Edgar Mueller

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.

Phase-sensitive inversion recovery (PSIR) single-shot TrueFISP for assessment of myocardial infarction at 3 tesla.

Purpose:The aim of the current study was to show if contrast-to-noise ratio (CNR) could be improved without loss of diagnostic accuracy if a phase-sensitive inversion recovery (PSIR) single-shot TrueFISP sequence is used at 3.0 T instead of 1.5 T. Material and Methods:Ten patients with myocardial infarction were examined on a 1.5 T magnetic resonance (MR) system (Avanto, Siemens Medical Systems) and at a 3.0 T MR system. Imaging delayed contrast enhancement was started 10 minutes after application of contrast material. A phase-sensitive inversion recovery (PSIR) single-shot TrueFISP sequence was used at 1.5 and 3.0 T and compared with a segmented IR turboFLASH sequence at 1.5 T, which served as the reference method. Infarct volumes and CNR of infarction and normal myocardium were compared with the reference method. Results:The PSIR Single-Shot TrueFISP technique allows for imaging nine slices during a single breathhold without adaptation of the inversion time. The mean value of CNR between infarction and normal myocardium was 5.9 at 1.5 T and 12.2 at 3.0 T (magnitude images). The CNR mean value of the reference method was 8.4. The CNR mean value at 3.0 T was significantly (P = 0.03) higher than the mean value of the reference method. The correlation coefficients of the infarct volumes, determined with the PSIR single-shot TrueFISP technique at 1.5 T and at 3.0 T and compared with the reference method, were r = 0.96 (P = 0.001) and r = 0.99 (P = 0.0001). Conclusion:The use of PSIR single-shot TrueFISP at 3.0 T allows for accurate detection and assessment of myocardial infarction. CNR is significantly higher at 3.0 T compared with 1.5 T. The PSIR single-shot technique at 3.0 T provides a higher CNR than the segmented reference technique at 1.5 T.

A possible role of in-flow effects in functional MR-imaging

The potential of functional MR-imaging at 1.5 T was studied using optical stimulation of the human brain. Differences in the image intensity of up to 30% were obtained in the visual cortex area, correlating with the time course of the stimuli. A simplified semi-quantitative model describing the role of in-flow effects in brain vessels on signal increase during the stimulation period is presented.

Discriminative Joint Context for Automatic Landmark Set Detection from a Single Cardiac MR Long Axis Slice

Cardiac magnetic resonance (MR) imaging has advanced to become a powerful diagnostic tool in clinical practice. Automatic detection of anatomic landmarks from MR images is important for structural and functional analysis of the heart. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, context, around the target. Conventional context is associated with each individual landmark to encode local shape and appearance evidence. We extend this concept to a landmark set , where multiple landmarks have connections at the semantic level, e.g., landmarks belonging to the same anatomy. We propose a joint context approach to construct contextual regions between landmarks. A discriminative model is learned to utilize inter-landmark features for landmark set detection as an entirety. This helps resolve ambiguities of individual landmark detection results. A probabilistic boosting tree is used to learn a discriminative model based on contextual features. We adopt a marginal space learning strategy to efficiently learn and search a high dimensional parameter space. A fully automatic system is developed to detect the set of three landmarks of the left ventricle, the apex and the two basal annulus points, from a single cardiac MR long axis image. We test the proposed approach on a database of 795 long axis images from 116 patients. A 4-fold cross validation results show that about 15% reduction of the errors is obtained by integrating joint context into a conventional landmark detection system.

Robust object detection using marginal space learning and ranking-based multi-detector aggregation: Application to left ventricle detection in 2D MRI images

Magnetic resonance imaging (MRI) is currently the gold standard for left ventricle (LV) quantification. Detection of the LV in an MRI image is a prerequisite for functional measurement. However, due to the large variations in the orientation, size, shape, and image intensity of the LV, automatic LV detection is challenging. In this paper, we propose to use marginal space learning (MSL) to exploit the recent advances in learning discriminative classifiers. Unlike full space learning (FSL) where a monolithic classifier is trained directly in the five dimensional object pose space (two for position, one for rotation, and two for anisotropic scaling), we train three detectors, namely, the position detector, the position-orientation detector, and the position-orientation-scale detector. As a contribution of this paper, we perform thorough comparison between MSL and FSL. Experiments show MSL significantly outperforms FSL on both the training and test sets. Additionally, we also detect several LV landmarks, such as the LV apex and two annulus points. If we combine the detected candidates from both the whole-object detector and landmark detectors, we can further improve the system robustness even when one detector fails. A novel ranking-based strategy is proposed to combine the detected candidates from all detectors. Experiments show our ranking-based aggregation approach can significantly reduce the detection outliers.

Automatic view planning for cardiac MRI acquisition

Conventional cardiac MRI acquisition involves a multi-step approach, requiring a few double-oblique localizers in order to locate the heart and prescribe long- and short-axis views of the heart. This approach is operator-dependent and time-consuming. We propose a new approach to automating and accelerating the acquisition process to improve the clinical workflow. We capture a highly accelerated static 3D full-chest volume through parallel imaging within one breath-hold. The left ventricle is localized and segmented, including left ventricle outflow tract. A number of cardiac landmarks are then detected to anchor the cardiac chambers and calculate standard 2-, 3-, and 4-chamber long-axis views along with a short-axis stack. Learning-based algorithms are applied to anatomy segmentation and anchor detection. The proposed algorithm is evaluated on 173 localizer acquisitions. The entire view planning is fully automatic and takes less than 10 seconds in our experiments.

Time-resolved 3D-CMR using free-breathing 2D-acquisitions.

Background A typical CMR exam consists of a limited number of 2D scans that provide standard views of the heart. Diagnosis is limited to these select views. For the acquisition, multiple breath-holds are required a challenge for many patients. As an improvement, we have investigated a free-breathing (FB) 2D acquisition protocol in conjunction with a novel reconstruction approach. The method provides 3D+time cine data with full heart coverage while simplifying the acquisition.

Automatic Left Ventricle Detection in MRI Images Using Marginal Space Learning and Component-Based Voting

Magnetic resonance imaging (MRI) is currently the gold standard for left ventricle (LV) quantification. Detection of the LV in an MRI image is a prerequisite for functional measurement. However, due to the large variations in orientation, size, shape, and image intensity of the LV, automatic detection of the LV is still a challenging problem. In this paper, we propose to use marginal space learning (MSL) to exploit the recent advances in learning discriminative classifiers. Instead of learning a monolithic classifier directly in the five dimensional object pose space (two dimensions for position, one for orientation, and two for anisotropic scaling) as full space learning (FSL) does, we train three detectors, namely, the position detector, the position-orientation detector, and the position-orientation-scale detector. Comparative experiments show that MSL significantly outperforms FSL in both speed and accuracy. Additionally, we also detect several LV landmarks, such as the LV apex and two annulus points. If we combine the detected candidates from both the whole-object detector and landmark detectors, we can further improve the system robustness. A novel voting based strategy is devised to combine the detected candidates by all detectors. Experiments show component-based voting can reduce the detection outliers.

Flow Measurement By Magnetic Resonance Imaging

Magnetic resonance images from 0.35T and 0.5T imaging systems are processed and evaluated in order to visualize and quantify blood flow. The velocity of moving substances is either measured by local and temporal tracking or by motion dependent encoding and decoding. With the first method the passage time through an imaged slice of known thickness may be measured. The signal intensity at an offset position also indicates how far a bolus has advanced under consideration of its velocity distribution. The second principle depends on MR gradient field encoding.

Tight frame learning for cardiovascular MRI

Dynamic cardiovascular MRI facilitates the assessment of the structure and function of the cardiovascular system. One of the challenges in dynamic MRI is the prolonged data acquisition time. In order to fit the data acquisition time inside the motion cycles of the imaging subject, the data must be highly undersampled. Compressed sensing or sparsity based MR reconstruction takes advantage of the fact that the image is compressible in some transform domain, and enables reconstruction based on under-sampled k-space data thereby reducing the acquisition time. The design of such transform is key to the success of the reconstruction. In this paper, we propose to use tight frame learning for computing data-driven transforms. Empirical results demonstrate improvement over the transform associated with the redundant Haar Wavelets.