AIM:To evaluate the reliability of multi-contrast dental MRI (dMRI) for periodontal lesion assessment in comparison to cone-beam computed tomography (CBCT). Associations between imaging findings and clinical indicators of periodontal inflammation (i.e., bleeding on probing [BOP], periodontal probing depth [PPD] and periodontal inflamed surface area [PISA]) were analysed to explore the potential of dMRI to reflect disease status and inflammatory burden. MATERIALS AND METHODS:In this prospective study, patients with severe periodontitis underwent clinical examination, CBCT and 3T dMRI. Site-level detectability of periodontal osseous defects and/or marrow signal changes at the six standard clinical probing sites per tooth, as well as tooth-level lesion volumetry, were evaluated across CBCT, T1-weighted (T1W), contrast-enhanced T1W and T2-weighted (T2W). Reliability (κ, ICC) and clinical-imaging associations (Kruskal-Wallis and mixed-effect models) were analysed. RESULTS:Nineteen patients with severe periodontitis stages III and IV were included. Reliability of dMRI matched CBCT (site-level κ = 0.631-0.771; tooth-level ICC = 0.832-0.973). Increasing PPD and BOP raised lesion detectability and volumes across all modalities, with dMRI being consistently more sensitive than CBCT. T1W MRI showed lesions and bone loss similar to CBCT and was unaffected by BOP (p = 0.132) or PPD (p = 0.252). T2W MRI identified most lesions regardless of PPD and reflected high correlations with BOP (p < 0.05), PPD (p < 0.001) and PISA (p < 0.001). CONCLUSIONS:Reliability of dMRI for lesion identification and quantification was comparable to that of CBCT. The presence of T2W marrow oedema at sites with a CBCT-normal periodontal ligament space and no radiographically detectable bone loss may represent a promising imaging marker for early periodontal disease recognition and assessment of inflammatory burden.

Abstract Purpose To evaluate the reliability and accuracy of optimized dental-MRI (dMRI) for detecting and classifying peri-implant bone defects around single titanium implants. Methods 48 titanium implants were inserted into bovine ribs. 24 implants had surrounding standardized defects (1-wall, 2-wall, 3-wall, 4-wall) in two sizes (1 mm and 3 mm), while the other 24 implants showed no peri-implant bone defects. CBCT and dMRI were performed and analyzed twice by two readers. 3 Tesla MRI was conducted using an innovative intraoral coil and a novel, T1-weighted sequence for metal artifact reduction (CS-SEMAC). Cohen's Kappa (κ), sensitivity, and specificity were assessed for bone defect detection, size, and type. Modality differences were evaluated using the exact McNemar test and Firth penalized-likelihood logistic regression. Results For bone defect detection, almost perfect intra-/inter-rater reliability ( κ- value range: 0.958–1) was found for both imaging modalities, with high sensitivity/specificity (CBCT 100%/100%, 95% CI 0.962–1.000/0.962–1.000; MRI 98%/100%, 95% CI 0.927–0.998/0.962–1.000), confirmed by Firth regression ( p = 0.22). For defect type classification, both CBCT ( κ- value range: 0.884–0.913) and MRI ( κ -value range: 0.794–0.942) demonstrated almost perfect intra-/inter-rater reliability. Overall sensitivity/ specificity were 86%/99% (95% CI 0.780–0.926 and 0.985–0.995) for CBCT and 84%/99% (95% CI 0.755–0.910 and 0.985–0.995) for dMRI, with no significant difference between modalities (Firth-adjusted OR 0.85, 95% CI 0.38–1.88, p = 0.68). Conclusions Under optimized ex vivo conditions, dMRI offers a promising reliability and accuracy for identifying and classifying peri-implant bone defects around single titanium implants. High-resolution dMRI facilitates radiation-free, three-dimensional peri-implant tissue visualization.

BACKGROUND:Magnetic resonance fingerprinting (MRF) is an emerging quantitative imaging technique that enables multiparametric tissue characterization, but its adoption has been hindered by the complexity of data acquisition and post-processing. These technical and implementation challenges have limited its broader clinical deployment. PURPOSE:To develop a modular MRF Development Kit (MRFDK) that enables efficient sequence design, streamlined implementation, and real-time image reconstruction. STUDY TYPE:Prospective. POPULATION:T1 and T2 relaxation phantom, nine volunteers (seven males and two females), five metastatic brain cancer patients. FIELD STRENGTH/SEQUENCE:3 T, MR Fingerprinting. ASSESSMENT:Accuracy of T1 and T2 quantification was estimated from phantom experiments. Manual ROIs were drawn on brain lesions and contralateral white matter for metastatic cancer patients. STATISTICAL TESTS:t-test, in vivo repeatability was calculated with Bland-Altman analysis on healthy volunteer scan-rescan data, significance level p < 0.01. RESULTS:Phantom results showed high accuracy in T1 and T2 assessment, with absolute percentage differences of 3% for T1 and 5% for T2 compared to offline MATLAB reconstruction. In vivo scans of eight healthy subjects further demonstrated excellent repeatability (bias and agreement: 0.95% ± 1.85% for T1; 1.78% ± 5.08% for T2). In patients, metastatic lesions showed significantly higher T1 and T2 values (T1, 1474 ms; T2, 61 ms) compared to normal white matter (T1, 913 ms; T2, 38 ms). With integrated B1 correction, all T1 and T2 maps were available for visualization within 1 min post-MRF scan, enabling immediate image assessment. DATA CONCLUSION:A modular MRF development package enabling efficient 3D acquisition and rapid inline reconstruction was developed and evaluated in this study. LEVEL OF EVIDENCE: 1: TECHNICAL EFFICACY:Stage 2.

Periodontitis is characterized by the inflammatory destruction of tooth-supporting alveolar bone. Dental magnetic resonance imaging (MRI) using dynamic contrast-enhanced perfusion can potentially detect vascular inflammatory responses. This study aims to assess the feasibility of perfusion dental MRI and characterize periodontal lesions with perfusion profiles. In this prospective study, 19 patients with severe periodontitis underwent pretreatment 3-T dental MRI with T2-weighted, high-resolution dynamic contrast-enhanced T1-weighted perfusion protocol, and contrast-enhanced T1-weighted fat-suppressed sequences as well as cone-beam computed tomography (CBCT). Periodontal bone lesions were segmented semiautomatically using a multistep threshold-based algorithm, guided by T1-weighted contrast enhancement, T2-weighted hyperintensity, as well as CBCT-based bone loss. Volumetric analyses and clinical data were compared with perfusion parameters. In all 95 assessed periodontal lesions, perfusion parameter elevations were significantly different when compared to normal distant bone (p < 0.001 to 0.026). Moreover, structurally normal-appearing bone adjacent to T2-hyperintense/T1-contrast-enhancing signal alterations exhibited increased permeability (p = 0.036–006) but showed no significant change in blood flow (p = 0.270) compared to bone control areas. Lesions with bleeding showed higher vascular permeability and blood flow markers than lesions without bleeding (p = 0.004–0.006). Additionally, lesions with excessive edema and areas of bone loss exhibited significantly elevated permeability and blood flow parameters (p = 0.001–0.028). Perfusion dental MRI for periodontal lesion assessment is feasible. Permeability/perfusion parameters elevations are related to clinical signs of inflammation and CBCT-based bone loss, with the potential for detecting early inflammatory responses. Perfusion dental MRI effectively characterizes periodontal disease by detecting inflammation-related vascular changes beyond structural imaging on CBCT and conventional MR, offering potential for improved diagnosis, monitoring, and treatment evaluation. Longitudinal studies are needed.

Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T1 and T2. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network.