Advanced Magnetic Resonance Imaging Techniques Research Articles

Advanced neuroimaging techniques to explore the effects of motor and cognitive rehabilitation in multiple sclerosis.

Progress in magnetic resonance imaging (MRI) technology and analyses is improving our comprehension of multiple sclerosis (MS) pathophysiology. These advancements, which enable the evaluation of atrophy, microstructural tissue abnormalities, and functional plasticity, are broadening our insights into the effectiveness and working mechanisms of motor and cognitive rehabilitative treatments. This narrative review with selected studies discusses findings derived from the application of advanced MRI techniques to evaluate structural and functional neuroplasticity modifications underlying the effects of motor and cognitive rehabilitative treatments in people with MS (PwMS). Current applications as outcome measure in longitudinal trials and observational studies, their interpretation and possible pitfalls and limitations in their use are covered. Finally, we examine how the use of these techniques could evolve in the future to improve monitoring of motor and cognitive rehabilitative treatments. Despite substantial variability in study design and participant characteristics in rehabilitative studies for PwMS, improvements in motor and cognitive functions accompanied by structural and functional brain modifications induced by rehabilitation can be observed. However, significant enhancements to refine rehabilitation strategies are needed. Future studies in this field should strive to implement standardized methodologies regarding MRI acquisition and processing, possibly integrating multimodal measures. This will help identifying relevant markers of treatment response in PwMS, thus improving the use of rehabilitative interventions at individual level. The combination of motor and cognitive strategies, longer periods of treatment, as well as adequate follow-up assessments will contribute to enhance the quality of evidence in support of their routine use.

DF-QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain.

Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique to quantify the magnetic susceptibility of the tissue under investigation. Deep learning methods have shown promising results in deconvolving the susceptibility distribution from the measured local field obtained from the MR phase. Although existing deep learning based QSM methods can produce high-quality reconstruction, they are highly biased toward training data distribution with less scope for generalizability. This work proposes a hybrid two-step reconstruction approach to improve deep learning based QSM reconstruction. The susceptibility map prediction obtained from the deep learning methods has been refined in the framework developed in this work to ensure consistency with the measured local field. The developed method was validated on existing deep learning and model-based deep learning methods for susceptibility mapping of the brain. The developed method resulted in improved reconstruction for MRI volumes obtained with different acquisition settings, including deep learning models trained on constrained (limited) data settings.

Peak Resembling N-acetylaspartate (NAA) on Magnetic Resonance Spectroscopy of Brain Metastases.

Background and Objectives: Differentiating between a high-grade glioma (HGG) and solitary cerebral metastasis presents a challenge when using standard magnetic resonance imaging (MRI) alone. Magnetic resonance spectroscopy (MRS), an advanced MRI technique, may assist in resolving this diagnostic dilemma. N-acetylaspartate (NAA), an amino acid found uniquely in the central nervous system and in high concentrations in neurons, typically suggests HGG over metastatic lesions in spectra from ring-enhancing lesions. This study investigates exceptions to this norm. Materials and Methods: We conducted an MRS study on 49 histologically confirmed and previously untreated patients with brain metastases, employing single-voxel (SVS) techniques with short and long echo times, as well as magnetic resonance spectroscopic imaging (MRSI). Results: In our cohort, 44 out of 49 (90%) patients demonstrated a typical MR spectroscopic profile consistent with secondary deposits: a Cho peak, very low or absent Cr, absence of NAA, and the presence of lipids. A peak at approximately 2 ppm, termed the "NAA-like peak", was present in spectra obtained with both short and long echo times. Among the MRS data from 49 individuals, we observed a peak at 2.0 ppm in five brain metastases from mucinous carcinoma of the breast, mucinous non-small-cell lung adenocarcinoma, two metastatic melanomas, and one metastatic non-small-cell lung cancer. Pathohistological verification of mucin in two of these five cases suggested this peak likely represents N-acetyl glycoproteins, indicative of mucin expression in cancer cells. Conclusions: The identification of a prominent peak at 2.0 ppm could be a valuable diagnostic marker for distinguishing single ring-enhancing lesions, potentially associated with mucin-expressing metastases, offering a new avenue for diagnostic specificity in challenging cases.

White Matter Alterations in Military Service Members With Remote Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) is the signature injury experienced by military service members and is associated with poor neuropsychiatric outcomes. Yet, there is a lack of reliable clinical tools for mTBI diagnosis and prognosis. To examine the white matter microstructure and neuropsychiatric outcomes of service members with a remote history of mTBI (ie, mTBI that occurred over 2 years ago) using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). This case-control study examined 98 male service members enrolled in a study at the National Intrepid Center of Excellence. Eligible participants were active duty status or able to enroll in the Defense Enrollment Eligibility Reporting system, ages 18 to 60 years, and had a remote history of mTBI; controls were matched by age. Remote history of mTBI. White matter microstructure was assessed using a region-of-interest approach of skeletonized diffusion images, including DTI (fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity) and NODDI (orientation dispersion index [ODI], isotropic volume fraction, intra-cellular volume fraction). Neuropsychiatric outcomes associated with posttraumatic stress disorder (PTSD) and postconcussion syndrome were assessed. A total of 65 male patients with a remote history of mTBI (mean [SD] age, 40.5 [5.0] years) and 33 age-matched male controls (mean [SD] age, 38.9 [5.6] years) were included in analysis. Compared with the control cohort, the 65 service members with mTBI presented with significantly more severe PTSD-like symptoms (mean [SD] PTSD CheckList-Civilian [PCL-C] version scores: control, 19.0 [3.8] vs mTBI, 41.2 [11.6]; P < .001). DTI and NODDI metrics were altered in the mTBI group compared with the control, including intra-cellular volume fraction of the right cortico-spinal tract (β = -0.029, Cohen d = 0.66; P < .001), ODI of the left posterior thalamic radiation (β = -0.006, Cohen d = 0.55; P < .001), and ODI of the left uncinate fasciculus (β = 0.013, Cohen d = 0.61; P < .001). In service members with mTBI, fractional anisotropy of the left uncinate fasciculus was associated with postconcussion syndrome (β = 5.4 × 10-3; P = .003), isotropic volume fraction of the genu of the corpus callosum with PCL-C (β = 4.3 × 10-4; P = .01), and ODI of the left fornix and stria terminalis with PCL-C avoidance scores (β = 1.2 × 10-3; P = .02). In this case-control study of military-related mTBI, the results suggest that advanced magnetic resonance imaging techniques using NODDI can reveal white matter microstructural alterations associated with neuropsychiatric symptoms in the chronic phase of mTBI. Diffusion trends observed throughout widespread white matter regions-of-interest may reflect mechanisms of neurodegeneration as well as postinjury tissue scarring and reorganization.

Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis.

Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders. We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data. A systematic review and diagnostic meta-analysis. Sixty-seven studies with 6,285 patients were included. Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included. The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures. Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias. sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies. The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance. 1 TECHNICAL EFFICACY: Stage 2.

Navigating Neural Landscapes: A Comprehensive Review of Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) Applications in Epilepsy.

This review comprehensively explores the evolving role of neuroimaging, specifically magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS), in epilepsy research and clinical practice. Beginning with a concise overview of epilepsy, the discussion emphasizes the crucial importance of neuroimaging in diagnosing and managing this complex neurological disorder. The review delves into the applications of advanced MRI techniques, including high-field MRI, resting-state fMRI, and connectomics, highlighting their impact on refining our understanding of epilepsy's structural and functional dimensions. Additionally, it examines the integration of machine learning in the analysis of intricate neuroimaging data. Moving to the clinical domain, the review outlines the utility of neuroimaging in pre-surgical evaluations and the monitoring of treatment responses and disease progression. Despite significant strides, challenges and limitations are discussed in the routine clinical incorporation of neuroimaging. The review explores promising developments in MRI and MRS technology, potential advancements in imaging biomarkers, and the implications for personalized medicine in epilepsy management. The conclusion underscores the transformative potential of neuroimaging and advocates for continued exploration, collaboration, and technological innovation to propel the field toward a future where tailored, effective interventions improve outcomes for individuals with epilepsy.

Post-COVID-19 conditions: a systematic review on advanced magnetic resonance neuroimaging findings.

Post-COVID conditions (PCCs) cover a wide spectrum of lingering symptoms experienced by survivors of coronavirus disease 2019 (COVID-19). Neurological and neuropsychiatric sequelae are common in PCCs. Advanced magnetic resonance imaging (MRI) techniques can reveal subtle alterations in brain structure, function, and perfusion that underlie these sequelae. This systematic review aimed to synthesize findings from studies that used advanced MRI to characterize brain changes in individuals with PCCs. A detailed literature search was conducted in the PubMed and Scopus databases to identify relevant studies that used advanced MRI modalities, such as structural MRI (sMRI), diffusion tensor imaging (DTI), functional MRI (fMRI), and perfusion-weighted imaging (PWI), to evaluate brain changes in PCCs. Twenty-five studies met the inclusion criteria, comprising 1219 participants with PCCs. The most consistent findings from sMRI were reduced gray matter volume (GMV) and cortical thickness (CTh) in cortical and subcortical regions. DTI frequently reveals increased mean diffusivity (MD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in white matter tracts (WMTs) such as the corpus callosum, corona radiata, and superior longitudinal fasciculus. fMRI demonstrated altered functional connectivity (FC) within the default mode, salience, frontoparietal, somatomotor, subcortical, and cerebellar networks. PWI showed decreased cerebral blood flow (CBF) in the frontotemporal area, thalamus, and basal ganglia. Advanced MRI shows changes in the brain networks and regions of the PCCs, which may cause neurological and neuropsychiatric problems. Multimodal neuroimaging may help understand brain-behavior relationships. Longitudinal studies are necessary to better understand the progression of these brain anomalies.

A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects.

Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC. This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies. Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival. Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.

Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study.

In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.

Assessment of PSR as a Novel Parameter in Perfusion Imaging for CNS Tumor Characterization: An Observational Study

Introduction: Magnetic resonance imaging (MRI) has emerged as the primary modality for diagnosing and evaluating central nervous system (CNS) tumors, transitioning from structural to functional assessment. Advanced MRI techniques, including MR spectroscopy and perfusion imaging, offer insights into functional, metabolic, and hemodynamic changes in addition to structural abnormalities. The study aims to assess the role of PSR (Post-Contrast Signal Recovery) alongside rCBV (Relative Cerebral Blood Volume) and rPSR (Relative Percentage of Signal Recovery) in differentiating grade IV glioma, solitary brain metastasis, and primary CNS lymphoma. Materials and Methods: An observational study was conducted on 38 patients with lymphoma, metastases, and grade IV glioma, utilizing DSC (Dynamic Susceptibility-Weighted Contrast-Enhanced) perfusion imaging and histopathology reports. Sociodemographic, clinical, and MRI parameters were collected. PSR measurements were obtained from perfusion maps using dedicated software. Statistical analysis was performed using SPSS version 27.0. Results: Among the study population, the mean age was 54.16±12.47, with male predominance (63.2%). Conventional imaging characteristics revealed significant differences, including T2 hypo intensity in lymphoma and FLAIR hyperintensity in all gliomas. Perfusion parameters showed higher rCBV in metastases and grade IV glioma compared to lymphoma, whereas PSR and rPSR were higher in lymphoma. PSR and rPSR effectively differentiated among the tumor types (p&lt;0.001). Conclusion: Our study underscores the critical role of advanced MRI techniques, particularly DSC perfusion imaging, in differentiating between grade IV gliomas, solitary brain metastases, and primary CNS lymphomas. PSR emerges as a valuable parameter alongside rCBV and rPSR, demonstrating potential for accurate tumor characterization.

State of the art in the diagnostic evaluation of osteomyelitis: exploring the role of advanced MRI sequences-a narrative review.

Osteomyelitis, a severe bone infection caused mainly by pyogenic organisms, poses diagnostic challenges due to its non-specific magnetic resonance imaging (MRI) manifestations. Conventional MRI, though the imaging modality of choice, often exhibits signal abnormalities with overlapping differential diagnoses, potentially leading to overestimation of infection extent and duration. To address these limitations, advanced MRI sequences, including dynamic contrast-enhanced (DCE) MRI, 1H magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), and Dixon techniques have emerged as promising alternatives. This narrative review explores the potential role of these sequences in aiding the differential diagnosis of osteomyelitis. We used the PubMed database to search for relevant articles using the MeSH keywords: (osteomyelitis) AND (advanced MRI sequences) and we manually selected the most suitable studies to include in our review. Articles outside of original studies were also included. Only records in English or French were considered. In particular, DWI is useful for characterizing fluid collections, distinguishing bone infarcts, and bacterial skull base osteomyelitis from neoplastic lesions. Moreover, DWI assists in differentiating diabetic foot osteomyelitis (DFO) from Charcot neuro-osteoarthropathy, facilitates the diagnosis of pediatric acute osteoarticular infections, and aids in distinguishing osteomyelitis from Modic I degenerative changes. Additionally, DWI proves valuable in monitoring spinal infections and distinguishing pedal osteomyelitis from other conditions, even in patients with renal impairment. DCE-MRI enhances MRI specificity by assessing contrast uptake over time, providing valuable insights into inflammatory microenvironments. It aids in detecting DFO, differentiating it from acute Charcot arthropathy, and distinguishing osteomyelitis from neuropathic arthropathy. Moreover, DCE-MRI shows potential in assessing response to antibiotic therapy in spinal infections. Dixon acquisition improves image quality and facilitates the detection of bone marrow abnormalities, aiding in the differentiation of diabetic foot from osteomyelitis. It also assists in distinguishing osteomyelitis from neuropathic arthropathy and provides valuable information in evaluating the diabetic foot. Proton MR spectroscopy, a well-established modality, offers metabolic information that can differentiate malignant from benign lesions. The role of advanced MRI techniques in evaluating osteomyelitis remains to be fully defined, and further research is required to explore its potential utility in this context. In conclusion, the incorporation of advanced MRI sequences has shown promise in improving the differential diagnosis of osteomyelitis. Future investigations exploring combinations of these techniques and their clinical applications hold significant potential to enhance diagnostic accuracy and patient outcomes.

Identifying Minimal Hepatic Encephalopathy: A New Perspective from Magnetic Resonance Imaging.

Type C hepatic encephalopathy (HE) is a condition characterized by brain dysfunction caused by liver insufficiency and/or portal-systemic blood shunting, which manifests as a broad spectrum of neurological or psychiatric abnormalities, ranging from minimal HE (MHE), detectable only by neuropsychological or neurophysiological assessment, to coma. Though MHE is the subclinical phase of HE, it is highly prevalent in cirrhotic patients and strongly associated with poor quality of life, high risk of overt HE, and mortality. It is, therefore, critical to identify MHE at the earliest and timely intervene, thereby minimizing the subsequent complications and costs. However, proper and sensitive diagnosis of MHE is hampered by its unnoticeable symptoms and the absence of standard diagnostic criteria. A variety of neuropsychological or neurophysiological tests have been performed to diagnose MHE. However, these tests are nonspecific and susceptible to multiple factors (eg, aging, education), thereby limiting their application in clinical practice. Thus, developing an objective, effective, and noninvasive method is imperative to help detect MHE. Magnetic resonance imaging (MRI), a noninvasive technique which can produce many objective biomarkers by different imaging sequences (eg, Magnetic resonance spectroscopy, DWI, rs-MRI, and arterial spin labeling), has recently shown the ability to screen MHE from NHE (non-HE) patients accurately. As advanced MRI techniques continue to emerge, more minor changes in the brain could be captured, providing new means for early diagnosis and quantitative assessment of MHE. In addition, the advancement of artificial intelligence in medical imaging also presents the potential to mine more effective diagnostic biomarkers and further improves the predictive efficiency of MHE. Taken together, advanced MRI techniques may provide a new perspective for us to identify MHE in the future. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Cognitive decline in thrombotic thrombocytopenic purpura survivors: The role of white matter health as assessed by MRI.

Immune-mediated thrombotic thrombocytopenic purpura (iTTP) is a rare condition caused by severe ADAMTS13 deficiency, leading to platelet aggregation and thrombosis. Despite treatment, patients are prone to cognitive impairment and depression. We investigated brain changes in iTTP patients during remission using advanced magnetic resonance imaging (MRI) techniques, correlating these changes with mood and neurocognitive tests. Twenty iTTP patients in remission (30 days post-haematological remission) were compared with six healthy controls. MRI scans, including standard and specialized sequences, were conducted to assess white matter health. Increased T1 relaxation times were found in the cingulate cortex (p < 0.05), and elevated T2 relaxation times were observed in the cingulate cortex, frontal, parietal and temporal lobes (p < 0.05). Pathological changes in these areas are correlated with impaired cognitive and depressive scores in concentration, short-term memory and verbal memory. This study highlights persistent white matter damage in iTTP patients, potentially contributing to depression and cognitive impairment. Key regions affected include the frontal lobe and cingulate cortex. These findings have significant implications for the acute and long-term management of iTTP, suggesting a need for re-evaluation of treatment approaches during both active phases and remission. Further research is warranted to enhance our understanding of these complexities.

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI.

Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice. To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined. We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength. We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.

Evaluations of the diagnostic performance of ZOOMit diffusion-weighted imaging and conventional diffusion-weighted imaging for breast lesions.

Diffusion-weighted imaging (DWI) is valuable in the screening, diagnosis, and grading of breast lesions. However, conventional DWI (C-DWI) is prone to chemical shift and distortion. ZOOMit DWI (Z-DWI), as an advanced magnetic resonance imaging (MRI) technique, applies two spatially selective parallel excitation pulses to focus sampling in the hope of obtaining more valuable information. This study aimed to evaluate and compare the image quality and feasibility of Z-DWI with those of C-DWI in breast lesions. The study included 51 patients with breast lesions who underwent breast MRI from May 2021 to February 2022. All patients received Z-DWI and C-DWI sequences, with b values selected as 50 and 800 s/mm2 (Z-DWIb50, Z-DWIb800, C-DWIb50, and C-DWIb800). Apparent diffusion coefficient (ADC) values based on Z-DWI and C-DWI were calculated. For qualitative analysis, four image quality parameters were selected and assessed on a 4-point Likert scale (1 = poor and 4 = excellent). For quantitative analysis, ADC, relative ADC (rADC), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and tumor-to-parenchymal contrast (TPC) values were selected for comparison. Z-DWI had higher scores compared to C-DWI in terms of lesion conspicuity, anatomical details, distortion and artifacts, and overall image quality (P<0.05). Meanwhile, the agreement between the two readers was reasonably good [intraclass correlation coefficient (ICC) range, 0.360-0.881]. The SNR of Z-DWIb800 was better than that of C-DWIb800 (P<0.001). The Z-DWI ADC and rADC values of breast lesions were statistically significantly lower than those of C-DWI (mean ADC: P<0.001; rADC; P=0.005). Z-DWI sequences were shown to have superior image quality. The ADC map of Z-DWI is more conducive to the imaging evaluation of breast lesions.

A Multi-Modality Magnetic Resonance Imaging Model for Predicting Traumatic Brain Injury Outcomes

Traumatic Brain Injury (TBI) is a heterogenous injury and a leading cause of long-term deficits and mortality in the United States. In order to improve TBI outcomes, an effective prognostication tool is necessary. Standard imaging modalities, computerized tomography (CT) and magnetic resonance imaging (MRI), have a limited ability to predict TBI outcomes. Currently, advanced MRI techniques are being studied for their efficacy. The aim of this study is to determine whether a multimodality MRI approach is superior to a single modality MRI approach in determining clinical outcomes of TBI. A secondary data analysis was conducted on TBI data obtained from 31 rat brains; 3-day MRI data in the Ipsilateral Perilesion Cortex and 28-day Behavioral Test data (Novel Object Recognition, Barnes Maze, and Open Field Test Total Distance and Total Act Time) were analyzed. A Best Subset Analysis was conducted for each of the behavioral tests. Three out of four behavioral tests show improved adjusted R2 values for models containing more than one imaging modality. A Multiple Linear Regression Analysis was then conducted on the MRIs from the highest predictive model determined by Best Subset Analysis. This analysis shows that a multimodality MRI approach can explain 25.2% of the variability in behavioral outcomes in the Novel Object Recognition Test with a P value of 0.012. Thus, the study demonstrates that a multi-modality MRI approach has a potential for effectively diagnosing and predicting TBI outcomes.

Advanced Magnetic Resonance Imaging and Molecular Imaging of the Painful Knee.

Chronic knee pain is a common condition. Causes of knee pain include trauma, inflammation, and degeneration, but in many patients the pathophysiology remains unknown. Recent developments in advanced magnetic resonance imaging (MRI) techniques and molecular imaging facilitate more in-depth research focused on the pathophysiology of chronic musculoskeletal pain and more specifically inflammation. The forthcoming new insights can help develop better targeted treatment, and some imaging techniques may even serve as imaging biomarkers for predicting and assessing treatment response in the future. This review highlights the latest developments in perfusion MRI, diffusion MRI, and molecular imaging with positron emission tomography/MRI and their application in the painful knee. The primary focus is synovial inflammation, also known as synovitis. Bone perfusion and bone metabolism are also addressed.

Assessment of longitudinal brain development using super-resolution magnetic resonance imaging following fetal surgery for open spina bifida.

Prenatal surgery is offered for selected fetuses with open spina bifida (OSB) to improve long-term outcome. We studied the effect of fetal OSB surgery on brain development using advanced magnetic resonance imaging (MRI) techniques to quantify the volume, surface area and shape of cerebral structures and to analyze surface curvature by means of parameters that correspond to gyrification. We compared MRI data from 29 fetuses with OSB before fetal surgery (mean gestational age (GA), 23 + 3 weeks) and at 1 and 6 weeks after surgery, with that of 36 GA-matched control fetuses (GA range, 21 + 2 to 36 + 2 weeks). Automated super-resolution reconstruction provided three-dimensional isotropic volumetric brain images. Unmyelinated white matter, cerebellum and ventricles were segmented automatically and refined manually, after which volume, surface area and shape parameter (volume/surface area) were quantified. Mathematical markers (shape index (SI) and curvedness) were used to measure gyrification. Parameters were assessed according to lesion type (myelomeningocele vs myeloschisis (MS)), postoperative persistence of hindbrain herniation (HH) and the presence of supratentorial anomalies, namely partial agenesis of the corpus callosum (pACC) and heterotopia (HT). Growth in ventricular volume per week and change in shape parameter per week were higher at 6 weeks after surgery in fetuses with OSB compared with controls (median, 2500.94 (interquartile range (IQR), 1689.70-3580.80) mm3 /week vs 708.21 (IQR, 474.50-925.00) mm3 /week; P < 0.001 and 0.075 (IQR, 0.047-0.112) mm/week vs 0.022 (IQR, 0.009-0.042) mm/week; P = 0.046, respectively). Ventricular volume growth increased 6 weeks after surgery in cases with pACC (P < 0.001) and those with persistent HH (P = 0.002). During that time period, the change in unmyelinated white-matter shape parameter per week was decreased in OSB fetuses compared with controls (0.056 (IQR, 0.044-0.092) mm/week vs 0.159 (IQR, 0.100-0.247) mm/week; P = 0.002), particularly in cases with persistent HH (P = 0.011), MS (P = 0.015), HT (P = 0.022), HT with corpus callosum anomaly (P = 0.017) and persistent HH with corpus callosum anomaly (P = 0.007). At 6 weeks postoperatively, despite OSB fetuses having a lower rate of change in curvedness compared with controls (0.061 (IQR, 0.040-0.093) mm-1 /week vs 0.094 (IQR, 0.070-0.146) mm-1 /week; P < 0.001), reversing the trend seen at 1 week after surgery (0.144 (IQR, 0.099-0.236) mm-1 /week vs 0.072 (IQR, 0.059-0.081) mm-1 /week; P < 0.001), gyrification, as determined using SI, appeared to be increased in OSB fetuses overall compared with controls. This observation was more prominent in fetuses with pACC and those with severe ventriculomegaly (P-value range, < 0.001 to 0.006). Following fetal OSB repair, volume, shape and curvedness of ventricles and unmyelinated white matter differed significantly compared with those of normal fetuses. Morphological brain changes after fetal surgery were not limited to effects on the circulation of cerebrospinal fluid. These observations may have implications for postnatal neurocognitive outcome. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

AQP4 is an Emerging Regulator of Pathological Pain: A Narrative Review.

Pathological pain presents significant challenges in clinical practice and research. Aquaporin-4 (AQP4), which is primarily found in astrocytes, is being considered as a prospective modulator of pathological pain. This review examines the association between AQP4 and pain-related diseases, including cancer pain, neuropathic pain, and inflammatory pain. In cancer pain, upregulated AQP4 expression in tumor cells is linked to increased pain severity, potentially through tumor-induced inflammation and edema. Targeting AQP4 may offer therapeutic strategies for managing cancer pain. AQP4 has also been found to play a role in nerve damage. Changes in AQP4 expression have been detected in pain-related regions of the brain and spinal cord; thus, modulating AQP4 expression or function may provide new avenues for treating neuropathic pain. Of note, AQP4-deficient mice exhibit reduced chronic pain responses, suggesting potential involvement of AQP4 in chronic pain modulation, and AQP4 is involved in pain modulation during inflammation, so understanding AQP4-mediated pain modulation may lead to novel anti-inflammatory and analgesic therapies. Recent advancements in magnetic resonance imaging (MRI) techniques enable assessment of AQP4 expression and localization, contributing to our understanding of its involvement in brain edema and clearance pathways related to pathological pain. Furthermore, targeting AQP4 through gene therapies and small-molecule modulators shows promise as a potential therapeutic intervention. Future research should focus on utilizing advanced MRI techniques to observe glymphatic system changes and the exchange of cerebrospinal fluid and interstitial fluid. Additionally, investigating the regulation of AQP4 by non-coding RNAs and exploring novel small-molecule medicines are important directions for future research. This review shed light on AQP4-based innovative therapeutic strategies for the treatment of pathological pain. Dark blue cells represent astrocytes, green cells represent microglia, and red ones represent brain microvasculature.

Three in One: In Vitro and In Vivo Evaluation of Anticancer Activity of a Theranostic Agent that Combines Magnetic Resonance Imaging, Optical Bioimaging, and Photodynamic Therapy Capabilities.

Cancer treatment is a crucial area of research and development, as current chemotherapeutic treatments can have severe side effects or poor outcomes. In the constant search for new strategies that are localized and minimally invasive and produce minimal side effects, photodynamic therapy (PDT) is an exciting therapeutic modality that has been gaining attention. The use of theranostics, which combine diagnostic and therapeutic capabilities, can further improve treatment monitoring through image guidance. This study explores the potential of a theranostic agent consisting of four Gd(III) DTTA complexes (DTTA: diethylenetriamine-N,N,N″,N″-tetraacetate) grafted to a meso-tetraphenylporphyrin core for PDT, fluorescence, and magnetic resonance imaging (MRI). The agent was first tested in vitro on both nonmalignant TIB-75 and MRC-5 and tumoral CT26 and HT-29 cell lines and subsequently evaluated in vivo in a preclinical colorectal tumor model. Advanced MRI and optical imaging techniques were employed with engineered quantitative in vivo molecular imaging based on dynamic acquisition sequences to track the biodistribution of agents in the body. With 3D quantitative volume computed by MRI and tumoral cell function assessed by bioluminescence imaging, we could demonstrate a significant impact of the molecular agent on tumor growth following light application. Further exhaustive histological analysis confirmed these promising results, making this theranostic agent a potential drug candidate for cancer.