Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility.

Cardiac magnetic resonance (CMR) imaging allows precise non-invasive quantification of cardiac function. It requires reliable image segmentation for myocardial tissue. Clinically used software usually offers automatic approaches for this step. These are, however, designed for segmentation of human images obtained at clinical field strengths. They reach their limits when applied to preclinical data and ultrahigh field strength (such as CMR of pigs at 7 T). In our study, eleven animals (seven with myocardial infarction) underwent four CMR scans each. Short-axis cine stacks were acquired and used for functional cardiac analysis. End-systolic and end-diastolic images were labelled manually by two observers and inter- and intra-observer variability were assessed. Aiming to make the functional analysis faster and more reproducible, an established deep learning (DL) model for myocardial segmentation in humans was re-trained using our preclinical 7 T data (n = 772 images and labels). We then tested the model on n = 288 images. Excellent agreement in parameters of cardiac function was found between manual and DL segmentation: For ejection fraction (EF) we achieved a Pearson's r of 0.95, an Intraclass correlation coefficient (ICC) of 0.97, and a Coefficient of variability (CoV) of 6.6%. Dice scores were 0.88 for the left ventricle and 0.84 for the myocardium.

Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI.

Purpose To develop a deep learning model for increasing cardiac cine frame rate while maintaining spatial resolution and scan time. Materials and Methods A transformer-based model was trained and tested on a retrospective sample of cine images from 5840 patients (mean age, 55 years ± 19 [SD]; 3527 male patients) referred for clinical cardiac MRI from 2003 to 2021 at nine centers; images were acquired using 1.5- and 3-T scanners from three vendors. Data from three centers were used for training and testing (4:1 ratio). The remaining data were used for external testing. Cines with downsampled frame rates were restored using linear, bicubic, and model-based interpolation. The root mean square error between interpolated and original cine images was modeled using ordinary least squares regression. In a prospective study of 49 participants referred for clinical cardiac MRI (mean age, 56 years ± 13; 25 male participants) and 12 healthy participants (mean age, 51 years ± 16; eight male participants), the model was applied to cines acquired at 25 frames per second (fps), thereby doubling the frame rate, and these interpolated cines were compared with actual 50-fps cines. The preference of two readers based on perceived temporal smoothness and image quality was evaluated using a noninferiority margin of 10%. Results The model generated artifact-free interpolated images. Ordinary least squares regression analysis accounting for vendor and field strength showed lower error (P < .001) with model-based interpolation compared with linear and bicubic interpolation in internal and external test sets. The highest proportion of reader choices was "no preference" (84 of 122) between actual and interpolated 50-fps cines. The 90% CI for the difference between reader proportions favoring collected (15 of 122) and interpolated (23 of 122) high-frame-rate cines was -0.01 to 0.14, indicating noninferiority. Conclusion A transformer-based deep learning model increased cardiac cine frame rates while preserving both spatial resolution and scan time, resulting in images with quality comparable to that of images obtained at actual high frame rates. Keywords: Functional MRI, Heart, Cardiac, Deep Learning, High Frame Rate Supplemental material is available for this article. © RSNA, 2024.

Imaging of the brain-heart axis: prognostic value in a European setting.

BACKGROUND AND AIMS: Increasing data suggest that stress-related neural activity (SNA) is associated with subsequent major adverse cardiovascular events (MACE) and may represent a therapeutic target. Current evidence is exclusively based on populations from the U.S. and Asia where limited information about cardiovascular disease risk was available. This study sought to investigate whether SNA imaging has clinical value in a well-characterized cohort of cardiovascular patients in Europe. METHODS: In this single-centre study, a total of 963 patients (mean age 58.4 ± 16.1 years, 40.7% female) with known cardiovascular status, ranging from 'at-risk' to manifest disease, and without active cancer underwent 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography between 1 January 2005 and 31 August 2019. Stress-related neural activity was assessed with validated methods and relations between SNA and MACE (non-fatal stroke, non-fatal myocardial infarction, coronary revascularization, and cardiovascular death) or all-cause mortality by time-to-event analysis. RESULTS: Over a maximum follow-up of 17 years, 118 individuals (12.3%) experienced MACE, and 270 (28.0%) died. In univariate analyses, SNA significantly correlated with an increased risk of MACE (sub-distribution hazard ratio 1.52, 95% CI 1.05-2.19; P = .026) or death (hazard ratio 2.49, 95% CI 1.96-3.17; P < .001). In multivariable analyses, the association between SNA imaging and MACE was lost when details of the cardiovascular status were added to the models. Conversely, the relationship between SNA imaging and all-cause mortality persisted after multivariable adjustments. CONCLUSIONS: In a European patient cohort where cardiovascular status is known, SNA imaging is a robust and independent predictor of all-cause mortality, but its prognostic value for MACE is less evident. Further studies should define specific patient populations that might profit from SNA imaging.

AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning.

In cardiac cine imaging, acquiring high-quality data is challenging and time-consuming due to the artifacts generated by the heart's continuous movement. Volumetric, fully isotropic data acquisition with high temporal resolution is, to date, intractable due to MR physics constraints. To assess whole-heart movement under minimal acquisition time, we propose a deep learning model that reconstructs the volumetric shape of multiple cardiac chambers from a limited number of input slices while simultaneously optimizing the slice acquisition orientation for this task. We mimic the current clinical protocols for cardiac imaging and compare the shape reconstruction quality of standard clinical views and optimized views. In our experiments, we show that the jointly trained model achieves accurate high-resolution multi-chamber shape reconstruction with errors of <13 mm HD95 and Dice scores of >80%, indicating its effectiveness in both simulated cardiac cine MRI and clinical cardiac MRI with a wide range of pathological shape variations.

Patient-independent, MHD-robust R-peak detection for retrospective gating in cardiac MRI imaging.

Objective.In cardiovascular magnetic resonance imaging, synchronization of image acquisition with heart motion (calledgating) is performed by detecting R-peaks in electrocardiogram (ECG) signals. Effective gating is challenging with 3T and 7T scanners, due to severe distortion of ECG signals caused by magnetohydrodynamic effects associated with intense magnetic fields. This work proposes an efficient retrospective gating strategy that requires no prior training outside the scanner and investigates the optimal number of leads in the ECG acquisition set.Approach.The proposed method was developed on a data set of 12-lead ECG signals acquired within 3T and 7T scanners. Independent component analysis is employed to effectively separate components related with cardiac activity from those associated to noise. Subsequently, an automatic selection process identifies the components best suited for accurate R-peak detection, based on heart rate estimation metrics and frequency content quality indexes.Main results.The proposed method is robust to different B0 field strengths, as evidenced by R-peak detection errors of 2.4 ± 3.1 ms and 10.6 ± 15.4 ms for data acquired with 3T and 7T scanners, respectively. Its effectiveness was verified with various subject orientations, showcasing applicability in diverse clinical scenarios. The work reveals that ECG leads can be limited in number to three, or at most five for 7T field strengths, without significant degradation in R-peak detection accuracy.Significance.The approach requires no preliminary ECG acquisition for R-peak detector training, reducing overall examination time. The gating process is designed to be adaptable, completely blind and independent of patient characteristics, allowing wide and rapid deployment in clinical practice. The potential to employ a significantly limited set of leads enhances patient comfort.

Light-Sheet Imaging to Reveal Cardiac Structure in Rodent Hearts.

Light-sheet microscopy (LSM) plays a pivotal role in comprehending the intricate three-dimensional (3D) structure of the heart, providing crucial insights into fundamental cardiac physiology and pathologic responses. We hereby delve into the development and implementation of the LSM technique to elucidate the micro-architecture of the heart in mouse models. The methodology integrates a customized LSM system with tissue clearing techniques, mitigating light scattering within cardiac tissues for volumetric imaging. The combination of conventional LSM with image stitching and multiview deconvolution approaches allows for the capture of the entire heart. To address the inherent trade-off between axial resolution and field of view (FOV), we further introduce an axially swept light-sheet microscopy (ASLM) method to minimize out-of-focus light and uniformly illuminate the heart across the propagation direction. In the meanwhile, tissue clearing methods such as iDISCO enhance light penetration, facilitating the visualization of deep structures and ensuring a comprehensive examination of the myocardium throughout the entire heart. The combination of the proposed LSM and tissue clearing methods presents a promising platform for researchers in resolving cardiac structures in rodent hearts, holding great potential for the understanding of cardiac morphogenesis and remodeling.

Intracortical brain-heart interplay: An EEG model source study of sympathovagal changes.

The interplay between cerebral and cardiovascular activity, known as the functional brain-heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain-heart axis have been proposed to estimate BHI non-invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies. This study proposes an analytical modeling framework for estimating the BHI at the source-brain level. This analysis relies on the low-resolution electromagnetic tomography sources localization from scalp electrophysiological recordings. BHI is then quantified as the functional correlation between the intracortical sources and cardiovascular dynamics. Using this approach, we aimed to evaluate the reliability of BHI estimates derived from source-localized EEG signals as compared with prior findings from neuroimaging methods. The proposed approach is validated using an experimental dataset gathered from 32 healthy individuals who underwent standard sympathovagal elicitation using a cold pressor test. Additional resting state data from 34 healthy individuals has been analysed to assess robustness and reproducibility of the methodology. Experimental results not only confirmed previous findings on activation of brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, and anterior and mid-cingulate cortices) but also provided insights into the anatomical bases of brain-heart axis. In particular, we show that the bidirectional activity of electrophysiological pathways of functional brain-heart communication increases during cold pressure with respect to resting state, mainly targeting neural oscillations in the δ $$ \delta $$ , ß $$ \beta $$ , and γ $$ \gamma $$ bands. The proposed approach offers new perspectives for the investigation of functional BHI that could also shed light on various pathophysiological conditions.

Genetic diversity, growth and heart function of Auckland Island pigs, a potential source for organ xenotransplantation.

One of the prerequisites for successful organ xenotransplantation is a reasonable size match between the porcine organ and the recipient's organ to be replaced. Therefore, the selection of a suitable genetic background of source pigs is important. In this study, we investigated body and organ growth, cardiac function, and genetic diversity of a colony of Auckland Island pigs established at the Center for Innovative Medical Models (CiMM), LMU Munich. Male and female Auckland Island pig kidney cells (selected to be free of porcine endogenous retrovirus C) were imported from New Zealand, and founder animals were established by somatic cell nuclear transfer (SCNT). Morphologically, Auckland Island pigs have smaller body stature compared to many domestic pig breeds, rendering their organ dimensions well-suited for human transplantation. Furthermore, echocardiography assessments of Auckland Island pig hearts indicated normal structure and functioning across various age groups throughout the study. Single nucleotide polymorphism (SNP) analysis revealed higher runs of homozygosity (ROH) in Auckland Island pigs compared to other domestic pig breeds and demonstrated that the entire locus coding the swine leukocyte antigens (SLAs) was homozygous. Based on these findings, Auckland Island pigs represent a promising genetic background for organ xenotransplantation.

Myocardial native T1 mapping at 3T cardiac magnetic resonance-closing the full-vendor reporting cycle of normal values.

AIM: Cardiac magnetic resonance is currently an indispensable tool in the diagnosis of cardiac pathologies, with mapping techniques being one of the most recent advances in this area. T1 mapping is a robust tool that uses the T1 magnetic relaxation time as a quantitative marker of myocardial tissue composition. However, multiple T1 mapping sequences are used, and data comparing them, especially on different vendors, is limited. This study aims to determine the T1 relaxation values in the cardiac muscle of healthy individuals using GE's Discovery 3T scanner, allowing the use of the T1 mapping technique in patients on a sustained basis. MATERIAL AND METHODS: Thirty-one healthy volunteers were submitted to T1 mapping at 3T magnetic resonance imaging (MRI) equipment, with 3 being excluded from the analysis (54% women; mean age: 39.2 ± 13.9 years). The MOLLI 5(3)3 sequence was used, acquiring one short axis slice at midventricular level. Native T1 values were presented as means (± standard deviation), and t-student independent samples tests evaluated gender differences in T1 values. RESULTS: The results show an average global native T1 value of 1193 ± 39 ms, with women's values being statistically higher than men (1211 ± 40 vs 1173 ± 27 ms, respectively, p<0.006). Gender remained the only determinant of native T1 times on a multiple linear regression model that included age, ejection fraction, and T2 status. CONCLUSION: This study has established one of the few native T1 values for a 3T GE Discovery scanner that are on par with those already reported by other vendors for a similar sequence, closing the circle in full-vendor reporting.

Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography.

Objective.Cardiac computed tomography (CT) is widely used for diagnosis of cardiovascular disease, the leading cause of morbidity and mortality in the world. Diagnostic performance depends strongly on the temporal resolution of the CT images. To image the beating heart, one can reduce the scanning time by acquiring limited-angle projections. However, this leads to increased image noise and limited-angle-related artifacts. The goal of this paper is to reconstruct high quality cardiac CT images from limited-angle projections.Approach. The ability to reconstruct high quality images from limited-angle projections is highly desirable and remains a major challenge. With the development of deep learning networks, such as U-Net and transformer networks, progresses have been reached on image reconstruction and processing. Here we propose a hybrid model based on the U-Net and Swin-transformer (U-Swin) networks. The U-Net has the potential to restore structural information due to missing projection data and related artifacts, then the Swin-transformer can gather a detailed global feature distribution.Main results. Using synthetic XCAT and clinical cardiac COCA datasets, we demonstrate that our proposed method outperforms the state-of-the-art deep learning-based methods.Significance. It has a great potential to freeze the beating heart with a higher temporal resolution.

Multi-residual 2D network integrating spatial correlation for whole heart segmentation.

Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy, modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on cardiac CT images, as well as the fast inference speed and low graphics processing unit (GPU) memory consumption required by practical clinical applications. Thus, we propose a multi-residual two-dimensional (2D) network integrating spatial correlation for WHS. The network performs slice-by-slice segmentation on three-dimensional cardiac CT images in a 2D encoder-decoder manner. In the network, a convolutional long short-term memory skip connection module is designed to perform spatial correlation feature extraction on the feature maps at different resolutions extracted by the sub-modules of the pre-trained ResNet-based encoder. Moreover, a decoder based on the multi-residual module is designed to analyze the extracted features from the perspectives of multi-scale and channel attention, thereby accurately delineating the various substructures of the heart. The proposed method is verified on a dataset of the multi-modality WHS challenge, an in-house WHS dataset, and a dataset of the abdominal organ segmentation challenge. The dice, Jaccard, average symmetric surface distance, Hausdorff distance, inference time, and maximum GPU memory of the WHS are 0.914, 0.843, 1.066 mm, 15.778 mm, 9.535 s, and 1905 MB, respectively. The proposed network has high accuracy, fast inference speed, minimal GPU memory consumption, strong robustness, and good generalization. It can be deployed to clinical practical applications for WHS and can be effectively extended and applied to other multi-organ segmentation fields. The source code is publicly available at https://github.com/nancy1984yan/MultiResNet-SC.

Manifold-based denoising for Ferumoxytol-enhanced 3D cardiac cine MRI.

The two-dimensional (2D) cine cardiovascular magnetic resonance (CMR) technique is the reference standard for assessing cardiac function. However, one challenge with 2D cine is that the acquisition time for the whole cine stack is long and requires multiple breath holds, which may not be feasible for pediatric or ill patients. Though single breath-hold multi-slice cine may address the issue, it can only acquire low-resolution images, and hence, affect the accuracy of cardiac function assessment. To address these challenges, a Ferumoxytol-enhanced, free breathing, isotropic high-resolution 3D cine technique was developed. The method produces high-contrast cine images with short acquisition times by using compressed sensing together with a manifold-based method for image denoising. This study included fifteen patients (9.1 $ \pm $ 5.6 yrs.) who were referred for clinical cardiovascular magnetic resonance imaging (MRI) with Ferumoxytol contrast and were prescribed the 3D cine sequence. The data was acquired on a 1.5T scanner. Statistical analysis shows that the manifold-based denoised 3D cine can accurately measure ventricular function with no significant differences when compared to the conventional 2D breath-hold (BH) cine. The multiplanar reconstructed images of the proposed 3D cine method are visually comparable to the golden standard 2D BH cine method in terms of clarity, contrast, and anatomical precision. The proposed method eliminated the need for breath holds, reduced scan times, enabled multiplanar reconstruction within an isotropic data set, and has the potential to be used as an effective tool to access cardiovascular conditions.

Self-supervised learning for medical image data with anatomy-oriented imaging planes.

Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work rarely paid attention to data with anatomy-oriented imaging planes, e.g., standard cardiac magnetic resonance imaging views. As these imaging planes are defined according to the anatomy of the imaged organ, pretext tasks effectively exploiting this information can pretrain the networks to gain knowledge on the organ of interest. In this work, we propose two complementary pretext tasks for this group of medical image data based on the spatial relationship of the imaging planes. The first is to learn the relative orientation between the imaging planes and implemented as regressing their intersecting lines. The second exploits parallel imaging planes to regress their relative slice locations within a stack. Both pretext tasks are conceptually straightforward and easy to implement, and can be combined in multitask learning for better representation learning. Thorough experiments on two anatomical structures (heart and knee) and representative target tasks (semantic segmentation and classification) demonstrate that the proposed pretext tasks are effective in pretraining deep networks for remarkably boosted performance on the target tasks, and superior to other recent approaches.

Automatic multi-view pose estimation in focused cardiac ultrasound.

Focused cardiac ultrasound (FoCUS) is a valuable point-of-care method for evaluating cardiovascular structures and function, but its scope is limited by equipment and operator's experience, resulting in primarily qualitative 2D exams. This study presents a novel framework to automatically estimate the 3D spatial relationship between standard FoCUS views. The proposed framework uses a multi-view U-Net-like fully convolutional neural network to regress line-based heatmaps representing the most likely areas of intersection between input images. The lines that best fit the regressed heatmaps are then extracted, and a system of nonlinear equations based on the intersection between view triplets is created and solved to determine the relative 3D pose between all input images. The feasibility and accuracy of the proposed pipeline were validated using a novel realistic in silico FoCUS dataset, demonstrating promising results. Interestingly, as shown in preliminary experiments, the estimation of the 2D images' relative poses enables the application of 3D image analysis methods and paves the way for 3D quantitative assessments in FoCUS examinations.

Advanced maternal age alters cardiac functional and structural adaptations to pregnancy in rats.

A significant number of pregnancies occur at advanced maternal age (>35 yr), which is a risk factor for pregnancy complications. Healthy pregnancies require massive hemodynamic adaptations, including an increased blood volume and cardiac output. There is growing evidence that these cardiovascular adaptations are impaired with age, however, little is known about maternal cardiac function with advanced age. We hypothesized that cardiac adaptations to pregnancy are impaired with advanced maternal age. Younger (4 mo; ∼early reproductive maturity in humans) and aged (9 mo; ∼35 yr in humans) pregnant Sprague-Dawley rats were assessed and compared with age-matched nonpregnant controls. Two-dimensional echocardiographic images were obtained (ultrasound biomicroscopy; under anesthesia) on gestational day 19 (term = 22 days) and compared with age-matched nonpregnant rats (n = 7-9/group). Left ventricular structure and function were assessed using short-axis images and transmitral Doppler signals. During systole, left ventricular anterior wall thickness increased with age in the nonpregnant rats, but there was no age-related difference between the pregnant groups. There were no significant pregnancy-associated differences in left ventricular wall thickness. Calculated left ventricular mass increased with age in nonpregnant rats and increased with pregnancy only in young rats. Compared with young pregnant rats, the aortic ejection time of aged pregnant rats was greater and Tei index was lower. Overall, the greater aortic ejection time and lower Tei index with age in pregnant rats suggest mildly altered cardiac adaptations to pregnancy with advanced maternal age, which may contribute to adverse outcomes in advanced maternal age pregnancies.NEW & NOTEWORTHY We demonstrated that even before the age of reproductive senescence, rats show signs of age-related alterations in cardiac structure that suggests increased cardiac work. Our data also demonstrate, using an in vivo echocardiographic approach, that advanced maternal age in a rat model is associated with altered cardiac function and structure relative to younger pregnant controls.

Cardiac 2-D Shear Wave Imaging Using a New Dedicated Clinical Ultrasound System: A Phantom Study.

OBJECTIVE: The purpose of this study was to assess cardiac shear wave imaging implemented in a new MACH 30 ultrasound machine (SuperSonic Imaging, Aix-en-Provence, France) and interfaced with a linear probe and a phased array probe, in comparison with a previously validated Aixplorer system connected to a linear probe (SuperSonic Imaging) using Elasticity QA phantoms (Models 039 and 049, CIRS Inc., Norfolk, VA, USA). METHODS: Quantile-quantile plots were used for distribution agreement. The accuracy of stiffness measurement was assessed by the percentage error and the mean percentage error (MPE), and its homogeneity, by the standard deviation of the MPE. A p value <0.01 was considered to indicate statistical significance. RESULTS: The accuracy of dedicated cardiac sequences for linear probes was similar for the two systems with an MPE of 8 ± 14% versus 20 ± 21% (p = not significant) with the SuperSonic MACH 30 and Aixplorer, respectively, and was influenced by target stiffness and location of the measurement in the field of view, but without drift over time. The optimal transthoracic cardiac probe workspace was located between 4 and 10 cm, with an MPE of 29.5 ± 25% compared with 93.3 ± 130% outside this area (p < 0.0001). In this area, stiffness below 20 kPa was significantly different from the reference (p < 0.0001). The sectorial probe revealed no MPE difference in any of the measurement areas, with no significant lateral or axial gradient. CONCLUSION: The new Supersonic MACH 30 system upgraded with a sectorial probe and specific cardiac settings provided homogenous stiffness measurements, especially when operating at depths between 4 and 10 cm. These phantom results may be useful in designing future in vivo studies.

STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution.

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively capture long-range or non-local features due to their limited receptive fields. Optical flow estimators are also commonly used to align neighboring frames, which may cause information loss and inaccurate motion estimation. Additionally, pre-warping strategies may involve interpolation, leading to potential loss of texture details and complicated anatomical structures. To overcome these challenges, we propose a novel Spatial-Temporal Attention-Guided Dual-Path Network (STADNet) for cardiac cine MRI super-resolution. We utilize transformers to model long-range dependencies in cardiac cine MR images and design a cross-frame attention module in the location-aware spatial path, which enhances the spatial details of the current frame by using complementary information from neighboring frames. We also introduce a recurrent flow-enhanced attention module in the motion-aware temporal path that exploits the correlation between cine MRI frames and extracts the motion information of the heart. Experimental results demonstrate that STADNet outperforms SOTA approaches and has significant potential for clinical practice.

Post-hoc standardisation of parametric T1 maps in cardiovascular magnetic resonance imaging: a proof-of-concept.

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.