Stefan Thesen

Prospective acquisition correction for head motion with image-based tracking for real-time fMRI.

In functional magnetic resonance imaging (fMRI) head motion can corrupt the signal changes induced by brain activation. This paper describes a novel technique called Prospective Acquisition CorrEction (PACE) for reducing motion‐induced effects on magnetization history. Full three‐dimensional rigid body estimation of head movement is obtained by image‐based motion detection to a high level of accuracy. Adjustment of slice position and orientation, as well as regridding of residual volume to volume motion, is performed in real‐time during data acquisition. Phantom experiments demonstrate a high level of consistency (translation < 40μm; rotation < 0.05°) for detected motion parameters. In vivo experiments were carried out and they showed a significant decrease of variance between successively acquired datasets compared to retrospective correction algorithms. Magn Reson Med 44:457–465, 2000.

Automatic Labeling of Anatomical Structures in MR FastView Images Using a Statistical Atlas

We present a method for fast and automatic labeling of anatomical structures in MR FastView localizer images, which can be useful for automatic MR examination planning. FastView is a modern MR protocol, that provides larger planning fields of view than previously available with isotropic 3D resolution by scanning during continuous movement of the patient table. Hence, full 3D information is obtained within short acquisition time. Anatomical labeling is done by registering the images to a statistical atlas created from training image data beforehand. The statistical atlas consists of a statistical model of deformation and a statistical model of grey value appearance. It is generated by non-rigid registration and principal component analysis of the resulting deformation fields and registered images. Labeling of an unseen FastView image is done by non-rigid registration of the image to the statistical atlas and propagating the labels from the atlas to the image. In our implementation, the statistical models of deformation and appearance are both implemented on the GPU (graphics processing unit), which permits computing the atlas based labeling using GPU hardware acceleration. The running times of about 10 to 30 seconds are of the same magnitude as the image acquisition itself, which allows for practical usage in clinical MR routine.

Scanning for the scanner: FMRI of audition by read-out omissions from echo-planar imaging.

Echo-planar imaging (EPI) generates considerable acoustic noise by rapidly oscillating gradients. In functional magnetic resonance imaging (FMRI), unshielded EPI sounds activate the auditory system inasmuch as it is responsive. Instead of attenuating EPI noise, our goal was to utilize it for auditory FMRI by omitting read-outs from the pulse sequences gradient train. Read-out gradient pulses are the primary noise determinant of EPI introducing its peak sound level and fundamental frequency peak which inversely relates to twice the echo spacing. Using model-driven analyses, we demonstrate that withholding read-outs from EPI is suited to reliably evoke hemodynamic blood oxygenation level-dependent (BOLD) signal modulations bilaterally in the auditory cortex of normal hearing subjects (n=60). To investigate the utility of EPI read-out omissions for auditory FMRI at an individual subjects level, we compare traditional Family-Wise-Error-Rate (FWER)-corrected maximum height thresholding to spatial mixture modeling (SMM). With the latter, appropriate bilateral auditory activations were confirmed in 95% of the individuals, whereas FWER-based voxel thresholding detected such activations in up to 72%. We illustrate the applicability of this novel EPI modification for clinical diagnostic purposes and report on a patient with bilateral large vestibular aqueducts (LVAs) and severe binaural sensorineural hearing loss (SNHL). In this particular case, read-out omissions from EPI were used to assert residual audition prior to cochlear implantation (CI). Requiring no specific task compliance or sophisticated stimulation equipment other than the scanner on its own, FMRI by read-out omissions lends itself to auditory investigations and to quickly probe audition.

Reconstructing liver shape and position from MR image slices using an active shape model

We present an algorithm for fully automatic reconstruction of 3D position, orientation and shape of the human liver from a sparsely covering set of

Method for operating a magnetic resonance imaging apparatus ensuring coincidence of images obtained in different examinations with respectively different positions of a subject

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Method for the implementation of a perfusion measurement with magnetic resonance imaging

2D MR slice images. Reconstructing the shape of an organ from slice images can be used for scan planning, for surgical planning or other purposes where 3D anatomical knowledge has to be inferred from sparse slices. The algorithm is based on adapting an active shape model of the liver surface to a given set of slice images. The active shape model is created from a training set of liver segmentations from a group of volunteers. The training set is set up with semi-manual segmentations of T1-weighted volumetric MR images. Searching for the optimal shape model that best fits to the image data is done by maximizing a similarity measure based on local appearance at the surface. Two different algorithms for the active shape model search are proposed and compared: both algorithms seek to maximize the a-posteriori probability of the grey level appearance around the surface while constraining the surface to the space of valid shapes. The first algorithm works by using grey value profile statistics in normal direction. The second algorithm uses average and variance images to calculate the local surface appearance on the fly. Both algorithms are validated by fitting the active shape model to abdominal 2D slice images and comparing the shapes, which have been reconstructed, to the manual segmentations and to the results of active shape model searches from 3D image data. The results turn out to be promising and competitive to active shape model segmentations from 3D data.