Virtual non-iodine photon-counting CT-angiography for aortic valve calcification scoring.

Photon-counting detector (PCD)-CT allows for reconstruction of virtual non-iodine (VNI) images from contrast-enhanced datasets. This study assesses the diagnostic performance of aortic valve calcification scoring (AVCS) derived from VNI datasets generated with a 1st generation clinical dual-source PCD-CT. AVCS was evaluated in 123 patients (statistical analysis only comprising patients with aortic valve calcifications [n = 56; 63.2 ± 11.6 years]), who underwent contrast enhanced electrocardiogram-gated (either prospective or retrospective or both) cardiac CT on a clinical PCD system. Patient data was reconstructed at 70 keV employing a VNI reconstruction algorithm. True non-contrast (TNC) scans at 70 keV without quantum iterative reconstruction served as reference in all individuals. Subgroup analysis was performed in 17 patients who received both, prospectively and retrospectively gated contrast enhanced scans (n = 8 with aortic valve calcifications). VNI images with prospective/retrospective gating had an overall sensitivity of 69.2%/56.0%, specificity of 100%/100%, accuracy of 85.4%/81.0%, positive predictive value of 100%/100%, and a negative predictive value of 78.2%/75.0%. VNI images with retrospective gating achieved similar results. For both gating approaches, AVCSVNI showed high correlation (r = 0.983, P < 0.001 for prospective; r = 0.986, P < 0.001 for retrospective) with AVCSTNC. Subgroup analyses demonstrated excellent intra-individual correlation between different acquisition modes (r = 0.986, P < 0.001). Thus, VNI images derived from cardiac PCD-CT allow for excellent diagnostic performance in the assessment of AVCS, suggesting potential for the omission of true non-contrast scans in the clinical workup of patients with aortic calcifications.

Image Quality and Radiation Dose of CTPA With Iodine Maps: A Prospective Randomized Study of High-Pitch Mode Photon-Counting Detector CT Versus Energy-Integrating Detector CT.

BACKGROUND. Dual-energy CT pulmonary angiography (CTPA) with energy-integrating detector (EID) technology is limited by the inability to use high-pitch technique. OBJECTIVE. The purpose of this study was to compare the image quality of anatomic images and iodine maps between high-pitch photon-counting detector (PCD) CTPA and dual-energy EID CTPA. METHODS. This prospective study included 117 patients (70 men and 47 women; median age, 65 years) who underwent CTPA to evaluate for pulmonary embolism between March 2022 and November 2022. Fifty-eight patients were randomized to undergo PCD CTPA (pitch, 2.0), and 59 were randomized to undergo EID CTPA (pitch, 0.55). For each examination, 120-kV polychromatic images, 60-keV virtual monogenetic images (VMIs), and iodine maps were reconstructed. One radiologist measured CNR and SNR. Three radiologists independently assessed subjective image quality (on a scale of 1-4, with a score of 1 denoting highest quality). Radiation dose was recorded. RESULTS. SNR and CNR were higher for PCD CTPA than for EID CTPA for polychromatic images and VMIs, for all assessed vessels other than the left upper lobe artery. For example, for PCD CTPA versus EID CTPA, the right lower lobe artery on polychromatic images had an SNR of 34.5 versus 28.0 (p = .003) and a CNR of 29.2 versus 24.4 (p = .001), and on VMIs it had an SNR of 43.2 versus 32.7 (p = .005) and a CNR of 37.4 versus 29.3 (p = .002). For both scanners for readers 1 and 2, the median image quality score for polychromatic images and VMIs was 1, although distributions indicated significantly better scores for PCD CTPA than for EID CTPA for polychromatic images for reader 1 (p = .02) and reader 2 (p = .005) and for VMIs for reader 1 (p = .001) and reader 2 (p = .006). The image quality of anatomic image sets was not different between PCD CTPA and EID CTPA for reader 3 (p > .05). The image quality of iodine maps was not different between PCD CTPA and EID CTPA for any reader (p > .05). For PCD CTPA versus EID CTPA, the CTDIvol was 3.9 versus 4.5 mGy (p = .03), and the DLP was 123.5 mGy × cm versus 157.0 mGy × cm (p < .001). CONCLUSION. High-pitch PCD CTPA provided anatomic images with better subjective and objective image quality versus dual-energy EID CTPA, with lower radiation dose. Iodine maps showed no significant difference in image quality between scanners. CLINICAL IMPACT. CTPA may benefit from the PCD CT technique.

Photon-counting CT for diagnosis of acute pulmonary embolism: potential for contrast medium and radiation dose reduction.

OBJECTIVE: To evaluate the image quality of an ultra-low contrast medium and radiation dose CT pulmonary angiography (CTPA) protocol for the diagnosis of acute pulmonary embolism using a clinical photon-counting detector (PCD) CT system and compare its performance to a dual-energy-(DE)-CTPA protocol on a conventional energy-integrating detector (EID) CT system. METHODS: Sixty-four patients either underwent CTPA with the novel scan protocol on the PCD-CT scanner (32 patients, 25 mL, CTDIvol 2.5 mGy·cm) or conventional DE-CTPA on a third-generation dual-source EID-CT (32 patients, 50 mL, CTDIvol 5.1 mGy·cm). Pulmonary artery CT attenuation, signal-to-noise ratio, and contrast-to-noise-ratio were assessed as objective criteria of image quality, while subjective ratings of four radiologists were compared at 60 keV using virtual monoenergetic imaging and polychromatic standard reconstructions. Interrater reliability was determined by means of the intraclass correlation coefficient (ICC). Effective dose was compared between patient cohorts. RESULTS: Subjective image quality was deemed superior by all four reviewers for 60-keV PCD scans (excellent or good ratings in 93.8% of PCD vs. 84.4% of 60 keV EID scans, ICC = 0.72). No examinations on either system were considered "non-diagnostic." Objective image quality parameters were significantly higher in the EID group (mostly p < 0.001), both in the polychromatic reconstructions and at 60 keV. The ED (1.4 vs. 3.3 mSv) was significantly lower in the PCD cohort (p < 0.001). CONCLUSIONS: PCD-CTPA allows for considerable reduction of contrast medium and radiation dose in the diagnosis of acute pulmonary embolism, while maintaining good to excellent image quality compared to conventional EID-CTPA. CLINICAL RELEVANCE STATEMENT: Clinical PCD-CT allows for spectral assessment of pulmonary vasculature with high scan speed, which is beneficial in patients with suspected pulmonary embolism, frequently presenting with dyspnea. Simultaneously PCD-CT enables substantial reduction of contrast medium and radiation dose. KEY POINTS: • The clinical photon-counting detector CT scanner used in this study allows for high-pitch multi-energy acquisitions. • Photon-counting computed tomography allows for considerable reduction of contrast medium and radiation dose in the diagnosis of acute pulmonary embolism. • Subjective image quality was rated best for 60-keV photon-counting scans.

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective.

Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

Real-time cardiac MRI using an undersampled spiral k-space trajectory and a reconstruction based on a variational network.

PURPOSE: Cardiac MRI represents the gold standard to determine myocardial function. However, the current clinical standard protocol, a segmented Cartesian acquisition, is time-consuming and can lead to compromised image quality in the case of arrhythmia or dyspnea. In this article, a machine learning-based reconstruction of undersampled spiral k-space data is presented to enable free breathing real-time cardiac MRI with good image quality and short reconstruction times. METHODS: Data were acquired in free breathing with a 2D spiral trajectory corrected by the gradient system transfer function. Undersampled data were reconstructed by a variational network (VN), which was specifically adapted to the non-Cartesian sampling pattern. The network was trained with data from 11 subjects. Subsequently, the imaging technique was validated in 14 subjects by quantifying the difference to a segmented reference acquisition, an expert reader study, and by comparing derived volumes and functional parameters with values obtained using the current clinical gold standard. RESULTS: The scan time for the entire heart was below 1 min. The VN reconstructed data in about 0.9 s per image, which is considerably shorter than conventional model-based approaches. The VN furthermore performed better than a U-Net and not inferior to a low-rank plus sparse model in terms of achieved image quality. Functional parameters agreed, on average, with reference data. CONCLUSIONS: The proposed VN method enables real-time cardiac imaging with both high spatial and temporal resolution in free breathing and with short reconstruction time.

Evaluation of combined late gadolinium-enhancement and functional cardiac magnetic resonance imaging using spiral real-time acquisition.

The purpose of the current study was to implement and validate joint real-time acquisition of functional and late gadolinium-enhancement (LGE) cardiac magnetic resonance (MR) images during free breathing. Inversion recovery cardiac real-time images with a temporal resolution of 50 ms were acquired using a spiral trajectory (IR-CRISPI) with a pre-emphasis based on the gradient system transfer function during free breathing. Functional and LGE cardiac MR images were reconstructed using a low-rank plus sparse model. Late gadolinium-enhancement appearance, image quality, and functional parameters of IR-CRISPI were compared with clinical standard balanced steady-state free precession breath-hold techniques in 10 patients. The acquisition of IR-CRISPI in free breathing of the entire left ventricle took 97 s on average. Bland-Altman analysis and Wilcoxon tests showed a higher artifact level for the breath-hold technique (p = 0.003), especially for arrhythmic patients or patients with dyspnea, but an increased noise level for IR-CRISPI of the LGE images (p = 0.01). The estimated transmural extent of the enhancement differed by not more than 25% and did not show a significant bias between the techniques (p = 0.50). The ascertained functional parameters were similar for the breath-hold technique and IR-CRISPI, that is, with a minor, nonsignificant (p = 0.16) mean difference of the ejection fraction of 2.3% and a 95% confidence interval from -4.8% to 9.4%. IR-CRISPI enables joint functional and LGE imaging in free breathing with good image quality but distinctly shorter scan times in comparison with breath-hold techniques.

Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction.

PURPOSE: To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy. METHODS: In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework ("nnU-net") was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels. Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the Sørensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson's r correlation and Bland-Altman analysis. RESULTS: The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ±â€¯0.03 for myocardium and 0.72 ±â€¯0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network. CONCLUSION: The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR.

Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times.

BACKGROUND: Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters. METHODS: In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen-Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson's correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer. RESULTS: The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson's correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L. CONCLUSIONS: Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.

Three-dimensional Ultrashort Echotime Magnetic Resonance Imaging for Combined Morphologic and Ventilation Imaging in Pediatric Patients With Pulmonary Disease.

PURPOSE: Ultrashort echotime (UTE) sequences aim to improve the signal yield in pulmonary magnetic resonance imaging (MRI). We demonstrate the initial results of spiral 3-dimensional (3D) UTE-MRI for combined morphologic and functional imaging in pediatric patients. METHODS: Seven pediatric patients with pulmonary abnormalities were included in this observational, prospective, single-center study, with the patients having the following conditions: cystic fibrosis (CF) with middle lobe atelectasis, CF with allergic bronchopulmonary aspergillosis, primary ciliary dyskinesia, air trapping, congenital lobar overinflation, congenital pulmonary airway malformation, and pulmonary hamartoma.Patients were scanned during breath-hold in 5 breathing states on a 3-Tesla system using a prototypical 3D stack-of-spirals UTE sequence. Ventilation maps and signal intensity maps were calculated. Morphologic images, ventilation-weighted maps, and signal intensity maps of the lungs of each patient were assessed intraindividually and compared with reference examinations. RESULTS: With a scan time of ∼15 seconds per breathing state, 3D UTE-MRI allowed for sufficient imaging of both "plus" pathologies (atelectasis, inflammatory consolidation, and pulmonary hamartoma) and "minus" pathologies (congenital lobar overinflation, congenital pulmonary airway malformation, and air trapping). Color-coded maps of normalized signal intensity and ventilation increased diagnostic confidence, particularly with regard to "minus" pathologies. UTE-MRI detected new atelectasis in an asymptomatic CF patient, allowing for rapid and successful therapy initiation, and it was able to reproduce atelectasis and hamartoma known from multidetector computed tomography and to monitor a patient with allergic bronchopulmonary aspergillosis. CONCLUSION: 3D UTE-MRI using a stack-of-spirals trajectory enables combined morphologic and functional imaging of the lungs within ~115 second acquisition time and might be suitable for monitoring a wide spectrum of pulmonary diseases.

Cardiac real-time MRI using a pre-emphasized spiral acquisition based on the gradient system transfer function.

PURPOSE: Segmented Cartesian acquisition in breath hold represents the current gold standard for cardiac functional MRI. However, it is also associated with long imaging times and severe restrictions in arrhythmic or dyspneic patients. Therefore, we introduce a real-time imaging technique based on a spoiled gradient-echo sequence with undersampled spiral k-space trajectories corrected by a gradient pre-emphasis. METHODS: A fully automatic gradient waveform pre-emphasis based on the gradient system transfer function was implemented to compensate for gradient inaccuracies, to optimize fast double-oblique spiral MRI. The framework was tested in a phantom study and subsequently transferred to compressed sensing-accelerated cardiac functional MRI in real time. Spiral acquisitions during breath hold and free breathing were compared with this reference method for healthy subjects (N = 7) as well as patients (N = 2) diagnosed with heart failure and arrhythmia. Left-ventricular volumes and ejection fractions were determined and analyzed using a Wilcoxon signed-rank test. RESULTS: The pre-emphasis successfully reduced typical artifacts caused by k-space misregistrations. Dynamic cardiac imaging was possible in real time (temporal resolution < 50 ms) with high spatial resolution (1.34 × 1.34 mm2 ), resulting in a total scan time of less than 50 seconds for whole heart coverage. Comparable image quality, as well as similar left-ventricular volumes and ejection fractions, were observed for the accelerated and the reference method. CONCLUSION: The proposed technique enables high-resolution real-time cardiac MRI with no need for breath holds and electrocardiogram gating, shortening the duration of an entire functional cardiac exam to less than 1 minute.

Three-dimensional Ultrashort Echo Time MRI for Functional Lung Imaging in Cystic Fibrosis.

Background In cystic fibrosis (CF), recurrent imaging and pulmonary function tests (PFTs) are needed for the assessment of lung function during disease management. Purpose To assess the clinical feasibility of pulmonary three-dimensional ultrashort echo time (UTE) MRI at breath holding for quantitative image analysis of ventilation inhomogeneity and hyperinflation in CF compared with PFT. Materials and Methods In this prospective study from May 2018 to June 2019, participants with CF and healthy control participants underwent PFTs and functional lung MRI by using a prototypical single breath-hold three-dimensional UTE sequence. Fractional ventilation (FV) was calculated from acquired data in normal inspiration and normal expiration. FV of each voxel was normalized to the whole lung mean (FVN), and interquartile range of normalized ventilation (IQRN; as a measure of ventilation heterogeneity) was calculated. UTE signal intensity (SI) was assessed in full expiration (SIN, normalized to aortic blood). Obtained metrics were compared between participants with CF and control participants. For participants with CF, MRI metrics were correlated with the standard lung clearance index (LCI) and PFT. Mann-Whitney U tests and Spearman correlation were used for statistical analysis. Results Twenty participants with CF (mean age, 17 years ± 9 [standard deviation]; 12 men) and 10 healthy control participants (24 years ± 8; five men) were included. IQRN was higher for participants with CF than for control participants (mean, 0.66 ± 0.16 vs 0.50 ± 0.04, respectively; P = .007). In the 20 participants with CF, IQRN correlated with obstruction markers forced expiratory volume in 1 second-to-forced vital capacity ratio (r = -0.70; 95% confidence interval [CI]: -0.92, -0.28; P < .001), mean expiratory flow 25% (r = 0.78; 95% CI: -0.95, -0.39; P < .001), and with the ventilation inhomogeneity parameter LCI (r = 0.90; 95% CI: 0.69, 0.96; P < .001). Mean SIN in full expiration was lower in participants with CF than in control participants (0.34 ± 0.08 vs 0.39 ± 0.03, respectively; P = .03). Conclusion Three-dimensional ultrashort echo time MRI in the lungs allowed for functional imaging of ventilation inhomogeneity within a few breath holds in patients with cystic fibrosis. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Functional MRI of the Lungs Using Single Breath-Hold and Self-Navigated Ultrashort Echo Time Sequences.

PURPOSE: To evaluate three-dimensional (3D) ultrashort echo time (UTE) MRI regarding image quality and suitability for functional image analysis using gradient-echo sequences in breath-hold and with self-navigation. MATERIALS AND METHODS: In this prospective exploratory study, 10 patients (mean age, 21 years; age range, 5-58 years; five men) and 10 healthy control participants (mean age, 25 years; age range, 10-39 years; five men) underwent 3D UTE MRI at 3.0 T. Imaging was performed with a prototypical stack-of-spirals 3D UTE sequence during single breath holds (echo time [TE], 0.05 msec) and with a self-navigated "Koosh ball" 3D UTE sequence at free breathing (TE, 0.03 msec). Image quality was rated on a four-point Likert scale. Edge sharpness was calculated. After semiautomated segmentation, fractional ventilation was calculated from MRI signal intensity (FVSI) and volume change (FVVol). The air volume fraction (AVF) was estimated from relative signal intensity (aortic blood signal intensity was used as a reference). Means were compared between techniques and participants. The Wilcoxon signed rank test and Spearman rank correlation were used for statistical analyses. RESULTS: Image quality ratings were equal for both techniques. However, stack-of-spirals breath-hold UTE was more susceptible to motion and aliasing artifacts. Mean FVSI was higher during breath hold than at free breathing (mean ± standard deviation in milliliters of gas per milliliters of parenchyma, 0.17 mL/mL ± 0.06 [minimum, 0.07; maximum, 0.34] vs 0.11 mL/mL ± 0.03 [minimum, 0.06; maximum, 0.17], P = .016). Mean FVSI and FVVol were in good agreement (mean difference: at breath hold, -0.008 [95% confidence interval {CI}: 0.007, -0.024]; ρ = 0.97 vs free breathing, -0.004 [95% CI: 0.007, -0.016]; ρ = 0.91). AVF correlated between both techniques (ρ = 0.94). CONCLUSION: Breath-hold and self-navigated 3D UTE sequences yield proton density-weighted images of the lungs that are similar in quality, and both techniques are suitable for functional image analysis.Supplemental material is available for this article.© RSNA, 2020.

T1- and ECV-mapping in clinical routine at 3 T: differences between MOLLI, ShMOLLI and SASHA.

BACKGROUND: T1 mapping sequences such as MOLLI, ShMOLLI and SASHA make use of different technical approaches, bearing strengths and weaknesses. It is well known that obtained T1 relaxation times differ between the sequence techniques as well as between different hardware. Yet, T1 quantification is a promising tool for myocardial tissue characterization, disregarding the absence of established reference values. The purpose of this study was to evaluate the feasibility of native and post-contrast T1 mapping methods as well as ECV maps and its diagnostic benefits in a clinical environment when scanning patients with various cardiac diseases at 3 T. METHODS: Native and post-contrast T1 mapping data acquired on a 3 T full-body scanner using the three pulse sequences 5(3)3 MOLLI, ShMOLLI and SASHA in 19 patients with clinical indication for contrast enhanced MRI were compared. We analyzed global and segmental T1 relaxation times as well as respective extracellular volumes and compared the emerged differences between the used pulse sequences. RESULTS: T1 times acquired with MOLLI and ShMOLLI exhibited systematic T1 deviation compared to SASHA. Myocardial MOLLI T1 times were 19% lower and ShMOLLI T1 times 25% lower compared to SASHA. Native blood T1 times from MOLLI were 13% lower than SASHA, while post-contrast MOLLI T1-times were only 5% lower. ECV values exhibited comparably biased estimation with MOLLI and ShMOLLI compared to SASHA in good agreement with results reported in literature. Pathology-suspect segments were clearly differentiated from remote myocardium with all three sequences. CONCLUSION: Myocardial T1 mapping yields systematically biased pre- and post-contrast T1 times depending on the applied pulse sequence. Additionally calculating ECV attenuates this bias, making MOLLI, ShMOLLI and SASHA better comparable. Therefore, myocardial T1 mapping is a powerful clinical tool for classification of soft tissue abnormalities in spite of the absence of established reference values.