Advanced Myocardial MRI Tissue Characterization Combining Contrast Agent-Free T1-Rho Mapping With Fully Automated Analysis.

BACKGROUND: Myocardial T1-rho (T1ρ) mapping is a promising method for identifying and quantifying myocardial injuries without contrast agents, but its clinical use is hindered by the lack of dedicated analysis tools. PURPOSE: To explore the feasibility of clinically integrated artificial intelligence-driven analysis for efficient and automated myocardial T1ρ mapping. STUDY TYPE: Retrospective. POPULATION: Five hundred seventy-three patients divided into a training (N = 500) and a test set (N = 73) including ischemic and nonischemic cases. FIELD STRENGTH/SEQUENCE: Single-shot bSSFP T1ρ mapping sequence at 1.5 T. ASSESSMENT: The automated process included: left ventricular (LV) wall segmentation, right ventricular insertion point detection and creation of a 16-segment model for segmental T1ρ value analysis. Two radiologists (20 and 7 years of MRI experience) provided ground truth annotations. Interobserver variability and segmentation quality were assessed using the Dice coefficient with manual segmentation as reference standard. Global and segmental T1ρ values were compared. Processing times were measured. STATISTICAL TESTS: Intraclass correlation coefficients (ICCs) and Bland-Altman analysis (bias ±2SD); Paired Student's t-tests and one-way ANOVA. A P value <0.05 was considered significant. RESULTS: The automated approach significantly reduced processing time (3 seconds vs. 1 minute 51 seconds ± 22 seconds). In the test set, automated LV wall segmentation closely matched manual results (Dice 81.9% ± 9.0) and closely aligned with interobserver segmentation (Dice 82.2% ± 6.5). Excellent ICCs were achieved on a patient basis (0.94 [95% CI: 0.91 to 0.96]) with bias of -0.93 cm2 ± 6.60. There was no significant difference in global T1ρ values between manual (54.9 msec ± 4.6; 95% CI: 53.8 to 56.0 msec, range: 46.6-70.9 msec) and automated processing (55.4 msec ± 5.1; 95% CI: 54.2 to 56.6 msec; range: 46.4-75.1 msec; P = 0.099). The pipeline demonstrated a high level of agreement with manual-derived T1ρ values at the patient level (ICC = 0.85; bias +0.52 msec ± 5.18). No significant differences in myocardial T1ρ values were found between methods across the 16 segments (P = 0.75). DATA CONCLUSION: Automated myocardial T1ρ mapping shows promise for the rapid and noninvasive assessment of heart disease. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Artificial intelligence-driven volumetric CT outcome score in cystic fibrosis: longitudinal and multicenter validation with/without modulators treatment.

OBJECTIVES: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations. MATERIALS AND METHODS: The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score. RESULTS: A total of 139 CF patients (median age, 15 years [interquartile range: 13-25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99. CONCLUSION: NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI. CLINICAL RELEVANCE STATEMENT: Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials. KEY POINTS: Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF). AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments. AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.

Relationships between asbestos exposure and pleural plaques: dose and time effects using fractional polynomials.

OBJECTIVE: The aim of this study was to confirm the relationship between several parameters of exposure to asbestos and pleural plaques (PP) using data from a large cohort of retired workers occupationally exposed to asbestos in France. METHOD: A large screening programme, including high-resolution CT (HRCT) examinations at inclusion and two other HRCT campaigns, was organised from 2003 to 2016 in four regions of France for voluntary, formerly asbestos-exposed workers. Exposure to asbestos has been evaluated by industrial hygienists based on the complete work history. The time since first exposure, the time since last exposure, Cumulative Exposure Index and maximum level of exposure to asbestos, were used in logistic regression using fractional polynomials to model the relationship with PP. RESULTS: The study included 5392 subjects with at least one HRCT available. There was a significant non-linear effect of time since first exposure, time since last exposure and Cumulative Exposure Index to asbestos on the presence of PP. The risk of PP increased with increasing Cumulative Exposure Index to asbestos adjusted for time since first exposure, age and smoking status. Models also show that PP odds rise with increasing time since first exposure adjusted for cumulative index exposure, age and smoking status. PP odds decrease when time since last exposure increases. CONCLUSION: The study provides new data on the link between asbestos exposure and the presence of PP using fractional polynomials with non-linear relationships for time exposure parameters and asbestos exposure parameters.

Radiological Diagnosis of Pulmonary Aspergillosis.

Imaging plays an important role in the various forms of Aspergillus-related pulmonary disease. Depending on the immune status of the patient, three forms are described with distinct imaging characteristics: invasive aspergillosis affecting severely immunocompromised patients, chronic pulmonary aspergillosis affecting less severely immunocompromised patients but suffering from a pre-existing structural lung disease, and allergic bronchopulmonary aspergillosis related to respiratory exposure to Aspergillus species in patients with asthma and cystic fibrosis. Computed tomography (CT) has been demonstrated more sensitive and specific than chest radiographs and its use has largely contributed to the diagnosis, follow-up, and evaluation of treatment in each condition. In the last few decades, CT has also been described in the specific context of cystic fibrosis. In this particular clinical setting, magnetic resonance imaging and the recent developments in artificial intelligence have shown promising results.