One serious problem affecting the rest and active state images obtained during a functional MRI (fMRI) study is that of involuntary subject movements inside the magnet while the imaging protocol is being carried out. The small signal intensity rise and small activation areas observed in the fMRI results, such as the statistical maps indicating the significance of the observed signal intensity difference between the rest and active states for each pixel, are greatly affected even by head displacements of less than one pixel. Near perfect alignment in the subpixel level of each image with respect to a reference, then, is necessary if the results are to be considered meaningful, specially in a clinical setting. In this paper we report the brain displacements that take place during a fMRI study with an image alignment method based on a refined crosscorrelation function which obtains fast (non-iterative) and precise values for the inplane rotation and X and Y translation correction factors. The performance of the method was tested with phantom experiments and fMRI studies using normal subjects executing a finger-tapping motor task. In all cases, subpixel translations and rotations were detected. The rest and active phases of the time course plots obtained from pixels in the primary motor area were well differentiated after only one pass of the motion correction program, giving enhanced activation zones. Other related areas such as the supplementary motor area became visible only after correction, and the number of pixels showing false activation was reduced.

This paper describes a simulation platform for use in the quantitative assessment of different respiratory motion correction techniques in Coronary MR angiography (CMRA). The simulator incorporates acquisition of motion parameters from heart motion tracking and applies it to a deformable heart model. To simulate respiratory motion, a high-resolution 3-D coronary heart reference image is deformed using the estimated linear transformation from a series of volunteer coronal scout scans. The deformed and motion-affected 3-D coronary images are used to generate segmented k-space data to represent MR data acquisition affected by respiratory motion. The acquired k-space data are then corrected using different respiratory motion correction methods and converted back to image data. The resulting images are quantitatively compared with each other using image-quality measures. Simulation experiment results are validated by acquiring CMRA scans using the correction methods used in the simulation.

Ultrafast MR imaging (e.g., echo-planar imaging) acquires all the data within only several tens of milliseconds. This method, however, is affected by static magnetic field inhomogeneities and chemical shift; therefore, a high degree of field homogeneity and water and fat signal separation are required. However, it is practically impossible to obtain an homogeneous field within a subject even if in vivo shimming has been performed. In this paper, we describe a new ultrafast MR imaging method called Ultrafast Single-shot water and fat Separated Imaging (USSI) and a correction method for field inhomogeneities and chemical shift. The magnetic field distribution whthin the subject is measured before thd scan and used to obtain images without field inhomogeneity distortions. Computer simulation results have shown that USSI and the correction method can obtain water and fat separated images as real and imaginary parts, respectively, of a complex Fourier transform with a single-shot scan. Image quality is maintained in the presence of field inhomogeneities of several ppm similar to those occurring under practical imaging conditions. Limitations of the correction method are also discussed.

We assess how well the recently created MRI reconstruction technique, Multi-Resolution Convolutional Neural Network (MRCNN), performs in the core medical vision field (classification). The primary goal of MRCNN is to identify the best k-space undersampling patterns to accelerate the MRI. In this study, we use the Figshare brain tumor dataset for MRI classification with 3064 T1-weighted contrast-enhanced MRI (CE-MRI) over three categories: meningioma, glioma, and pituitary tumors. We apply MRCNN to the dataset, which is a method to reconstruct high-quality images from under-sampled k-space signals. Next, we employ the pre-trained VGG16 model, which is a Deep Neural Network (DNN) based image classifier to the MRCNN restored MRIs to classify the brain tumors. Our experiments showed that in the case of MRCNN restored data, the proposed brain tumor classifier achieved 92.79% classification accuracy for a 10% sampling rate, which is slightly higher than that of SRCNN, MoDL, and Zero-filling methods have 91.89%, 91.89%, and 90.98% respectively. Note that our classifier was trained using the dataset consisting of the images with full sampling and their labels, which can be regarded as a model of the usual human diagnostician. Hence our results would suggest MRCNN is useful for human diagnosis. In conclusion, MRCNN significantly enhances the accuracy of the brain tumor classification system based on the tumor location using under-sampled k-space signals.