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.
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... In this paper, a new object-based method to estimate noise in magnitude MR images is proposed. The main advantage of this object-based method is its robustness to background artefacts such as ghosting. The proposed method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The MAD is a robust and efficient estimator initially proposed to estimate Gaussian noise. In this work, the adaptation of MAD operator for Rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the SNR of the image. During the evaluation, a comparison of the proposed method with several state-of-the-art methods is performed. A quantitative validation on synthetic phantom with and without artefacts is presented. A new validation framework is proposed to perform quantitative validation on real data. The impact of the accuracy of noise estimation on the performance of a denoising filter is also studied. The results obtained on synthetic images show the accuracy and the robustness of the proposed method. Within the validation on real data, the proposed method obtained very competitive results compared to the methods under study. 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. article i nfo Article history: Parallel imaging methods allow to increase the acquisition rate via subsampled acquisitions of the k- space. SENSE and GRAPPA are the most popular reconstruction methods proposed in order to suppress the artifacts created by this subsampling. The reconstruction process carried out by both methods yields to a variance of noise value which is dependent on the position within the fi nal image. Hence, the traditional noise estimation methods - based on a single noise level for the whole image - fail. In this paper we propose a novel methodology to estimate the spatial dependent pattern of the variance of noise in SENSE and GRAPPA reconstructed images. In both cases, some additional information must be known beforehand: the sensitivity maps of each receiver coil in the SENSE case and the reconstruction coefficients for GRAPPA.