Novel whole brain segmentation and volume estimation using quantitative MRI

3D Quantitative MRI: A Fast and Reliable Method for Ventricular Volumetry

PURPOSE

Volumetry of cerebral ventricles is a far more sensitive measure for shunt-induced reduction of ventricular size than traditional 2-dimensional (2D) measures, such as Evans index. However, available ventricle segmentation methods are time-consuming, resulting in limited use in clinical practice. Quantitative MRI (qMRI) obtains objective measurements of physical tissue properties, enabling automatic segmentation of white and gray matter and intracranial cerebrospinal fluid. The aim of this study was to evaluate the reliability and processing time of both manual and manually corrected automatic ventricular volumetry through the application of 3D qMRI.

METHODS

An independent examiner performed manual ventricular volumetry segmentations on 45 3D qMRI acquisitions (15 healthy individuals, 15 idiopathic normal pressure hydrocephalus (iNPH) patients, 15 shunted iNPH patients) twice. Another independent examiner manually segmented 15 of these acquisitions once. An automatic ventricle segmentation algorithm generated a third set of ventricular segmentations for all 45 data sets. The automatic segmentations were then corrected by both examiners to obtain a fourth set of data. All segmentations were assessed for intra- and interobserver reliability.

RESULTS

Intra- and interobserver reliability for all segmentations, manual, corrected, and automatic, was excellent (intra-class correlation coefficient 1.000, 1.000 and 0.999 respectively). Ventricular volumes were on average 42 ± 18 mL (mean ± SD) in healthy individuals, 140 ± 34 mL in iNPH patients, and 113 ± 35 mL in shunted iNPH patients.

CONCLUSIONS

3D qMRI is a reliable and time-efficient method to obtain relevant volumetric measures of intracranial cerebrospinal fluid spaces for both clinical and research purposes. The corrected automatic segmentations provide a feasible time expenditure for clinicians caring for patients with iNPH.

Synthetic versus conventional MRI for ring-enhancing brain lesions: A pre- and post-contrast comparison

PURPOSE

Synthetic MRI has potential to significantly reduce MR scan time by reconstructing multiple contrast images from a single acquisition. The aim of this study was to compare the image quality of both pre- and post-contrast synthetic MRI in subjects with ring-enhancing brain lesions with conventional images.

METHODS

50 patients with radiologically confirmed ring-enhancing brain lesions underwent TSE_MDME sequence before and after gadolinium administration along with conventional MRI sequences. Image quality was compared between synthetic and conventional sequences on a 4-point scale across 5 parameters, that is, grey white matter differentiation, demarcation of caudate nucleus, lentiform nucleus, demarcation of sulci, and SNR. Also, the artefacts, lesion conspicuity, and ability to diagnose on synthetic images were studied.

RESULTS

Image quality of synthetic MRI was relatively similar across all sequences except for FLAIR. The image quality comparison between synthetic and conventional images showed an agreement in 70.7% of the cases (Weighted Kappa = 0.043, p = <0.001). Artefacts were maximum in synthetic FLAIR sequence (52%). 50% cases showed a discordant enhancement pattern in post contrast synthetic images. Despite a higher occurrence of artefacts in synthetic post contrast images, diagnostic ability was comparable across pre- and post-contrast synthetic and conventional images.

CONCLUSION

Synthetic MRI provides comparable diagnostic quality of images with acceptable rate of artefacts in both pre and post contrast sequences. However, needs a careful interpretation especially when diagnosis is heavily relied on the enhancement pattern of lesions.

Biomarkers of preschool children with autism spectrum disorder: quantitative analysis of whole-brain tissue component volumes, intelligence scores, ADOS-CSS, and ages of first-word production and walking onset

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Evaluating T1, T2 Relaxation, and Proton Density in Normal Brain Using Synthetic MRI with Fast Imaging Protocol

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.

The value of synthetic MRI in detecting the brain changes and hearing impairment of children with sensorineural hearing loss

Introduction

Sensorineural hearing loss (SNHL) can arise from a diverse range of congenital and acquired factors. Detecting it early is pivotal for nurturing speech, language, and cognitive development in children with SNHL. In our study, we utilized synthetic magnetic resonance imaging (SyMRI) to assess alterations in both gray and white matter within the brains of children affected by SNHL.

Methods

The study encompassed both children diagnosed with SNHL and a control group of children with normal hearing {1.5-month-olds (n = 52) and 3-month-olds (n = 78)}. Participants were categorized based on their auditory brainstem response (ABR) threshold, delineated into normal, mild, moderate, and severe subgroups.Clinical parameters were included and assessed the correlation with SNHL. Quantitative analysis of brain morphology was conducted using SyMRI scans, yielding data on brain segmentation and relaxation time.Through both univariate and multivariate analyses, independent factors predictive of SNHL were identified. The efficacy of the prediction model was evaluated using receiver operating characteristic (ROC) curves, with visualization facilitated through the utilization of a nomogram. It's important to note that due to the constraints of our research, we worked with a relatively small sample size.

Results

Neonatal hyperbilirubinemia (NH) and children with inner ear malformation (IEM) were associated with the onset of SNHL both at 1.5 and 3-month groups. At 3-month group, the moderate and severe subgroups exhibited elevated quantitative T1 values in the inferior colliculus (IC), lateral lemniscus (LL), and middle cerebellar peduncle (MCP) compared to the normal group. Additionally, WMV, WMF, MYF, and MYV were significantly reduced relative to the normal group. Additionally, SNHL-children with IEM had high T1 values in IC, and LL and reduced WMV, WMF, MYV and MYF values as compared with SNHL-children without IEM at 3-month group. LL-T1 and WMF were independent risk factors associated with SNHL. Consequently, a prediction model was devised based on LL-T1 and WMF. ROC for training set, validation set and external set were 0.865, 0.806, and 0.736, respectively.

Conclusion

The integration of T1 quantitative values and brain volume segmentation offers a valuable tool for tracking brain development in children affected by SNHL and assessing the progression of the condition's severity.

Human-Prosthetic Interaction (HumanIT): A study protocol for a clinical trial evaluating brain neuroplasticity and functional performance after lower limb loss

BACKGROUND

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.

OBJECTIVES

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.

METHODS

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.

CONCLUSION

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.

TRIAL REGISTRATION

NCT05818410 (Clinicaltrials.gov).

Automatic estimation of brain parenchymal fraction in patients with multple sclerosis: a comparison between synthetic MRI and an established automated brain segmentation software based on FSL

PURPOSE

We aimed to validate the estimation of the brain parenchymal fraction (BPF) in patients with multiple sclerosis (MS) using synthetic magnetic resonance imaging (SyMRI) by comparison with software tools of the FMRIB Software Library (FSL). In addition to a cross-sectional method comparison, longitudinal volume changes were assessed to further elucidate the suitability of SyMRI for quantification of disease-specific changes.

METHODS

MRI data from 216 patients with MS and 28 control participants were included for volume estimation by SyMRI and FSL-SIENAX. Moreover, longitudinal data from 35 patients with MS were used to compare registration-based percentage brain volume changes estimated using FSL-SIENA to difference-based calculations of volume changes using SyMRI.

RESULTS

We observed strong correlations of estimated brain volumes between the two methods. While SyMRI overestimated grey matter and BPF compared to FSL-SIENAX, indicating a systematic bias, there was excellent agreement according to intra-class correlation coefficients for grey matter and good agreement for BPF and white matter. Bland-Altman plots suggested that the inter-method differences in BPF were smaller in patients with brain atrophy compared to those without atrophy. Longitudinal analyses revealed a tendency for higher atrophy rates for SyMRI than for SIENA, but SyMRI had a robust correlation and a good agreement with SIENA.

CONCLUSION

In summary, BPF based on data from SyMRI and FSL-SIENAX is not directly transferable because an overestimation and higher variability of SyMRI values were observed. However, the consistency and correlations between the two methods were satisfactory, and SyMRI was suitable to quantify disease-specific atrophy in MS.

Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool

Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multiparametric imaging techniques. We introduce PySynthMRI, an open-source tool with a user-friendly interface that uses a set of input images to generate synthetic images with diverse radiological contrasts by varying representative parameters of the desired target sequence, including the echo time, repetition time and inversion time(s). PySynthMRI is written in Python 3.6, and it can be executed under Linux, Windows, or MacOS as a python script or an executable. The tool is free and open source and is developed while taking into consideration the possibility of software customization by the end user. PySynthMRI generates synthetic images by calculating the pixelwise signal intensity as a function of a set of input images (e.g., T1 and T2 maps) and simulated scanner parameters chosen by the user via a graphical interface. The distribution provides a set of default synthetic contrasts, including T1w gradient echo, T2w spin echo, FLAIR and Double Inversion Recovery. The synthetic images can be exported in DICOM or NiFTI format. PySynthMRI allows for the fast synthetization of differently weighted MR images based on quantitative maps. Specialists can use the provided signal models to retrospectively generate contrasts and add custom ones. The modular architecture of the tool can be exploited to add new features without impacting the codebase.

Clinical Feasibility of Automated Brain Tissue and Myelin Volumetry of Normal Brian Using Synthetic Magnetic Resonance Imaging With Fast Imaging Protocol: A Single-Center Pilot Study

OBJECTIVE

This study aimed to investigate the clinical feasibility of synthetic magnetic resonance imaging (MRI) with fast imaging protocol for automated brain tissue and myelin volumetry in healthy volunteers at 3.0-T MRI.

METHODS

Thirty-four healthy volunteers were scanned using synthetic MRI with 3 sets of scan parameters: groups Fast (FAS; 2 minutes, 29 seconds), Routine (ROU; 4 minutes, 7 seconds), and Research (RES; 7 minutes, 46 seconds). White matter (WM), gray matter (GM), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), brain parenchymal volume (BPV), intracranial volume (ICV), and myelin volume (MYV) were compared between 3 groups. Linear correlation analysis was performed for measured volumes of groups FAS and ROU versus group RES.

RESULTS

Significant differences were found in all the measured brain tissue volumes between groups FAS and ROU (P < 0.001), FAS and RES (P < 0.05), and ROU and RES (P < 0.05), except for NoN between groups ROU and RES (P = 0.0673), ICV between groups FAS and ROU (P = 0.2552), and ICV between groups FAS and RES (P = 0.4898). The intergroup coefficients of variation were 4.36% for WM, 6.39% for GM, 10.14% for CSF, 67.5% for NoN, 1.21% for BPV, 0.08% for ICV, and 5.88% for MYV. Strong linear correlation was demonstrated for WM, GM, CSF, BPV, ICV, and MYV (R = 0.9230-1.131) between FAS versus RES, and ROU versus RES.

CONCLUSIONS

Using synthetic MRI with fast imaging protocol can change the measured brain tissue volumes of volunteers. It is necessary to use consistent acquisition protocols for comparing or following up cases quantitatively.

Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging

Objective

Brain tissue changes dynamically during aging. The purpose of this study was to use synthetic magnetic resonance imaging (syMRI) to evaluate the changes in relaxation values in different brain regions during brain aging and to construct a brain age prediction model.

Materials and methods

Quantitative MRI was performed on 1,000 healthy people (≥ 18 years old) from September 2020 to October 2021. T1, T2 and proton density (PD) values were simultaneously measured in 17 regions of interest (the cerebellar hemispheric cortex, pons, amygdala, hippocampal head, hippocampal tail, temporal lobe, occipital lobe, frontal lobe, caudate nucleus, lentiform nucleus, dorsal thalamus, centrum semiovale, parietal lobe, precentral gyrus, postcentral gyrus, substantia nigra, and red nucleus). The relationship between the relaxation values and age was investigated. In addition, we analyzed the relationship between brain tissue values and sex. Finally, the participants were divided into two age groups: < 60 years old and ≥ 60 years old. Logistic regression analysis was carried out on the two groups of data. According to the weight of related factors, a brain age prediction model was established and verified.

Results

We obtained the specific reference value range of different brain regions of individuals in different age groups and found that there were differences in relaxation values in brain tissue between different sexes in the same age group. Moreover, the relaxation values of most brain regions in males were slightly higher than those in females. In the study of age and brain relaxation, it was found that brain relaxation values were correlated with age. The T1 values of the centrum semiovale increased with age, the PD values of the centrum semiovale increased with age, while the T2 values of the caudate nucleus and lentiform nucleus decreased with age. Seven brain age prediction models were constructed with high sensitivity and specificity, among which the combined T1, T2 and PD values showed the best prediction efficiency. In the training set, the area under the curve (AUC), specificity and sensitivity were 0.959 [95% confidence interval (CI): 0.945–0.974], 91.51% and 89.36%, respectively. In the test cohort, the above indicators were 0.916 (95% CI: 0.882–0.951), 89.24% and 80.33%, respectively.

Conclusion

Our study provides specific reference ranges of T1, T2, and PD values in different brain regions from healthy adults of different ages. In addition, there are differences in brain relaxation values in some brain regions between different sexes, which help to provide new ideas for brain diseases that differ according to sex. The brain age model based on synthetic MRI is helpful to determine brain age.

Accelerated Synthetic MRI with Deep Learning-Based Reconstruction for Pediatric Neuroimaging

BACKGROUND AND PURPOSE

Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learning-based reconstruction in pediatric neuroimaging and to investigate the impact of deep learning-based reconstruction on image quality and quantitative values in synthetic MR imaging.

MATERIALS AND METHODS

This study included 47 children 2.3-14.7 years of age who underwent both standard and accelerated synthetic MR imaging at 3T. The accelerated synthetic MR imaging was reconstructed using a deep learning pipeline. The image quality, lesion detectability, tissue values, and brain volumetry were compared among accelerated deep learning and accelerated and standard synthetic data sets.

RESULTS

The use of deep learning-based reconstruction in the accelerated synthetic scans significantly improved image quality for all contrast weightings (P < .001), resulting in image quality comparable with or superior to that of standard scans. There was no significant difference in lesion detectability between the accelerated deep learning and standard scans (P > .05). The tissue values and brain tissue volumes obtained with accelerated deep learning and the other 2 scans showed excellent agreement and a strong linear relationship (all, R 2 > 0.9). The difference in quantitative values of accelerated scans versus accelerated deep learning scans was very small (tissue values, <0.5%; volumetry, -1.46%-0.83%).

CONCLUSIONS

The use of deep learning-based reconstruction in synthetic MR imaging can reduce scan time by 42% while maintaining image quality and lesion detectability and providing consistent quantitative values. The accelerated deep learning synthetic MR imaging can replace standard synthetic MR imaging in both contrast-weighted and quantitative imaging.

Time-saving synthetic magnetic resonance imaging protocols for pediatric neuroimaging: impact of echo train length and bandwidth on image quality

BACKGROUND

Synthetic MRI is a time-efficient imaging technique that provides both quantitative MRI and contrast-weighted images simultaneously. However, a rather long single scan time can be challenging for children.

OBJECTIVE

To evaluate the clinical feasibility of time-saving synthetic MRI protocols adjusted for echo train length and receiver bandwidth in pediatric neuroimaging based on image quality assessment and quantitative data analysis.

MATERIALS AND METHODS

In total, we included 33 children ages 1.6-17.4 years who underwent synthetic MRI using three sets of echo train length and receiver bandwidth combinations (echo train length [E]12-bandwidth [B in KHz]22, E16-B22 and E16-B83) at 3 T. The image quality and lesion conspicuity of synthetic contrast-weighted images were compared between the suggested protocol (E12-B22) and adjusted protocols (E16-B22 and E16-B83). We also compared tissue values (T1, T2, proton-density values) and brain volumetry.

RESULTS

For the E16-B83 combination, image quality was sufficient except for 15.2% of T1-W and 3% of T2-W fluid-attenuated inversion recovery (FLAIR) images, with remarkable scan time reduction (up to 35%). The E16-B22 combination demonstrated a comparable image quality to E12-B22 (P&gt;0.05) with a scan time reduction of up to 8%. There were no significant differences in lesion conspicuity among the three protocols (P&gt;0.05). Tissue value measurements and brain tissue volumes obtained with the E12-B22 protocol and adjusted protocols showed excellent agreement and strong correlations except for gray matter volume and non-white matter/gray matter/cerebrospinal fluid volume in E12-B22 vs. E16-B83.

CONCLUSION

The adjusted synthetic protocols produced image quality sufficient or comparable to that of the suggested protocol while maintaining lesion conspicuity with reduced scan time. The quantitative values were generally consistent with the suggested MRI-protocol-derived values, which supports the clinical application of adjusted protocols in pediatric neuroimaging.

Relaxometry and brain myelin quantification with synthetic MRI in MS subtypes and their associations with spinal cord atrophy

Immune-mediated demyelination and neurodegeneration are pathophysiological hallmarks of Multiple Sclerosis (MS) and main drivers of disease related disability. The principal method for evaluating qualitatively demyelinating events in the clinical context is contrast-weighted magnetic resonance imaging (MRI). Moreover, advanced MRI sequences provide reliable quantification of brain myelin offering new opportunities to study tissue pathology in vivo. Towards neurodegenerative aspects of the disease, spinal cord atrophy - besides brain atrophy - is a powerful and validated predictor of disease progression. The etiology of spinal cord volume loss is still a matter of research, as it remains unclear whether the impact of local lesion pathology or the interaction with supra- and infratentorial axonal degeneration and demyelination of the long descending and ascending fiber tracts are the determining factors. Quantitative synthetic MR using a multiecho acquisition of saturation recovery pulse sequence provides fast automatic brain tissue and myelin volumetry based on R1 and R2 relaxation rates and proton density quantification, making it a promising modality for application in the clinical routine. In this cross sectional study a total of 91 MS patients and 31 control subjects were included to investigate group differences of global and regional measures of brain myelin and relaxation rates, in different MS subtypes, using QRAPMASTER sequence and SyMRI postprocessing software. Furthermore, we examined associations between these quantitative brain parameters and spinal cord atrophy to draw conclusions about possible pathophysiological relationships. Intracranial myelin volume fraction of the global brain exhibited statistically significant differences between control subjects (10.4%) and MS patients (RRMS 9.4%, PMS 8.1%). In a LASSO regression analysis with total brain lesion load, intracranial myelin volume fraction and brain parenchymal fraction, the intracranial myelin volume fraction was the variable with the highest impact on spinal cord atrophy (standardized coefficient 4.52). Regional supratentorial MRI metrics showed altered average myelin volume fraction, R1, R2 and proton density in MS patients compared to controls most pronounced in PMS. Interestingly, quantitative MRI parameters in supratentorial regions showed strong associations with upper cord atrophy, suggesting an important role of brain diffuse demyelination on spinal cord pathology possibly in the context of global disease activity. R1, R2 or proton density of the thalamus, cerebellum and brainstem correlated with upper cervical cord atrophy, probably reflecting the direct functional connection between these brain structures and the spinal cord as well as the effects of retrograde and anterograde axonal degeneration. By using Synthetic MR-derived myelin volume fraction, we were able to effectively detect significant differences of myelination in relapsing and progressive MS subtypes. Total intracranial brain myelin volume fraction seemed to predict spinal cord volume loss better than brain atrophy or total lesion load. Furthermore, demyelination in highly myelinated supratentorial regions, as an indicator of diffuse disease activity, as well as alterations of relaxation parameters in adjacent infratentorial and midbrain areas were strongly associated with upper cervical cord atrophy.

Synthetic MRI for stroke: a qualitative and quantitative pilot study

Synthetic MR provides qualitative and quantitative multi-parametric data about tissue properties in a single acquisition. Its use in stroke imaging is not yet established. We compared synthetic and conventional image quality and studied synthetic relaxometry of acute and chronic ischemic lesions to investigate its interest for stroke imaging. We prospectively acquired synthetic and conventional brain MR of 43 consecutive adult patients with suspected stroke. We studied a total of 136 lesions, of which 46 DWI-positive with restricted ADC (DWI + /rADC), 90 white matter T2/FLAIR hyperintensities (WMH) showing no diffusion restriction, and 430 normal brain regions (NBR). We assessed image quality for lesion definition according to a 3-level score by two readers of different experiences. We compared relaxometry of lesions and regions of interest. Synthetic images were superior to their paired conventional images for lesion definition except for sFLAIR (sT1 or sPSIR vs. cT1 and sT2 vs. cT2 for DWI + /rADC and WMH definition; p values < .001) with substantial to almost perfect inter-rater reliability (κ ranging from 0.711 to 0.932, p values < .001). We found significant differences in relaxometry between lesions and NBR and between acute and chronic lesions (T1, T2, and PD of DWI + /rADC or WMH vs. mirror NBR; p values < .001; T1 and PD of DWI + /rADC vs. WMH; p values of 0.034 and 0.008). Synthetic MR may contribute to stroke imaging by fast generating accessible weighted images for visual inspection derived from rapidly acquired relaxometry data. Moreover, this synthetic relaxometry could differentiate acute and chronic ischemic lesions.

The effect of scan parameters on T1, T2 relaxation times measured with multi-dynamic multi-echo sequence: a phantom study

Multi-Dynamic Multi-Echo (MDME) Sequence is a new method which can acquire various contrast-weighted images using quantitative relaxometric parameters measured from multicontrast images. The purpose of our study was to investigate the effect of scan parameters of MDME Sequence on measured T1, T2 values of phantoms at 3.0 T MRI scanner. Gray matter, white matter and cerebrospinal fluid simulation phantoms with different relaxation times (named GM, WM, CSF, respectively) were used in our study. All the phantoms were scanned 9 times on different days using MDME sequence with variations of echo train length, matrix, and acceleration factor. The T1, T2 measurements were acquired after each acquisition. The repeatability was characterized as the intragroup coefficient of variation (CV) of measured values over 9 times, and the discrepancies of measurements across different groups were characterized as intergroup CVs. The highest intragroup CVs of T1-GM, T2-GM, T1-WM, T2-WM, T1-CSF, T2-SCF were 1.36%, 1.75%, 0.74%, 1.41%, 1.70%, 7.79%, respectively. The highest intergroup CVs of T1-GM, T2-GM, T1-WM, T2-WM, T1-CSF, T2-SCF were 0.54%, 1.86%, 1.70%, 0.94%, 1.00%, 2.17%, respectively. Quantitative T1, T2 measurements of gray matter, white matter and cerebrospinal fluid simulation phantoms derived from the MDME sequence were not obviously affected by variations of scanning parameters, such as echo train length, matrix, and acceleration factor on 3T scanner.

Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation

Purpose

We aimed to evaluate the accuracy of T₁ and T₂ mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC^®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning.

Methods

The accuracy of T₁ and T₂ measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland–Altman analysis. The reproducibility and repeatability of T₁ and T₂ measurements were performed on 15 sessions with the HNU coil. The T₁ and T₂ synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T₁ and T₂in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared.

Results

The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T₁ values between 490 and 2,385 ms and T₂ values between 25 and 400 ms. MAGiC^® gave discordant results for T₁ values below 220 ms (bias with the reference values, from 38% to 1,620%). T₂ measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T₁ and T₂ by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%).

Conclusion

This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC^®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC^® could potentially be used for prostate qMRI.

Relaxation time of brain tissue in the elderly assessed by synthetic MRI

BACKGROUND

Synthetic MRI (SyMRI) is a quantitative technique that allows measurements of T1 and T2 relaxation times (RTs). Brain RT evolution across lifespan is well described for the younger population. The aim was to study RTs of brain parenchyma in a healthy geriatric population in order to define the normal value of structures in this group population. Normal values for geriatric population could help find biomarker for age-related brain disease.

MATERIALS AND METHODS

Fifty-four normal-functioning individuals (22 females, 32 males) with mean age of 83 years (range 56-98) underwent SyMRI. RT values in manually defined ROIs (centrum semiovale, middle cerebellar peduncles, thalamus, and insular cortex) and in segmented whole-brain components (brain parenchyma, gray matter, white matter, myelin, CSF, and stromal structures) were extracted from the SyMRI segmentation software. Patients' results were combined into the group age. Main ROI-based and whole-brain results were compared for the all dataset and for age group results as well.

RESULTS

For white matter, RTs between ROI-based analyses and whole-brain results for T2 and for T1 were statistically different and a trend of increasing T1 in centrum semiovale and cerebellar peduncle was observed. For gray matter, thalamic T1 was statistically different from insular T1. A difference was also found between left and right insula (p < .0001). T1 RTs of ROI-based and whole-brain-based analyses were statistically different (p < .0001). No significant difference in T1 and T2 was found between age groups on ROI-based analysis, but T1 in centrum semiovale and thalamus increased with age. No statistical difference between age groups was found for the various segmented volumes except for myelin between 65-74 years of age and the 95-105 years of age groups (p = .038).

CONCLUSIONS

SyMRI is a new tool that allows faster imaging and permits to obtain quantitative T1 and T2. By defining RT values of different brain components of normal-functioning elderly individuals, this technique may be used as a biomarker for clinical disorders like dementia.

Brain Volumetric Measurements in Children With Attention Deficit Hyperactivity Disorder: A Comparative Study Between Synthetic and Conventional Magnetic Resonance Imaging

Objective: To investigate the profiles of brain volumetric measurements in children with attention deficit hyperactivity disorder (ADHD), and the consistency of these brain volumetric measurements derived from the synthetic and conventional T1 weighted MRI (SyMRI and cT1w MRI).

Methods: Brain SyMRI and cT1w images were prospectively collected for 38 pediatric patients with ADHD and 38 healthy children (HC) with an age range of 6–14 years. The gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), myelin, myelin fraction (MYF), brain parenchyma volume (BPV), and intracranial volume (ICV) were automatically estimated from SyMRI data, and the four matching measurements (GMV, WMV, BPV, ICV) were extracted from cT1w images. The group differences of brain volumetric measurements were performed, respectively, using analysis of covariance. Pearson correlation analysis and interclass correlation coefficient (ICC) were applied to evaluate the association between synthetic and cT1w MRI-derived measurements.

Results: As for the brain volumetric measurements extracted from SyMRI, significantly decreased GMV, WMV, BPV, and increased NON volume (p < 0.05) were found in the ADHD group compared with HC; No group differences were found in ICV, CSF, myelin volume and MYF (p > 0.05). With regard to GMV, WMV, BPV, and ICV estimated from cT1w images, the group differences between ADHD and HC were consistent with the results estimated from SyMRI. And these four measurements showed noticeable correlation between the two approaches (r = 0.692, 0.643, 0.898, 0.789, respectively, p < 0.001; ICC values are 0.809, 0.782, 0.946, 0.873, respectively).

Conclusion: Our study demonstrated a global brain development disability, but normal whole-brain myelination in children with ADHD. Moreover, our results demonstrated the high consistency of brain volumetric indices between synthetic and cT1w MRI in children, which indicates the high reliability of SyMRI in the child-brain volumetric analysis.

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.

Clinical adaptation of synthetic MRI-based whole brain volume segmentation in children at 3 T: comparison with modified SPM segmentation methods

PURPOSE

To validate the use of synthetic magnetic resonance imaging (SyMRI) volumetry by comparing with child-optimized SPM 12 volumetry in 3 T pediatric neuroimaging.

METHODS

In total, 106 children aged 4.7-18.7 years who underwent both synthetic and 3D T1-weighted imaging and had no abnormal imaging/neurologic findings were included for the SyMRI vs. SPM T1-only segmentation (SPM T1). Forty of the 106 children who underwent an additional 3D T2-weighted imaging were included for the SyMRI vs. SPM multispectral segmentation (SPM multi). SPM segmentation using an age-appropriate atlas and inverse-transforming template-space intracranial mask was compared with SyMRI segmentation. Volume differences between SyMRI and SPM T1 were plotted against age to evaluate the influence of age on volume difference.

RESULTS

Measurements derived from SyMRI and two SPM methods showed excellent agreements and strong correlations except for the CSF volume (CSFV) (intraclass correlation coefficients = 0.87-0.98; r = 0.78-0.96; relative volume difference other than CSFV = 6.8-18.5% [SyMRI vs. SPM T1] and 11.3-22.7% [SyMRI vs. SPM multi]). Dice coefficients of all brain tissues (except CSF) were in the range 0.78-0.91. The Bland-Altman plot and age-related volume difference change suggested that the volume differences between the two methods were influenced by the volume of each brain tissue and subject's age (p < 0.05).

CONCLUSION

SyMRI and SPM segmentation results were consistent except for CSFV, which supports routine clinical use of SyMRI-based volumetry in pediatric neuroimaging. However, caution should be taken in the interpretation of the CSF segmentation results.

Brain volumetric and fractal analysis of synthetic MRI: A comparative study with conventional 3D T1-weighted images

PURPOSE

The estimation of brain volumetric measurements based on Synthetic MRI (SyMRI) is easy and fast, however, the consistency of brain volumetric and morphologic measurements based on SyMRI and 3D T1WI should be further addressed. The current study evaluated the impact of spatial resolution on brain volumetric and morphologic measurements using SyMRI, and test whether the brain measurements derived from SyMRI were consistent with those resulted from 3D T1WI.

METHOD

Brain volumetric and fractal analysis were applied to thirty healthy subjects, each underwent four SyMRI acquisitions with different spatial resolutions (1 × 1 × 2 mm, 1 × 1x3mm, 1 × 1 × 4 mm, 2 × 2 × 2 mm) and a 3D T1WI (1 × 1 × 1 mm isotropic). The consistency of the SyMRI measurements was tested using one-way non-parametric Kruskal-Wallis test and post hoc Dwass-Steel-Critchlow-Fligner test. The association between SyMRI and 3D T1WI derived measurements was evaluated using linear regression models.

RESULTS

Our results demonstrated that both in- and through-plane resolutions show an impact on brain volumetric measurements, while brain parenchymal volume showed high consistency across the SyMRI acquisitions, and high association with the measurements from 3D T1WI. In addition, SyMRI with 1 × 1 × 4 mm resolution showed the strongest association with 3D T1WI compared to other SyMRI acquisitions in both volumetric and fractal analyses. Moreover, substantial differences were found in fractal dimension of both gray and white matter between the SyMRI and 3D T1WI tissue segmentations.

CONCLUSIONS

Our results suggested that the measurements from SyMRI with relatively higher in-plane and lower through-plane resolution (1 × 1 × 4 mm) are much closer to 3D T1WI.

Accelerated Isotropic Multiparametric Imaging by High Spatial Resolution 3D-QALAS With Compressed Sensing: A Phantom, Volunteer, and Patient Study

OBJECTIVES

The aims of this study were to develop an accelerated multiparametric magnetic resonance imaging method based on 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) combined with compressed sensing (CS) and to evaluate the effect of CS on the quantitative mapping, tissue segmentation, and quality of synthetic images.

MATERIALS AND METHODS

A magnetic resonance imaging system phantom, containing multiple compartments with standardized T1, T2, and proton density (PD) values; 10 healthy volunteers; and 12 patients with multiple sclerosis were scanned using the 3D-QALAS sequence with and without CS and conventional contrast-weighted imaging. The scan times of 3D-QALAS with and without CS were 5:56 and 11:11, respectively. For healthy volunteers, brain volumetry and myelin estimation were performed based on the measured T1, T2, and PD. For patients with multiple sclerosis, the mean T1, T2, PD, and the amount of myelin in plaques and contralateral normal-appearing white matter (NAWM) were measured. Simple linear regression analysis and Bland-Altman analysis were performed for each metric obtained from the datasets with and without CS. To compare overall image quality and structural delineations on synthetic and conventional contrast-weighted images, case-control randomized reading sessions were performed by 2 neuroradiologists in a blinded manner.

RESULTS

The linearity of both phantom and volunteer measurements in T1, T2, and PD values obtained with and without CS was very strong (R2 = 0.9901-1.000). The tissue segmentation obtained with and without CS also had high linearity (R2 = 0.987-0.999). The quantitative tissue values of the plaques and NAWM obtained with CS showed high linearity with those without CS (R2 = 0.967-1.000). There were no significant differences in overall image quality between synthetic contrast-weighted images obtained with and without CS (P = 0.17-0.99).

CONCLUSIONS

Multiparametric imaging of the whole brain based on 3D-QALAS can be accelerated using CS while preserving tissue quantitative values, tissue segmentation, and quality of synthetic images.

Age-Related Changes in Relaxation Times, Proton Density, Myelin, and Tissue Volumes in Adult Brain Analyzed by 2-Dimensional Quantitative Synthetic Magnetic Resonance Imaging

OBJECTIVES

Quantitative synthetic magnetic resonance imaging (MRI) enables the determination of fundamental tissue properties, namely, T1 and T2 relaxation times and proton density (PD), in a single scan. Myelin estimation and brain segmentation based on these quantitative values can also be performed automatically. This study aimed to reveal the changes in tissue characteristics and volumes of the brain according to age and provide age-specific reference values obtained by quantitative synthetic MRI.

MATERIALS AND METHODS

This was a prospective study of healthy subjects with no history of brain diseases scanned with a multidynamic multiecho sequence for simultaneous measurement of relaxometry of T1, T2, and PD. We performed myelin estimation and brain volumetry based on these values. We performed volume-of-interest analysis on both gray matter (GM) and white matter (WM) regions for T1, T2, PD, and myelin volume fraction maps. Tissue volumes were calculated in the whole brain, producing brain parenchymal volume, GM volume, WM volume, and myelin volume. These volumes were normalized by intracranial volume to a brain parenchymal fraction, GM fraction, WM fraction, and myelin fraction (MyF). We examined the changes in the mean regional quantitative values and segmented tissue volumes according to age.

RESULTS

We analyzed data of 114 adults (53 men and 61 women; median age, 66.5 years; range, 21-86 years). T1, T2, and PD values showed quadratic changes according to age and stayed stable or decreased until around 60 years of age and increased thereafter. Myelin volume fraction showed a reversed trend. Brain parenchymal fraction and GM fraction decreased throughout all ages. The approximation curves showed that WM fraction and MyF gradually increased until around the 40s to 50s and decreased thereafter. A significant decline in MyF was first noted in the 60s age group (Tukey test, P < 0.001).

CONCLUSIONS

Our study showed changes according to age in tissue characteristic values and brain volumes using quantitative synthetic MRI. The reference values for age demonstrated in this study may be useful to discriminate brain disorders from healthy brains.

Synthetic MRI: Technologies and Applications in Neuroradiology

Synthetic MRI is a technique that synthesizes contrast-weighted images from multicontrast MRI data. There have been advances in synthetic MRI since the technique was introduced. Although a number of synthetic MRI methods have been developed for quantifying one or more relaxometric parameters and for generating multiple contrast-weighted images, this review focuses on several methods that quantify all three relaxometric parameters (T₁ , T₂ , and proton density) and produce multiple contrast-weighted images. Acquisition, quantification, and image synthesis techniques are discussed for each method. We discuss the image quality and diagnostic accuracy of synthetic MRI methods and their clinical applications in neuroradiology. Based on this analysis, we highlight areas that need to be addressed for synthetic MRI to be widely implemented in the clinic. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.

An information theory model for optimizing quantitative magnetic resonance imaging acquisitions

Acquisition parameter selection is currently performed empirically for many quantitative MRI (qMRI) acquisitions. Tuning parameters for different scan times, tissues, and resolutions requires some amount of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to minimize variability of quantitative maps and post-processing techniques such as synthetic image generation. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for qMRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of potential acquisitions and allowing informed parameter selection. The information theory framework was tested on artificial data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The phantom measurements showed that higher mutual information calculated by the model correlated with smaller coefficient of variation in the reconstructed parametric maps, and in vivo measurements demonstrated that information-based calibration of acquisition parameters resulted in a decrease in parametric map variability consistent with model predictions.

Normal volumetric and T1 relaxation time values at 1.5 T in segmented pediatric brain MRI using a MP2RAGE acquisition

OBJECTIVES

This study introduced a tailored MP2RAGE-based brain acquisition for a comprehensive assessment of the normal maturing brain.

METHODS

Seventy normal patients (35 girls and 35 boys) from 1 to 16 years of age were recruited within a prospective monocentric study conducted from a single University Hospital. Brain MRI examinations were performed at 1.5 T using a 20-channel head coil and an optimized 3D MP2RAGE sequence with a total acquisition time of 6:36 min. Automated 38 region segmentation was performed using the MorphoBox (template registration, bias field correction, brain extraction, and tissue classification) which underwent a major adaptation of three age-group T1-weighted templates. Volumetry and T1 relaxometry reference ranges were established using a logarithmic model and a modified Gompertz growth respectively.

RESULTS

Detailed automated brain segmentation and T1 mapping were successful in all patients. Using these data, an age-dependent model of normal brain maturation with respect to changes in volume and T1 relaxometry was established. After an initial rapid increase until 24 months of life, the total intracranial volume was found to converge towards 1400 mL during adolescence. The expected volumes of white matter (WM) and cortical gray matter (GM) showed a similar trend with age. After an initial major decrease, T1 relaxation times were observed to decrease progressively in all brain structures. The T1 drop in the first year of life was more pronounced in WM (from 1000-1100 to 650-700 ms) than in GM structures.

CONCLUSION

The 3D MP2RAGE sequence allowed to establish brain volume and T1 relaxation time normative ranges in pediatrics.

KEY POINTS

• The 3D MP2RAGE sequence provided a reliable quantitative assessment of brain volumes and T1 relaxation times during childhood. • An age-dependent model of normal brain maturation was established. • The normative ranges enable an objective comparison to a normal cohort, which can be useful to further understand, describe, and identify neurodevelopmental disorders in children.

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

Synthetic MRI of Preterm Infants at Term-Equivalent Age: Evaluation of Diagnostic Image Quality and Automated Brain Volume Segmentation

BACKGROUND AND PURPOSE

Neonatal MR imaging brain volume measurements can be used as biomarkers for long-term neurodevelopmental outcome, but quantitative volumetric MR imaging data are not usually available during routine radiologic evaluation. In the current study, the feasibility of automated quantitative brain volumetry and image reconstruction via synthetic MR imaging in very preterm infants was investigated.

MATERIALS AND METHODS

Conventional and synthetic T1WIs and T2WIs from 111 very preterm infants were acquired at term-equivalent age. Overall image quality and artifacts of the conventional and synthetic images were rated on a 4-point scale. Legibility of anatomic structures and lesion conspicuity were assessed on a binary scale. Synthetic MR volumetry was compared with that generated via MANTiS, which is a neonatal tissue segmentation toolbox based on T2WI.

RESULTS

Image quality was good or excellent for most conventional and synthetic images. The 2 methods did not differ significantly regarding image quality or diagnostic performance for focal and cystic WM lesions. Dice similarity coefficients had excellent overlap for intracranial volume (97.3%) and brain parenchymal volume (94.3%), and moderate overlap for CSF (75.6%). Bland-Altman plots demonstrated a small systematic bias in all cases (1.7%-5.9%) CONCLUSIONS: Synthetic T1WI and T2WI sequences may complement or replace conventional images in neonatal imaging, and robust synthetic volumetric results are accessible from a clinical workstation in less than 1 minute. Via the above-described methods, volume assessments could be routinely used in daily clinical practice.

Technical Note: Simultaneous segmentation and relaxometry for MRI through multitask learning

PURPOSE

This study demonstrated a magnetic resonance (MR) signal multitask learning method for three-dimensional (3D) simultaneous segmentation and relaxometry of human brain tissues.

MATERIALS AND METHODS

A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multicontrast brain images. The deep neural network contained three residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online-synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter, and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on three healthy volunteers. The mean and standard deviation for the T1 and T2 values in vivo were reported and compared to literature values. Additional animal (N = 6) and prostate patient (N = 1) experiments were performed to compare the estimated T1 and T2 values with those from gold standard methods and to demonstrate clinical applications of the proposed method.

RESULTS

In animal validation experiment, the differences/errors (mean difference ± standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 ± 486 and 154 ± 512 ms for T1, and 5 ± 33 and 7 ± 41 ms for T2, respectively. In healthy volunteer experiments (N = 3), whole brain segmentation and relaxometry were finished within ~ 5 s. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1- and T2-weighted images.

CONCLUSION

The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1- and T2-weighted images, in conjunction with segmentation of gray matter, white matter, and CSF.

Recent Advances in Pediatric Brain, Spine, and Neuromuscular Magnetic Resonance Imaging Techniques

Magnetic resonance imaging (MRI) is a powerful radiologic tool with the ability to generate a variety of proton-based signal contrast from tissues. Owing to this immense flexibility in signal generation, new MRI techniques are constantly being developed, tested, and optimized for clinical utility. In addition, the safe and nonionizing nature of MRI makes it a suitable modality for imaging in children. In this review article, we summarize a few of the most popular advances in MRI techniques in recent years. In particular, we highlight how these new developments have affected brain, spine, and neuromuscular imaging and focus on their applications in pediatric patients. In the first part of the review, we discuss new approaches such as multiphase and multidelay arterial spin labeling for quantitative perfusion and angiography of the brain, amide proton transfer MRI of the brain, MRI of brachial plexus and lumbar plexus nerves (i.e., neurography), and T2 mapping and fat characterization in neuromuscular diseases. In the second part of the review, we focus on describing new data acquisition strategies in accelerated MRI aimed collectively at reducing the scan time, including simultaneous multislice imaging, compressed sensing, synthetic MRI, and magnetic resonance fingerprinting. In discussing the aforementioned, the review also summarizes the advantages and disadvantages of each method and their current state of commercial availability from MRI vendors.

Partial volume mapping using magnetic resonance fingerprinting

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction. The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error. Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient. PV-MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T₁ resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi-automatically constructed in vivo using k-means clustering of MRF-mapped relaxation times. Dictionary-based PV-MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV-MRF could also estimate partial volumes of solid tumor and peritumoral edema. We conclude that PV-MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.

Age-Related Changes in Tissue Value Properties in Children: Simultaneous Quantification of Relaxation Times and Proton Density Using Synthetic Magnetic Resonance Imaging

OBJECTIVES

The properties of brain tissue undergo dynamic changes during maturation. T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) are now simultaneously quantifiable within a clinically acceptable time, using a synthetic magnetic resonance imaging (MRI) sequence. This study aimed to provide age-specific reference values for T1, T2, and PD in children, using synthetic MRI.

MATERIALS AND METHODS

We included 89 children (median age, 18 months; range, 34 weeks of gestational age to 17 years) who underwent quantitative MRI, using a multidynamic, multiecho sequence on 3 T MRI, between December 2015 and November 2016, and had no abnormal MRI/neurologic assessment findings. T1, T2, and PD were simultaneously measured in each of the 22 defined white matter and gray matter regions of interest. The measured values were plotted against age, and a curve fitting model that best explained the age dependence of tissue values was identified. Age-specific regional tissue values were calculated using a fit equation.

RESULTS

The tissue values of all brain regions, except cortical PD, decreased with increasing age, and the robust negative association was best explained by modified biexponential model of the form Tissue values = T1 × exp (-C1 × age) + T2 × exp (-C2 × age). The quality of fit to the modified biexponential model was high in white matter and deep gray matter (white matter, R = 97%-99% [T1], 88%-95% [T2], 88%-97% [PD]; deep gray matter, R = 96%-97% [T1], 96% [T2], 49%-88% [PD]; cortex, 70%-83% [T1], 87%-90% [T2], 5%-27% [PD]). The white matter and deep gray matter changed the most dynamically within the first year of life.

CONCLUSIONS

Our study provides age-specific regional reference values, from the neonate to adolescent, of T1, T2, and PD, which could be objective tools for assessment of normal/abnormal brain development using synthetic MRI.

Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative physical tissue properties independent of the hardware and the sequence parameters, it is still difficult to leverage these physical properties to segment and classify pelvic tissues. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times and pure synthetic weighted images) and machine learning (Support Vector Machine (SVM)) to segment and classify tissues in the pelvic region, i.e.: fat, muscle, prostate, bone marrow, bladder, and air. Twenty-two men with a mean age of 30±14 years were included in this prospective study. The images were acquired with a 3 Tesla MRI scanner. An inversion recovery-prepared turbo spin echo sequence was used to obtain T1-weighted images at different inversion times with a TR of 14000 ms. A 32-echo spin echo sequence was used to obtain the T2-weighted images at different echo times with a TR of 5000 ms. T1 and T2 relaxation times, synthetic T1- and T2-weighted images and anatomical probabilistic maps were calculated and used as input features of a SVM for segmenting and classifying tissues within the pelvic region. The mean SVM classification accuracy across subjects was calculated for the different tissues: prostate (94.2%), fat (96.9%), muscle (95.8%), bone marrow (91%) and bladder (82.1%) indicating an excellent classification performance. However, the segmentation and classification for air (within the rectum) may not always be successful (mean SVM accuracy 47.5%) due to the lack of air data in the training and testing sets. Our findings suggest that SVM can reliably segment and classify tissues in the pelvic region.

Synthetic Brain MRI: Review of Current Concepts and Future Directions

Synthetic magnetic resonance imaging is a novel imaging technique that allows generating multiple contrast-weighted images based on relaxivity measurements of tissue properties in a single acquisition using a multiecho, multidelay saturation recovery spin-echo sequence. The synthetic images can be generated postacquisition from the parametric tissue maps, which can be beneficial to reduce scan time and improve patient throughput. Based on relaxometry maps, synthetic magnetic resonance imaging can also perform brain tissue segmentation and myelin quantification without additional scan time. The quantitative analysis may have implications for understanding and monitoring of the evolution of the maturation process. Similarly, the myelination process is vitally important to central nervous system functioning. Measuring myelin volume could provide relevant information for the diagnosis and treatment of patients with myelination disorders.

Effect of Gadolinium on the Estimation of Myelin and Brain Tissue Volumes Based on Quantitative Synthetic MRI

BACKGROUND AND PURPOSE

The effect of gadolinium on the estimation of myelin has not been reported. The aim of the current study was to investigate the effects of gadolinium on automatic myelin and brain tissue volumetry via quantitative synthetic MR imaging.

MATERIALS AND METHODS

The study included 36 patients who were referred for brain metastases screening, and quantitative synthetic MR imaging data before and after gadolinium-based contrast agent administration were analyzed retrospectively. Brain metastases were detected in 17 patients. WM volume, GM volume, CSF volume, non-WM/GM/CSF volume, myelin volume, brain parenchymal volume, myelin fraction (myelin volume/brain parenchymal volume), and intracranial volume were estimated. T1 and T2 relaxation times, proton density, and myelin partial volume per voxel averaged across the brain parenchyma were also analyzed.

RESULTS

In patients with and without metastases after gadolinium-based contrast agent administration, measurements of WM and myelin volumes, and myelin fraction were significantly increased (+26.65 and +29.42 mL, +10.14 and +12.46 mL, +0.88% and +1.09%, respectively), whereas measurements of GM, CSF, brain parenchymal, and intracranial volumes were significantly decreased (-36.23 and -34.49 mL, -20.77 and -18.94 mL, -6.76 and -2.84 mL, -27.41 and -21.84 mL, respectively). Non-WM/GM/CSF volume did not show a significant change. T1, T2, and proton density were significantly decreased (-51.34 and -46.84 ms, -2.67 and -4.70 ms, -1.05%, and -1.28%, respectively) after gadolinium-based contrast agent administration, whereas measurements of myelin partial volume were significantly increased (+0.78% and +0.75%, respectively).

CONCLUSIONS

Gadolinium had a significant effect on the automatic calculation of myelin and brain tissue volumes using quantitative synthetic MR imaging, which can be explained by decreases in T1, T2, and proton density.

Myelin loss in white matter hyperintensities and normal-appearing white matter of cognitively impaired patients: a quantitative synthetic magnetic resonance imaging study

OBJECTIVES

White matter hyperintensities (WMHs) are implicated in the etiology of dementia. The underlying pathology of WMHs involves myelin and axonal loss due to chronic ischemia. We investigated myelin loss in WMHs and normal-appearing white matter (NAWM) in patients with various degrees of cognitive impairment using quantitative synthetic magnetic resonance imaging (MRI).

METHODS

We studied 99 consecutive patients with cognitive complaints who underwent 3 T brain MRI between July 2016 and August 2017. Myelin partial volume maps were generated with synthetic MRI. Region-of-interest-based analysis was performed on these maps to compare the myelin partial volumes of NAWM and periventricular and deep WMHs. The effects of myelin partial volume of NAWMs on clinical cognitive function were evaluated using multivariate linear regression analysis.

RESULTS

WMHs were present in 30.3% of patients. Myelin partial volume in NAWM was lower in patients with WMHs than in those without (37.5 ± 2.7% vs. 39.9 ± 2.4%, p < 0.001). In patients with WMHs, myelin partial volume was highest in NAWMs (median [interquartile range], 37.2% [35.5-39.0%]), followed by deep WMHs (7.2% [3.2-10.5%]) and periventricular WMHs (2.1% [1.1-3.9%], p < 0.001). After adjusting for sex and education years, myelin partial volume in NAWMs was associated with the Clinical Dementia Rating Scale Sum of Box (β = -0.189 [95% CI, -0.380 to -0.012], p = 0.031).

CONCLUSION

Myelin loss occurs in both NAWM and WMHs of cognitively impaired patients. Synthetic MRI-based myelin quantification may be a useful imaging marker of cognitive dysfunction in patients with cognitive complaints.

KEY POINTS

• Quantitative synthetic MRI allows simultaneous acquisition of conventional MRI and myelin quantification without additional scanning time. • Normal-appearing and hyperintense white matter demonstrate myelin loss in cognitively impaired patients. • This myelin loss partially explains cognitive dysfunction in patients with cognitive complaints.

Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis

OBJECTIVES

To compare the cross-sectional robustness of commonly used volumetric software and effects of lesion filling in multiple sclerosis (MS).

METHODS

Nine MS patients (six females; age 38±13 years, disease duration 7.3±5.2 years) were scanned twice with repositioning on three MRI scanners (Siemens Aera 1.5T, Avanto 1.5T, Trio 3.0T) the same day. Volumetric T₁-weighted images were processed with FreeSurfer, FSL-SIENAX, SPM and SPM-CAT before and after 3D FLAIR lesion filling with LST. The whole-brain, grey matter (GM) and white matter (WM) volumes were calculated with and without normalisation to the intracranial volume or FSL-SIENAX scaling factor. Robustness was assessed using the coefficient of variation (CoV).

RESULTS

Variability in volumetrics was lower within than between scanners (CoV 0.17-0.96% vs. 0.65-5.0%, p<0.001). All software provided similarly robust segmentations of the brain volume on the same scanner (CoV 0.17-0.28%, p=0.076). Normalisation improved inter-scanner reproducibility in FreeSurfer and SPM-based methods, but the FSL-SIENAX scaling factor did not improve robustness. Generally, SPM-based methods produced the most consistent volumetrics, while FreeSurfer was more robust for WM volumes on different scanners. FreeSurfer had more robust normalised brain and GM volumes on different scanners than FSL-SIENAX (p=0.004). MS lesion filling changed the output of FSL-SIENAX, SPM and SPM-CAT but not FreeSurfer.

CONCLUSIONS

Consistent use of the same scanner is essential and normalisation to the intracranial volume is recommended for multiple scanners. Based on robustness, SPM-based methods are particularly suitable for cross-sectional volumetry. FreeSurfer poses a suitable alternative with WM segmentations less sensitive to MS lesions.

KEY POINTS

• The same scanner should be used for brain volumetry. If different scanners are used, the intracranial volume normalisation improves the FreeSurfer and SPM robustness (but not the FSL scaling factor). • FreeSurfer, FSL and SPM all provide robust measures of the whole brain volume on the same MRI scanner. SPM-based methods overall provide the most robust segmentations (except white matter segmentations on different scanners where FreeSurfer is more robust). • MS lesion filling with Lesion Segmentation Toolbox changes the output of FSL-SIENAX and SPM. FreeSurfer output is not affected by MS lesion filling since it already takes white matter hypointensities into account and is therefore particularly suitable for MS brain volumetry.

Myelin Measurement: Comparison Between Simultaneous Tissue Relaxometry, Magnetization Transfer Saturation Index, and T1w/T2w Ratio Methods

Magnetization transfer (MT) imaging has been widely used for estimating myelin content in the brain. Recently, two other approaches, namely simultaneous tissue relaxometry of R1 and R2 relaxation rates and proton density (SyMRI) and the ratio of T1-weighted to T2-weighted images (T1w/T2w ratio), were also proposed as methods for measuring myelin. SyMRI and MT imaging have been reported to correlate well with actual myelin by histology. However, for T1w/T2w ratio, such evidence is limited. In 20 healthy adults, we examined the correlation between these three methods, using MT saturation index (MTsat) for MT imaging. After calibration, white matter (WM) to gray matter (GM) contrast was the highest for SyMRI among these three metrics. Even though SyMRI and MTsat showed strong correlation in the WM (r = 0.72), only weak correlation was found between T1w/T2w and SyMRI (r = 0.45) or MTsat (r = 0.38) (correlation coefficients significantly different from each other, with p values < 0.001). In subcortical and cortical GM, these measurements showed moderate to strong correlations to each other (r = 0.54 to 0.78). In conclusion, the high correlation between SyMRI and MTsat indicates that both methods are similarly suited to measure myelin in the WM, whereas T1w/T2w ratio may be less optimal.

Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting

PURPOSE

To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori.

THEORY

Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown. Assuming that only a few entries from the dictionary contribute to a mixed signal, sparsity-promoting priors can be placed upon the solution.

METHODS

An iterative algorithm is applied to compute the maximum a posteriori estimator of the posterior probability density to determine the magnetic resonance fingerprinting dictionary entries that contribute most significantly to mixed or pure voxels.

RESULTS

Simulation results show that the algorithm is robust in finding the component tissues of mixed voxels. Preliminary in vivo data confirm this result, and show good agreement in voxels containing pure tissue.

CONCLUSIONS

The Bayesian framework and algorithm shown provide accurate solutions for the partial-volume problem in magnetic resonance fingerprinting. The flexibility of the method will allow further study into different priors and hyperpriors that can be applied in the model. Magn Reson Med 80:159-170, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

SyMRI of the Brain: Rapid Quantification of Relaxation Rates and Proton Density, With Synthetic MRI, Automatic Brain Segmentation, and Myelin Measurement

Conventional magnetic resonance images are usually evaluated using the image signal contrast between tissues and not based on their absolute signal intensities. Quantification of tissue parameters, such as relaxation rates and proton density, would provide an absolute scale; however, these methods have mainly been performed in a research setting. The development of rapid quantification, with scan times in the order of 6 minutes for full head coverage, has provided the prerequisites for clinical use. The aim of this review article was to introduce a specific quantification method and synthesis of contrast-weighted images based on the acquired absolute values, and to present automatic segmentation of brain tissues and measurement of myelin based on the quantitative values, along with application of these techniques to various brain diseases. The entire technique is referred to as “SyMRI” in this review. SyMRI has shown promising results in previous studies when used for multiple sclerosis, brain metastases, Sturge-Weber syndrome, idiopathic normal pressure hydrocephalus, meningitis, and postmortem imaging.

Clinical equivalence assessment of T2 synthesized pediatric brain magnetic resonance imaging

BACKGROUND AND PURPOSE

Automated synthetic magnetic resonance imaging (MRI) provides qualitative, weighted image contrasts as well as quantitative information from one scan and is well-suited for various applications such as analysis of white matter disorders. However, the synthesized contrasts have been poorly evaluated in pediatric applications. The purpose of this study was to compare the image quality of synthetic T2 to conventional turbo spin-echo (TSE) T2 in pediatric brain MRI.

MATERIALS AND METHODS

This was a mono-center prospective study. Synthetic and conventional MRI acquisitions at 1.5 Tesla were performed for each patient during the same session using a prototype accelerated T2 mapping sequence package (TA_synthetic=3:07min, TA_conventional=2:33min). Image sets were blindly and randomly analyzed by pediatric neuroradiologists. Global image quality, morphologic legibility of standard structures and artifacts were assessed using a 4-point Likert scale. Inter-observer kappa agreements were calculated. The capability of the synthesized contrasts and conventional TSE T2 to discern normal and pathologic cases was evaluated.

RESULTS

Sixty patients were included. The overall diagnostic quality of the synthesized contrasts was non-inferior to conventional imaging scale (P=0.06). There was no significant difference in the legibility of normal and pathological anatomic structures of synthetized and conventional TSE T2 (all P>0.05) as well as for artifacts except for phase encoding (P=0.008). Inter-observer agreement was good to almost perfect (kappa between 0.66 and 1).

CONCLUSIONS

T2 synthesized contrasts, which also provides quantitative T2 information that could be useful, could be suggested as an equivalent technique in pediatric neuro-imaging, compared to conventional TSE T2.