Diffusion Tensor Imaging in the Diagnosis of Perianal Abscess: Protocol for a Single-Blind Randomized Controlled Trial

Background: Perianal abscesses are common anorectal conditions that often necessitate surgical intervention. Accurate preoperative assessment is crucial for effective treatment and reducing recurrence rates. Diffusion tensor imaging (DTI) is a valuable method for visualizing the degree of infection and infiltration, the extent of abscess formation, and the relationship between perianal abscess erosion. However, there is currently a lack of specific studies focusing on perianal abscesses. Objective: The objective of this study is to investigate the diagnostic utility of DTI in the preoperative assessment of perianal abscesses. By enhancing the precision of preoperative evaluation, we aim to minimize damage to the anal sphincter, reduce the recurrence rate, and improve the prognosis and quality of life for patients. Methods: This study adopts a randomized, prospective, single-blind design. Ninety participants are being randomized into 2 groups: a DTI group and a conventional magnetic resonance imaging group. A structured report is being completed based on imaging of the perianal abscesses in terms of location, number, specific pus cavity alignment, thickness, and relationship to the surrounding muscles. In addition, the patient condition is being assessed, and corresponding surgical treatment is being performed. If the patient's blood routine shows infection, cefuroxime sodium combined with metronidazole is being administered intravenously as an anti-infective therapy. Postoperatively, the primary variable is being assessed for recurrence rate at 6 months, and the secondary variables, including postoperative pain scores on days 3 and 7, peripheral blood inflammatory factors, and assessment of anal function, are being evaluated. Normally distributed continuous data will be presented as mean (SD) and analyzed using independent or paired t tests. Non-normally continuous data will be analyzed with rank-sum tests. Categorical data will be expressed as frequency (%) and compared using a chi-square test or appropriate nonparametric tests. Ordinal data will be analyzed using the Ridit test. A P value