Characterization of hardware-related spatial distortions for IR-PETRA pulse sequence using a brain specific phantom.

OBJECTIVE: Inversion recovery-pointwise encoding time reduction with radial acquisition (IR-PETRA) is an effective magnetic resonance (MR) pulse sequence in generating pseudo-CTs. The hardware-related spatial-distortion (HRSD) in MR images potentially deteriorates the accuracy of pseudo-CTs. Thus, we aimed at characterizing HRSD for IR-PETRA. MATERIALS AND METHODS: gross-HRSDoverall (Euclidean-sum of gross-HRSDi (i = x, y, z)) for IR-PETRA was assessed using a brain-specific phantom for two MR scanners (1.5 T-Aera and 3.0 T-Prisma). Moreover, hardware imperfections were analyzed by determining gradient-nonlinearity spatial-distortion (GNSD) and B0-inhomogeneity spatial-distortion (B0ISD) for magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) which has well-known distortion characteristics. RESULTS: In 3.0 T, maximum of gross-GNSDoverall (Euclidean-sum of gross-GNSDi) and gross-B0ISD for MP-RAGE was 2.77 mm and 0.57 mm, respectively. For this scanner, the mean and maximum of gross-HRSDoverall for IR-PETRA were 0.63 ± 0.38 mm and 1.91 mm, respectively. In 1.5 T, maximum of gross-GNSDoverall and gross-B0ISD for MP-RAGE was 3.41 mm and 0.78 mm, respectively. The mean and maximum of gross-HRSDoverall for IR-PETRA were 1.02 ± 0.50 mm and 3.12 mm, respectively. DISCUSSION: The spatial accuracy of MR images, besides being impacted by hardware performance, scanner capabilities, and imaging parameters, is mainly affected by its imaging strategy and data acquisition scheme. In 3.0 T, even without applying vendor correction algorithms, spatial accuracy of IR-PETRA image is sufficient for generating pseudo-CTs. In 1.5 T, distortion-correction is required to provide this accuracy.

Evaluating the accuracy of geometrical distortion correction of magnetic resonance images for use in intracranial brain tumor radiotherapy.

AIM: Determine the 1) effectiveness of correction for gradient-non-linearity and susceptibility effects on both QUASAR GRID3D and CIRS phantoms; and 2) the magnitude and location of regions of residual distortion before and after correction. BACKGROUND: Using magnetic resonance imaging (MRI) as a primary dataset for radiotherapy planning requires correction for geometrical distortion and non-uniform intensity. MATERIALS AND METHODS: Phantom Study: MRI, computed tomography (CT) and cone beam CT images of QUASAR GRID3D and CIRS head phantoms were acquired. Patient Study: Ten patients were MRI-scanned for stereotactic radiosurgery treatment. Correction algorithm: Two magnitude and one phase difference image were acquired to create a field map. A MATLAB program was used to calculate geometrical distortion in the frequency encoding direction, and 3D interpolation was applied to resize it to match 3D T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) images. MPRAGE images were warped according to the interpolated field map in the frequency encoding direction. The corrected and uncorrected MRI images were fused, deformable registered, and a difference distortion map generated. RESULTS: Maximum deviation improvements: GRID3D , 0.27 mm y-direction, 0.07 mm z-direction, 0.23 mm x-direction. CIRS, 0.34 mm, 0.1 mm and 0.09 mm at 20-, 40- and 60-mm diameters from the isocenter. Patient data show corrections from 0.2 to 1.2 mm, based on location. The most-distorted areas are around air cavities, e.g. sinuses. CONCLUSIONS: The phantom data show the validity of our fast distortion correction algorithm. Patient-specific data are acquired in <2 min and analyzed and available for planning in less than a minute.

Estimation of the minimum detectable phase change of surface coil for neural current MRI.

Neuronal current magnetic resonance imaging (NC-MRI) is a new method in functional imaging of the brain that could cause the alteration in the phase of magnetic resonance signal. The phase variance is defined as the inverse of the signal to noise ratio (SNR). The intrinsic SNR of the MRI signal is characterized by the coil performance. We evaluated the relation between the geometry and the shape of coils in order to find the minimum detectable change in the signal phase and the possibility of direct detection of neuronal activity by MRI. Full wave equations were solved by the finite element method to calculate the SNR for circular, elliptical, and square shape surface coils. The simulation was repeated for Larmor frequencies of 64 MHz and 85.2 MHz and the coil sizes between 1.5 and 7.5 cm. Relative intrinsic signal to noise ratio (rISNR) of coils with a respect to a selected reference coil and a reference point in the sample was estimated. The circular coil had higher rISNR than other shapes. The increase of the strip width in the coils raised the rISNR 5-20%. For typical imaging parameters, rISNR reference was about 66 which led to a minimum detectable change in MRI signal phase of 0.87° (11.4 nT). It may also be reduced up to tenfold in a 1.5 cm circular coil. Detection of subtle phase signal change due to neuronal activity in surface coils needs a large amount of data acquisition and averaging, but it is intrinsically feasible.

Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model.

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot-savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm(3). With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.

Computational Modeling of Neuronal Current MRI Signals with Rat Somatosensory Cortical Neurons.

Magnetic field generated by active neurons has recently been considered to determine location of neuronal activity directly with magnetic resonance imaging (MRI), but controversial results have been reported about detection of such small magnetic fields. In this study, multiple neuronal morphologies of rat tissue were modeled to investigate better estimation of MRI signal change produced by neuronal magnetic field (NMF). Ten pyramidal neurons from layer II to VI of rat somatosensory area with realistic morphology, biophysics, and neuronal density were modeled to simulate NMF of neuronal tissue, from which effects of NMF on MRI signals were obtained. Neuronal current MRI signals, which consist of relative magnitude signal change (RMSC) and phase signal change (PSC), were at least three and one orders of magnitude less than a tissue with single neuron type, respectively. Also, a reduction in voxel size could increase signal alterations. Furthermore, with selection of zenith angle of external main magnetic field related to tissue surface near to 90°, RMSC could be maximized. This value for PSC would be 90° for small voxel size and zero degree for large ones.