Improvement of the Diagnostic Performance of Facial Neuritis Using Contrast-Enhanced 3D T1 Black-Blood Imaging: Comparison with Contrast-Enhanced 3D T1-Spoiled Gradient-Echo Imaging

Lee, Seun-Ah; Jo, Sang-Won; Chang, Suk-Ki; Kwon, Ki-Han

doi:10.3390/jcm10091850

Cited by 2 publications

(1 citation statement)

References 44 publications

(64 reference statements)

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“…Recently, a contrast-enhanced 3D-T1-turbo spin-echo (TSE) black-blood sequence has gained attention, showing optimal diagnostic performance in depicting cranial nerve enhancement as it suppresses signals from vessels, including the near venous plexuses, and provides an increased contrast-to-noise ratio [ 9 , 10 ].…”

Section: Data Collectionmentioning

confidence: 99%

Extra-axial cranial nerve enhancement: a pattern-based approach

Moltoni,

Romano,

Blandino

et al. 2023

Radiol med

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Cranial nerve enhancement is a common and challenging MRI finding that requires a meticulous and systematic evaluation to identify the correct diagnosis. Literature mainly describes the various pathologies with the associated clinic-radiological characteristics, while the radiologist often needs a reverse approach that starts from the radiological findings to reach the diagnosis. Therefore, our aim is to provide a new and practical pattern-based approach to cranial nerve enhancement, which starts from the radiological findings and follows pattern-driven pipelines to navigate through multiple differential diagnoses, guiding the radiologist to reach the proper diagnosis. Firstly, we reviewed the literature and identified four patterns to categorize the main pathologies presenting with cranial nerve enhancement: unilateral linear pattern, bilateral linear pattern, unilateral thickened pattern, and bilateral thickened pattern. For each pattern, we describe the underlying pathogenic origin, and the main radiological features are displayed through high-quality MRI images and illustrative panels. A suggested MRI protocol for studying cranial nerve enhancement is also provided. In conclusion, our approach for cranial nerve enhancement aims to be an easy tool immediately applicable to clinical practice for converting challenging findings into specific pathological patterns.

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Section: Data Collectionmentioning

confidence: 99%

Extra-axial cranial nerve enhancement: a pattern-based approach

Moltoni,

Romano,

Blandino

et al. 2023

Radiol med

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Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection

Huang

Zheng

et al. 2021

Journal of Healthcare Engineering

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The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with severe pulmonary infection. A new lung CT image segmentation algorithm (U-Net+ deep convolution (DC)) was proposed based on U-Net network and compared with convolutional neural network (CNN) algorithm. Then, it was applied to CT image diagnosis of 100 patients with severe lung infection in The Second Affiliated Hospital of Fujian Medical University hospital and compared with traditional methods, and its sensitivity, specificity, and accuracy were compared. It was found that the single training time and loss of U-Net + DC algorithm were reduced by 59.4% and 9.8%, respectively, compared with CNN algorithm, while Dice increased by 3.6%. The lung contour segmented by the proposed model was smooth, which was the closest to the gold standard. Fungal infection, bacterial infection, viral infection, tuberculosis infection, and mixed infection accounted for 28%, 18%, 7%, 7%, and 40%, respectively. 36%, 38%, 26%, 17%, and 20% of the patients had ground-glass shadow, solid shadow, nodule or mass shadow, reticular or linear shadow, and hollow shadow in CT, respectively. The incidence of various CT characteristics in patients with fungal and bacterial infections was statistically significant ( P < 0.05 ). The specificity (94.32%) and accuracy (97.22%) of CT image diagnosis based on U-Net + DC algorithm were significantly higher than traditional diagnostic method (75.74% and 74.23%), and the differences were statistically significant ( P < 0.05 ). The network of the algorithm in this study demonstrated excellent image segmentation effect. The CT image based on the U-Net + DC algorithm can be used for the diagnosis of patients with severe pulmonary infection, with high diagnostic value.

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Improvement of the Diagnostic Performance of Facial Neuritis Using Contrast-Enhanced 3D T1 Black-Blood Imaging: Comparison with Contrast-Enhanced 3D T1-Spoiled Gradient-Echo Imaging

Cited by 2 publications

References 44 publications

Extra-axial cranial nerve enhancement: a pattern-based approach

Extra-axial cranial nerve enhancement: a pattern-based approach

Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection

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