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Objective: To measure prefrontal cortical volume in a group of schizophrenic subjects who presented with mainly positive symptoms and who were previously shown to have volume reductions in left temporal lobe structures. Method: Fourteen men with chronic schizophrenia and 15 male control subjects were matched for age, IQ, handedness, and parental socioeconomic status. Magnetic resonance images were obtained by means of a 1.5-T magnet, and contiguous 1.5-mm slices of the entire brain were obtained. Results: No significant differences were found between schizophrenic and control subjects in mean values for prefrontal white or gray matter on either the right or the left side. However, within the schizophrenic group, there was evidence of a relationship between the volumes of left prefrontal gray matter and left temporal lobe structures that was not present in the control group. Conclusions: At least in this group of schizophrenic subjects with mainly positive symptoms, temporal lobe abnormalities can exist in conjunction with no gross volumetric abnormalities of the prefrontal cortex.

We read the article by Killiany et al 1 with considerable interest. We note that it confirmed our previously published data regarding the use of discriminant analysis to identify patients with early Alzheimer's disease (AD). 2 However, the very preliminary nature of their observations deserves comment. To date, we have studied 31 patients with dementia of the Alzheimer's type and have compared these with 29 healthy age- and sex-matched control subjects. All patients with dementia of the Alzheimer's type and control subjects were followed up longitudinally, including autopsy in some patients with dementia. This enabled us to test the diagnostic advantage of quantitative magnetic resonance imaging at the time of initial evaluation of patients subsequently confirmed to have probable AD. Image segmentation 3 determined various brain and cerebrospinal fluid volumes. Initial discriminant analysis revealed a significant gender difference among the control subjects. Subsequent discriminant analyses were applied according to gender.

Multiple point T1 and T2 values of 424 vertebral bodies were measured and analysed. The influence of several factors including age, sex, location in the spine and status of neighbouring discs on the measured relaxation times were evaluated. The results indicate limitations in the region of interest approach. Vertebral bodies of different age, sex and location in the spine could not be distinguished. For heterogeneous tissues a more advanced form of image analysis appears to be essential. Diurnal factors resulting from the stress of normal ambulatory activity caused increased variation in vertebral body relaxation time values.

The T2 component of the magnetic resonance imaging (MRI) signal was measured in 11 brain loci in six elderly patients diagnosed as having probable Alzheimer's disease. T2 values and relative amount of periventricular high-intensity foci were significantly correlated with dementia severity, indicated by the Blessed-Roth Dementia Scale score. Although the mean T2 value for left hemispheric structures was more closely correlated with the dementia score, T2 values did not differ significantly in the right and left hemispheres or in gray and white matter. These findings suggest that more severe dementia in Alzheimer's disease is associated with more water in the brain.

Although numerous studies have confirmed the presence of larger cerebral ventricles in schizophrenia, the locus of tissue loss remains elusive. By analyzing magnetic resonance scans with computerized image analysis, the authors determined gray and white matter volumes in the temporal lobes and prefrontal regions of 17 patients with schizophrenia and 17 age- and sex-matched normal subjects. The volume of temporal lobe gray matter was 20% smaller in the patients than in the control subjects. The lateral ventricular volume was 67% larger in the patients and, when normalized for brain size, correlated inversely with the volume of temporal lobe gray matter.

Quantitative magnetic resonance imaging (qMRI) goes beyond conventional MRI, which aims primarily at local image contrast. It provides specific physical parameters related to the nuclear spin of protons in water, such as relaxation times. These parameters carry information about the local microstructural environment of the protons (such as myelin in the brain). Non-invasive in vivo histology using MRI (hMRI) aims to use this information to directly characterize biological tissue microstructure, partially replacing or complementing classical invasive histology. The understanding of MRI tissue contrast provided by hMRI is, in turn, crucial for further improvements of qMRI, and they should be considered closely interlinked. We discuss concepts, models and validation approaches, pointing out challenges and the latest advances in this field. Further, we point out links to physics, including computational and analytical approaches and developments in materials science and photonics, that aid in reference data acquisition and model validation. Quantitative magnetic resonance imaging and in vivo histology go beyond standard magnetic resonance imaging, aiming at characterizing tissue microstructure of the living brain. This Technical Review discusses advances in concepts, instrumentation, biophysical models and validation approaches facilitating this rapidly developing field.

Aktuell beschrankt sich die computationale Neuroanatomie auf morphologische Mase des Gehirns und basiert auf herkommlichen Magnetresonanztomographie (MRT)-Aufnahmetechniken. Im Vergleich dazu besitzt die quantitative MRT (qMRT) in der Regel nicht nur einen besseren Gewebekontrast, sondern hat auch eine deutlich verbesserte Sensitivitat und Spezifitat gegenuber mikrostrukturellen Eigenschaften des Gewebes. Aktuelle methodische Entwicklungen in der qMRT werden dargestellt, die uber die Morphologie hinausgehen, indem sie standardisierte Informationen uber die Mikrostruktur des Gehirns liefern. Das Konzept der In-vivo-Histologie wird eingefuhrt, basierend auf biophysikalischer Modellierung von qMRT-Daten (hMRT) zur Bestimmung von quantitativen Histologie-ahnlichen Markern der Mikrostruktur. qMRT-Mase konnen als direkte Biomarker fur mikrostrukturelle Mechanismen verwendet werden, die mit morphologischen Veranderungen einhergehen oder diese verursachen. hMRT-Metriken verwenden biophysikalische Modelle des MRT-Signals, um 3‑dimensionale quantitative Karten von Histologie-ahnlichen Masen in weiser Substanz zu bestimmen. Nichtinvasive Hirngewebecharakterisierung mit qMRT oder hMRT hat ein erhebliches Potenzial fur wissenschaftlichen und klinischen Einsatz. Beide Ansatze verbessern die Vergleichbarkeit uber Standorte und Zeitpunkte, erleichtern multizentrische sowie Langsschnittstudien und eine standardisierte Diagnostik. hMRT kann einen wesentlichen Beitrag liefern, das Verhaltnis zwischen Gehirnmikrostruktur, Funktion und Verhalten besser zu verstehen und auserdem die Mechanismen, die Gesundheit und Krankheit zugrunde liegen. In Zukunft wird hMRT eine unverzichtbare Rolle im Feld der computationalen Neuroanatomie spielen.

The improvement in radiological diagnosis of lumbar disc disease achieved by the development of spinal computerized tomography and by quantitative magnetic resonance imaging has increased the reliability of patient selection for percutaneous discectomy.

A method has been developed that substantially increases quantitative tissue discrimination of pathology in in-vivo magnetic resonance (MR) imaging. Results from a study of carcinoma and benign hypertrophy of the prostate show the potential for classification of tissues from individual patients and demonstrate increased class separation. This appears to be the firstapplication of pattern recognition techniques to relaxation-time data from in-vivo MR images. Multiple-point relaxation-time maps were classified using both statistical measures and texture descriptors derived from spatial grey level dependency matrices. These parameters were combined in a new pattern-recognition method based upon the Karhunen- Loeve expansion.

Several investigators have emphasized the potential value of quantitative relaxation times in the assessment of diseases. In performing such measurements using the spin-echo technique, we have encountered several anomalous results, whereby the intensity of the organ parenchyma on second-echo images is greater than on first echo images. This is most likely a result of respiratory motion, and it occurs only rarely. Several volunteers were studied before and after exercise to see if respiratory motion could reproduce the anomalous intensity reverse; a reversal of intensities in renal parenchyma was observed in two of five individuals. We conclude that respiratory motion artifacts will seriously limit quantitative magnetic resonance imaging of the upper abdomen if respiratory gating during imaging is not used.

Magnetic resonance imaging (MRI) has been used for the determination in vivo of rat pituitary size. In midsagittal T2-weighted sections the pituitary, having a lower T2 value than the surrounding tissue, was visible with pronounced contrast. The size has been estimated by pixel counting. A close correlation (r = 0.96) with the pituitary weights determined postmortem has been obtained, demonstrating the reliability of the in vivo method. Using MRI the effects of Sandostatin, a somatostatin analog, on the pituitary size have been monitored in a rat model of prolactinoma (estradiol-induced hyperplasia of the pituitary). Treatment with Sandostatin over 4 weeks resulted in a 40% reduction of the hyperplastic pituitaries. These results have been confirmed by determination of pituitary weights postmortem. However, due to a large interindividual variation in size of hyperplastic pituitaries, more animals are required to reach statistical significance when only endpoints of treatment can be measured. In contrast, MRI allows one to monitor individually the drug effects over a long period of time, eliminating interindividual variations. © 1988 Academic Press, Inc.

Background Determination of the relaxation time constants T1 and T2 with quantitative magnetic resonance imaging is increasingly used for both research and clinical practice. Recently, groups have been formed within the Society of Cardiovascular Magnetic Resonance to address issues with relaxometry. However, so far they have avoided specific recommendations on methodology due to lack of consensus and current evolving research. Standardised widely available software may simplify this process. The purpose of the current study was to develop and validate vendor-independent T1 and T2 mapping modules and implement those in the versatile and widespread software Segment, freely available for research and FDA approved for clinical applications. Results The T1 and T2 mapping modules were developed and validated in phantoms at 1.5 T and 3 T with reference standard values calculated from reference pulse sequences using the Nelder-Mead Simplex optimisation method. The proposed modules support current commonly available MRI pulse sequences and both 2- and 3-parameter curve fitting. Images acquired in patients using three major vendors showed vendor-independence. Bias and variability showed high agreement with T1 and T2 reference standards for T1 (range 214–1752 ms) and T2 (range 45–338 ms), respectively. Conclusions The developed and validated T1 and T2 mapping and quantification modules generated relaxation maps from current commonly used MRI sequences and multiple signal models. Patient applications showed usability for three major vendors.

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R 1 and R 2 ⋆ , proton density P D and magnetisation transfer M T saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.

An emerging hypothesis in developmental and behavioral disorders is that they arise from disorganized brain messaging or reduced connectivity. Given the importance of myelin to efficient brain communication, characterization of myelin development in infancy and childhood may provide salient information related to early connectivity deficits. In this work, we investigate regional and whole brain growth trajectories of the myelin water fraction, a quantitative magnetic resonance imaging measure sensitive and specific to myelin content, in data acquired from 122 healthy male children from 3 to 60 months of age. We examine common growth functions to find the most representative model of myelin maturation and subsequently use the best of these models to develop a continuous population-averaged, four-dimensional model of normative myelination. Through comparisons with an independent sample of 63 male children across the same age span, we show that the developed model is representative of this population. This work contributes to understanding the trajectory of myelination in healthy infants and toddlers, furthering our knowledge of early brain development, and provides a model that may be useful for identifying developmental abnormalities.

OBJECTIVES: T(2) mapping of the intervertebral disc (IVD) can depict quantitative changes reflecting biochemical change due to loss of glycosaminoglycan content. Conventional T(2) mapping is usually performed using a 2-dimensional multi-echo-spin echo sequence (2D-MESE) with long acquisition times that are generally not compatible with clinical routine. This study investigates the applicability of GRAPPATINI, a T(2) mapping sequence combining undersampling, model-based reconstruction, and parallel imaging, to offer clinically feasible acquisition times in T(2) mapping of the lumbar IVD. MATERIALS AND METHODS: Fifty-eight individuals (26 female; mean age, 23.3 ±8.1 years) were prospectively studied at 3 T. GRAPPATINI was conducted with the same parameters as the 2D-MESE while shortening the acquisition time from 13:18 to 2:27 minutes. The setup was also validated in a phantom experiment using a 6.48-hour-long single echo-spin echo sequence as reference. The IVDs were manually segmented on 4 central slices. RESULTS: The median nucleus pulposus showed a strong Pearson correlation coefficient between T(2GRAPPATINI) and T(2MESE) (r (p) = 0.919; P < 0.001). There was also a significant correlation for the ventral (r (p) = 0.241; P < 0.001) and posterior (r (p) = 0.418; P < 0.001) annular regions. In the single spin-echo phantom experiment, the most accurate T(2) estimation was achieved using T(2GRAPPATINI) with a median absolute deviation of 15.3 milliseconds as compared with T(2MESE) with 26.5 milliseconds. CONCLUSIONS: GRAPPATINI facilitates precise T(2) mapping at 3 T in accordance with clinical standards and reference methods using the same parameters while shortening acquisition times from 13:18 to 2:27 minutes with the same parameters.

Background Assessing the reproducibility of new quantitative magnetic resonance imaging (MRI) biomarkers is an important part of validation [1-3]. Fourier Analysis of STimulated Echoes (FAST) [4] is a new MRI tissue tagging method that has recently been shown to compare favorably to conventional estimates of left ventricular (LV) twist from cardiac tagged images, but with significantly reduced user interaction time (less than ~3 minutes) and low intra- and interobserver variability. The purpose of this study was to assess the intra- and inter-exam reproducibility of LV twist using FAST.