Multiparametric Diagnostic Model of Thick-section Clinical-quality MRI Data in Detecting Migraine Without Aura

Multiparametric Diagnostic Model of Thick-section Clinical-quality MRI Data in Detecting Migraine Without Aura

Recently, radiomics combined with machine learning method has been widely used in clinical practice. Compared with traditional imaging studies that explore the underlying mechanisms, the machine learning method focuses on classification and prediction to propose personalized diagnosis and treatment strategies. However, these studies were based on thin-section research-quality brain MR imaging with section thickness of < 2 mm. Clinical, the usage of thick-section clinical setting instead of thin-section research setting is especially important to shorten the acquisition time to reduce the patient's suffering. Here investigators want to build multiparametric diagnostic model of migraineurs without aura using radiomics features extracted from thick-section clinical-quality brain MR images.

Inclusion Criteria:

• right-handed
• International Headache Society criteria for episodic migraine without aura

Exclusion Criteria:

• addition (including alcohol, nicotine, or drug)
• physical illness