesis during the prodromal phase of the disease.

Can Apparent Diffusion Coefficient (ADC) maps replace Diffusion Tensor Imaging (DTI) maps to predict the volumetric response of meningiomas to Gamma Knife Radiosurgery?

PURPOSE

Noninvasive methods are desired to predict the treatment response to Stereotactic Radiosurgery (SRS) to improve individual tumor management. In a previous study, we demonstrated that Diffusion Tensor Imaging (DTI)-derived parameter maps significantly correlate to SRS response. This study aimed to analyze and compare the predictive value of intratumoral ADC and DTI parameters in patients with meningiomas undergoing radiosurgery.

METHODS

MR images of 70 patients treated with Gamma Knife SRS for WHO grade I meningiomas were retrospectively reviewed. MR acquisition included pre- and post-treatment DWI and DTI sequences, and subtractions were calculated to assess for radiation-induced changes in the parameter values.

RESULTS

After a mean follow-up period (FUP) of 52.7 months, 69 of 70 meningiomas were controlled, with a mean volume reduction of 34.9%. Whereas fractional anisotropy (FA) values of the initial exam showed the highest correlation to tumor volume change at the last FU (CC = - 0.607), followed by the differences between first and second FU values of FA (CC = - 0.404) and the first longitudinal diffusivity (LD) value (CC = - 0.375), the correlation coefficients of all ADC values were comparably low. Nevertheless, all these correlations, except for ADC measured at the first follow-up, reached significance.

CONCLUSION

For the first time, the prognostic value of ADC maps measured in meningiomas before and at first follow-up after Gamma Knife SRS, was compared to simultaneously acquired DTI parameter maps. Quantities assessed from ADC maps present significant correlations to the volumetric meningioma response but are less effective than correlations with DTI parameters.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Spatiotemporal encoding MRI using subspace-constrained sampling and locally-low-rank regularization: Applications to diffusion weighted and diffusion kurtosis imaging of human brain and prostate

The benefits of performing locally low-rank (LLR) reconstructions on subsampled diffusion weighted and diffusion kurtosis imaging data employing spatiotemporal encoding (SPEN) methods, is investigated. SPEN allows for self-referenced correction of motion-induced phase errors in case of interleaved diffusion-oriented acquisitions, and allows one to overcome distortions otherwise observed along EPI's phase-encoded dimension. In combination with LLR-based reconstructions of the pooled imaging data and with a joint subsampling of b-weighted and interleaved images, additional improvements in terms of sensitivity as well as shortened acquisition times are demonstrated, without noticeable penalties. Details on how the LLR-regularized, subspace-constrained image reconstructions were adapted to SPEN are given; the improvements introduced by adopting these reconstruction frameworks for the accelerated acquisition of diffusivity and of kurtosis imaging data in both relatively homogeneous regions like the human brain and in more challenging regions like the human prostate, are presented and discussed within the context of similar efforts in the field.

Temporal and sequence-related variability in diffusion-weighted imaging of presumed cerebrovascular accidents in the dog brain

Diffusion-weighted MRI (DWI) is often used to guide clinical interpretation of intraparenchymal brain lesions when there is suspicion for a cerebrovascular accident (CVA). Despite widespread evidence that imaging and patient parameters can influence diffusion-weighted measurements, such as apparent diffusion coefficient (ADC), there is little published data on such measurements for naturally occurring CVA in clinical cases in dogs. We describe a series of 22 presumed and confirmed spontaneous canine CVA with known time of clinical onset imaged on a single 3T magnet between 2011 and 2021. Median ADC values of < 1.0x10⁻³ mm²/s were seen in normal control tissues as well as within CVAs. Absolute and relative ADC values in CVAs were well-correlated (R² = 0.82). Absolute ADC values < 1.0x10⁻³ mm²/s prevailed within ischemic CVAs, though there were exceptions, including some lesions of < 5 days age. Some lesions showed reduced absolute but not relative ADC values when compared to matched normal contralateral tissue. CVAs with large hemorrhagic components did not show restricted diffusion. Variation in the DWI sequence used impacted the ADC values obtained. Failure to identify a region of ADC < 1.0x10⁻³ mm²/s should not exclude CVA from the differential list when clinical suspicion is high.

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman's correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as "virtual biopsy", introducing new insights for omics integration toward a personalized medicine approach.

Multinuclear MRI in Drug Discovery

The continuous development of magnetic resonance imaging broadens the range of applications to newer areas. Using MRI, we can not only visualize, but also track pharmaceutical substances and labeled cells in both in vivo and in vitro tests. ¹H is widely used in the MRI method, which is determined by its high content in the human body. The potential of the MRI method makes it an excellent tool for imaging the morphology of the examined objects, and also enables registration of changes at the level of metabolism. There are several reports in the scientific publications on the use of clinical MRI for in vitro tracking. The use of multinuclear MRI has great potential for scientific research and clinical studies. Tuning MRI scanners to the Larmor frequency of a given nucleus, allows imaging without tissue background. Heavy nuclei are components of both drugs and contrast agents and molecular complexes. The implementation of hyperpolarization techniques allows for better MRI sensitivity. The aim of this review is to present the use of multinuclear MRI for investigations in drug delivery.

Utility of the Diffusion Weighted Sequence in Gynecological Imaging: Review Article

Simple Summary

Diffusion weighted imaging (DWI) is a magnetic resonance imaging sequence with diverse clinical applications in malignant and nonmalignant gynecological conditions. It provides vital supplemental information in the diagnosis and management of various gynecological conditions. Radiologists should be aware of fundamental concepts, clinical applications and pitfalls of DWI. Additionally we briefly discuss potential scope of newer advanced techniques based on DWI including diffusion tensor imaging and diffusion-weighted whole-body imaging with background signal suppression.

Abstract

Functional imaging with diffusion-weighted imaging (DWI) is a complementary tool to conventional diagnostic magnetic resonance imaging sequences. It is being increasingly investigated to predict tumor response and assess tumor recurrence. We elucidate the specific technical modifications of DWI preferred for gynecological imaging, including the different b-values and planes for image acquisition. Additionally, we discuss the problems and potential pitfalls encountered during DWI interpretation and ways to overcome them. DWI has a wide range of clinical applications in malignant and non-malignant gynecological conditions. It provides supplemental information helpful in diagnosing and managing tubo-ovarian abscess, uterine fibroids, endometriosis, adnexal torsion, and dermoid. Similarly, DWI has diverse applications in gynecological oncology in diagnosis, staging, detection of recurrent disease, and tumor response assessment. Quantitative evaluation with apparent diffusion coefficient (ADC) measurement is being increasingly evaluated for correlation with various tumor parameters in managing gynecological malignancies aiding in preoperative treatment planning. Newer advanced DWI techniques of diffusion tensor imaging (DTI) and whole body DWI with background suppression (DWIBS) and their potential uses in pelvic nerve mapping, preoperative planning, and fertility-preserving surgeries are briefly discussed.

Neuroinvasive disease due to West Nile virus: Clinical and imaging findings associated with a re-emerging pathogen

The West Nile virus (WNV) is an arbovirus than can infect human beings and cause severe neuroinvasive disease. Taking the outbreak that occurred in Spain in 2020 as a reference, this article reviews the clinical and imaging findings for neuroinvasive disease due to WNV. We collected demographic, clinical, laboratory, and imaging (CT and MRI) variables for 30 patients with WNV infection diagnosed at our center. The main clinical findings were fever, headache, and altered levels of consciousness. Neuroimaging studies, especially MRI, are very useful in the diagnosis and follow-up of these patients. The most common imaging findings were foci of increased signal intensity in the thalamus and brainstem in T2-weighted sequences; we illustrate these findings in cases from our hospital.

Development of a standard phantom for diffusion-weighted magnetic resonance imaging quality control studies: A review

Various materials and compounds have been used in the design of diffusion-weighted magnetic resonance imaging (DWMRI) phantoms to mimic biological tissue properties, including diffusion. This review thus provides an overview of the preparations of the various DW-MRI phantoms available in relation to the limitations and strengths of materials/solutions used to fill them. The narrative review conducted from relevant databases shows that synthesizing all relevant compounds from individual liquids, gels, and solutions based on their identified strengths could contribute to the development of a novel multifunctional DW-MRI phantom. The proposed multifunctional material at varied concentrations, when filled into a multi-compartment Perspex container of cylindrical or spherical geometry, could serve as a standard DW-MRI phantom. The standard multifunctional phantom could potentially provide DW-MRI quality control test parameters in one study session.

Assessment of Rat Sciatic Nerve Using Diffusion-Tensor Imaging With Readout-Segmented Echo Planar Imaging

Objectives

This study aimed to compare readout-segmented-3, readout-segmented-5, and readout-segmented-7 echo-planar imaging (RS3-EPI, RS5-EPI, and RS7-EPI) of DTI in the assessment of rat sciatic nerve at 3T MR.

Methods

Eight male adult healthy Sprague-Dawley rats were scanned at 3T MR with RS-3 EPI, RS5-EPI, and RS-7 EPI DTI. The image quality of RS-3 EPI, RS-5 EPI, and RS-7 EPI in terms of the nerve morphology, distortions of the nearby femur, muscles, and homogeneity of neuromuscular were evaluated by two experienced radiologists. The correlations between the histopathological and DTI parameters, including fractional anisotropy (FA) and radial diffusivity (RD), were calculated, respectively, and compared in RS-3, RS-5, and RS-7 EPI. The image quality scores for RS-3 EPI, RS-5 EPI, and RS-7 EPI were compared using the Wilcoxon rank-sum test. The correlation between DTI and histopathological parameters was calculated using the Pearson correlation coefficient.

Results

RS-5 EPI yielded the best SNR-values corrected for the acquisition time compared to RS3-EPI and RS7-EPI. The image quality scores of RS-5 EPI were superior to those of RS-3 and RS-7 EPI (P = 0.01–0.014) and lower artifacts of the ventral/dorsal margin and femur (P = 0.008–0.016) were shown. DTT analysis yielded a significantly higher number of tracts for RS5-EPI compared to RS3-EPI (P = 0.007) but no significant difference with RS7-EPI (P = 0.071). For the three sequences, FA and RD were well-correlated with the myelin-related histopathological parameters (|r| 0.709–0.965, P = 0.001–0.049). The overall correlation coefficients of FA and RD obtained from RS-5 EPI were numerically higher than that with both RS3-EPI and RS7-EPI.

Conclusion

For the rat sciatic nerve DTI imaging, RS-5 EPI offered the best image quality and SNR-values corrected for the acquisition time. The FA and RD derived from the RS-5 EPI were the most sensitive quantitative biomarkers to detect rat sciatic nerve histopathological change.