Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection. Issue 10 (12th September 2022)

• Record Type: Journal Article
• Authors: Kawashima, Hiroki; Ichikawa, Katsuhiro; Takata, Tadanori; Seto, Issei
• Is Part Of: Medical physics. Volume 49:Issue 10(2022)
• Page Start: 6359
• Page End: 6367
• Publication Date: 2022-09-12
• Subjects: computed tomography -- deep learning image reconstruction -- noise reduction; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15918
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Two deep learning image reconstruction (DLIR) techniques from two different computed tomography (CT) vendors have recently been introduced into clinical practice. Purpose: To characterize the noise properties of two DLIR techniques with different training methods, using a phantom containing a simple uniform and a complex non‐uniform region. Methods: A water‐bath phantom with a diameter of 300 mm was used as a base phantom. A textured phantom with a diameter of 128 mm, which was made of two materials, one equivalent to water and the other being 12 mg/ml diluted iodine, irregularly mixed to create a complex texture (non‐uniform region), was placed in the base phantom. Thirty repeated phantom scans were performed using two CT scanners (Revolution CT with Apex Edition, GE Healthcare; Aquilion One PRISM Edition, Canon Medical Systems) at two dose levels (CT dose index: 5 and 15 mGy). Images were reconstructed with each CT system's filtered back projection (FBP) and DLIR [TrueFidelity (TF), GE Healthcare; Advanced intelligent Clear‐IQ Engine Body Sharp (AC), Canon Medical Systems] for three process strengths. For basic characteristics of noise, the standard deviation (SD) and noise power spectrum (NPS) were measured for the uniform (water) region. A noise magnitude map was generated by calculating the inter‐image SD at each pixel position across the 30 images. Then, a noise reduction map (NRM), which visualizes the relative differences in noise magnitude between FBP and DLIR, was calculated. The NRM values ranged from 0.0 to 1.0. A low NRM value represents a less aggressive noise reduction. The histograms of the NRM value were analyzed for the uniform and non‐uniform regions. Results: The reduction in noise magnitude compared with FBP tended to be greater with AC (45%–85%) than with TF (32%–65%). The average NPS frequencies of TF and AC were almost comparable to those of FBP, except for the low‐dose condition and the high noise reduction strength for AC. The NRM values of TF and AC were higher in the uniform region than in the non‐uniform region. In the non‐uniform region, TF's average NRM values (0.21–0.48) tended to be lower than AC's (0.39–0.78). The histograms for TF showed a small overlap between the uniform and the non‐uniform regions; in contrast, those for AC showed a greater overlap. This difference seems to indicate that TF processes the uniform and non‐uniform regions more differently than AC does. Conclusion: This study has revealed a distinct difference in characteristics between the two DLIR techniques: TF tends to offer less aggressive noise reduction in non‐uniform regions and preserve the original signals, whereas AC tends to prioritize noise filtering over edge‐preservation, especially at the low‐dose condition and with the high noise reduction strength.