[Development of CT Pelvimetry Using Deep Learning Based Reconstruction].

Purpose: X-ray pelvimetry is typically performed for the diagnosis of the cephalopelvic disproportion (CPD). The purpose of this study was to assess the utility of new computed tomography (CT) reconstruction "deep learning based reconstruction (DLR) " in ultra-low dose CT pelvimetry.

Method: CT pelvimetry was performed 320-row CT. All CT images were reconstructed with and without DLR and transferred for workstation to processing martius and guthmann view. Radiologist and obstetrician-gynecologist subjectively ranked overall image quality of each CT image from the best to the worst. Exposure dose of the CT pelvimetry used a following calculated value, displayed CT dose index (CTDI) multiplied by measured value using the thimble chamber and pelvic phantom, and of the X-ray pelvimetry used Japan-Diagnositic Refernce Levels 2015 as a reference, were compared.

Result: 3D images obtained from CT pelvimetry with DLR showed accurate biparietal diameter and obstetric conjugate as compared to without DLR. Radiation dose of CT pelvimetry is 0.39 mGy, of X-ray pelvimetry is 1.18 mGy, respectively. Conculusion: Although the visualizing high contrast object, such as bone morphology, is likely to reduce exposure dose in CT examination generally, DLR enable to further dose reduction to keep image quality. 3D image processing from CT pelvimetry solves the problem of expansion rate in X-P pelvimetry and provide accurate measurements. Furthermore, CT pelvimetry can undergo more comfortable position for Pregnant Woman in Labor.