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Summary: Background: In cardiovascular magnetic resonance imaging parametric T1 mapping lacks universally valid reference values. This limits its extensive use in the clinical routine. The aim of this work was the introduction of our self-developed Magnetic Resonance Imaging Software for Standardization (MARISSA) as a post-hoc standardisation approach. Methods: Our standardisation approach minimises the bias of confounding parameters (CPs) on the base of regression models. 214 healthy subjects with 814 parametric T1 maps were used for training those models on the CPs: age, gender, scanner and sequence. The training dataset included both sex, eleven different scanners and eight different sequences. The regression model type and four other adjustable standardisation parameters were optimised among 240 tested settings to achieve the lowest coefficient of variation, as measure for the inter-subject variability, in the mean T1 value across the healthy test datasets (HTE, N = 40, 156 T1 maps). The HTE were then compared to 135 patients with left ventricular hypertrophy including hypertrophic cardiomyopathy (HCM, N = 112, 121 T1 maps) and amyloidosis (AMY, N = 24, 24 T1 maps) after applying the best performing standardisation pipeline (BPSP) to evaluate the diagnostic accuracy. Findings: The BPSP reduced the COV of the HTE from 12.47% to 5.81%. Sensitivity and specificity reached 95.83% / 91.67% between HTE and AMY, 71.90% / 72.44% between HTE and HCM, and 87.50% / 98.35% between HCM and AMY. Interpretation: Regarding the BPSP, MARISSA enabled the comparability of T1 maps independently of CPs while keeping the discrimination of healthy and patient groups as found in literature. Funding: This study was supported by the BMBF / DZHK.

BackgroundCardiac function quantification in cardiovascular magnetic resonance requires precise contouring of the heart chambers. This time-consuming task is increasingly being addressed by a plethora of ever more complex deep learning methods. However, only a small fraction of these have made their way from academia into clinical practice. In the quality assessment and control of medical artificial intelligence, the opaque reasoning and associated distinctive errors of neural networks meet an extraordinarily low tolerance for failure.AimThe aim of this study is a multilevel analysis and comparison of the performance of three popular convolutional neural network (CNN) models for cardiac function quantification.MethodsU-Net, FCN, and MultiResUNet were trained for the segmentation of the left and right ventricles on short-axis cine images of 119 patients from clinical routine. The training pipeline and hyperparameters were kept constant to isolate the influence of network architecture. CNN performance was evaluated against expert segmentations for 29 test cases on contour level and in terms of quantitative clinical parameters. Multilevel analysis included breakdown of results by slice position, as well as visualization of segmentation deviations and linkage of volume differences to segmentation metrics via correlation plots for qualitative analysis.ResultsAll models showed strong correlation to the expert with respect to quantitative clinical parameters (rz′ = 0.978, 0.977, 0.978 for U-Net, FCN, MultiResUNet respectively). The MultiResUNet significantly underestimated ventricular volumes and left ventricular myocardial mass. Segmentation difficulties and failures clustered in basal and apical slices for all CNNs, with the largest volume differences in the basal slices (mean absolute error per slice: 4.2 ± 4.5 ml for basal, 0.9 ± 1.3 ml for midventricular, 0.9 ± 0.9 ml for apical slices). Results for the right ventricle had higher variance and more outliers compared to the left ventricle. Intraclass correlation for clinical parameters was excellent (≥0.91) among the CNNs.ConclusionModifications to CNN architecture were not critical to the quality of error for our dataset. Despite good overall agreement with the expert, errors accumulated in basal and apical slices for all models.

Respiratory and cardiac motion can strongly impair cardiac PET image quality and tracer uptake quantification. Standard gating techniques can minimize these motion artefacts but suffer from low signal-to-noise ratio because only a small percentage of the total data is utilized. Motion correction approaches have been proposed to overcome this problem but require accurate knowledge of such physiological motion. Here we present a joint PET-MR motion estimation approach which combines complimentary dynamic image information from simultaneously acquired MR and PET to ensure improved cardiac and respiratory motion estimation for motion-corrected image reconstruction (MCIR) of PET images. A 3D triple-echo Dixon MR scan is used both for calculation of MR-based attenuation correction (AC) maps and estimation of physiological motion. PET listmode data is obtained simultaneously to the MR acquisition which is used for a joint motion estimation and reconstruction of the final MCIR PET. In a first step, dynamic cardiac and respiratory motion resolved 4D MR and PET images are reconstructed. These image series are used in a joint image registration to estimate non-rigid cardiac and respiratory motion fields. In a second step, the motion fields are utilized in a MR MCIR to obtain cardiac and respiratory resolved dynamic MR-based AC maps. In the last step, the non-rigid motion fields and the dynamic AC maps are applied in a PET MCIR to obtain the final motion-corrected PET images. PET-MR data has been obtained in six patients without any known heart disease. Motion amplitudes were between 5.6 and 16 mm, with higher values in the basal compared to the mid-ventricular and apical segments. The proposed joint PET-MR motion estimation provided more accurate motion estimation than using either modality separately. The underestimation of PET uptake due to respiratory and cardiac motion artefacts in the AC maps was up to 17%. The average increase in uptake values using MCIR was 23%  ±  10% (p  <  0.0001), with values of 28%  ±  11% (p  <  0.0001) for basal, 21%  ±  8% (p  <  0.0001) for mid-cavity and 17%  ±  7% (p  <  0.0001) for apical segments. With the proposed scheme we could ensure high PET image quality and improve local PET uptake quantification by up to 30%. Attenuation correction and motion information was obtained from the same PET-MR raw data, which was obtained during free-breathing to minimize scan times and to increase patient comfort. [ABSTRACT FROM AUTHOR]

High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times. Here we present a novel compressed sensing (CS) reconstruction approach using shearlets as a sparsifying transform allowing for fast 3D CMR (3DShearCS) using 3D radial phase encoding (RPE). An iterative reweighting scheme was applied during image reconstruction to ensure fast convergence and high image quality. Shearlets are mathematically optimal for a simplified model of natural images and have been proven to be more efficient than classical systems such as wavelets. 3DShearCS was compared to three other commonly used reconstruction approaches. Image quality was assessed quantitatively using general image quality metrics and using clinical diagnostic scores from expert reviewers. The proposed technique had lower relative errors, higher structural similarity and higher diagnostic scores compared to the other reconstruction techniques especially for high undersampling factors, i.e. short scan times. 3DShearCS provided ensured accurate depiction of cardiac anatomy for fast imaging and could help to promote 3D high-resolution CMR in clinical practice. [ABSTRACT FROM AUTHOR]

Quantification of myocardial perfusion by contrast-enhanced cardiovascular magnetic resonance imaging (CMR) aims for an observer independent and reproducible risk assessment of cardiovascular disease. Currently, the data used for the pixel-wise analysis of cardiac perfusion are either filtered prior to a fitting procedure, which inherently reduces the spatial resolution of data; or all pixels are considered without any regularization or prior filtering, which yields an unstable fit in the presence of low signal-to-noise ratio. Here, we propose a new pixel-wise analysis based on spatial Tikhonov regularization which exploits the spatial smoothness of the data and ensures accurate quantification even for images with low signal-to-noise ratio. The regularization parameter is determined automatically by an L-curve criterion. We study the performance of our method on a numerical phantom and demonstrate that the method reduces significantly the root-mean square error in the perfusion estimate compared to a non-regularized fit. In patient data our method allows us to recover the myocardial perfusion and to distinguish between healthy and ischemic regions. [ABSTRACT FROM AUTHOR]

Respiratory motion during cardiac PET acquisitions can cause image blurring and erroneous uptake quantification. In particular the misalignment of attenuation correction (AC) maps and PET emission data can lead to severe quantification errors, because the AC value of the heart is five times higher than of the surrounding lung tissue. Standard PET-MR approaches assume accurate alignment between breathhold MR-based AC maps and free-breathing PET emission data but cannot necessarily ensure it. Here we propose a 75 s free-breathing MR-acquisition, which provides respiratory-resolved AC maps (ACDyn) and non-rigid respiratory motion information. This approach ensures accurate AC for free-breathing PET data and the motion information can be utilized to reduce image blurring caused by respiratory motion. 3D multi-echo MR data was acquired during a 75 s free-breathing scan in six patients. Both a respiratory-resolved dynamic AC map (ACDyn) and a non-rigid respiratory motion field are provided by the MR scan. ACDyn yielded AC values for different breathing phases ensuring accurate AC for each respiratory phase of the free-breathing PET data. In addition, motion-corrected image reconstruction (MCIR) of MR and PET data was used to minimize breathing artefacts. Motion amplitudes in the left ventricle were 8.2  ±  2.9 mm with a dominant motion direction along the anterior-anterolateral and inferior-inferoseptal axis of the heart. The proposed ACDyn-MCIR technique led to significant signal recovery of PET tracer uptake by 24  ±  5% (p  <  0.05). The maximum improvement was observed in patients with large misalignment between standard breathhold MR-based AC maps and PET emission data. PET image resolution was improved by 20  ±  12% (p  <  0.05). We have presented an efficient MR-scan, which ensures accurate motion information and AC values to improve PET quantification for cardiac PET-MR scans. The short scan time of 75 s makes this free-breathing acquisition easy to integrate into standard clinical PET-MR protocols. [ABSTRACT FROM AUTHOR]

An experimental design for noninvasive assessment of neural retinal tissue function with enhanced sensitivity is presented. By matching the response detection to a defined flicker frequency stimulus similar to heterodyne detection, the response signal will be shifted out of the low-frequency noise and the specificity of response detection will be strongly enhanced. Optimal measurement parameters are discussed, such as the function and timing of the response function to a single flash stimulus. The results indicate responses on the order of 200 msthat have been probed with our frequency-encoded approach using 5 Hzflickering. Preliminary results indicate the feasibility of our measurement concept to assess small changes in reflectivity with enhanced sensitivity. A functional tomogram for response localization and quantification is introduced. [ABSTRACT FROM AUTHOR]