title: Improved image quality and diagnostic potential using ultra-high-resolution computed tomography of the lung with small scan FOV: A prospective study.
abstract: The aim of this study was to assess whether CT imaging using an ultra-high-resolution CT (UHRCT) scan with a small scan field of view (FOV) provides higher image quality and helps to reduce the follow-up period compared with a conventional high-resolution CT (CHRCT) scan. We identified patients with at least one pulmonary nodule at our hospital from July 2015 to November 2015. CHRCT and UHRCT scans were conducted in all enrolled patients. Three experienced radiologists evaluated the image quality using a 5-point score and made diagnoses. The paired images were displayed side by side in a random manner and annotations of scan information were removed. The following parameters including image quality, diagnostic confidence of radiologists, follow-up recommendations and diagnostic accuracy were assessed. A total of 52 patients (62 nodules) were included in this study. UHRCT scan provides a better image quality regarding the margin of nodules and solid internal component compared to that of CHRCT (P < 0.05). Readers have higher diagnostic confidence based on the UHRCT images than of CHRCT images (P<0.05). The follow-up recommendations were significantly different between UHRCT and CHRCT images (P<0.05). Compared with the surgical pathological findings, UHRCT had a relative higher diagnostic accuracy than CHRCT (P > 0.05). These findings suggest that the UHRCT prototype scanner provides a better image quality of subsolid nodules compared to CHRCT and contributes significantly to reduce the patients' follow-up period.
authors.affiliation: Department of Radiology, Pulmonary Hospital Affiliated to Tongji University, Shanghai, China.
authors.forename: Huiyuan
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Department of Radiology, Pulmonary Hospital Affiliated to Tongji University, Shanghai, China.|Department of Radiology, Jiading Hospital of Traditional Chinese Medicine, Shanghai, China.
authors.forename: Lian
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Pulmonary Hospital Affiliated to Tongji University, Shanghai, China.
authors.forename: Yali
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Radiology, Pulmonary Hospital Affiliated to Tongji University, Shanghai, China.
authors.forename: Preeti
authors.initials: P
authors.lastname: Hamal
authors.affiliation: Department of Radiology, Pulmonary Hospital Affiliated to Tongji University, Shanghai, China.
authors.forename: Xiaofang
authors.initials: X
authors.lastname: You
authors.affiliation: Department of Radiology, Pulmonary Hospital Affiliated to Tongji University, Shanghai, China.
authors.forename: Haixia
authors.initials: H
authors.lastname: Mao
authors.affiliation: Department of Radiology, Jiading Hospital of Traditional Chinese Medicine, Shanghai, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Li
authors.affiliation: Department of Radiology, Pulmonary Hospital Affiliated to Tongji University, Shanghai, China.
authors.forename: Xiwen
authors.initials: X
authors.lastname: Sun
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*