Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled echo acquisition.

The aim of this study was to demonstrate a new automatic brain segmentation method in magnetic resonance imaging (MRI)

Radiofrequency bias correction of magnetization prepared rapid gradient echo MRI at 7.0 Tesla using an external reference in a sequential protocol

At field strengths of 7 T and above, T1-weighted imaging of human brain suffers increasingly from radiofrequency (RF) B1 inhomogeneities. The well-known MP2RAGE (magnetization prepared two rapid acquisition gradient echoes) sequence provides a solution but may not be readily available for all MR systems. Here, we describe the implementation and evaluation of a sequential protocol to obtain normalized magnetization prepared rapid gradient echo (MPRAGE) images at 0.7,0.8, or 0.9-mm isotropic spatial resolution. Optimization focused on the reference gradient-recalled echo (GRE) that was used for normalization of the MPRAGE. A good compromise between white-gray matter contrast and the signal-to-noise ratio (SNR) was reached at a flip angle of 3° and total scan time was reduced by increasing the reference voxel size by a factor of 8 relative to the MPRAGE resolution. The average intra-subject coefficient-of-variation (CV) in segmented white matter (WM) was 7.9 ±3.3% after normalization, compared to 20 ±8.4% before. The corresponding inter-subject average CV in WM as 7.6 ±7.6% and 13 ±7.8%. Maps of T1 derived from forward signal modelling showed no obvious bias after correction by a separately acquired flip angle map. To conclude, a non-interleaved acquisition for normalization of MPRAGE offers a simple alternative to MP2RAGE to obtain semi-quantitative purely T1-weighted images. These images can be converted to T1 maps, analogously to the established MP2RAGE approach. Scan time can be reduced by increasing the reference voxel size which has only a miniscule effect on image quality

Variability in diffusion kurtosis imaging: Impact on study design, statistical power and interpretation.

Diffusion kurtosis imaging (DKI) is an emerging technique with the potential to quantify properties of tissue microstructure that may not be observable using diffusion tensor imaging (DTI). In order to help design DKI studies and improve interpretation of DKI results, we employed statistical power analysis to characterize three aspects of variability in four DKI parameters; the mean diffusivity, fractional anisotropy, mean kurtosis, and radial kurtosis. First, we quantified the variability in terms of the group size required to obtain a statistical power of 0.9. Second, we investigated the relative contribution of imaging and post-processing noise to the total variance, in order to estimate the benefits of longer scan times versus the inclusion of more subjects. Third, we evaluated the potential benefit of including additional covariates such as the size of the structure when testing for differences in group means. The analysis was performed in three major white matter structures of the brain: the superior cingulum, the corticospinal tract, and the mid-sagittal corpus callosum, extracted using diffusion tensor tractography and DKI data acquired in a healthy cohort. The results showed heterogeneous variability across and within the white matter structures. Thus, the statistical power varies depending on parameter and location, which is important to consider if a pathogenesis pattern is inferred from DKI data. In the data presented, inter-subject differences contributed more than imaging noise to the total variability, making it more efficient to include more subjects rather than extending the scan-time per subject. Finally, strong correlations between DKI parameters and the structure size were found for the cingulum and corpus callosum. Structure size should thus be considered when quantifying DKI parameters, either to control for its potentially confounding effect, or as a means of reducing unexplained variance

Correlation between arterial blood volume obtained by arterial spin labelling and cerebral blood volume in intracranial tumours.

OBJECTIVE: To compare measurements of the arterial blood volume (aBV), a perfusion parameter calculated from arterial spin labelling (ASL), and cerebral blood volume (CBV), calculated from dynamic susceptibility contrast (DSC) MRI. In the clinic, CBV is used for grading of intracranial tumours. MATERIALS AND METHODS: Estimates of aBV from the model-free ASL technique quantitative STAR labelling of arterial regions (QUASAR) experiment and of DSC-CBV were obtained at 3T in ten patients with eleven tumours (three grade III gliomas, four glioblastomas and four meningiomas, two in one patient). Parametric values of aBV and CBV were determined in the tumour as well as in normal grey matter (GM), and tumour-to-GM aBV and CBV ratios were calculated. RESULTS: In a 4-pixel ROI representing maximal tumour values, the coefficient of determination R (2) was 0.61 for the comparison of ASL-based aBV tumour-to-GM ratios and DSC-MRI-based CBV tumour-to-GM ratios and 0.29 for the comparison of parametric values of ASL-aBV and DSC-CBV, under the assumption of proportionality. Both aBV and CBV showed a non-significant tendency to increase when going from grade III gliomas to glioblastomas to meningiomas. CONCLUSION: These results suggest that measurement of aBV is a potential tool for non-invasive assessment of blood volume in intracranial tumours

Absolute quantification of perfusion by dynamic susceptibility contrast MRI using Bookend and VASO steady-state CBV calibration: a comparison with pseudo-continuous ASL.

Dynamic susceptibility contrast MRI (DSC-MRI) tends to return elevated estimates of cerebral blood flow (CBF) and cerebral blood volume (CBV). In this study, subject-specific calibration factors (CFs), based on steady-state CBV measurements, were applied to rescale the absolute level of DSC-MRI CBF

Core curriculum for medical physicists in radiology. Recommendations from an EFOMP/ESR working group

Some years ago it was decided that a European curriculum should be developed for medical physicists professionally engaged in the support of clinical diagnostic imaging departments. With this in mind, EFOMP (European Federation of Organisations for Medical Physics) in association with ESR (European Society of Radiology) nominated an expert working group. This curriculum is now to hand. The curriculum is intended to promote best patient care in radiology departments through the harmonization of education and training of medical physicists to a high standard in diagnostic radiology. It is recommended that a medical physicist working in a radiology department should have an advanced level of professional expertise in X-ray imaging, and additionally, depending on local availability, should acquire knowledge and competencies in overseeing ultrasound imaging, nuclear medicine, and MRI technology. By demonstrating training to a standardized curriculum, medical physicists throughout Europe will enhance their mobility, while maintaining local high standards of medical physics expertise. This document also provides the basis for improved implementation of articles in the European medical exposure directives related to the medical physics expert. The curriculum is divided into three main sections: The first deals with general competencies in the principles of medical physics. The second section describes specific knowledge and skills required for a medical physicist (medical physics expert) to operate clinically in a department of diagnostic radiology. The final section outlines research skills that are also considered to be necessary and appropriate competencies in a career as medical physicist

EMERALD and EMIT—worldwide computer aided education and training packages in medical physics

This paper describes the development of two web based education and training packages EMERALD and EMIT designed to meet the training needs of professional medical physicists. The program has been developed over a number of years by collaboration between hospitals and universities across Europe. The program concentrates on assisting competence development in five initial areas: diagnostic radiology; nuclear medicine; magnetic resonance tomography; ultrasound; and radiotherapy. Each of the topic areas includes around 50 training tasks in five hypertext workbooks, supplemented by a topical image database. The training materials have been extensively refereed during the development phase and are now in use in 65 countries across the globe. Initial evaluation has shown that the material enhances the training experience and produces a more consistent output