Image quality improvement of liver ultrasound using unsupervised deep learning

From January to April 2022, we prepared two categories of datasets for training our deep learning-based image quality improvement algorithm, i.e., 1) liver US obtained by a US machine >10 years old, consequently having quality deterioration, served as an input, and 2) liver US obtained by a high-end US machine manufactured within last five years, served as a target (Fig. 1). Basically, all liver ultrasound scans included a minimum of 10 images (ranging from 10 to 38). These images encompassed suitable liver and gallbladder visuals, with no cine clips present. Additionally, all images were acquired using a 1-6 MHz convex transducer. For an input dataset, we randomly selected 500 liver US (training sets: validation sets: test sets = 350: 50:100) from 746 consecutively enrolled examinations from January 2016 to February 2018, performed by a hepatologist with 20 years of experience, using a 12-year-old US system (SSD-alpha 10 Ultrasound System, Aloka Co., Ltd., Japan). For a target dataset, we randomly selected 400 out of 2,652 liver US performed by one of three board-certified abdominal radiologists with more than 15 years of experience (E.S.L., H.J.P., and B.I.C.) from December 2020 to December 2021. All US of target datasets were obtained by a high-end US machine (Aplio i900, Canon Medical Systems, Japan), manufactured within the last 5 years from the date of examination.

2022年1月至4月，我们为训练基于深度学习的图像质量增强算法构建了两类数据集：1）以使用时长超10年的超声设备采集的肝脏超声（Ultrasound，以下简称US）（存在质量退化问题）作为输入样本；2）以检查当日起5年内出厂的高端超声设备采集的肝脏超声（Ultrasound，以下简称US）作为目标样本（图1）。
原则上，所有纳入的肝脏超声扫描均包含至少10幅图像（图像数量范围为10至38幅），涵盖合格的肝脏与胆囊成像视野，且不含动态cine片段。此外，所有图像均采用1-6 MHz凸阵探头采集。
对于输入数据集，我们从2016年1月至2018年2月期间连续入组的746例检查中随机选取500例肝脏超声，按照训练集:验证集:测试集=350:50:100的比例划分；该系列检查由拥有20年从业经验的肝脏科医师使用一台使用时长12年的超声系统（SSD-alpha 10 Ultrasound System，日本Aloka株式会社）完成。
对于目标数据集，我们从2020年12月至2021年12月期间，由3名具备15年以上腹部放射诊断从业经验的持证腹部放射医师（E.S.L.、H.J.P.与B.I.C.）完成的2652例肝脏超声中随机选取400例。所有目标数据集的超声图像均采用检查当日起5年内出厂的高端超声设备（Aplio i900，日本佳能医疗系统株式会社）采集。