More than 5 years have passed since the Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS) method was proposed with the intention of evaluating the glymphatic system. This method is handy due to its noninvasiveness, provision of a simple index in a straightforward formula, and the possibility of retrospective analysis. Therefore, the ALPS method was adopted to evaluate the glymphatic system for many disorders in many studies. The purpose of this review is to look back and discuss the ALPS method at this moment.

The ALPS-index was found to be an indicator of a number of conditions related to the glymphatic system. Thus, although this was expected in the original report, the results of the ALPS method are often interpreted as uniquely corresponding to the function of the glymphatic system. However, a number of subsequent studies have pointed out the problems on the data interpretation. As they rightly point out, a higher ALPS-index indicates predominant Brownian motion of water molecules in the radial direction at the lateral ventricular body level, no more and no less. Fortunately, the term “ALPS-index” has become common and is now known as a common term by many researchers. Therefore, the ALPS-index should simply be expressed as high or low, and whether it reflects a glymphatic system is better to be discussed carefully. In other words, when a decreased ALPS-index is observed, it should be expressed as “decreased ALPS-index” and not directly as “glymphatic dysfunction”. Recently, various methods have been proposed to evaluate the glymphatic system. It has become clear that these methods also do not seem to reflect the entirety of the extremely complex glymphatic system. This means that it would be desirable to use various methods in combination to evaluate the glymphatic system in a comprehensive manner.

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

In MRI, researchers have long endeavored to effectively visualize myelin distribution in the brain, a pursuit with significant implications for both scientific research and clinical applications. Over time, various methods such as myelin water imaging, magnetization transfer imaging, and relaxometric imaging have been developed, each carrying distinct advantages and limitations. Recently, an innovative technique named as magnetic susceptibility source separation has emerged, introducing a novel surrogate biomarker for myelin in the form of a diamagnetic susceptibility map. This paper comprehensively reviews this cutting-edge method, providing the fundamental concepts of magnetic susceptibility, susceptibility imaging, and the validation of the diamagnetic susceptibility map as a myelin biomarker that indirectly measures myelin content. Additionally, the paper explores essential aspects of data acquisition and processing, offering practical insights for readers. A comparison with established myelin imaging methods is also presented, and both current and prospective clinical and scientific applications are discussed to provide a holistic understanding of the technique. This work aims to serve as a foundational resource for newcomers entering this dynamic and rapidly expanding field.

Purpose: Brain MRI with high spatial resolution allows for a more detailed delineation of multiple sclerosis (MS) lesions. The recently developed deep learning-based reconstruction (DLR) technique enables image denoising with sharp edges and reduced artifacts, which improves the image quality of thin-slice 2D MRI. We, therefore, assessed the diagnostic value of 1 mm-slice-thickness 2D T2-weighted imaging (T2WI) with DLR (1 mm T2WI with DLR) compared with conventional MRI for identifying MS lesions.

Methods: Conventional MRI (5 mm T2WI, 2D and 3D fluid-attenuated inversion recovery) and 1 mm T2WI with DLR (imaging time: 7 minutes) were performed in 42 MS patients. For lesion detection, two neuroradiologists counted the MS lesions in two reading sessions (conventional MRI interpretation with 5 mm T2WI and MRI interpretations with 1 mm T2WI with DLR). The numbers of lesions per region category (cerebral hemisphere, basal ganglia, brain stem, cerebellar hemisphere) were then compared between the two reading sessions.

Results: For the detection of MS lesions by 2 neuroradiologists, the total number of detected MS lesions was significantly higher for MRI interpretation with 1 mm T2WI with DLR than for conventional MRI interpretation with 5 mm T2WI (765 lesions vs. 870 lesions at radiologist A, < 0.05). In particular, of the 33 lesions in the brain stem, radiologist A detected 21 (63.6%) additional lesions by 1 mm T2WI with DLR.

Conclusion: Using the DLR technique, whole-brain 1 mm T2WI can be performed in about 7 minutes, which is feasible for routine clinical practice. MRI with 1 mm T2WI with DLR enabled increased MS lesion detection, particularly in the brain stem.

Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time reduction is a natural requirement in neuroimaging due to detailed structures requiring high resolution imaging and often volumetric (3D) acquisitions, and numerous studies have recently attempted to harness deep learning (DL) technology in enabling scan time reduction and image quality improvement. Various DL-based image reconstruction products allow for additional scan time reduction on top of existing accelerated acquisition methods without compromising the image quality.

RF electromagnetic wave exposure during MRI scans induces heat and occasionally causes burn injuries to patients. Among all the types of physical injuries that have occurred during MRI examinations, RF burn injuries are the most common ones. The number of RF burn injuries increases as the static magnetic field of MRI systems increases because higher RFs lead to higher heating. The commonly believed mechanisms of RF burn injuries are the formation of a conductive loop by the patient’s posture or cables, such as an electrocardiogram lead; however, the mechanisms of RF burn injuries that occur at the contact points, such as the bore wall and the elbow, remain unclear. A comprehensive understanding of RF heating is needed to address effective countermeasures against all RF burn injuries for safe MRI examinations. In this review, we summarize the occurrence of RF burn injury cases by categorizing RF burn injuries reported worldwide in recent decades. Safety standards and regulations governing RF heating that occurs during MRI examinations are presented, along with their theoretical and physiological backgrounds. The experimental assessment techniques for RF heating are then reviewed, and the development of numerical simulation techniques is explained. In addition, a comprehensive theoretical interpretation of RF burn injuries is presented. By including the results of recent experimental and numerical simulation studies on RF heating, this review describes the progress achieved in understanding RF heating from the standpoint of MRI burn injury prevention.

Arterial spin labeling (ASL), a non-invasive MRI technique, has emerged as a valuable tool for researchers that can measure blood flow and related parameters. This review aims to provide a qualitative overview of the technical principles and recent developments in ASL and to highlight its potential clinical applications. A growing literature demonstrates impressive ASL sensitivity to a range of neuropathologies and treatment responses. Despite its potential, challenges persist in the translation of ASL to widespread clinical use, including the lack of standardization and the limited availability of comprehensive training. As experience with ASL continues to grow, the final stage of translation will require moving beyond single site observational studies to multi-site experience and measurement of the added contribution of ASL to patient care and outcomes.

Purpose: The assessment of metastatic cervical lymph nodes in head and neck squamous cell carcinoma patients is crucial; as such, many studies focusing on non-invasive imaging techniques to evaluate metastatic cervical lymph nodes have been performed. The aim of our study was to assess the usefulness of elasticity values on diffusion weighted imaging (DWI)-based virtual MR elastography in the evaluation of metastatic cervical lymph nodes from head and neck squamous cell carcinoma.

Methods: Two head and neck radiologists measured the elasticity values of 16 metastatic cervical lymph nodes from head and neck squamous cell carcinoma and 13 benign cervical lymph nodes on DWI-based virtual MR elastography maps. Mean, minimum, maximum, and median elasticity values were evaluated for lymph nodes between the two groups and interobserver agreement in measuring the elasticity was also evaluated.

Results: The mean, maximum, and median elasticity values of metastatic cervical lymph nodes were significantly higher than those of benign cervical lymph nodes (P = 0.001, 0.01, and 0.002, respectively). Diagnostic accuracy, sensitivity, and specificity of the mean elasticity were 82.8%, 93.8%, and 69.2%, respectively. Interobserver agreement was excellent for the mean and median elasticity (intraclass correlation coefficients were 0.98 for both).

Conclusion: Estimated elasticity values based on DWI-based virtual MR elastography show significant difference between benign and metastatic cervical lymph nodes from head and neck squamous cell carcinoma. While precise modulation of MR sequences and calibration parameters still needs to be established, elasticity values can be useful in differentiating between these lymph nodes.

Purpose: To investigate the characteristics of the putative meningeal lymphatics located at the posterior wall of the sigmoid sinus (PML-PSS) in human subjects imaged before and after intravenous administration (IV) of a gadolinium-based contrast agent (GBCA). The appearance of the PML-PSS and the enhancement of the perivascular space of the basal ganglia (PVS-BG) were analyzed for an association with gender, age, and clearance of the GBCA from the cerebrospinal fluid (CSF).

Methods: Forty-two patients with suspected endolymphatic hydrops were included. Heavily T2-weighted 3D-fluid attenuated inversion recovery (hT2w-3D-FLAIR) and 3D-real inversion recovery (IR) images were obtained at pre-administration, immediately post-administration, and at 4 and 24 hours after IV-GBCA. The appearance of the PML-PSS and the presence of enhancement in the PVS-BG were analyzed for a relationship with age, gender, contrast enhancement of the CSF at 4 hours after IV-GBCA, and the washout ratio of the GBCA in the CSF from 4 to 24 hours after IV-GBCA.

Results: The PML-PSS and PVS-BG were seen in 23 of 42 and 21 of 42 cases, respectively, at 4 hours after IV-GBCA. In all PML-PSS positive cases, hT2w-3D-FLAIR signal enhancement was highest at 4 hours after IV-GBCA. A multivariate analysis between gender, age, CSF signal elevation at 4 hours, and washout ratio indicated that only the washout ratio was independently associated with the enhancement of the PML-PSS or PVS-BG. The odds ratios (95% CIs; P value) were 4.09 × 10^–5 (2.39 × 10^–8 – 0.07; 0.0078) for the PML-PSS and 1.7 × 10^–4 (1.66 × 10^–7 – 0.174; 0.014) for the PVS-BG.

Conclusion: The PML-PSS had the highest signal enhancement at 4 hours after IV-GBCA. When the PML-PSS was seen, there was also often enhancement of the PVS-BG at 4 hours after IV-GBCA. Both observed enhancements were associated with delayed GBCA excretion from the CSF.

Purpose: To evaluate the clinical value of early renal changes in type 2 diabetes mellitus (T2DM) using multiparameter MRI.

Methods: The study included 41 diabetics (normoalbuminuria: n = 23; microalbuminuria: n = 18) and 30 healthy controls. All subjects underwent intravoxel incoherent motion diffusion-weighted imaging (IVIM), blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) examinations. One-way analysis of variance was used to compare MRI parameters among the three groups. Pearson correlation analysis was used to evaluate the relationship between MRI parameters and estimated glomerular filtration rate (eGFR) and albumin–creatinine ratio (ACR). Receiver operating characteristic analysis was performed to assess the diagnostic performance.

Results: There were statistical differences in cortical D, D*, f, renal blood flow (RBF) and medulla D, D*, f, R2* among the three groups (P < 0.05). The cortical or medullary D, cortical f, and RBF were significantly positively correlated with eGFR (all P < 0.01). The cortical or medullary D, D*, f, cortical RBF were negatively correlated with ACR (all P < 0.05).

To evaluate early kidney changes and degree of diabetes, cortical combined D and RBF (AUC [area under the curve]  = 0.796 and 0.947, respectively) was better than single D or RBF (all P > 0.05); medullary combined D and R2* (AUC = 0.899 and 0.923, respectively) was better than single D or R2* (all P > 0.05), except single D (P = 0.005) in differentiating normoalbuminuria group from control group.

Conclusion: The early changes of renal diffusion and perfusion, oxygenation level, and blood flow in T2DM could be evaluated noninvasively and quantitatively using IVIM, BOLD and ASL. Renal medullary combined IVIM-derived D and BOLD-derived R2* and cortical combined IVIM-derived D and ASL-derived RBF were better for evaluating early renal changes in T2DM.

The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques—for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer’s disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.

MRI is widely used in clinical practice for detecting liver diseases. Since the introduction of gadoxetic acid, MRI has become the most effective modality for the detection and characterization of focal liver lesions. According to previous meta-analyses, the area under the receiver operating characteristic curve (AUROC) was 0.97–0.99 for the diagnosis of small hepatocellular carcinoma (≥ 2 cm) by gadoxetic-acid-enhanced MRI. Moreover, the AUROC for the diagnosis of colorectal liver metastases was significantly high (0.98). Despite gadoxetic acid’s drawbacks, its clinical utility outweighs them, making it the contrast agent of choice in routine liver MRIs. Moreover, clinically, liver MRI has become more prevalent for a quantitative assessment. Liver fibrosis can be evaluated using MR elastography; whereas, hepatic steatosis and iron overload can be evaluated using proton density fat fraction, with high accuracy and reproducibility. This article reviewed the usefulness of liver MRI, which can be a comprehensive imaging modality in clinical practice.

Free-running 5D whole-heart coronary MR angiography (MRA) is gaining in popularity because it reduces scanning complexity by removing the need for specific slice orientations, respiratory gating, or cardiac triggering. At 3T, a gradient echo (GRE) sequence is preferred in combination with contrast injection. However, neither the injection scheme of the gadolinium (Gd) contrast medium, the choice of the RF excitation angle, nor the dedicated image reconstruction parameters have been established for 3T GRE free-running 5D whole-heart coronary MRA. In this study, a Gd injection scheme, RF excitation angles of lipid-insensitive binominal off-resonance RF excitation (LIBRE) pulse for valid fat suppression and continuous data acquisition, and compressed-sensing reconstruction regularization parameters were optimized for contrast-enhanced free-running 5D whole-heart coronary MRA using a GRE sequence at 3T. Using this optimized protocol, contrast-enhanced free-running 5D whole-heart coronary MRA using a GRE sequence is feasible with good image quality at 3T.

The incidence of hepatocellular carcinoma (HCC) is still on the rise in North America and Europe and is the second leading cause of cancer-related mortality. The treatment of HCC varies, with surgery and locoregional therapy (LRT) such as radiofrequency ablation and transcatheter arterial chemoembolization, and radiation therapy being the primary treatment. Currently, systemic therapy with molecular-targeted agents and immune checkpoint inhibitors (ICIs) is becoming a major treatment option for the unresectable HCC. As the HCC after LRT or systemic therapy often remains unchanged in size and shows loss of contrast effect in contrast-enhanced CT or MRI, the response evaluation criteria in solid tumors (RECIST) and World Health Organization criteria, which are usually used to evaluate the treatment response of solid tumors, are not appropriate for HCC. The modified RECIST (mRECIST) and the European Association for the Study of the Liver (EASL) criteria were developed for HCC, with a focus on viable lesions. The latest 2018 edition of the Liver Imaging Reporting and Data System (LI-RADS) also includes a section on the evaluation of treatment response. The cancer microenvironment influences the therapeutic efficacy of ICIs. Several studies have examined the utility of gadoxetic acid-enhanced MRI for predicting the pathological and molecular genetic patterns of HCC. In the future, it may be possible to stratify prognosis and predict treatment response prior to systemic therapy by using pre-treatment imaging findings.