Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction

Abstract

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images.

Materials and methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM).

Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581).

Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.

Keywords: Abdomen; Computed tomography; Deep learning; Image reconstruction; Radiation dose.

Fig. 1. Flow chart of patient selection.

LDCT = lower-dose CT, SDCT = standard-dose CT

Fig. 2. Follow-up SDCT and LDCT protocol contrast-enhanced abdominopelvic CT images of a 61-year-old male with colon cancer; SDCT and LDCT studies were performed within one year of each other.

A-D. Axial CT images were taken at the same anatomic level to compare image quality between the SDCT protocol (A) and LDCT protocol (B-D). SDCT images were reconstructed with h-IR (A), while LDCT images were reconstructed with h-IR (B), DLIR-M (C), and DLIR-H (D). Image noise in the liver in CT images A, B, C, and D is 10.8, 16.1, 12.8, and 8.4, respectively. Quantitative measures of image noise, signal-to-noise ratio and contrast-to-noise ratio, show equivalence between SDCT with h-IR images (A) and LDCT with DLIR-M images (C). DLIR-H = deep learning image reconstruction high-strength, DLIR-M = deep learning image reconstruction medium-strength, h-IR = hybrid iterative reconstruction, LDCT = lower-dose CT, SDCT = standard-dose CT

Fig. 3. A side-to-side comparison of qualitative measures between SDCT with h-IR images (A) and LDCT with DLIR-M images (B) of a 55-year-old male with colon cancer.

A, B. Black arrows show a focal liver lesion being assessed for lesion conspicuity. In terms of overall image quality and lesion conspicuity, all readers interpreted the LDCT with DLIR-M images (B) as being marginally superior (all score 4) to the SDCT with h-IR images (A). Although the image texture of SDCT with h-IR images was preferred over that of LDCT with DLIR-M images, the qualitative parameters of LDCT with DLIR-M images including overall image quality, image noise, image sharpness, and lesion conspicuity were comparable or superior to those of SDCT with h-IR images. DLIR-M = deep learning image reconstruction medium-strength, h-IR = hybrid iterative reconstruction, LDCT = lower-dose CT, SDCT = standard-dose CT

Fig. 4. Results of the quantitative image analysis

^*Statistically significant difference (p < 0.001). A, C. The bar graphs show no significant difference between SDCT with h-IR images and LDCT with DLIR-M images in terms of image noise (A) and SNR (C). B, D, F. Equivalence tests show that LDCT with DLIR-M images are equivalent to SDCT with h-IR images with their prespecified margins (colored boxes in each figure part) in terms of image noise (B), SNR (D), and CNR (F). E. Regarding CNR, all LDCT images are significantly different from SDCT with h-IR images regardless of reconstruction method, but the difference between the mean values in LDCT with DLIR-M images and SDCT with h-IR images is the smallest. CNR = contrast-to-noise ratio, DLIR-H = deep learning image reconstruction high-strength, DLIR-M = deep learning image reconstruction medium-strength, h-IR = hybrid iterative reconstruction, LDCT = lower-dose CT, SDCT = standard-dose CT, SNR = signal-to-noise ratio