Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences.

OBJECTIVES: The unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or image quality. This study investigates MR energy consumption and identifies strategies for improving energy efficiency, focusing on musculoskeletal MRI. We assess the potential savings achievable through (1) optimizing protocols, (2) incorporating deep learning (DL) accelerated acquisitions, and (3) optimizing the cooling system. MATERIALS AND METHODS: Energy consumption measurements were performed on two MRI scanners (1.5-T Aera, 1.5-T Sola) in practices in Munich, Germany, between December 2022 and March 2023. Three levels of energy reduction measures were implemented and compared to the baseline. Wilcoxon signed-rank test with Bonferroni correction was conducted to evaluate the impact of sequence scan times and energy consumption. RESULTS: Our findings showed significant energy savings by optimizing protocol settings and implementing DL technologies. Across all body regions, the average reduction in energy consumption was 72% with DL and 31% with economic protocols, accompanied by time reductions of 71% (DL) and 18% (economic protocols) compared to baseline. Optimizing the cooling system during the non-scanning time showed a 30% lower energy consumption. CONCLUSION: Implementing energy-saving strategies, including economic protocols, DL accelerated sequences, and optimized magnet cooling, can significantly reduce energy consumption in MRI scanners. Radiology departments and practices should consider adopting these strategies to improve energy efficiency and reduce costs. CLINICAL RELEVANCE STATEMENT: MRI scanner energy consumption can be substantially reduced by incorporating protocol optimization, DL accelerated acquisition, and optimized magnetic cooling into daily practice, thereby cutting costs and environmental impact. KEY POINTS: Optimization of protocol settings reduced energy consumption by 31% and imaging time by 18%. DL technologies led to a 72% reduction in energy consumption of and a 71% reduction in time, compared to the standard MRI protocol. During non-scanning times, activating Eco power mode (EPM) resulted in a 30% reduction in energy consumption, saving 4881 € ($5287) per scanner annually.

Multiparametric Cardiac Magnetic Resonance Imaging to Discriminate Endomyocardial Biopsy-Proven Chronic Myocarditis From Healed Myocarditis.

BACKGROUND: Detecting ongoing inflammation in myocarditis patients has prognostic relevance, but there are limited data on the detection of chronic myocarditis and its differentiation from healed myocarditis. OBJECTIVES: This study sought to assess the performance of cardiac magnetic resonance (CMR) for the detection of ongoing inflammation and the discrimination of chronic myocarditis from healed myocarditis. METHODS: Consecutive patients with persistent symptoms (>30 days) suggestive of myocarditis were prospectively enrolled from a single tertiary center. All patients underwent a multiparametric 1.5-T CMR protocol including biventricular strain, T1/T2 mapping, and late gadolinium enhancement (LGE). Endomyocardial biopsy was chosen for the reference standard diagnosis. RESULTS: Among 452 consecutive patients, 103 (median age: 50 years; 66 men) had evaluable CMR and cardiopathologic reference diagnosis: 53 (51%) with chronic lymphocytic myocarditis and 50 (49%) with healed myocarditis. T2 mapping as a single parameter showed the best accuracy in detecting chronic myocarditis, if abnormal in ≥3 segments (92%; 95% CI: 85-97), and provided the best discrimination from healed myocarditis, as defined by the area under the receiver-operating characteristic curve (0.87 [95% CI: 0.79-0.93]; P < 0.001), followed by radial peak systolic strain rate of the left ventricle (0.86) and the right ventricle (0.84); T1 mapping (0.64), extracellular volume fraction (0.62), and LGE (0.57). Specificity increased when T2 mapping was combined with elevation of either troponin or C-reactive protein. CONCLUSIONS: A multiparametric CMR protocol allows detection of ongoing myocardial inflammation and discrimination of chronic myocarditis from healed myocarditis, with segmental T2 mapping and biventricular strain analysis showing higher diagnostic accuracy compared with T1 mapping, extracellular volume fraction, and LGE. The use of biomarkers (troponin or C-reactive protein) may improve specificity.

Generative Pre-trained Transformer 4 analysis of cardiovascular magnetic resonance reports in suspected myocarditis: A multicenter study.

BACKGROUND: Diagnosing myocarditis relies on multimodal data, including cardiovascular magnetic resonance (CMR), clinical symptoms, and blood values. The correct interpretation and integration of CMR findings require radiological expertise and knowledge. We aimed to investigate the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model, for report-based medical decision-making in the context of cardiac MRI for suspected myocarditis. METHODS: This retrospective study includes CMR reports from 396 patients with suspected myocarditis and eight centers, respectively. CMR reports and patient data including blood values, age, and further clinical information were provided to GPT-4 and radiologists with 1 (resident 1), 2 (resident 2), and 4 years (resident 3) of experience in CMR and knowledge of the 2018 Lake Louise Criteria. The final impression of the report regarding the radiological assessment of whether myocarditis is present or not was not provided. The performance of Generative pre-trained transformer 4 (GPT-4) and the human readers were compared to a consensus reading (two board-certified radiologists with 8 and 10 years of experience in CMR). Sensitivity, specificity, and accuracy were calculated. RESULTS: GPT-4 yielded an accuracy of 83%, sensitivity of 90%, and specificity of 78%, which was comparable to the physician with 1 year of experience (R1: 86%, 90%, 84%, p = 0.14) and lower than that of more experienced physicians (R2: 89%, 86%, 91%, p = 0.007 and R3: 91%, 85%, 96%, p < 0.001). GPT-4 and human readers showed a higher diagnostic performance when results from T1- and T2-mapping sequences were part of the reports, for residents 1 and 3 with statistical significance (p = 0.004 and p = 0.02, respectively). CONCLUSION: GPT-4 yielded good accuracy for diagnosing myocarditis based on CMR reports in a large dataset from multiple centers and therefore holds the potential to serve as a diagnostic decision-supporting tool in this capacity, particularly for less experienced physicians. Further studies are required to explore the full potential and elucidate educational aspects of the integration of large language models in medical decision-making.

Prospective Deployment of Deep Learning Reconstruction Facilitates Highly Accelerated Upper Abdominal MRI.

RATIONALE AND OBJECTIVES: To compare a conventional T1 volumetric interpolated breath-hold examination (VIBE) with SPectral Attenuated Inversion Recovery (SPAIR) fat saturation and a deep learning (DL)-reconstructed accelerated VIBE sequence with SPAIR fat saturation achieving a 50 % reduction in breath-hold duration (hereafter, VIBE-SPAIRDL) in terms of image quality and diagnostic confidence. MATERIALS AND METHODS: This prospective study enrolled consecutive patients referred for upper abdominal MRI from November 2023 to December 2023 at a single tertiary center. Patients underwent upper abdominal MRI with acquisition of non-contrast and gadobutrol-enhanced conventional VIBE-SPAIR (fourfold acceleration, acquisition time 16 s) and VIBE-SPAIRDL (sixfold acceleration, acquisition time 8 s) on a 1.5 T scanner. Image analysis was performed by four readers, evaluating homogeneity of fat suppression, perceived signal-to-noise ratio (SNR), edge sharpness, artifact level, lesion detectability and diagnostic confidence. A statistical power analysis for patient sample size estimation was performed. Image quality parameters were compared by a repeated measures analysis of variance, and interreader agreement was assessed using Fleiss' κ. RESULTS: Among 450 consecutive patients, 45 patients were evaluated (mean age, 60 years ± 15 [SD]; 27 men, 18 women). VIBE-SPAIRDL acquisition demonstrated superior SNR (P < 0.001), edge sharpness (P < 0.001), and reduced artifacts (P < 0.001) with substantial to almost perfect interreader agreement for non-contrast (κ: 0.70-0.91) and gadobutrol-enhanced MRI (κ: 0.68-0.87). No evidence of a difference was found between conventional VIBE-SPAIR and VIBE-SPAIRDL regarding homogeneity of fat suppression, lesion detectability, or diagnostic confidence (all P > 0.05). CONCLUSION: Deep learning reconstruction of VIBE-SPAIR facilitated a reduction of breath-hold duration by half, while reducing artifacts and improving image quality. SUMMARY: Deep learning reconstruction of prospectively accelerated T1 volumetric interpolated breath-hold examination for upper abdominal MRI enabled a 50 % reduction in breath-hold time with superior image quality. KEY RESULTS: 1) In a prospective analysis of 45 patients referred for upper abdominal MRI, accelerated deep learning (DL)-reconstructed VIBE images with spectral fat saturation (SPAIR) showed better overall image quality, with better perceived signal-to-noise ratio and less artifacts (all P < 0.001), despite a 50 % reduction in acquisition time compared to conventional VIBE. 2) No evidence of a difference was found between conventional VIBE-SPAIR and accelerated VIBE-SPAIRDL regarding lesion detectability or diagnostic confidence.

Viabahn endoprosthesis for femoropopliteal aneurysm repair: safety, success rates, and long-term patency.

BACKGROUND: The Viabahn endoprosthesis has become a vital option for endovascular therapy, yet there is limited long-term data on its effectiveness for peripheral aneurysm repair. This study aimed to evaluate the safety, technical and clinical success, and long-term patency of the Viabahn endoprosthesis for treating femoropopliteal aneurysms. METHODS: This retrospective tertiary single-center study analyzed patients who underwent a Viabahn endoprosthesis procedure for femoropopliteal aneurysm repair from 2010 to 2020. Intraoperative complications, technical and clinical success rates, and major adverse events (MAE, including acute thrombotic occlusion, major amputation, myocardial infarction, and device- or procedure-related death) at 30 days were assessed. Incidence of clinically-driven target lesion revascularisation (cdTLR) was noted. Patency rates were evaluated by Kaplan-Meier analysis. RESULTS: Among 19 patients (mean age, 72 ± 12 years; 18 male, 1 female) who underwent aneurysm repair using the Viabahn endoprosthesis, there were no intraoperative adverse events, with 100% technical and clinical success rates. At the 30-day mark, all patients (19/19, 100%) were free of MAE. The median follow-up duration was 1,009 days [IQR, 462-1,466]. Popliteal stent graft occlusion occurred in 2/19 patients (10.5%) after 27 and 45 months, respectively. Consequently, the primary patency rates were 100%, 90%, 74% at 12, 24, and 36-72 months, respectively. Endovascular cdTLR was successful in both cases, resulting in sustained secondary patency at 100%. CONCLUSION: The use of Viabahn endoprostheses for femoropopliteal aneurysm repair demonstrated technical and clinical success rates of 100%, a 0% 30-day MAE rate, and excellent long-term patency.

Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression.

OBJECTIVE: Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-DixonDL). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). METHODS: This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed. RESULTS: Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-DixonDL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-DixonDL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-DixonDL (P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-DixonDL technique (P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-DixonDL. Interreader agreement between VIBE-Dixon and VIBE-DixonDL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXONDL was observed in both the precontrast (P = 0.025) and postcontrast images (P < 0.001). Also, an increase of splenic SNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.34 and P = 0.003, respectively). Similarly, an increase of pancreas CNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.557 and P = 0.026, respectively). CONCLUSIONS: The prospectively accelerated, DL-enhanced VIBE with Dixon fat suppression was clinically feasible. It enabled a 52% reduction in breath-hold time and provided superior image quality, diagnostic confidence, and pancreatic lesion conspicuity. This technique might be especially useful for patients with limited breath-hold capacity.

Cooperative AI training for cardiothoracic segmentation in computed tomography: An iterative multi-center annotation approach.

PURPOSE: Radiological reporting is transitioning to quantitative analysis, requiring large-scale multi-center validation of biomarkers. A major prerequisite and bottleneck for this task is the voxelwise annotation of image data, which is time-consuming for large cohorts. In this study, we propose an iterative training workflow to support and facilitate such segmentation tasks, specifically for high-resolution thoracic CT data. METHODS: Our study included 132 thoracic CT scans from clinical practice, annotated by 13 radiologists. In three iterative training experiments, we aimed to improve and accelerate segmentation of the heart and mediastinum. Each experiment started with manual segmentation of 5-25 CT scans, which served as training data for a nnU-Net. Further iterations incorporated AI pre-segmentation and human correction to improve accuracy, accelerate the annotation process, and reduce human involvement over time. RESULTS: Results showed consistent improvement in AI model quality with each iteration. Resampled datasets improved the Dice similarity coefficients for both the heart (DCS 0.91 [0.88; 0.92]) and the mediastinum (DCS 0.95 [0.94; 0.95]). Our AI models reduced human interaction time by 50 % for heart and 70 % for mediastinum segmentation in the most potent iteration. A model trained on only five datasets achieved satisfactory results (DCS > 0.90). CONCLUSIONS: The iterative training workflow provides an efficient method for training AI-based segmentation models in multi-center studies, improving accuracy over time and simultaneously reducing human intervention. Future work will explore the use of fewer initial datasets and additional pre-processing methods to enhance model quality.

Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence.

PURPOSE: The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up. MATERIALS AND METHODS: Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed. RESULTS: A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51-93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0-98.7), 68.7% (95% CI: 60.1-76.4), 74.3 % (95% CI: 69.1-78.8), 94.8% (95% CI: 88.5-97.8), and 81.9% (95% CI: 76.7-86.4) for PC-CCTA, and 96.8% (95% CI: 92.1-99.1), 87.3% (95% CI: 80.5-92.4), 87.8% (95% CI: 82.2-91.8), 96.7% (95% CI: 91.7-98.7), and 91.9% (95% CI: 87.9-94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88-0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77-0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001). CONCLUSION: Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

Viabahn stent graft for arterial injury management: safety, technical success, and long-term outcome.

BACKGROUND: The Viabahn stent graft has emerged as an integral tool for managing vascular diseases, but there is limited long-term data on its performance in emergency endovascular treatment. This study aimed to assess safety, technical success, and long-term efficacy of the Viabahn stent graft in emergency treatment of arterial injury. METHODS: We conducted a retrospective single tertiary centre analysis of patients who underwent Viabahn emergency arterial injury treatment between 2015 and 2020. Indication, intraoperative complications, technical and clinical success, and major adverse events at 30 days were evaluated. Secondary efficacy endpoints were the primary and secondary patency rates assessed by Kaplan-Meier analysis. RESULTS: Forty patients (71 ± 13 years, 19 women) were analyzed. Indications for Viabahn emergency treatment were extravasation (65.0%), arterio-venous fistula (22.5%), pseudoaneurysm (10.0%), and arterio-ureteral fistula (2.5%). No intraoperative adverse events occurred, technical and clinical success rates were 100%. One acute stent graft occlusion occurred in the popliteal artery on day 9, resulting in a 30-day device-related major-adverse-event rate of 2.5%. Median follow-up was 402 days [IQR, 43-1093]. Primary patency rate was 97% (95% CI: 94-100) in year 1, and 92% (95% CI: 86-98) from years 2 to 6. One stent graft occlusion occurred in the external iliac artery at 18 months; successful revascularization resulted in secondary patency rates of 97% (95% CI: 94-100) from years 1 to 6. CONCLUSION: Using Viabahn stent graft in emergency arterial injury treatment had 100% technical and clinical success rates, a low 30-day major-adverse-event rate of 2.5%, and excellent long-term patency rates.

T2* map at cardiac MRI reveals incidental hepatic and cardiac iron overload.

PURPOSE: The purpose of this study was to assess the diagnostic capabilities of cardiac magnetic resonance (CMR) T2* mapping in detecting incidental hepatic and cardiac iron overload. MATERIALS AND METHODS: Patients with various clinical indications for CMR examination were consecutively included at a single center from January 2019 to April 2023. All patients underwent T2* mapping at 1.5 T in a single mid-ventricular short-axis as part of a comprehensive routine CMR protocol. T2* measurements were performed of the heart (using a region-of-interest in the interventricular septum) and the liver, categorized according to the severity of iron overload. The degree of cardiac iron overload was categorized as mild (15 ms < T2* < 20 ms), moderate (10 ms < T2* < 15 ms) and severe (T2* < 10 ms). The degree of hepatic iron overload was categorized as mild (4 ms < T2* < 8 ms), moderate (2 ms < T2* < 4 ms), severe (T2* < 2 ms). Image quality and inter-reader agreement were assessed using intraclass correlation coefficient (ICC). RESULTS: CMR examinations from 614 patients (374 men, 240 women) with a mean age of 50 ± 18 (standard deviation) years were fully evaluable. A total of 24/614 patients (3.9%) demonstrated incidental hepatic iron overload; of these, 22/614 patients (3.6%) had mild hepatic iron overload, and 2/614 patients (0.3%) had moderate hepatic iron overload. Seven out of 614 patients (1.1%) had incidental cardiac iron overload; of these, 5/614 patients (0.8%) had mild iron overload, 1/614 patients (0.2%) had moderate iron overload, and 1/614 patients (0.2%) had severe iron overload. Good to excellent inter-reader agreement was observed for the assessment of T2* values (ICC, 0.90 for heart [95% confidence interval: 0.88-0.91]; ICC, 0.91 for liver [95% confidence interval: 0.89-0.92]). CONCLUSION: Analysis of standard CMR T2* maps detects incidental cardiac and hepatic iron overload in 1.1% and 3.9% of patients, respectively, which may have implications for further patient management. Therefore, despite an overall low number of incidental abnormal findings, T2* imaging may be included in a standardized comprehensive CMR protocol.

Dark-Blood Late Gadolinium Enhancement MRI Is Noninferior to Bright-Blood LGE in Non-Ischemic Cardiomyopathies.

(1) Background and Objectives: Dark-blood late gadolinium enhancement has been shown to be a reliable cardiac magnetic resonance (CMR) method for assessing viability and depicting myocardial scarring in ischemic cardiomyopathy. The aim of this study was to evaluate dark-blood LGE imaging compared with conventional bright-blood LGE for the detection of myocardial scarring in non-ischemic cardiomyopathies. (2) Materials and Methods: Patients with suspected non-ischemic cardiomyopathy were prospectively enrolled in this single-centre study from January 2020 to March 2023. All patients underwent 1.5 T CMR with both dark-blood and conventional bright-blood LGE imaging. Corresponding short-axis stacks of both techniques were analysed for the presence, distribution, pattern, and localisation of LGE, as well as the quantitative scar size (%). (3) Results: 343 patients (age 44 ± 17 years; 124 women) with suspected non-ischemic cardiomyopathy were examined. LGE was detected in 123 of 343 cases (36%) with excellent inter-reader agreement (κ 0.97-0.99) for both LGE techniques. Dark-blood LGE showed a sensitivity of 99% (CI 98-100), specificity of 99% (CI 98-100), and an accuracy of 99% (CI 99-100) for the detection of non-ischemic scarring. No significant difference in total scar size (%) was observed. Dark-blood imaging with mean 5.35 ± 4.32% enhanced volume of total myocardial volume, bright-blood with 5.24 ± 4.28%, p = 0.84. (4) Conclusions: Dark-blood LGE imaging is non-inferior to conventional bright-blood LGE imaging in detecting non-ischemic scarring. Therefore, dark-blood LGE imaging may become an equivalent method for the detection of both ischemic and non-ischemic scars.

T1 Mapping MOLLI 5(3)3 Acquisition Scheme Yields High Accuracy in 1.5 T Cardiac Magnetic Resonance.

Objectives: To systematically compare two modified Look-Locker inversion recovery (MOLLI) T1 mapping sequences and their impact on (1) myocardial T1 values native, (2) post-contrast and (3) extracellular volume (ECV). Methods: 200 patients were prospectively included for 1.5 T CMR for work-up of ischemic or non-ischemic cardiomyopathies. To determine native and post-contrast T1 for ECV calculation, two different T1 mapping MOLLI acquisition schemes, 5(3)3 (designed for native scans with long T1) and 4(1)3(1)2 (designed for post-contrast scans with short T1), were acquired in identical mid-ventricular short-axis slices. Both schemes were acquired in native and post-contrast scans. Results: Datasets from 163 patients were evaluated (age 55 ± 17 years; 38% female). Myocardial T1 native for 5(3)3 was 1017 ± 42 ms vs. 956 ± 40 ms for 4(1)3(1)2, with mean intraindividual difference −61 ms (p < 0.0001). Post-contrast myocardial T1 in patients was similar for both acquisition schemes, with 494 ± 48 ms for 5(3)3 and 490 ± 45 ms for 4(1)3(1)2 and mean intraindividual difference −4 ms. Myocardial ECV for 5(3)3 was 27.6 ± 4% vs. 27 ± 4% for 4(1)3(1)2, with mean difference −0.6 percentage points (p < 0.0001). Conclusions: The T1 MOLLI 5(3)3 acquisition scheme provides a reliable estimation of myocardial T1 for the clinically relevant range of long and short T1 values native and post-contrast. In contrast, the T1 MOLLI 4(1)3(1)2 acquisition scheme may only be used for post-contrast scans according to its designed purpose.

Using Multiparametric Cardiac Magnetic Resonance to Phenotype and Differentiate Biopsy-Proven Chronic from Healed Myocarditis and Dilated Cardiomyopathy.

(1) Objectives: To discriminate biopsy-proven myocarditis (chronic vs. healed myocarditis) and to differentiate from dilated cardiomyopathy (DCM) using cardiac magnetic resonance (CMR). (2) Methods: A total of 259 consecutive patients (age 51 ± 15 years; 28% female) who underwent both endomyocardial biopsy (EMB) and CMR in the years 2008−2021 were evaluated. According to right-ventricular EMB results, patients were divided into either chronic (n = 130, 50%) or healed lymphocytic myocarditis (n = 60, 23%) or DCM (n = 69, 27%). The CMR protocol included functional, strain, and late gadolinium enhancement (LGE) imaging, T2w imaging, and T2 mapping. (3) Results: Left-ventricular ejection fraction (LV-EF) was higher, and the indexed end-diastolic volume (EDV) was lower in myocarditis patients (chronic: 42%, median 96 mL/m²; healed: 49%, 86 mL/m²) compared to the DCM patients (31%, 120 mL/m²), p < 0.0001. Strain analysis demonstrated lower contractility in DCM patients vs. myocarditis patients, p < 0.0001. Myocarditis patients demonstrated a higher LGE prevalence (68% chronic; 59% healed) than the DCM patients (45%), p = 0.01. Chronic myocarditis patients showed a higher myocardial edema prevalence and ratio (59%, median 1.3) than healed myocarditis (23%, 1.3) and DCM patients (13%, 1.0), p < 0.0001. T2 mapping revealed elevated values more frequently in chronic (90%) than in healed (21%) myocarditis and DCM (23%), p < 0.0001. T2 mapping yielded an AUC of 0.89 (sensitivity 90%, specificity 76%) in the discrimination of chronic from healed myocarditis and an AUC of 0.92 (sensitivity 86%, specificity 91%) in the discrimination of chronic myocarditis from DCM, both p < 0.0001. (4) Conclusions: Multiparametric CMR imaging, including functional parameters, LGE and T2 mapping, may allow differentiation of chronic from healed myocarditis and DCM and therefore help to optimize patient management in this clinical setting.

Comprehensive Cardiac Magnetic Resonance to Detect Subacute Myocarditis.

(1) Background: Compared to acute myocarditis in the initial phase, detection of subacute myocarditis with cardiac magnetic resonance (CMR) parameters can be challenging due to a lower degree of myocardial inflammation compared to the acute phase. (2) Objectives: To systematically evaluate non-invasive CMR imaging parameters in acute and subacute myocarditis. (3) Methods: 48 patients (age 37 (IQR 28−55) years; 52% female) with clinically suspected myocarditis were consecutively included. Patients with onset of symptoms ≤2 weeks prior to 1.5T CMR were assigned to the acute group (n = 25, 52%), patients with symptom duration >2 to 6 weeks were assigned to the subacute group (n = 23, 48%). CMR protocol comprised morphology, function, 3D-strain, late gadolinium enhancement (LGE) imaging and mapping (T1, ECV, T2). (4) Results: Highest diagnostic performance in the detection of subacute myocarditis was achieved by ECV evaluation either as single parameter or in combination with T1 mapping (applying a segmental or global increase of native T1 > 1015 ms and ECV > 28%), sensitivity 96% and accuracy 91%. Compared to subacute myocarditis, acute myocarditis demonstrated higher prevalence and extent of LGE (AUC 0.76) and increased T2 (AUC 0.66). (5) Conclusions: A comprehensive CMR approach allows reliable diagnosis of clinically suspected subacute myocarditis. Thereby, ECV alone or in combination with native T1 mapping indicated the best performance for diagnosing subacute myocarditis. Acute vs. subacute myocarditis is difficult to discriminate by CMR alone, due to chronological connection and overlap of pathologic findings.