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.

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.

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.

GPT-4 Analysis of MRI Reports in Suspected Myocarditis: A Multicenter Study.

PURPOSE: Diagnosing myocarditis relies on multimodal data including magnetic resonance imaging (MRI), clinical symptoms, and blood values. The correct interpretation and integration of MRI findings requires 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 MRI reports from 396 patients with suspected myocarditis and eight centers, respectively. MRI reports and patient data including blood values, age, and further clinical information were provided to GPT-4 and to radiologists with 1 (Resident 1), 2 (Resident 2), and 4 years (Resident 3) of experience in cardiovascular MRI 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 GPT-4 and of the human readers were compared to a consensus reading (two board-certified radiologists with 8 and 10 years of experience in cardiovascular MRI). 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=.14) and lower than that of more experienced physicians (R2: 89%, 86%, 91%, p=.007 and R3: 91%, 85%, 96%, p<.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 Resident 3 with statistical significance (p=.004 and p=.02, respectively). CONCLUSION: GPT-4 yielded good accuracy for diagnosing myocarditis based on MRI 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.

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.

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.

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.