T1 and T2 Mapping for Characterization of Mediastinal Masses: A Feasibility study.
Kyongmin Sarah BeckSuyon ChangKwanyong HyunYeoun Eun SungKyo-Young LeeJung Im JungPublished in: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes (2023)
Purpose: To evaluate the feasibility and usefulness of T1 and T2 mapping in characterization of mediastinal masses. Methods: From August 2019 through December 2021, 47 patients underwent 3.0-T chest MRI with T1 and post-contrast T1 mapping using modified look-locker inversion recovery sequences and T2 mapping using a T2-prepared single-shot shot steady-state free precession technique. Mean native T1, native T2, and post-contrast T1 values were measured by drawing the region of interest in the mediastinal masses, and enhancement index (EI) was calculated using these values. Results: All mapping images were acquired successfully, without significant artifact. There were 25 thymic epithelial tumors (TETs), 3 schwannomas, 6 lymphomas, and 9 thymic cysts, and 4 other cystic tumors. TET, schwannoma, and lymphoma were grouped together as "solid tumor," to be compared with thymic cysts and other tumors ("cystic tumors"). The mean post-contrast T1 mapping ( P < .001), native T2 mapping ( P < .001), and EI ( P < .001) values showed significant difference between these two groups. Among TETs, high risk TETs (thymoma types B2, B3, and thymic carcinoma) showed significantly higher native T2 mapping values (P = .002) than low risk TETs (thymoma types A, B1, and AB). For all measured variables, interrater reliability was good to excellent (intraclass coefficient [ICC]: .869∼.990) and intrarater reliability was excellent (ICC: .911∼.995). Conclusion: The use of T1 and T2 mapping in MRI of mediastinal masses is feasible and may provide additional information in the evaluation of mediastinal masses.
Keyphrases
- contrast enhanced
- high resolution
- ultrasound guided
- high density
- lymph node
- magnetic resonance
- magnetic resonance imaging
- fine needle aspiration
- computed tomography
- diffusion weighted imaging
- ejection fraction
- machine learning
- mass spectrometry
- healthcare
- deep learning
- diffuse large b cell lymphoma
- chronic kidney disease
- optical coherence tomography