Arne Littmann

Motion correction for myocardial T1 mapping using image registration with synthetic image estimation

Quantification of myocardial T1 relaxation has potential value in the diagnosis of both ischemic and nonischemic cardiomyopathies. Image acquisition using the modified Look‐Locker inversion recovery technique is clinically feasible for T1 mapping. However, respiratory motion limits its applicability and degrades the accuracy of T1 estimation. The robust registration of acquired inversion recovery images is particularly challenging due to the large changes in image contrast, especially for those images acquired near the signal null point of the inversion recovery and other inversion times for which there is little tissue contrast. In this article, we propose a novel motion correction algorithm. This approach is based on estimating synthetic images presenting contrast changes similar to the acquired images. The estimation of synthetic images is formulated as a variational energy minimization problem. Validation on a consecutive patient data cohort shows that this strategy can perform robust nonrigid registration to align inversion recovery images experiencing significant motion and lead to suppression of motion induced artifacts in the T1 map. Magn Reson Med, 2011.

Respiratory self‐navigation for whole‐heart bright‐blood coronary MRI: Methods for robust isolation and automatic segmentation of the blood pool

Free‐breathing three‐dimensional whole‐heart coronary MRI is a noninvasive alternative to X‐ray coronary angiography. However, the existing navigator‐gated approaches do not meet the requirements of clinical practice, as they perform with suboptimal accuracy and require prolonged acquisition times. Self‐navigated techniques, applied to bright‐blood imaging sequences, promise to detect the position of the blood pool directly in the readouts acquired for imaging. Hence, the respiratory displacement of the heart can be calculated and used for motion correction with high accuracy and 100% scan efficiency. However, additional bright signal from the chest wall, spine, arms, and liver can render the isolation of the blood pool impossible. In this work, an innovative method based on a targeted combination of the output signals of an anterior phased‐array surface coil is implemented to efficiently suppress such additional bright signal. Furthermore, an algorithm for the automatic segmentation of the blood pool is proposed. Robust self‐navigation is achieved by cross‐correlation. These improvements were integrated into a three‐dimensional radial whole‐heart coronary MRI sequence and were compared with navigator‐gated imaging in vivo. Self‐navigation was successful in all cases and the acquisition time was reduced up to 63%. Equivalent or slightly superior image quality, vessel length, and sharpness were achieved. Magn Reson Med, 2012.

Spiral Phyllotaxis: The Natural Way to Construct a 3D Radial Trajectory in MRI

While radial 3D acquisition has been discussed in cardiac MRI for its excellent results with radial undersampling, the self‐navigating properties of the trajectory need yet to be exploited. Hence, the radial trajectory has to be interleaved such that the first readout of every interleave starts at the top of the sphere, which represents the shell covering all readouts. If this is done sub‐optimally, the image quality might be degraded by eddy current effects, and advanced density compensation is needed. In this work, an innovative 3D radial trajectory based on a natural spiral phyllotaxis pattern is introduced, which features optimized interleaving properties: ( 1 ) overall uniform readout distribution is preserved, which facilitates simple density compensation, and ( 2 ) if the number of interleaves is a Fibonacci number, the interleaves self‐arrange such that eddy current effects are significantly reduced. These features were theoretically assessed in comparison with two variants of an interleaved Archimedean spiral pattern. Furthermore, the novel pattern was compared with one of the Archimedean spiral patterns, with identical density compensation, in phantom experiments. Navigator‐gated whole‐heart coronary imaging was performed in six healthy volunteers. High reduction of eddy current artifacts and overall improvement in image quality were achieved with the novel trajectory. Magn Reson Med, 2011.

Cardiac anchoring in MRI through context modeling

Cardiac magnetic resonance imaging (MRI) has advanced to become a powerful diagnostic tool in clinical practice. Robust and fast cardiac modeling is important for structural and functional analysis of the heart. Cardiac anchors provide strong cues to extract morphological and functional features for diagnosis and disease monitoring. We present a fully automatic method and system that is able to detect these cues. The proposed approach explores expert knowledge embedded in a large annotated database. Exemplar cues in our experiments include left ventricle (LV) base plane and LV apex from long-axis images, and right ventricle (RV) insertion points from short-axis images. We evaluate the proposed approach on 8304 long-axis images from 188 patients and 891 short-axis images from 338 patients that are acquired from different vendors. In addition, another evaluation is conducted on an independent 7140 images from 87 patient studies. Experimental results show promise of the proposed approach.

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.

Is there an optimal respiratory reference position for self‐navigated whole‐heart coronary MR angiography?

To test the direct influence of the reference respiratory position on image quality for self‐navigated whole‐heart coronary MRI.

Effects of MRI scan acceleration on brain volume measurement consistency

To evaluate the effects of recent advances in magnetic resonance imaging (MRI) radiofrequency (RF) coil and parallel imaging technology on brain volume measurement consistency.

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.