Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

• Published in: European radiology 2023-03, Vol.33 (3), p.1629-1640
• Main Authors: Lyu, Peijie, Liu, Nana, Harrawood, Brian, Solomon, Justin, Wang, Huixia, Chen, Yan, Rigiroli, Francesca, Ding, Yuqin, Schwartz, Fides Regina, Jiang, Hanyu, Lowry, Carolyn, Wang, Luotong, Samei, Ehsan, Gao, Jianbo, Marin, Daniele
• Format: Article
• Language: English
• Subjects: Algorithms; Computed Tomography; Datasets; Deep Learning; Diagnostic Radiology; Humans; Image acquisition; Image contrast; Image processing; Image Processing, Computer-Assisted; Image quality; Image reconstruction; Imaging; Internal Medicine; Interventional Radiology; Lesions; Liver; Liver cancer; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - secondary; Medical imaging; Medicine; Medicine & Public Health; Metastases; Metastasis; Neuroradiology; Phantoms, Imaging; Prospective Studies; Radiation; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Reduction; Tomography, X-Ray Computed - methods; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-09206-3

Objectives To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). Methods A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. Results The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: −0.112; 95% confidence interval [CI]: −0.178 to 0.047) and full-dose IR (difference: −0.123; 95% CI: −0.182 to 0.053) ( p < 0.001). Conclusion DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. Key Points • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).