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Purpose: This study aimed to investigate differences in cervical lymph node image quality on dual-energy computed tomography (CT) scan with datasets reconstructed using filter back projection (FBP), hybrid iterative reconstruction (IR), and deep learning-based image reconstruction (DLIR) in patients with head and neck cancer. Method: Seventy patients with head and neck cancer underwent follow-up contrast-enhanced dual-energy CT examinations. All datasets were reconstructed using FBP, hybrid IR with 30 % adaptive statistical IR (ASiR-V), and DLIR with three selectable levels (low, medium, and high) at 2.5- and 0.625-mm slice thicknesses. Herein, signal, image noise, signal-to-noise ratio, and contrast-to-noise ratio of lymph nodes and overall image quality, artifact, and noise of selected regions of interest were evaluated by two radiologists. Next, cervical lymph node sharpness was evaluated using full width at half maximum. Results: DLIR exhibited significantly reduced noise, ranging from 3.8 % to 35.9 % with improved signal-to-noise ratio (11.5-105.6 %) and contrast-to-noise ratio (10.5-107.5 %) compared with FBP and ASiR-V, for cervical lymph nodes (p < 0.001). Further, 0.625-mm-thick images reconstructed using DLIR-medium and DLIR-high had a lower noise than 2.5-mm-thick images reconstructed using FBP and ASiR-V. The lymph node margins and vessels on DLIR-medium and DLIR-high were sharper than those on FBP and ASiR-V (p < 0.05). Both readers agreed that DLIR had a better image quality than the conventional reconstruction algorithms. Conclusion: DLIR-medium and -high provided superior cervical lymph node image quality in head and neck CT. Improved image quality affords thin-slice DLIR images for dose-reduction protocols in the future.
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OBJECTIVES: This study assessed the incidence of postfracture radiological temporomandibular joint (TMJ) degeneration in patients with different types of mandibular fractures, focusing on the impact of condylar fractures. METHODS: This retrospective review included patients diagnosed as having mandibular fractures from 2016 to 2020 who had undergone initial computed tomography (CT) and a follow-up CT scan at least 1-month postfracture. Patient demographics, fracture details, treatment methods, and radiological signs of TMJ degeneration on CT were analyzed to identify risk factors for postfracture TMJ degeneration, with a focus on condylar head fracture and non-head (condylar neck or base) fractures. RESULTS: The study included 85 patients (mean age: 38.95 ± 17.64 years). The per-patient analysis indicated that the incidence of new radiologic TMJ degeneration on CT was significantly the highest (p < 0.001) in patients with condylar head fractures (90.91%), followed by those with non-head condylar fractures (57.14%), and those without condylar involvement (24.49%). The per-joint analysis indicated nearly inevitable degeneration (93.94%) in 33 TMJs with ipsilateral condylar head fractures. For the remaining 137 TMJs, multivariate logistic regression revealed that other patterns (ipsilateral non-head, contralateral, or both) of condylar fractures (odds ratio (OR) = 3.811, p = 0.007) and the need for open reduction and internal fixation (OR = 5.804, p = 0.005) significantly increased the risk of TMJ degeneration. CONCLUSIONS: Ipsilateral non-head condylar fractures and contralateral condylar fractures are associated with a high risk of postfracture TMJ degeneration. Indirect trauma plays a vital role in postfracture TMJ degeneration.