Brain Parcellation Repeatability and Reproducibility Using Conventional and Quantitative 3D MR Imaging.

BACKGROUND AND PURPOSE: Automatic brain parcellation is typically performed on dedicated MR imaging sequences, which require valuable examination time. In this study, a 3D MR imaging quantification sequence to retrieve R1 and R2 relaxation rates and proton density maps was used to synthesize a T1-weighted image stack for brain volume measurement, thereby combining image data for multiple purposes. The repeatability and reproducibility of using the conventional and synthetic input data were evaluated. MATERIALS AND METHODS: Twelve subjects with a mean age of 54 years were scanned twice at 1.5T and 3T with 3D-QALAS and a conventionally acquired T1-weighted sequence. Using SyMRI, we converted the R1, R2, and proton density maps into synthetic T1-weighted images. Both the conventional T1-weighted and the synthetic 3D-T1-weighted inversion recovery images were processed for brain parcellation by NeuroQuant. Bland-Altman statistics were used to correlate the volumes of 12 brain structures. The coefficient of variation was used to evaluate the repeatability. RESULTS: A high correlation with medians of 0.97 for 1.5T and 0.92 for 3T was found. A high repeatability was shown with a median coefficient of variation of 1.2% for both T1-weighted and synthetic 3D-T1-weighted inversion recovery at 1.5T, and 1.5% for T1-weighted imaging and 4.4% for synthetic 3D-T1-weighted inversion recovery at 3T. However, significant biases were observed between the methods and field strengths. CONCLUSIONS: It is possible to perform MR imaging quantification of R1, R2, and proton density maps to synthesize a 3D-T1-weighted image stack, which can be used for automatic brain parcellation. Synthetic parameter settings should be reinvestigated to reduce the observed bias.

Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema.

Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R1), transverse relaxation (R2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R1-difference-map. The quantitative R1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.

Improved Precision of Automatic Brain Volume Measurements in Patients with Clinically Isolated Syndrome and Multiple Sclerosis Using Edema Correction.

BACKGROUND AND PURPOSE: The presence of edema will result in increased brain volume, which may obscure progressing brain atrophy. Similarly, treatment-induced edema reduction may appear as accelerated brain tissue loss (pseudoatrophy). The purpose of this study was to correlate brain tissue properties to brain volume, to investigate the possibilities for edema correction and the resulting improvement of the precision of automated brain volume measurements. MATERIALS AND METHODS: A group of 38 patients with clinically isolated syndrome or newly diagnosed MS were imaged at inclusion and after 1, 2, and 4 years using an MR quantification sequence. Brain volume, relaxation rates (R1 and R2), and proton density were measured by automated software. RESULTS: The reduction of normalized brain volume with time after inclusion was 0.273%/year. The mean SDs were 0.508%, 0.526%, 0.454%, and 0.687% at baseline and 1, 2, and 4 years. Linear regression of the relative change of normalized brain volume and the relative change of R1, R2, and proton density showed slopes of -0.198 (P < .001), 0.156 (P = .04), and 0.488 (P < .001), respectively. After we applied the measured proton density as a correction factor, the mean SDs decreased to 24.2%, 4.8%, 33.3%, and 17.4%, respectively. The observed atrophy rate reduced from 0.273%/year to 0.238%/year. CONCLUSIONS: Correlations between volume and R1, R2, and proton density were observed in the brain, suggesting that a change of brain tissue properties can affect brain volume. Correction using these parameters decreased the variation of brain volume measurements and may have reduced the effect of pseudoatrophy.

Myelin Detection Using Rapid Quantitative MR Imaging Correlated to Macroscopically Registered Luxol Fast Blue-Stained Brain Specimens.

BACKGROUND AND PURPOSE: Myelin detection is of great value in monitoring diseases such as multiple sclerosis and dementia. However, most MR imaging methods to measure myelin are challenging for routine clinical use. Recently, a novel method was published, in which the presence of myelin is inferred by using its effect on the intra- and extracellular water relaxation rates and proton density, observable by rapid quantitative MR imaging. The purpose of this work was to validate this method further on the brains of 12 fresh, intact cadavers. MATERIALS AND METHODS: The 12 brains were scanned with a quantification sequence to determine the longitudinal and transverse relaxation rates and proton density as input for the myelin estimations. Subsequently, the brains were excised at postmortem examination, and brain slices were stained with Luxol fast blue to verify the presence of myelin. The optical density values of photographs of the stained brain slices were registered with the MR images and correlated with the myelin estimation performed by quantitative MR imaging. RESULTS: A correlation was found between the 2 methods with a mean Spearman ρ for all subjects of 0.74 ± 0.11. Linear regression showed a mean intercept of 1.50% ± 2.84% and a mean slope of 4.37% ± 1.73%/%. A lower correlation was found for the separate longitudinal relaxation rates and proton density (ρ = 0.63 ± 0.12 and -0.73 ± 0.09, respectively). For transverse relaxation rates, the ρ was very low (0.11 ± 0.28). CONCLUSIONS: The observed correlation supports the validity of myelin measurement by using the MR imaging quantification method.

Effects of gadolinium contrast agent administration on automatic brain tissue classification of patients with multiple sclerosis.

BACKGROUND AND PURPOSE: The administration of gadolinium contrast agent is a common part of MR imaging examinations in patients with MS. The presence of gadolinium may affect the outcome of automated tissue classification. The purpose of this study was to investigate the effects of the presence of gadolinium on the automatic segmentation in patients with MS by using the synthetic tissue-mapping method. MATERIALS AND METHODS: A cohort of 20 patients with clinically definite multiple sclerosis were recruited, and the T1 and T2 relaxation times and proton density were simultaneously quantified before and after the administration of gadolinium. Synthetic tissue-mapping was used to measure white matter, gray matter, CSF, brain parenchymal, and intracranial volumes. For comparison, 20 matched controls were measured twice, without gadolinium. RESULTS: No differences were observed for the control group between the 2 measurements. For the MS group, significant changes were observed pre- and post-gadolinium in intracranial volume (-13 mL, P < .005) and cerebrospinal fluid volume (-16 mL, P < .005) and the remaining, unclassified non-WM/GM/CSF tissue volume within the intracranial volume (+8 mL, P < .05). The changes in the patient group were much smaller than the differences, compared with the controls, which were -129 mL for WM volume, -22 mL for GM volume, +91 mL for CSF volume, 24 mL for the remaining, unclassified non-WM/GM/CSF tissue volume within the intracranial volume, and -126 mL for brain parenchymal volume. No significant differences were observed for linear regression values against age and Expanded Disability Status Scale. CONCLUSIONS: The administration of gadolinium contrast agent had a significant effect on automatic brain-tissue classification in patients with MS by using synthetic tissue-mapping. The observed differences, however, were much smaller than the group differences between MS and controls.

Automated determination of brain parenchymal fraction in multiple sclerosis.

BACKGROUND AND PURPOSE: Brain atrophy is a manifestation of tissue damage in MS. Reduction in brain parenchymal fraction is an accepted marker of brain atrophy. In this study, the approach of synthetic tissue mapping was applied, in which brain parenchymal fraction was automatically calculated based on absolute quantification of the tissue relaxation rates R1 and R2 and the proton attenuation. MATERIALS AND METHODS: The BPF values of 99 patients with MS and 35 control subjects were determined by using SyMap and tested in relationship to clinical variables. A subset of 5 patients with MS and 5 control subjects were also analyzed with a manual segmentation technique as a reference. Reproducibility of SyMap was assessed in a separate group of 6 healthy subjects, each scanned 6 consecutive times. RESULTS: Patients with MS had significantly lower BPF (0.852 ± 0.0041, mean ± SE) compared with control subjects (0.890 ± 0.0040). Significant linear relationships between BPF and age, disease duration, and Expanded Disability Status Scale scores were observed (P < .001). A strong correlation existed between SyMap and the reference method (r = 0.96; P < .001) with no significant difference in mean BPF. Coefficient of variation of repeated SyMap BPF measurements was 0.45%. Scan time was <6 minutes, and postprocessing time was <2 minutes. CONCLUSIONS: SyMap is a valid and reproducible method for determining BPF in MS within a clinically acceptable scan time and postprocessing time. Results are highly congruent with those described using other methods and show high agreement with the manual reference method.

Procedure for quantitative (1)H magnetic resonance spectroscopy and tissue characterization of human brain tissue based on the use of quantitative magnetic resonance imaging.

PURPOSE: Existing methods for quantitative magnetic resonance spectroscopy are not widely used for magnetic resonance spectroscopy examinations in clinical practice due to the lengthy and difficult workflow. In this report, we aimed to investigate whether metabolite concentrations show co-variation with relaxation parameters (R1,H2O,R2,H2O), water concentration (CH2O), and age, using a quantitative magnetic resonance spectroscopy method, which is suitable for a clinical setting. METHODS: We performed 166 single voxel magnetic resonance spectroscopy measurements in the white matter and thalamus in 47 healthy subjects, aged 18-72 years. Whole brain R1,H2O, R2,H2O, and CH2O maps were determined for each subject using quantitative magnetic resonance imaging. Absolute metabolite concentrations were calculated by calibrating the water-scaled magnetic resonance spectroscopy, using the quantitative magnetic resonance imaging maps of R1,H2O, R2,H2O, and CH2O. RESULTS: Absolute concentrations in white matter of total Creatine and myo-Inositol were correlated with age (total Creatine: 12 ± 4 µM/year, P < 0.01; myo-Inositol: 23 ± 9 µM/year, P < 0.05), suggesting a process of increased glia density in aging white matter. Moreover, total Creatine and total N-acetylaspartate were inversely correlated with the R1,H2O and positively correlated with the CH2O of white matter. In addition, the Cramér-Rao lower bound was biased regarding the metabolite concentration, suggesting that should not be used as a quality assessment. CONCLUSION: The implemented method was fast, robust, and user-independent.

Synthetic MRI of the brain in a clinical setting.

BACKGROUND: Conventional magnetic resonance imaging (MRI) has relatively long scan times for routine examinations, and the signal intensity of the images is related to the specific MR scanner settings. Due to scanner imperfections and automatic optimizations, it is impossible to compare images in terms of absolute image intensity. Synthetic MRI, a method to generate conventional images based on MR quantification, potentially both decreases examination time and enables quantitative measurements. PURPOSE: To evaluate synthetic MRI of the brain in a clinical setting by assessment of the contrast, the contrast-to-noise ratio (CNR), and the diagnostic quality compared with conventional MR images. MATERIAL AND METHODS: Twenty-two patients had synthetic imaging added to their clinical MR examination. In each patient, 12 regions of interest were placed in the brain images to measure contrast and CNR. Furthermore, general image quality, probable diagnosis, and lesion conspicuity were investigated. RESULTS: Synthetic T1-weighted turbo spin echo and T2-weighted turbo spin echo images had higher contrast but also a higher level of noise, resulting in a similar CNR compared with conventional images. Synthetic T2-weighted FLAIR images had lower contrast and a higher level of noise, which led to a lower CNR. Synthetic images were generally assessed to be of inferior image quality, but agreed with the clinical diagnosis to the same extent as the conventional images. Lesion conspicuity was higher in the synthetic T1-weighted images, which also had a better agreement with the clinical diagnoses than the conventional T1-weighted images. CONCLUSION: Synthetic MR can potentially shorten the MR examination time. Even though the image quality is perceived to be inferior, synthetic images agreed with the clinical diagnosis to the same extent as the conventional images in this study.

Novel whole brain segmentation and volume estimation using quantitative MRI.

OBJECTIVES: Brain segmentation and volume estimation of grey matter (GM), white matter (WM) and cerebro-spinal fluid (CSF) are important for many neurological applications. Volumetric changes are observed in multiple sclerosis (MS), Alzheimer's disease and dementia, and in normal aging. A novel method is presented to segment brain tissue based on quantitative magnetic resonance imaging (qMRI) of the longitudinal relaxation rate R(1), the transverse relaxation rate R(2) and the proton density, PD. METHODS: Previously reported qMRI values for WM, GM and CSF were used to define tissues and a Bloch simulation performed to investigate R(1), R(2) and PD for tissue mixtures in the presence of noise. Based on the simulations a lookup grid was constructed to relate tissue partial volume to the R(1)-R(2)-PD space. The method was validated in 10 healthy subjects. MRI data were acquired using six resolutions and three geometries. RESULTS: Repeatability for different resolutions was 3.2% for WM, 3.2% for GM, 1.0% for CSF and 2.2% for total brain volume. Repeatability for different geometries was 8.5% for WM, 9.4% for GM, 2.4% for CSF and 2.4% for total brain volume. CONCLUSION: We propose a new robust qMRI-based approach which we demonstrate in a patient with MS. KEY POINTS: • A method for segmenting the brain and estimating tissue volume is presented • This method measures white matter, grey matter, cerebrospinal fluid and remaining tissue • The method calculates tissue fractions in voxel, thus accounting for partial volume • Repeatability was 2.2% for total brain volume with imaging resolution <2.0 mm.

Rapid magnetic resonance quantification on the brain: Optimization for clinical usage.

A method is presented for rapid simultaneous quantification of the longitudinal T(1) relaxation, the transverse T(2) relaxation, the proton density (PD), and the amplitude of the local radio frequency B(1) field. All four parameters are measured in one single scan by means of a multislice, multiecho, and multidelay acquisition. It is based on a previously reported method, which was substantially improved for routine clinical usage. The improvements comprise of the use of a multislice spin-echo technique, a background phase correction, and a spin system simulation to compensate for the slice-selective RF pulse profile effects. The aim of the optimization was to achieve the optimal result for the quantification of magnetic resonance parameters within a clinically acceptable time. One benchmark was high-resolution coverage of the brain within 5 min. In this scan time the measured intersubject standard deviation (SD) in a group of volunteers was 2% to 8%, depending on the tissue (voxel size = 0.8 x 0.8 x 5 mm). As an example, the method was applied to a patient with multiple sclerosis in whom the diseased tissue could clearly be distinguished from healthy reference values. Additionally it was shown that, using the approach of synthetic MRI, both accurate conventional contrast images as well as quantification maps can be generated based on the same scan.

Novel method for rapid, simultaneous T1, T2*, and proton density quantification.

An imaging method called "quantification of relaxation times and proton density by twin-echo saturation-recovery turbo-field echo" (QRAPTEST) is presented as a means of quickly determining the longitudinal T(1) and transverse T(2) (*) relaxation time and proton density (PD) within a single sequence. The method also includes an estimation of the B(1) field inhomogeneity. High-resolution images covering large volumes can be achieved within clinically acceptable times of 5-10 min. The range of accuracy for determining T(1), T(2) (*), and PD values is flexible and can be optimized relative to any anticipated values. We validated the experimental results against existing methods, and provide a clinical example in which quantification of the whole brain using 1.5 mm(3) voxels was achieved in less than 8 min.