Self-Navigation with Compressed Sensing for 2D Translational Motion Correction in Free-Breathing Coronary MRI: A Feasibility Study.

PURPOSE: Respiratory motion correction remains a challenge in coronary magnetic resonance imaging (MRI) and current techniques, such as navigator gating, suffer from sub-optimal scan efficiency and ease-of-use. To overcome these limitations, an image-based self-navigation technique is proposed that uses "sub-images" and compressed sensing (CS) to obtain translational motion correction in 2D. The method was preliminarily implemented as a 2D technique and tested for feasibility for targeted coronary imaging. METHODS: During a 2D segmented radial k-space data acquisition, heavily undersampled sub-images were reconstructed from the readouts collected during each cardiac cycle. These sub-images may then be used for respiratory self-navigation. Alternatively, a CS reconstruction may be used to create these sub-images, so as to partially compensate for the heavy undersampling. Both approaches were quantitatively assessed using simulations and in vivo studies, and the resulting self-navigation strategies were then compared to conventional navigator gating. RESULTS: Sub-images reconstructed using CS showed a lower artifact level than sub-images reconstructed without CS. As a result, the final image quality was significantly better when using CS-assisted self-navigation as opposed to the non-CS approach. Moreover, while both self-navigation techniques led to a 69% scan time reduction (as compared to navigator gating), there was no significant difference in image quality between the CS-assisted self-navigation technique and conventional navigator gating, despite the significant decrease in scan time. CONCLUSIONS: CS-assisted self-navigation using 2D translational motion correction demonstrated feasibility of producing coronary MRA data with image quality comparable to that obtained with conventional navigator gating, and does so without the use of additional acquisitions or motion modeling, while still allowing for 100% scan efficiency and an improved ease-of-use. In conclusion, compressed sensing may become a critical adjunct for 2D translational motion correction in free-breathing cardiac imaging with high spatial resolution. An expansion to modern 3D approaches is now warranted.

Enhanced compressed sensing recovery with level set normals.

We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality from few measurements. The image reconstruction is done by iterating the two following steps: 1) estimation of normal vectors of the image level curves, and 2) reconstruction of an image fitting the normal vectors, the compressed sensing measurements, and the sparsity constraint. The proposed technique can naturally extend to nonlocal operators and graphs to exploit the repetitive nature of textured images to recover fine detail structures. In both cases, the problem is reduced to a series of convex minimization problems that can be efficiently solved with a combination of variable splitting and augmented Lagrangian methods, leading to fast and easy-to-code algorithms. Extended experiments show a clear improvement over related state-of-the-art algorithms in the quality of the reconstructed images and the robustness of the proposed method to noise, different kind of images, and reduced measurements.

Compressed Sensing Single-Breath-Hold CMR for Fast Quantification of LV Function, Volumes, and Mass.

OBJECTIVES: The purpose of this study was to compare a novel compressed sensing (CS)-based single-breath-hold multislice magnetic resonance cine technique with the standard multi-breath-hold technique for the assessment of left ventricular (LV) volumes and function. BACKGROUND: Cardiac magnetic resonance is generally accepted as the gold standard for LV volume and function assessment. LV function is 1 of the most important cardiac parameters for diagnosis and the monitoring of treatment effects. Recently, CS techniques have emerged as a means to accelerate data acquisition. METHODS: The prototype CS cine sequence acquires 3 long-axis and 4 short-axis cine loops in 1 single breath-hold (temporal/spatial resolution: 30 ms/1.5 × 1.5 mm(2); acceleration factor 11.0) to measure left ventricular ejection fraction (LVEFCS) as well as LV volumes and LV mass using LV model-based 4D software. For comparison, a conventional stack of multi-breath-hold cine images was acquired (temporal/spatial resolution 40 ms/1.2 × 1.6 mm(2)). As a reference for the left ventricular stroke volume (LVSV), aortic flow was measured by phase-contrast acquisition. RESULTS: In 94% of the 33 participants (12 volunteers: mean age 33 ± 7 years; 21 patients: mean age 63 ± 13 years with different LV pathologies), the image quality of the CS acquisitions was excellent. LVEFCS and LVEFstandard were similar (48.5 ± 15.9% vs. 49.8 ± 15.8%; p = 0.11; r = 0.96; slope 0.97; p < 0.00001). Agreement of LVSVCS with aortic flow was superior to that of LVSVstandard (overestimation vs. aortic flow: 5.6 ± 6.5 ml vs. 16.2 ± 11.7 ml, respectively; p = 0.012) with less variability (r = 0.91; p < 0.00001 for the CS technique vs. r = 0.71; p < 0.01 for the standard technique). The intraobserver and interobserver agreement for all CS parameters was good (slopes 0.93 to 1.06; r = 0.90 to 0.99). CONCLUSIONS: The results demonstrated the feasibility of applying the CS strategy to evaluate LV function and volumes with high accuracy in patients. The single-breath-hold CS strategy has the potential to replace the multi-breath-hold standard cardiac magnetic resonance technique.

Spread spectrum magnetic resonance imaging.

We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s(2)MRI, consists of premodulating the signal of interest by a linear chirp before random k-space under-sampling, and then reconstructing the signal with nonlinear algorithms that promote sparsity. The effectiveness of the procedure is theoretically underpinned by the optimization of the coherence between the sparsity and sensing bases. The proposed technique is thoroughly studied by means of numerical simulations, as well as phantom and in vivo experiments on a 7T scanner. Our results suggest that s(2)MRI performs better than state-of-the-art variable density k-space under-sampling approaches.

Single breath-hold 3D measurement of left atrial volume using compressed sensing cardiovascular magnetic resonance and a non-model-based reconstruction approach.

BACKGROUND: Left atrial (LA) dilatation is associated with a large variety of cardiac diseases. Current cardiovascular magnetic resonance (CMR) strategies to measure LA volumes are based on multi-breath-hold multi-slice acquisitions, which are time-consuming and susceptible to misregistration. AIM: To develop a time-efficient single breath-hold 3D CMR acquisition and reconstruction method to precisely measure LA volumes and function. METHODS: A highly accelerated compressed-sensing multi-slice cine sequence (CS-cineCMR) was combined with a non-model-based 3D reconstruction method to measure LA volumes with high temporal and spatial resolution during a single breath-hold. This approach was validated in LA phantoms of different shapes and applied in 3 patients. In addition, the influence of slice orientations on accuracy was evaluated in the LA phantoms for the new approach in comparison with a conventional model-based biplane area-length reconstruction. As a reference in patients, a self-navigated high-resolution whole-heart 3D dataset (3D-HR-CMR) was acquired during mid-diastole to yield accurate LA volumes. RESULTS: Phantom studies. LA volumes were accurately measured by CS-cineCMR with a mean difference of -4.73 ± 1.75 ml (-8.67 ± 3.54%, r2 = 0.94). For the new method the calculated volumes were not significantly different when different orientations of the CS-cineCMR slices were applied to cover the LA phantoms. Long-axis "aligned" vs "not aligned" with the phantom long-axis yielded similar differences vs the reference volume (-4.87 ± 1.73 ml vs. -4.45 ± 1.97 ml, p = 0.67) and short-axis "perpendicular" vs. "not-perpendicular" with the LA long-axis (-4.72 ± 1.66 ml vs. -4.75 ± 2.13 ml; p = 0.98). The conventional bi-plane area-length method was susceptible for slice orientations (p = 0.0085 for the interaction of "slice orientation" and "reconstruction technique", 2-way ANOVA for repeated measures). To use the 3D-HR-CMR as the reference for LA volumes in patients, it was validated in the LA phantoms (mean difference: -1.37 ± 1.35 ml, -2.38 ± 2.44%, r2 = 0.97). Patient study: The CS-cineCMR LA volumes of the mid-diastolic frame matched closely with the reference LA volume (measured by 3D-HR-CMR) with a difference of -2.66 ± 6.5 ml (3.0% underestimation; true LA volumes: 63 ml, 62 ml, and 395 ml). Finally, a high intra- and inter-observer agreement for maximal and minimal LA volume measurement is also shown. CONCLUSIONS: The proposed method combines a highly accelerated single-breathhold compressed-sensing multi-slice CMR technique with a non-model-based 3D reconstruction to accurately and reproducibly measure LA volumes and function.

Access to information in Switzerland : From secrecy to transparency

Access to information legislations are now present in over 50 countries world-wide. Lagging behind some of its own Cantons, the Swiss Federal government was until recently one of the few hold outs in Europe. But, in December 2004, the Confederation voted the 'Loi sur la Transparence de l'administration' or Law on Transparency (LTrans) a Law that came into effect in July 2006. This paper presents an overview of the new Law and underlines the main institutional challenges to its introduction in Switzerland.

Insight into oversight : how information commissioners contribute to the achievement of access-to-information policy objectives

This thesis examines how oversight bodies, as part of an ATI policy, contribute to the achievement of the policy's objectives. The aim of the thesis is to see how oversight bodies and the work they do affects the implementation of their respective ATI policies and thereby contributes to the objectives of those policies using a comparative case study approach. The thesis investigates how federal/central government level information commissioners in four jurisdictions - Germany, India, Scotland, and Switzerland - enforce their respective ATI policies, which tasks they carry out in addition to their enforcement duties, the challenges they face in their work and the ways they overcome these. Qualitative data were gathered from primary and secondary documents as well as in 37 semi-structured interviews with staff of the commissioners' offices, administrative officials whose job entails complying with ATI, people who have made ATI requests and appealed to their respective oversight body, and external experts who have studied ATI implementation in their particular jurisdiction. The thesis finds that while the aspect of an oversight body's formal independence that has the greatest impact on its work is resource control and that although the powers granted by law set the framework for ensuring that the administration is properly complying with the policy, the commissioner's leadership style - a component of informal independence - has more influence than formal attributes of independence in setting out how resources are obtained and used as well as how staff set priorities and utilize the powers they are granted by law. The conclusion, therefore, is that an ATI oversight body's ability to contribute to the achievement of the policy's objectives is a function of three main factors: a. commissioner's leadership style; b. adequacy of resources and degree of control the organization has over them; c. powers and the exercise of discretion in using them. In effect, the thesis argues that it is difficult to pinpoint the value of the formal powers set out for the oversight body in the ATI law, and that their decisions on whether and how to use them are more important than the presumed strength of the powers. It also claims that the choices made by the commissioners and their staff regarding priorities and use of powers are determined to a large extent by the adequacy of resources and the degree of control the organization has over those resources. In turn, how the head of the organization leads and manages the oversight body is crucial to both the adequacy of the organization's resources and the decisions made about the use of powers. Together, these three factors have a significant impact on the body's effectiveness in contributing to ATI objectives.

Altered processing of contextual information during fear extinction in PTSD: an fMRI study.

Medial prefrontal cortical areas have been hypothesized to underlie altered contextual processing in posttraumatic stress disorder (PTSD). We investigated brain signaling of contextual information in this disorder. Eighteen PTSD subjects and 16 healthy trauma-exposed subjects underwent a two-day fear conditioning and extinction paradigm. On day 1, within visual context A, a conditioned stimulus (CS) was followed 60% of the time by an electric shock (conditioning). The conditioned response was then extinguished (extinction learning) in context B. On day 2, recall of the extinction memory was tested in context B. Skin conductance response (SCR) and functional magnetic resonance imaging (fMRI) data were collected during context presentations. There were no SCR group differences in any context presentation. Concerning fMRI data, during late conditioning, when context A signaled danger, PTSD subjects showed dorsal anterior cingulate cortical (dACC) hyperactivation. During early extinction, when context B had not yet fully acquired signal value for safety, PTSD subjects still showed dACC hyperactivation. During late extinction, when context B had come to signal safety, they showed ventromedial prefrontal cortex (vmPFC) hypoactivation. During early extinction recall, when context B signaled safety, they showed both vmPFC hypoactivation and dACC hyperactivation. These findings suggest that PTSD subjects show alterations in the processing of contextual information related to danger and safety. This impairment is manifest even prior to a physiologically-measured, cue-elicited fear response, and characterized by hypoactivation in vmPFC and hyperactivation in dACC.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.