Synthetic MRI (SyMRI) is a short-time acquisition sequence that generates different contrast-weighted images based on the measurement of tissue properties and provides quantitative volumetric, relaxation, and myelin maps. It has been used as an alternative to conventional MRI sequences in relapsing-remitting MS for detecting focal lesions and volumetric analysis. This study aimed to find an SyMRI variable associated with an Expanded Disability Status Scale (EDSS) ≥ 6 in progressive patients.

Twenty-four patients with progressive MS underwent SyMRI with a 2D axial QRAPMASTER pulse sequence. We analyzed volumetric parameters, global myelin fraction (MyCF), and quantitative values derived from maps of proton density, R1, R2, and myelin for the masks: normal-appearing white and gray matter, lesion, and corpus callosum. A t test compared SyMRI variables between groups, followed by univariate binary logistic regression for significant (P < .05) or trending results (P < .09).

Patients were categorized into 2 groups (EDSS < 6 versus ≥ 6). Variables with significant differences between groups were: brain parenchymal fraction (P = .05), white matter fraction (P = .05), MyCF (P = .04), and corpus callosum volume (P = .04). In the binary logistic regression analysis, the best predictor of the EDSS category was MyCF, with a P value of .08, and an OR of 0.59.

Our results confirm differences in volumetric parameters by EDSS by using a single MRI acquisition. Additionally, higher MyCF values were associated with lower disability, highlighting SyMRI and myelin quantification as potential tools for clinical practice.

To report T1/T2-values of normal and normal appearing brain tissues (NBTs, healthy volunteers; NABTs, patients) acquired with a whole-brain 5-minute Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) protocol, and to explore relaxometry behavior in a brain tumor and in a multiple sclerosis patient.

MR-STAT was acquired in 49 participants (39 patients with neurological pathologies, age range: 21-79 years) at 3 T. Mean T1/T2-values were computed for: normal and normal appearing grey matter (NGM/NAGM)/white matter (NWM/NAWM)/thalamus/putamen/caudate nucleus (CN)/globus pallidus (GP). Differences between sex, brain lobes, and left/right were assessed. The age-dependency of T1/T2-values in N(A)BTs was investigated. Relaxometry analysis was performed in two clinical case examples.

Mean (standard deviation) T1/T2-values were measured in N(A)GM = 1086(73)/74(9) ms; N(A)WM = 658(24)/48(3) ms; thalamus = 783(51)/42(4) ms; putamen = 863(40)/46(3) ms; CN = 1042(97)/63(9) ms; GP = 652(36)/36(3) ms. Differences between sex were not significant. T1/T2-values between the left/right parietal lobe and the left/right temporal lobe were significantly different. The quadratic age-dependency of T1-values in the CN (p = 0.00039) and GP (p = 0.00037), and of T2-values in the thalamus (p = 0.00044) and GP (p = 0.003) were significant. Pathological tissues could be discerned from NABTs using T1/T2-values.

T1/T2-values and data trends agree with literature, supporting the validity of MR-STAT as a clinical option for fast relaxometry despite the relatively low number of subjects in the study. Future work should aim to include healthy participants of a wider age-range and to include B1-field corrections.

To evaluate the utility of synthetic MRI of the shoulder compared with conventional MRI and to optimize the parameters of morphological images through post-processing. Additionally, we aimed to assess the agreement of T2 values between multi-echo spin-echo (MESE) and multi-dynamic multi-echo (MDME) sequences.

Twenty healthy volunteers underwent shoulder MRI. The optimal post-processing parameters for the tendon-muscle contrast were examined using synthetic MRI, and two radiologists analyzed three image sets: conventional images, synthetic images using preset parameters, and optimized images. Qualitative analysis included assessment of the visibility of anatomical structures, overall image quality, and magic angle effect, whereas the quantitative analysis included measurement of the relative signal intensity and relative contrast. The T2 values of the articular cartilage and supraspinatus muscle were measured for each sequence.

Images synthesized with short echo times and long repetition times showed high tendon-muscle contrast. For fat-suppressed T2-weighted images, conventional images showed the highest image quality (p < 0.001), whereas the optimized images achieved comparable visibility of the rotator cuff (p = 0.031-1.0). No significant differences were observed among image sets in proton density-weighted images and T1-weighted images (p > 0.05). The T2 values of the MDME sequence were consistent with those of the MESE sequence at the muscle (p = 0.81), but were approximately 8.3 ms longer at the cartilage (p < 0.001).

Synthetic MRI provided acceptable image quality using appropriate post-processing parameters. The simultaneous acquisition of multiple morphological images and quantitative maps within five minutes holds promise for shoulder examination.

To explore the role of Synthetic MRI (SyMRI) histogram parameters in differentiating metastatic from non-metastatic cervical lymph nodes (LNs) in head and neck squamous cell carcinoma (HNSCC) patients, and construct a practical model.

A total of 149 pathologically confirmed LNs (metastatic LNs: 58, non-metastatic LNs: 91) were included in the study. LNs were stratified and randomly divided into a training set and an independent validation set in a ratio of 7:3. Histogram parameters derived from SyMRI, ADC values, and the short and long diameters of each LN were obtained. Significantly different parameters between metastatic and non-metastatic LNs were selected in the training set, and logistic regression analysis was adopted to construct different models. ROC analysis and AUC were performed to assess the diagnostic performance of different models and subjective analysis.

The AUCs of the three models were 0.882 (SyMRI_model), 0.755 (DWI), and 0.952 (Combined_model) in the validation set. The Combined_model, constructed based on SyMRI, ADC values, and size, had the highest diagnostic potency in both training and validation sets, with an accuracy of 0.905 and 0.864 in the two sets, respectively. The diagnostic performance of the Combined_model was superior to multi-radiologists' subjective experience, not only in LNs from validation set (AUC: 0.952 vs. 0.705 ∼ 0.845) but also in the cohort of sub-centimeter LNs (AUC: 0.878 vs. 0.429 ∼ 0.628) (all P < 0.001).

Histogram parameters derived from SyMRI are feasible in discriminating metastatic from non-metastatic cervical LNs in HNSCC, and the diagnostic efficacy is optimal when combined with DWI and size.

We aimed to elucidate the correlation between cognitive function and relaxation rates of the cerebral cortex in the early stages of cognitive decline.

Brain MRI was performed on 97 community-dwelling elderly participants aged 65-84 years. R1 (1/T1) and R2 (1/T2) maps were obtained with synthetic MRI (SyMRI). Cognitive function was evaluated using the Japanese version of the Montreal Cognitive Assessment (MoCA). Participants were categorized into mild cognitive impairment (n = 47) and healthy control (n = 50) groups. Voxel-based quantification (VBQ) and voxel-based morphometry (VBM) analyses were conducted using two-sample t-tests and multiple regression models, with age and sex as covariates.

VBQ revealed a significant negative correlation between R1 values and MoCA visuospatial/executive score in the bilateral frontal pole and left superior frontal gyrus (family-wise error-corrected p < 0.05). No significant correlations were found between R2 values and visuospatial/executive score. The multiple regression analysis for VBM showed no significant correlations, and the two-sample t-tests for both VBQ and VBM revealed no significant group differences.

Visuospatial/executive impairment correlated with higher R1 and R2 values in the frontal cortex, suggesting their potential as biomarkers for early cognitive decline.

Magnetic resonance imaging (MRI)-based lesion quantification is essential for the diagnosis of and prognosis in multiple sclerosis (MS). This study compares an established software's performance for automated volumetric and numerical segmentation of MS brain lesions using synthetic T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI, based on a multi-dynamic, multi-echo sequence (MDME), vs. conventional FLAIR imaging.

To ensure comparability, 3D FLAIR images were resampled to 4 mm axial slices to match the synthetic images' slice thickness. Lesion segmentation was performed using the Lesion Prediction Algorithm within the Lesion Segmentation Toolbox. For the assessment of spatial differences between lesion segmentations from both sequences, all lesion masks were registered to a brain template in the standard space. Spatial agreement between the two sequences was evaluated by calculating Sørensen-Dice coefficients (SDC) of the segmented and registered lesion masks. Additionally, average lesion masks for both synthetic and conventional FLAIR were created and displayed as overlays on a brain template to visualize segmentation differences.

Both total lesion volume (TLV) and total lesion number (TLN) were significantly higher for synthetic MRI (11.0 ± 12.8 mL, 19.5 ± 12.1 lesions) than for conventional images (6.1 ± 8.5 mL, 17.9 ± 12.5 lesions). Bland-Altman plot analysis showed minimal TLV differences between synthetic and conventional FLAIR in patients with low overall lesion loads. The intraclass coefficient (ICC) indicated excellent agreement between both measurements, with values of 0.88 for TLV and 0.89 for TLN. The mean SDC was 0.47 ± 0.15.

Despite some limitations, synthetic FLAIR imaging holds promise as an alternative to conventional FLAIR for assessing MS lesions, especially in patients with low lesion load. However, further refinement is needed to reduce unwanted artifacts that may affect image quality.

The aging process induces a variety of changes in the brain detectable by magnetic resonance imaging (MRI). These changes include alterations in brain volume, fluid-attenuated inversion recovery (FLAIR) white matter hyperintense lesions, and variations in tissue properties such as relaxivity, myelin, iron content, neurite density, and other microstructures. Each MRI technique offers unique insights into the structural and compositional changes occurring in the brain due to normal aging or neurodegenerative diseases. Age-related brain volume changes encompass a decrease in gray matter and an increase in ventricular volume, associated with cognitive decline. White matter hyperintensities, detected by FLAIR, are common and linked to cognitive impairments and increased risk of stroke and dementia. Tissue relaxometry reveals age-related changes in relaxivity, aiding the distinction between normal aging and pathological conditions. Myelin content, measurable by MRI, changes with age and is associated with cognitive and motor function alterations. Iron accumulation, detected by susceptibility-sensitive MRI, increases in certain brain regions with age, potentially contributing to neurodegenerative processes. Diffusion MRI provides detailed insights into microstructural changes such as neurite density and orientation. Neurofluid imaging, using techniques like gadolinium-based contrast agents and diffusion MRI, reveals age-related changes in cerebrospinal and interstitial fluid dynamics, crucial for brain health and waste clearance. This review offers a comprehensive overview of age-related brain changes revealed by various MRI techniques. Understanding these changes helps differentiate between normal aging and pathological conditions, aiding the development of interventions to mitigate age-related cognitive decline and other symptoms. Recent advances in machine learning and artificial intelligence have enabled novel methods for estimating brain age, offering also potential biomarkers for neurological and psychiatric disorders.

Brain segmentation and multi-parameter mapping (MPM) are important steps in neurodegenerative disease characterization. However, acquiring both a high-resolution T1w sequence like MPRAGE (standard input to brain segmentation) and an MPM in the same neuroimaging protocol increases scan time and patient discomfort, making it difficult to combine both in clinical examinations.

A novel approach to synthesize T1w images from MPM images, named MPRAGElike, is proposed and compared to the standard technique used to produce synthetic MPRAGE images (synMPRAGE). Twenty-three healthy subjects were scanned with the same imaging protocol at three different 7T sites using universal parallel transmit RF pulses. SNR, CNR, and automatic brain segmentation results from both MPRAGElike and synMPRAGE were compared against an acquired MPRAGE.

The proposed MPRAGElike technique produced higher SNR values than synMPRAGE for all regions evaluated while also having higher CNR values for subcortical structures. MPRAGE was still the image with the highest SNR values overall. For automatic brain segmentation, MPRAGElike outperformed synMPRAGE when compared to MPRAGE (median Dice Similarity Coefficient of 0.90 versus 0.29 and Average Asymmetric Surface Distance of 0.33 versus 2.93 mm, respectively), in addition to being simple, flexible, and considerably more robust to low image quality than synMPRAGE.

The MPRAGElike technique can provide a better and more reliable alternative to synMPRAGE as a substitute for MPRAGE, especially when automatic brain segmentation is of interest and scan time is limited.

Synthetic MR is a quantitative MRI method that measures tissue relaxation times and generates multiple contrast-weighted images using suitable algorithms. The present article principally discusses the multiple dynamic multiple echo (MDME) technique of synthetic MR and briefly describes other quantitative MR sequences.

Using illustrative cases, various applications of the MDME sequence in neuroradiology are explained. The MDME sequence allows rapid quantification of tissue relaxation times in a scan duration of 5-7 minutes for full brain coverage. It also has the additional advantages of myelin quantification and automatic segmentation of brain volumes.

Applications including reducing scan time, improved detection of demyelinating plaques in Multiple Sclerosis (MS), objective assessment and follow-up for brain atrophy in neurodegenerative MS and dementia cases, and applications in stroke imaging and neuro-oncology are discussed. Uses in the pediatric population, including assessment of brain development and progression of myelination in children, evaluation of white matter disorders, and evaluation of pediatric and adult epilepsy, are elaborated. Quantitative evaluation by synthetic MR is discussed, which allows homogenization and objectification of the radiology data and can serve as a valuable source for artificial intelligence and future multicentre studies. A brief discussion on the technique, other quantitative MR methods, and limitations of the MDME sequence is also presented.

The article intends to provide an explicit and comprehensive review of the applications of synthetic MR in neuroradiology, exploring its potential as a routine sequence in daily neuroimaging practice.

Pediatric growth hormone deficiency (GHD) is a disease resulting from the impaired growth hormone-insulin-like growth factor-1 (GH-IGF-1) axis, but the effects of GHD on children's behavior and brain microstructural structure alterations have not yet been fully clarified. We aimed to investigate the quantitative profiles of gray matter and white matter in pediatric GHD using synthetic magnetic resonance imaging (MRI).

The data of 50 children with GHD and 50 typically developing (TD) children were prospectively collected. Group differences in brain volumetric parameters, individual-level T1 and T2 relaxometry values, and myelin volume fraction (MVF) were assessed. Subsequently, magnetic resonance-based indices with significant differences between groups were correlated with clinical variables via partial correlation.

Compared with TD children, children with GHD showed significantly decreased whole-brain gray-matter volume [P false discovery rate (PFDR) <0.001] and increased non-gray-matter/white-matter/cerebrospinal fluid (NoN) volume (PFDR<0.001). For gray-matter microstructural profiles, altered T1 and T2 relaxometry values in children with GHD were mainly distributed in the default mode (PFDR<0.001) and central executive networks (PFDR<0.001). For white-matter microstructural profiles, widespread increased regional MVF was mainly distributed in the corpus callosum, corticospinal tract, internal capsule, external capsule, and cingulum (all PFDR values <0.001). Meanwhile, the T2 relaxation values in the left cuneus (r=0.400; P=0.005) and MVF in the right corticospinal tract (r=0.313; P=0.032) had a positive relationship with IGF-1.

Altered T1 and T2 relaxometry values and MVF in gray and white matter indicate the relevance of the default mode, central executive, somatosensory, visual, and cerebellar networks underlying pediatric GHD, which may imply the involvement of the GH-IGF-1 axis and myelin in the pathophysiological mechanism of GHD. Moreover, the brain microstructure alteration in cortico-striatal-limbic loop might be influenced by the GH-IGF-1 axis and play an important role in the behavioral impairments in children with GHD.

As a common disease in the elderly, the diagnosis of Alzheimer's disease (AD) is of great significance to the treatment and prognosis of the patients. Studies have found that magnetic resonance imaging plays an important role in the early diagnosis of Alzheimer's disease. This article tries to review the application of magnetic resonance imaging in the diagnosis and differential diagnosis of Alzheimer's disease.

Synthetic MRI utilizes the quantitative relaxometry parameters to generate multiple contrast images through a single acquisition. We tried to explore the utility of synthetic MRI derived relaxometry parameters in evaluation of ring enhancing lesions of brain.

This was a prospective study. 40 subjects with ring enhancing lesions in brain underwent pre and post contrast synthetic MRI using MDME sequence. Pre and post contrast R1, R2 and PD values were recorded from the core, wall and perilesional edema of lesions and sub group analysis was done among infective, primary neoplastic and secondary neoplastic (metastatic) lesion groups.

Pre and post contrast R1, R2 values from core were higher in the infective group compared to the others. Pre and post contrast R1, R2 values were lower in the wall where as it was significantly higher in the perilesional edema of primary neoplastic group. Post-pre the values increased significantly in the perilesional edema of primary neoplasms. R1 value of ≥ 0.689 and R2 value of ≥ 7.481 in the perilesional edema predicts a primary neoplasm over infection with 70.6% sensitivity and 85.7% specificity and over secondary neoplasm with 64.7% sensitivity and 100% specificity.

Synthetic MRI derived relaxometry parameters in ring enhancing lesions were found to be significantly different across sub groups and can be used to differentiate between primary neoplastic, secondary neoplastic and infective group with parameters from perilesional edema being the most useful.

To investigate the feasibility of synthetic magnetic resonance imaging (syMRI) in predicting the lymphatic vascular space invasion (LVSI) status of early-stage cervical cancer, and its added value to morphological MRI.

A total of 72 patients with pathology-confirmed early-stage cervical cancer were enrolled, and classified into LVSI- positive (n=41) and LVSI- negative (n=31) groups. Together with morphological parameters including gross tumor volume (GTV) and maximum tumor diameter (MTD), the T1, T2, and proton density (PD) values of the tumors were also measured and compared between two groups. Binary logistic regression analysis was used to identify the independent variable associated with LVSI. Receiver operating characteristic curve analyses and DeLong tests were used to evaluate and compare the performances of significant parameters or their combination in predicting LVSI.

LVSI- positive group showed significantly higher GTV (P=0.008) and MTD (P=0.019), and lower T1 (P<0.001) and PD values (P=0.041) than LVSI- negative group. However, no statistical significance was observed regarding the T2 values (P=0.331). Binary logistic regression indicated that T1 value (odds ratio [OR] = 0.993; P=0.001) and MTD (OR=1.903, P=0.027) were independent variables associated with LVSI in early cervical cancer. Optimal performance could be achieved [area under ROC curve (AUC) = 0.784; cut-off value = 0.56; sensitivity = 80.5%; specificity = 71.0%] when combining T1 and MTD for predicting LVSI. Its performance was significantly better than that of MTD alone (AUC, 0.784 vs 0.662, P=0.035).

syMRI might be a feasible approach, and it can provide added value to morphological MRI in predicting the LVSI status of early-stage cervical cancer.

The T2-FLAIR mismatch sign is a characteristic imaging biomarker for astrocytoma, isocitrate dehydrogenase (IDH)-mutant. However, investigators have provided varying interpretations of the positivity/negativity of this sign given for individual cases the nature of qualitative visual assessment. Moreover, MR sequence parameters also influence the appearance of the T2-FLAIR mismatch sign. To resolve these issues, we used synthetic MR technique to quantitatively evaluate and differentiate astrocytoma from oligodendroglioma.

This study included 20 patients with newly diagnosed non-enhanced IDH-mutant diffuse glioma who underwent preoperative synthetic MRI using the Quantification of Relaxation Times and Proton Density by Multiecho acquisition of a saturation-recovery using Turbo spin-Echo Readout (QRAPMASTER) sequence at our institution. Two independent reviewers evaluated preoperative conventional MR images to determine the presence or absence of the T2-FLAIR mismatch sign. Synthetic MRI was used to measure T1, T2 and proton density (PD) values in the tumor lesion. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance.

The pathological diagnoses included astrocytoma, IDH-mutant (n = 12) and oligodendroglioma, IDH-mutant and 1p/19q-codeleted (n = 8). The sensitivity and specificity of T2-FLAIR mismatch sign for astrocytoma were 66.7% and 100% [area under the ROC curve (AUC) = 0.833], respectively. Astrocytoma had significantly higher T1, T2, and PD values than did oligodendroglioma (p < 0.0001, < 0.0001, and 0.0154, respectively). A cutoff lesion T1 value of 1580 ms completely differentiated astrocytoma from oligodendroglioma (AUC = 1.00).

Quantitative evaluation of non-enhanced IDH-mutant diffuse glioma using synthetic MRI allowed for better differentiation between astrocytoma and oligodendroglioma than did conventional T2-FLAIR mismatch sign. Measurement of T1 and T2 value by synthetic MRI could improve the differentiation of IDH-mutant diffuse gliomas.

Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges.

In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis.

Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs.

Lumbar intervertebral disc and paravertebral muscle degeneration are common causes of chronic low back pain (CLBP). However, the exact etiology of CLBP in young patients remains unclear. Identifying the risk factors for CLBP in young patients could expedite the development of effective preventive recommendations.

To identify the factors influencing the presence and severity of CLBP in young patients by analyzing the associations between the fat content of the paravertebral muscles, T2 value of the lumbar intervertebral disc (LIVD), and visual analog scale (VAS) score.

Data for 23 patients diagnosed with CLBP were compared to those of 20 healthy young individuals.

The T2 values of the LIVD and fat content of the psoas major (PM), multifidus (MF), and erector spinae (ES) muscles for 23 young patients with CLBP and 20 healthy individuals were measured and compared using synthetic magnetic resonance imaging and proton density fat fraction analyses. Moreover, the factors (T2 values and fat content) associated with severe CLBP (assessed using the VAS score) were analyzed.

The fat content of the right MF and ES was higher in patients with CLBP than in healthy individuals (p < 0.05). The T2 values of each LIVD in the CLBP and control groups were not significantly different (p > 0.05). Moreover, the VAS scores did not correlate with the T2 values of the patients (p > 0.05). The fat content of the bilateral MF and ES muscles was positively associated with the VAS score in young patients with CLBP (left MF: r = 0.506, p = 0.01; right MF: r = 0.532, p = 0.01; left ES: r = 0.636, p < 0.01; and right ES: r = 0.716, p < 0.01).

Degeneration of the MF and ES may contribute to CLBP in young patients. In addition, the severity of CLBP is positively correlated with the degree of fat infiltration in the MF and ES.

Preschooling is a critical time for intervention in children with autism spectrum disorder (ASD); thus, we analyzed brain tissue component volumes (BTCVs) and clinical indicators in preschool children with ASD to identify new biomarkers for early screening.

Eighty preschool children (3-6 years) with ASD were retrospectively included. The whole-brain myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF brain component volumes were obtained using synthetic magnetic resonance imaging (SyMRI). Clinical data, such as intelligence scores, autism diagnostic observation schedule-calibrated severity scores, age at first production of single words (AFSW), age at first production of phrases (AFP), and age at walking onset (AWO), were also collected. The correlation between the BTCV and clinical data was evaluated, and the effect of BTCVs on clinical data was assessed by a regression model.

WM and GM volumes were positively correlated with intelligence scores (both P < 0.001), but WM and GM did not affect intelligence scores (P = 0.116, P = 0.290). AWO was positively correlated with AFSW and AFP (both P < 0.001). The multivariate linear regression analysis revealed that MyC, AFSW, AFP, and AWO were significantly different (P = 0.005, P < 0.001, P < 0.001).

This study revealed positive correlations between WM and GM volumes and intelligence scores. Whole-brain MyC affected AFSW, AFP, and AWO in preschool children with ASD. Noninvasive quantification of BTCVs via SyMRI revealed a new visualizable and quantifiable biomarker (abnormal MyC) for early ASD screening in preschool children.

Synthetic MRI is being increasingly used for the quantification of brain longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) values. However, the effect of fast imaging protocols on these quantitative values has not been fully estimated. The purpose of this study was to investigate the effect of fast scan parameters on T1, T2, and PD measured with a multi-dynamic multi-echo (MDME) sequence of normal brain at 3.0T. Thirty-four volunteers were scanned using 3 MDME sequences with different scan times (named Fast, 2 min, 29 sec; Routine, 4 min, 07 sec; and Research, 7 min, 46 sec, respectively). The measured T1, T2, and PD in 18 volumes of interest (VOI) of brain were compared between the 3 sequences using rank sum test, t test, coefficients of variation (CVs) analysis, correlation analysis, and Bland-Altman analysis. We found that even though T1, T2, and PD were significantly different between the 3 sequences in most of the brain regions, the intersequence CVs were relatively low and linear correlation were high. Bland-Altman plots showed that most of the values fall within the 95% prediction limits. We concluded that fast imaging protocols of MDME sequence used in our study can potentially be used for quantitative evaluation of brain tissues. Since changing scan parameters can affect the measured T1, T2, and PD values, it is necessary to use consistent scan parameter for comparing or following up cases quantitatively.

To evaluate the potential of Synthetic Magnetic Resonance Imaging (SynMRI) in identifying muscular invasion in bladder cancer (BCa), and explore whether there is additional value in combination with the Vesical Imaging-Reporting and Data System (VI-RADS).

In this prospective single-center study, pathologically-confirmed BCa were enrolled between May 2023 and November 2023. All participants underwent preoperative multiparametric MRI, including T1/T2 weighted, SynMRI and diffusion-weighted imaging. T1/T2/PD values and apparent diffusion coefficient (ADC) values were compared between muscle invasive (MIBC) and non-invasive (NMIBC) groups. Receiver operating characteristic (ROC) analysis with the variables and their combination was performed to explore the performance of distinguishing the MIBC from NMIBC, and the ROC curves were compared using DeLong's test.

A total of 54 BCa patients were enrolled (38 males; NMIBC/MIBC=37/19) and all assessed with VI-RADS without dynamic enhanced imaging (DCE). Compared to NMIBC group, MIBC group had significantly larger diameter, higher VI-RADS score, lower T2 and ADC values (P < 0.05). VI-RADS score and T2 showed independent predictive value in differentiating NMIBC and MIBC. The combined model (T2 + VI-RADS+Diameter) resulted in significantly improved specificity (0.842), sensitivity (0.914), and AUC (0.943), in comparison to VI-RADS or ADC alone (P < 0.05).

T2 relaxation time can be easily obtained from SynMRI in routine clinical protocol and assist VI-RADS score system without DCE to improve differentiation performance in identifying NMIBC and MIBC.

The purpose of this study was to investigate the technical feasibility of integrating the quantitative maps available from SyntheticMR into the head and neck adaptive radiation oncology workflow. While SyntheticMR has been investigated for diagnostic applications, no studies have investigated its feasibility and potential for MR-Simulation or MR-Linac workflow. Demonstrating the feasibility of using this technique will facilitate rapid quantitative biomarker extraction which can be leveraged to guide adaptive radiation therapy decision making.

Two phantoms, two healthy volunteers, and one patient were scanned using SyntheticMR on the MR-Simulation and MR-Linac devices with scan times between four to six minutes. Images in phantoms and volunteers were conducted in a test/retest protocol. The correlation between measured and reference quantitative T1, T2, and PD values were determined across clinical ranges in the phantom. Distortion was also studied. Contours of head and neck organs-at-risk (OAR) were drawn and applied to extract T1, T2, and PD. These values were plotted against each other, clusters were computed, and their separability significance was determined to evaluate SyntheticMR for differentiating tumor and normal tissue.

The Lin's Concordance Correlation Coefficient between the measured and phantom reference values was above 0.98 for both the MR-Sim and MR-Linac. No significant levels of distortion were measured. The mean bias between the measured and phantom reference values across repeated scans was below 4% for T1, 7% for T2, and 4% for PD for both the MR-Sim and MR-Linac. For T1 vs. T2 and T1 vs. PD, the GTV contour exhibited perfect purity against neighboring OARs while being 0.7 for T2 vs. PD. All cluster significance levels between the GTV and the nearest OAR, the tongue, using the SigClust method was p < 0.001.

The technical feasibility of SyntheticMR was confirmed. Application of this technique to the head and neck adaptive radiation therapy workflow can enrich the current quantitative biomarker landscape.

Pre-surgical information about tumor consistency could facilitate neurosurgical planning. This study used multi-dynamic-multi-echo (MDME)-based relaxometry for the quantitative determination of pituitary tumor consistency, with the aim of predicting lesion resectability.

Seventy-two patients with suspected pituitary adenomas, who underwent preoperative 3 T MRI between January 2020 and January 2022, were included in this prospective study. Lesion-specific T1-/T2-relaxation times (T1R/T2R) and proton density (PD) metrics were determined. During surgery, data about tumor resectability were collected. A Receiver Operating Characteristic (ROC) curve analysis was performed to investigate the diagnostic performance (sensitivity/specificity) for discriminating between easy- and hard-to-remove by aspiration (eRAsp and hRAsp) lesions. A Mann-Whitney-U-test was done for group comparison.

A total of 65 participants (mean age, 54 years ± 15, 33 women) were enrolled in the quantitative analysis. Twenty-four lesions were classified as hRAsp, while 41 lesions were assessed as eRAsp. There were significant differences in T1R (hRAsp: 1221.0 ms ± 211.9; eRAsp: 1500.2 ms ± 496.4; p = 0.003) and T2R (hRAsp: 88.8 ms ± 14.5; eRAsp: 137.2 ms ± 166.6; p = 0.03) between both groups. The ROC analysis revealed an area under the curve of 0.72 (95% CI: 0.60-0.85) at p = 0.003 for T1R (cutoff value: 1248 ms; sensitivity/specificity: 78%/58%) and 0.66 (95% CI: 0.53-0.79) at p = 0.03 for T2R (cutoff value: 110 ms; sensitivity/specificity: 39%/96%).

MDME-based relaxometry enables a non-invasive, pre-surgical characterization of lesion consistency and, therefore, provides a modality with which to predict tumor resectability.

To determine whether synthetic MR imaging can distinguish between benign and malignant salivary gland lesions.

The study population included 44 patients with 33 benign and 11 malignant salivary gland lesions. All MR imaging was obtained using a 3 Tesla system. The QRAPMASTER pulse sequence was used to acquire images with four TI values and two TE values, from which quantitative images of T1 and T2 relaxation times and proton density (PD) were generated. The Mann-Whitney U test was used to compare T1, T2, PD, and ADC values among the subtypes of salivary gland lesions. ROC analysis was used to evaluate diagnostic capability between malignant tumors (MTs) and either pleomorphic adenomas (PAs) or Warthin tumors (WTs). We further calculated diagnostic accuracy for distinguishing malignant from benign lesions when combining these parameters.

PAs demonstrated significantly higher T1, T2, PD, and ADC values than WTs (all p < 0.001). Compared to MTs, PAs had significantly higher T1, T2, and ADC values (all p < 0.001), whereas WTs had significantly lower T1, T2, and PD values (p < 0.001, p = 0.008, and p = 0.003, respectively). T2 and ADC were most effective in differentiating between MTs and PAs (AUC = 0.928 and 0.939, respectively), and T1 and PD values for differentiating between MTs and WTs (AUC = 0.915 and 0.833, respectively). Combining T1 with T2 or ADC achieved accuracy of 86.4% in distinguishing between malignant and benign tumors. Similarly, combining PD with T2 or ADC reached accuracy of 86.4% for differentiating between malignant and benign tumors.

Utilizing a combination of synthetic MRI parameters may assist in differentiating malignant from benign salivary gland lesions.

Automated methods for quantifying brain tissue volumes have gained clinical interest for their objective assessment of neurological diseases. This study aimed to establish reference curves for brain volumes and fractions in the Indian population using Synthetic MRI (SyMRI), a quantitative imaging technique providing multiple contrast-weighted images through fast postprocessing.

The study included a cohort of 314 healthy individuals aged 15-65 years from multiple hospitals/centers across India. The SyMRI-quantified brain volumes and fractions, including brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and myelin.

Normative age-stratified quantification curves were created based on the obtained data. The results showed significant differences in brain volumes between the sexes, but not after normalization by intracranial volume.

The findings provide normative data for the Indian population and can be used for comparative analysis of brain structure values. Furthermore, our data indicate that the use of fractions rather than absolute volumes in normative curves, such as BPF, GMF, and WMF, can mitigate sex and population differences as they account for individual differences in head size or brain volume.

This review aimed to assess the usefulness of various magnetic resonance imaging (MRI) techniques for the quantification of neonatal white matter myelination. The Scopus, PubMed, and Web of Science databases were searched to identify studies following the PRISMA (preferred reporting items for systematic reviews and meta-analyses) statement using quantitative MRI techniques to examine samples collected from neonates to quantify myelin. Twelve studies were ultimately included. The results demonstrated that in validation studies, relaxometry is the most frequently explored approach (83.33%), followed by magnetization transfer imaging (8.33%) and a new automatic segmentation technique (8.33%). Synthetic MRI is recommended for quantifying myelin in neonates because of several advantages that outweigh a few negligible limitations.

Normal aging is associated with brain volume change, and brain segmentation can be performed within an acceptable scan time using synthetic magnetic resonance imaging (MRI). This study aimed to investigate the brain volume changes in healthy adult according to age and gender, and provide age- and gender-specific reference values using synthetic MRI.

A total of 300 healthy adults (141 males, median age 48; 159 females, median age 50) were underwent synthetic MRI on 3.0 T. Brain parenchymal volume (BPV), gray matter volume (GMV), white matter volume (WMV), myelin volume (MYV), and cerebrospinal fluid volume (CSFV) were calculated using synthetic MRI software. These volumes were normalized by intracranial volume to normalized GMV (nGMV), normalized WMV (nWMV), normalized MYV (nMYV), normalized BPV (nBPV), and normalized CSFV (nCSFV). The normalized brain volumes were plotted against age in both males and females, and a curve fitting model that best explained the age dependence of brain volume was identified. The normalized brain volumes were compared between different age and gender groups.

The approximate curves of nGMV, nWMV, nCSFV, nBPV, and nMYV were best fitted by quadratic curves. The nBPV decreased monotonously through all ages in both males and females, while the changes of nCSFV showed the opposite trend. The nWMV and nMYV in both males and females increased gradually and then decrease with age. In early adulthood (20s), nWMV and nMYV in males were lower and peaked later than that in females (p < .005). The nGMV in both males and females decreased in the early adulthood until the 30s and then remains stable. A significant decline in nWMV, nBPV, and nMYV was noted in the 60s (Turkey test, p < .05).

Our study provides age- and gender-specific reference values of brain volumes using synthetic MRI, which could be objective tools for discriminating brain disorders from healthy brains.

To investigate the feasibility of synthetic MRI (syMRI), diffusion-weighted imaging (DWI), and their combination with morphological features for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC).

Sixty-nine patients with nasopharyngeal tumours (NPL, n = 22; NPC, n = 47) who underwent syMRI and DWI were retrospectively enrolled between October 2020 and May 2022. syMRI and DWI quantitative parameters (T1, T2, PD, ADC) and morphological features were obtained. Diagnostic performance was assessed by independent sample t-test, chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test.

NPL has significantly lower T2, PD, and ADC values compared to NPC (all P < .05), whereas no significant difference was found in T1 value between these two entities (P > .05). The morphological features of tumour type, skull-base involvement, Waldeyer ring involvement, and lymph nodes involvement region were significantly different between NPL and NPC (all P < .05). The syMRI (T2 + PD) model has better diagnostic efficacy, with AUC, sensitivity, specificity, and accuracy of 0.875, 77.27%, 89.36%, and 85.51%. Compared with syMRI model, syMRI + Morph (PD + Waldeyer ring involvement + lymph nodes involvement region), syMRI + DWI (T2 + PD + ADC), and syMRI + DWI + Morph (PD + ADC + skull-base involvement + Waldeyer ring involvement) models can further improve the diagnostic efficiency (all P < .05). Furthermore, syMRI + DWI + Morph model has excellent diagnostic performance, with AUC, sensitivity, specificity, and accuracy of 0.986, 95.47%, 97.87%, and 97.10%, respectively.

syMRI and DWI quantitative parameters were helpful in discriminating NPL from NPC. syMRI + DWI + Morph model has the excellent diagnostic efficiency in differentiating these two entities.

syMRI + DWI + morphological feature method can differentiate NPL from NPC with excellent diagnostic performance.

Neonates born at < 28 weeks of gestation are at risk for neurodevelopmental delay. The aim of this study was to identify quantitative MR-based metrics for the prediction of neurodevelopmental outcomes in extremely preterm neonates.

T1-/T2-relaxation times (T1R/T2R), ADC, and fractional anisotropy (FA) of the left/right posterior limb of the internal capsule (PLIC) and the brainstem were determined at term-equivalent ages in a sample of extremely preterm infants (n = 33). Scores for cognitive, language, and motor outcomes were collected at one year corrected-age. Pearson's correlation analyses detected relationships between quantitative measures and outcome data. Stepwise regression procedures identified imaging metrics to estimate neurodevelopmental outcomes.

Cognitive outcomes correlated significantly with T2R (r = 0.412; p = 0.017) and ADC (r = -0.401; p = 0.021) (medulla oblongata). Furthermore, there were significant correlations between motor outcomes and T1R (pontine tegmentum (r = 0.346; p = 0.049), midbrain (r = 0.415; p = 0.016), right PLIC (r = 0.513; p = 0.002), and left PLIC (r = 0.504; p = 0.003)); T2R (right PLIC (r = 0.405; p = 0.019)); ADC (medulla oblongata (r = -0.408; p = 0.018) and pontine tegmentum (r = -0.414; p = 0.017)); and FA (pontine tegmentum (r = -0.352; p = 0.045)). T2R/ADC (medulla oblongata) (cognitive outcomes (R2 = 0.296; p = 0.037)) and T1R (right PLIC)/ADC (medulla oblongata) (motor outcomes (R2 = 0.405; p = 0.009)) revealed predictive potential for neurodevelopmental outcomes.

There are relationships between relaxometry‑/DTI-based metrics determined by neuroimaging near term and neurodevelopmental outcomes collected at one year of age. Both modalities bear prognostic potential for the prediction of cognitive and motor outcomes. Thus, quantitative MRI at term-equivalent ages represents a promising approach with which to estimate neurologic development in extremely preterm infants.

Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.

Evaluation of myelin content is crucial for attention-deficit/hyperactivity disorder (ADHD). To estimate myelin content in ADHD based on synthetic MRI-based method and compare it with established diffusion tensor imaging (DTI) method. Fifth-nine ADHD and fifty typically developing (TD) children were recruited. Global and regional myelin content (myelin volume fraction [MVF] and myelin volume [MYV]) were assessed using SyMRI and compared with DTI metrics (fractional anisotropy and mean/radial/axial diffusivity). The relationship between significant MRI parameters and clinical variables were assessed in ADHD. No between-group differences of whole-brain myelin content were found. Compared to TDs, ADHD showed higher mean MVF in bilateral internal capsule, external capsule, corona radiata, and corpus callosum, as well as in left tapetum, left superior fronto-occipital fascicular, and right cingulum (all PFDR-corrected < 0.05). Increased MYV were found in similar regions. Abnormalities of DTI metrics were mainly in bilateral corticospinal tract. Besides, MVF in right retro lenticular part of internal capsule was negatively correlated with cancellation test scores (r = - 0.41, P = 0.002), and MYV in right posterior limb of internal capsule (r = 0.377, P = 0.040) and left superior corona radiata (r = 0.375, P = 0.041) were positively correlated with cancellation test scores in ADHD. Increased myelin content underscored the important pathway of frontostriatal tract, posterior thalamic radiation, and corpus callosum underlying ADHD, which reinforced the insights into myelin quantification and its potential role in pathophysiological mechanism and disease diagnosis. Prospectively registered trials number: ChiCTR2100048109; date: 2021-07.

This retrospective study aimed to identify quantitative magnetic resonance imaging markers in the brainstem of preterm neonates with intraventricular hemorrhages. It delves into the intricate associations between quantitative brainstem magnetic resonance imaging metrics and neurodevelopmental outcomes in preterm infants with intraventricular hemorrhage, aiming to elucidate potential relationships and their clinical implications.

Neuroimaging was performed on preterm neonates with intraventricular hemorrhage using a multi-dynamic multi-echo sequence to determine T1 relaxation time, T2 relaxation time, and proton density in specific brainstem regions. Neonatal outcome scores were collected using the Bayley Scales of Infant and Toddler Development. Statistical analysis aimed to explore potential correlations between magnetic resonance imaging metrics and neurodevelopmental outcomes.

Sixty preterm neonates (mean gestational age at birth 26.26 ± 2.69 wk; n = 24 [40%] females) were included. The T2 relaxation time of the midbrain exhibited significant positive correlations with cognitive (r = 0.538, P < 0.0001, Pearson's correlation), motor (r = 0.530, P < 0.0001), and language (r = 0.449, P = 0.0008) composite scores at 1 yr of age.

Quantitative magnetic resonance imaging can provide valuable insights into neurodevelopmental outcomes after intraventricular hemorrhage, potentially aiding in identifying at-risk neonates. Multi-dynamic multi-echo sequence sequences hold promise as an adjunct to conventional sequences, enhancing the sensitivity of neonatal magnetic resonance neuroimaging and supporting clinical decision-making for these vulnerable patients.

Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired contrasts. Synthetic MRI aims to accelerate examinations by synthesizing any desirable contrast from a single acquisition.

We developed a physics-informed deep learning-based method to synthesize multiple brain MRI contrasts from a single 5-min acquisition and investigate its ability to generalize to arbitrary contrasts.

A dataset of 55 subjects acquired with a clinical MRI protocol and a 5-min transient-state sequence was used. The model, based on a generative adversarial network, maps data acquired from the five-minute scan to "effective" quantitative parameter maps (q*-maps), feeding the generated PD, T1, and T2 maps into a signal model to synthesize four clinical contrasts (proton density-weighted, T1-weighted, T2-weighted, and T2-weighted fluid-attenuated inversion recovery), from which losses are computed. The synthetic contrasts are compared to an end-to-end deep learning-based method proposed by literature. The generalizability of the proposed method is investigated for five volunteers by synthesizing three contrasts unseen during training and comparing these to ground truth acquisitions via qualitative assessment and contrast-to-noise ratio (CNR) assessment.

The physics-informed method matched the quality of the end-to-end method for the four standard contrasts, with structural similarity metrics above0.75 ± 0.08 $0.75\pm 0.08$ ( ± $\pm$ std), peak signal-to-noise ratios above22.4 ± 1.9 $22.4\pm 1.9$ , representing a portion of compact lesions comparable to standard MRI. Additionally, the physics-informed method enabled contrast adjustment, and similar signal contrast and comparable CNRs to the ground truth acquisitions for three sequences unseen during model training.

The study demonstrated the feasibility of physics-informed, deep learning-based synthetic MRI to generate high-quality contrasts and generalize to contrasts beyond the training data. This technology has the potential to accelerate neuroimaging protocols.

This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.

The value of SyMRI-derived parameters from lumbar marrow for predicting early treatment response and optimizing the risk stratification of the Revised International Staging System (R-ISS) in participants with multiple myeloma (MM) is unknown.

We prospectively enrolled participants with newly diagnosed MM before treatment. The SyMRI of lumbar marrow was used to calculate T1, T2, and PD values and the clinical features were collected. All participants were divided into good response (≥VGPR) and poor response (

RESULTS

Fifty-nine participants (good response, n = 33; poor response, n = 26) were evaluated. The bone marrow plasma cell percentage, β2-microglobulin, T1 and T2 value were difference between two groups (all p < 0.05). The T1 (odds ratio 1.003, p = 0.005) and T2 values (odds ratio 0.910, p = 0.002) were independent predictors and the AUC and cut-off values were 0.787, 967.2 ms and 0.784, 75.9 ms, respectively. There were no significant differences in SyMRI parameters between genders. Participants with both T1 value ≥967.2 ms and T2 value ≤75.9 ms in the R-ISS II stage were potentially to get poor response.

CONCLUSIONS

Synthetic MRI is a promising tool for predicting early treatment response to MM and promoting R-ISS II stage risk stratification.

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Lower limb amputation contributes to structural and functional brain alterations, adversely affecting gait, balance, and overall quality of life. Therefore, selecting an appropriate prosthetic ankle is critical in enhancing the well-being of these individuals. Despite the availability of various prostheses, their impact on brain neuroplasticity remains poorly understood.

The primary objective is to examine differences in the degree of brain neuroplasticity using magnetic resonance imaging (MRI) between individuals wearing a new passive ankle prosthesis with an articulated ankle joint and a standard passive prosthesis, and to examine changes in brain neuroplasticity within these two prosthetic groups. The second objective is to investigate the influence of prosthetic type on walking performance and quality of life. The final objective is to determine whether the type of prosthesis induces differences in the walking movement pattern.

Participants with a unilateral transtibial amputation will follow a 24-week protocol. Prior to rehabilitation, baseline MRI scans will be performed, followed by allocation to the intervention arms and commencement of rehabilitation. After 12 weeks, baseline functional performance tests and a quality of life questionnaire will be administered. At the end of the 24-week period, participants will undergo the same MRI scans, functional performance tests and questionnaire to evaluate any changes. A control group of able-bodied individuals will be included for comparative analysis.

This study aims to unravel the differences in brain neuroplasticity and prosthesis type in patients with a unilateral transtibial amputation and provide insights into the therapeutic benefits of prosthetic devices. The findings could validate the therapeutic benefits of more advanced lower limb prostheses, potentially leading to a societal impact ultimately improving the quality of life for individuals with lower limb amputation.

NCT05818410 (Clinicaltrials.gov).

There is increasing evidence of abnormal neurodevelopmental outcomes in preterm infants with low-grade intraventricular hemorrhage (IVH). The purpose of the study was to explore whether brain microstructure and volume are associated with neuro-behavioral outcomes at 40 weeks corrected gestational age in preterm infants with low-grade IVH. MR imaging at term-equivalent age (TEA) was performed in 25 preterm infants with mild IVH (Papile grading I/II) and 40 control subjects without IVH. These subjects all had neonatal behavioral neurological assessment (NBNA) at 40 weeks' corrected age. Microstructure and volume evaluation of the brain were performed by using diffusion kurtosis imaging (DKI) and Synthetic MRI. Correlations among microstructure parameters, volume, and developmental outcomes were explored by using Spearman's correlation. In preterm infants with low-grade IVH, the volume of brain parenchymal fraction (BPF) was reduced. In addition, mean kurtosis (MK), fractional anisotropy (FA), radial kurtosis (RK), axial kurtosis (AK) in several major brain regions were reduced, while mean diffusivity (MD) was increased (P < 0.05). BPF, RK in the cerebellum, MK in the genu of the corpus callosum, and MK in the thalamus of preterm infants with low-grade IVH were associated with lower NBNA scores (r = 0.831, 0.836, 0.728, 0.772, P < 0.05). DKI and Synthetic MRI can quantitatively evaluate the microstructure alterations and brain volumes in preterm infants with low-grade IVH, which provides clinicians with a more comprehensive and accurate neurobehavioral assessment of preterm infants with low-grade IVH.