Deep learning versus iterative image reconstruction algorithm for head CT in trauma.

PURPOSE: To compare the image quality between a deep learning-based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT. METHODS: Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa. RESULTS: DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader. CONCLUSION: The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography.

OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V). METHODS: We prospectively recruited 52 patients. All images were reconstructed with ASiR-V 30%, ASiR-V 70%, and DLIR at low, medium, and high reconstruction strengths. CT number, noise, noise reduction rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated within the region of interest (ROI) on subcutaneous fat, bilateral renal cortices, renal medulla, renal arteries, and adrenal glands. For qualitative analyses, the differentiation of the renal cortex and medulla, conspicuity of the adrenal gland boundary, sharpness, artifacts, and subjective noise were assessed. The overall image quality was calculated on a scale from 0 (worst) to 15 (best) based on the five values above and the score≥9 was acceptable. RESULTS: CT number does not significantly differ between the reconstruction datasets. Noise does not significantly differ between ASiR-V 30% and DLIR-L, but it is significantly lower using ASiR-V 70%, DLIR-M, and DLIR-H. The noise reduction rate relative to ASiR-V 30% is significantly different between the DLIR groups and ASiR-V 70%, and DLIR-H yields the highest noise reduction rate (61.6%). SNR and CNR are higher for DLIR-M, DLIR-H, and ASiR-V 70% than for ASiR-V 30% and DLIR-L. DLIR-H shows the best SNR and CNR. The overall image quality yields the same pattern for DLIR-H, with the highest score. Percentages of cases with overall image quality score≥9 are 100% (DLIR-H), 94.23% (DLIR-M), 90.38% (ASiR-V70%), 67.31% (DLIR-L), and 63.46% (ASiR-V30%), respectively. CONCLUSIONS: DLIR significantly improved the objective and subjective image quality of renal and adrenal CTs, yielding superior noise reduction compared with ASiR-V.

Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V).

PURPOSE: To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS: Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement. RESULTS: There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all P < 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all P < 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR. CONCLUSION: On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.

Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses.

Background: Numerous computed tomography (CT) image reconstruction algorithms have been developed to improve image quality, and high-quality renal CT images are crucial to clinical diagnosis. This study evaluated the image quality and lesion visibility of deep learning-based image reconstruction (DLIR) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V) in contrast-enhanced renal CT at different reconstruction strengths and doses. Methods: From January 2020 to May 2021, we prospectively included 101 patients who underwent renal contrast-enhanced CT scanning (69 at 120 kV; 32 at 80 kV). All image data were reconstructed with ASiR-V (30% and 70%) and DLIR at low, medium, and high reconstruction strengths (DLIR-L, DLIR-M, and DLIR-H, respectively). The CT number, noise, noise reduction rate (NRR), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall image quality, and the proportion of acceptable images were compared. Lesions of DLIR groups were evaluated, and the conspicuity-to-noise ratio (C/N) was calculated. Results: Quantitative values (noise, SNR, CNR, and NRR) significantly differed between all reconstructions at 120 and 80 kV (P<0.001) and between each reconstruction, except ASiR-V 70% vs. DLIR-M. At 120 kV, the overall image quality and the proportion of acceptable images significantly differed between all reconstructions (P<0.001) and between each reconstruction, except ASiR-V 30% vs. DLIR-L and ASiR-V 70% vs. DLIR-M. At 80 kV, the overall image quality significantly differed between all reconstructions (P<0.001) and between each reconstruction, except between ASiR-V 30%, ASiR-V 70%, and DLIR-L. Quantitative and qualitative values were highest in DLIR-H, while the values were close in DLIR-H (80 kV) vs. ASiR-V 70% (120 kV) and DLIR-M (80 kV) vs. ASiR-V 30% (120 kV). The lesion conspicuity and noise significantly differed in DLIR at 120 kV and 80 kV (P<0.001). C/N significantly differed in DLIR at 120 kV (P<0.001) but not at 80 kV. DLIR-L and DLIR-M exhibited much-improved lesion display (C/N >1), and DLIR-H exhibited much-improved noise (C/N <1) at 120 kV. Conclusions: DLIR significantly improved the image quality and lesion visibility of renal CT compared with ASiR-V, even at a low dose.

Associations between paraspinal muscles fatty infiltration and lumbar vertebral bone mineral density - An investigation by fast kVp switching dual-energy CT and QCT.

Purpose: To investigate the relationship between paraspinal muscles fat content and lumbar bone mineral density (BMD). Methods: A total of 119 participants were enrolled in our study (60 males, age: 50.88 ± 17.79 years, BMI: 22.80 ± 3.80 kg·m-2; 59 females, age: 49.41 ± 17.69 years, BMI: 22.22 ± 3.12 kg·m-2). Fat content of paraspinal muscles (erector spinae (ES), multifidus (MS), and psoas (PS)) were measured at (ES L1/2-L4/5; MS L2/3-L5/S1; PS L2/3-L5/S1) levels using dual-energy computed tomography (DECT). Quantitative computed tomography (QCT) was used to assess BMD of L1 and L2. Linear regression analysis was used to assess the relationship between BMD of the lumbar spine and paraspinal muscles fat content with age, sex, and BMI. The variance inflation factor (VIF) was used to detect the degree of multicollinearity among the variables. P < .05 was considered to indicate a statistically significant difference. Results: The paraspinal muscles fat content had a fairly significant inverse association with lumbar BMD after controlling for age, sex, and BMI (adjusted R 2 = 0.584-0.630, all P < .05). Conclusion: Paraspinal muscles fat content was negatively associated with BMD.Paraspinal muscles fatty infiltration may be considered as a potential marker to identify BMD loss.

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy.

BACKGROUND: Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference. METHODS: This retrospective study includes 43 patients who underwent clinically indicated CCTA and ICA. Datasets were reconstructed with ASiR-V 70% (using standard [SD] and high-definition [HD] kernels) and with DLIR at different levels (i.e., medium [M] and high [H]). Image noise, image quality, and coronary luminal narrowing were evaluated by three blinded readers. Diagnostic accuracy was compared against ICA. RESULTS: Noise did not significantly differ between ASiR-V SD and DLIR-M (37 vs. 37 HU, p = 1.000), but was significantly lower in DLIR-H (30 HU, p < 0.001) and higher in ASiR-V HD (53 HU, p < 0.001). Image quality was higher for DLIR-M and DLIR-H (3.4-3.8 and 4.2-4.6) compared to ASiR-V SD and HD (2.1-2.7 and 1.8-2.2; p < 0.001), with DLIR-H yielding the highest image quality. Consistently across readers, no significant differences in sensitivity (88% vs. 92%; p = 0.453), specificity (73% vs. 73%; p = 0.583) and diagnostic accuracy (80% vs. 82%; p = 0.366) were found between ASiR-V HD and DLIR-H. CONCLUSION: DLIR significantly reduces noise in CCTA compared to ASiR-V, while yielding superior image quality at equal diagnostic accuracy.