The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation; 2) inter-slice motion detection; 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method—integrating both regression and structured classification models—to extract landmarks and probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank and on 100 cases from the UK Digital Heart Project and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88 and 99 for heart coverage estimation and 85 and 95 for motion detection), allowing their exclusion from the analyzed dataset or the triggering of a new acquisition.
In the past, several research efforts have been made towards the correct quantification of signal-to-noise (SNR) or contrast-to-noise (CNR) ratios in MR images @cite_15 . However, modern acquisition techniques making use of parallel imaging produce images with spatially-varying noise distributions, rendering image-based estimators unreliable @cite_4 . To overcome this limitation, more elaborate methods have been proposed exploiting information about coil sensitivity or reconstruction coefficients @cite_8 . Unfortunately, these data are very often not available, making the estimation of noise, and consequently of SNR and CNR, practically unfeasible in most scenarios. At the same time, image contrast between two objects - simply defined as the difference between their signal intensity - has long been used to determine their visual differentiability in the acquired MR image @cite_13 . In CMR imaging, images with poor contrast between the LV cavity and myocardium can potentially hinder the assessment of cardiac structure and function: consequently, contrast estimation in the cardiac region can provide a useful metric for quality control purposes, either triggering the use of contrast-enhancing techniques or a new acquisition.
@cite_13 @cite_15 @cite_4 @cite_8
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Magnetic resonance imaging pulse sequences are frequently judged by their ability to facilitate discrimination between pathologic and normal tissue. Objective analysis is usually based on signal intensity measurements. However, the literature shows disagreement as to how this analysis should be performed. The ability to visually differentiate two objects on the basis of signal intensity depends on the contrast-to-noise ratio (CNR). This parameter, however, can be calculated only by measuring the intensity of photons reaching the eye from two distinct objects and, hence, is display dependent. The signal difference-to-noise ratio (SDNR) is a display-independent parameter that reflects the contrast-generating ability of a pulse sequence. When comparing two imaging sequences, the SDNR is proportional to the CNR, assuming the images being compared are displayed so that corresponding regions have the same intensity (i.e., photon fluxes). Because the SDNR is display independent, it should be the preferred parame... Purpose To evaluate the validity of different approaches to determine the signal-to-noise ratio (SNR) in MRI experiments with multi-element surface coils, parallel imaging, and different reconstruction filters. Materials and Methods Four different approaches of SNR calculation were compared in phantom measurements and in vivo based on: 1) the pixel-by-pixel standard deviation (SD) in multiple repeated acquisitions; 2) the signal statistics in a difference image; and 3) and 4) the statistics in two separate regions of a single image employing either the mean value or the SD of background noise. Different receiver coil systems (with one and eight channels), acquisitions with and without parallel imaging, and five different reconstruction filters were compared. Results Averaged over all phantom measurements, the deviations from the reference value provided by the multiple-acquisitions method are 2.7 (SD 1.6 ) for the difference method, 37.7 (25.9 ) for the evaluation of the mean value of background noise, and 34.0 (38.1 ) for the evaluation of the SD of background noise. Conclusion The conventionally determined SNR based on separate signal and noise regions in a single image will in general not agree with the true SNR measured in images after the application of certain reconstruction filters, multichannel reconstruction, or parallel imaging. J. Magn. Reson. Imaging 2007. © 2007 Wiley-Liss, Inc. Incorrect ECG gating of cardiac magnetic resonance (CMR) acquisitions can lead to artefacts, which hampers the accuracy of diagnostic imaging. Therefore, there is a need for robust reconstruction methods to ensure high image quality. In this paper, we propose a method to automatically correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data. Our method is based on the Automap reconstruction method, which directly reconstructs high quality MR images from k-space using deep learning. Our main methodological contribution is the addition of an adversarial element to this architecture, in which the quality of image reconstruction (the generator) is increased by using a discriminator. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted CMR k-space data and uncorrupted reconstructed images. Using 25000 images from the UK Biobank dataset we achieve good image quality in the presence of synthetic motion artefacts, but some structural information was lost. We quantitatively compare our method to a standard inverse Fourier reconstruction. In addition, we qualitatively evaluate the proposed technique using k-space data containing real motion artefacts. Abstract A fundamental step in the diagnosis of cardiovascular diseases, automatic left ventricle (LV) segmentation in cardiac magnetic resonance images (MRIs) is still acknowledged to be a difficult problem. Most of the existing algorithms require either extensive training or intensive user inputs. This study investigates fast detection of the left ventricle (LV) endo- and epicardium surfaces in cardiac MRI via convex relaxation and distribution matching. The algorithm requires a single subject for training and a very simple user input, which amounts to a single point (mouse click) per target region (cavity or myocardium). It seeks cavity and myocardium regions within each 3D phase by optimizing two functionals, each containing two distribution-matching constraints: (1) a distance-based shape prior and (2) an intensity prior. Based on a global measure of similarity between distributions, the shape prior is intrinsically invariant with respect to translation and rotation. We further introduce a scale variable from which we derive a fixed-point equation (FPE), thereby achieving scale-invariance with only few fast computations. The proposed algorithm relaxes the need for costly pose estimation (or registration) procedures and large training sets, and can tolerate shape deformations, unlike template (or atlas) based priors. Our formulation leads to a challenging problem, which is not directly amenable to convex-optimization techniques. For each functional, we split the problem into a sequence of sub-problems, each of which can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Unlike related graph-cut approaches, the proposed convex-relaxation solution can be parallelized to reduce substantially the computational time for 3D domains (or higher), extends directly to high dimensions, and does not have the grid-bias problem. Our parallelized implementation on a graphics processing unit (GPU) demonstrates that the proposed algorithm requires about 3.87 s for a typical cardiac MRI volume, a speed-up of about five times compared to a standard implementation. We report a performance evaluation over 400 volumes acquired from 20 subjects, which shows that the obtained 3D surfaces correlate with independent manual delineations. We further demonstrate experimentally that (1) the performance of the algorithm is not significantly affected by the choice of the training subject and (2) the shape description we use does not change significantly from one subject to another. These results support the fact that a single subject is sufficient for training the proposed algorithm.