Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction

Background: X-ray computed tomography is the first imaging technology that supports accurate nondestructive interior image reconstruction of an object from sufficient projection data. Low-dose computed tomography (LDCT) has been considered to relieve the harm to patients caused by X-ray radiation. However, LDCT images can be degraded by quantum noise and streak artifacts. Methods: The objective of the authors’ study is to evaluate the optimal level of the hybrid iterative reconstruction (HIR) that generates images with the best diagnostic quality on different dose and noise levels. HIR with optimizations is proposed to reduce image noise and provide better performance at a low dose. The Catphan R 504 phantom is employed to assess various image qualities (IQ). Results: For any given scanning protocols, there is linear noise reduction and linear increase of contrast-to- noise ratio (CNR) using optimal HIR. The evidence from various module tests demonstrates that the shape of the noise power spectrum is continuously shifted to low frequency with increasing HIR levels compared with that of filtered-back-projection (FBP). This may describe the difference between the human observer performance and features of the ideal low-contrast objects. Conclusion: Optimal HIR is clearly demonstrated to be a superior method for reducing image noise and improving CNR compared to FBP. Optimal HIR also inhibits texture change or spectrum shift compared with the pure IR method. Even though there are continuous noise reduction and CNR increase with HIR at increasing levels, the human observer performance does not seem to improve simultaneously due to coarser noise (low-frequency noise). HIR level 3 to 5 is optimal for their study. It is possible for the optimal HIR to offer equivalent diagnostic IQ at a lower dose compared with FBP at a routine dose.

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Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

British Journal of Radiology ◽

10.1259/bjr.20201329 ◽

2021 ◽

Vol 94 (1121) ◽

pp. 20201329

Author(s):

Yoshifumi Noda ◽

Tetsuro Kaga ◽

Nobuyuki Kawai ◽

Toshiharu Miyoshi ◽

Hiroshi Kawada ◽

...

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Image Quality ◽

Low Dose ◽

Standard Dose ◽

Lesion Detection ◽

Whole Body ◽

Hybrid Iterative Reconstruction ◽

Whole Body Ct ◽

Attenuation Values

Objectives: To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR). Methods: The study cohort of 59 consecutive patients (mean age, 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDIvol) and dose-length product (DLP) were compared between SD and LD scans. Results: The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28–0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDIvol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy•cm) than in the SD (13.5 mGy and 1011.6 mGy•cm) (p < 0.0001). Conclusion: LD CT images reconstructed with DLIR enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR. Advances in knowledge: Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body CT.

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Low Dose Computed Tomography in Diagnosis of Ureteric Calculus in Obese Patients

Journal of Nobel Medical College ◽

10.3126/jonmc.v9i1.29464 ◽

2020 ◽

Vol 9 (1) ◽

pp. 27-31

Author(s):

Mahesh Gautam ◽

Aziz Ullah ◽

Manish Raj Pathak

Keyword(s):

Computed Tomography ◽

Image Quality ◽

Radiation Dose ◽

Iterative Reconstruction ◽

Low Dose ◽

Standard Dose ◽

Reconstruction Technique ◽

Obese Patients ◽

Low Dose Computed Tomography ◽

Effective Radiation

Background: Standard dose computed tomography is standard imaging modality in diagnosis of urolithiasis. The introduction of low dose techniques results in decrease radiation dose without significant change in image quality. However, the image quality of low dose computed tomography is affected by skin fold thickness and subcutaneous abdominal adipose tissue. The aim of this study to evaluate stone location, size, and density using low dose computed tomography compared with standard dose computed tomography in obese population. Material and Methods: This non-randomized non-inferiority trial includes 120 patient having BMI≥25kg/m2 with acute ureteric colic. The low dose and standard dose computed tomography were performed accordingly. Effective radiation doses were calculated from dose-length product obtained from scan report using conversion factor of 0.015. The images were reconstructed using iterative reconstruction algorithm. Effective dose, number and size of stone, Hounsfield Unit value of stone and image quality was assessed. Results: Stones were located in 69 (57.5%) in right and 51 (42.5%) in left ureter. There was no statistical difference in mean diameter, number and density of stones in low dose as compared with standard dose. The radiation dose was significantly lower with low dose. (3.68 mSv) The delineation of the ureter, outline of the stones and image quality in low dose was overall sufficient for diagnosis. No images of low dose scan were subjectively rated as non-diagnostics. Conclusion: Low dose computed tomography with iterative reconstruction technique is as effective as standard dose in diagnosis of ureteric stones in obese patients with lower effective radiation dose.

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