The optimal monochromatic spectral computed tomographic imaging plus adaptive statistical iterative reconstruction algorithm can improve the superior mesenteric vessel image quality

Assessment of Spectral Computed Tomography Image Quality and Detection of Lesions in the Liver Based on Image Reconstruction Algorithms and Virtual Tube Voltage

Background: Spectral detector computed tomography (SDCT) has demonstrated superior diagnostic performance and image quality in liver disease assessment compared with traditional CT. Selecting the right reconstruction algorithm and tube voltage is essential to avoid increased noise and diagnostic errors. Objectives: This study evaluated improvements in image quality achieved using various virtual tube voltages and reconstruction algorithms for diagnosing common liver diseases with spectral CT. Methods: This retrospective study involved forty-seven patients who underwent spectral CT scans for liver conditions, including fatty liver, hemangiomas, and metastatic lesions. The assessment utilized signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), with images reconstructed using various algorithms (IMR, iDose) at different levels and virtual tube voltages. Three experienced radiologists analyzed the reconstructed images to identify the best reconstruction methods and tube voltage combinations for diagnosing these liver pathologies. Results: The signal-to-noise ratio (SNR) was highest for spectral CT images using the IMR3 algorithm in metastatic, hemangioma, and fatty liver cases. A strong positive correlation was found between IMR3 at 120 keV and 70 keV (p-value = 0.000). In contrast, iDOSE2 at 120 keV and 70 keV showed a low correlation of 0.291 (p-value = 0.045). Evaluators noted that IMR1 at 70 keV provided the best visibility for liver lesions (mean = 3.58), while IMR3 at 120 keV had the lowest image quality (mean = 2.65). Conclusions: Improvements in image quality were noted with SDCT, especially in SNR values for liver tissues at low radiation doses and a specific IMR level. The IMR1 algorithm reduced noise, enhancing the visibility of liver lesions for better diagnosis.

A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results

This study aimed to compare the performance of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in improving image quality and diagnostic performance using virtual monochromatic spectral images in abdominal dual-energy computed tomography (DECT). Sixty-two patients [mean age ± standard deviation (SD): 56 years ± 13; 30 men] who underwent abdominal DECT were prospectively included in this study. The 70-keV DECT images in the portal phase were reconstructed at 5-mm and 1.25-mm slice thicknesses with 40% ASIR-V (ASIR-V40%) and at 1.25-mm slice with deep learning image reconstruction at medium (DLIR-M) and high (DLIR-H) levels and then compared. Computed tomography (CT) attenuation, SD values, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured in the liver, spleen, erector spinae, and intramuscular fat. The lesions in each reconstruction group at 1.25-mm slice thickness were counted. The image quality and diagnostic confidence were subjectively evaluated by two radiologists using a 5-point scale. For the 1.25-mm images, DLIR-M and DLIR-H had lower SD, higher SNR and CNR, and better subjective image quality compared with ASIR-V40%; DLIR-H performed the best (all P values < 0.001). Furthermore, the 1.25-mm DLIR-H images had similar SD, SNR, and CNR values as the 5-mm ASIR-V40% images (all P > 0.05). Three image groups had similar lesion detection rates, but DLIR groups exhibited higher confidence in diagnosing lesions. Compared with ASIR-V40% at 70 keV, 70-keV DECT with DLIR-H further reduced image noise and improved image quality. Additionally, it improved diagnostic confidence while ensuring a consistent lesion detection rate of liver lesions.

Spectral detector CT applications in advanced liver imaging

OBJECTIVE

Spectral detector CT (SDCT) has many applications in advanced liver imaging. If appropriately utilized, this technology has the potential to improve image quality, provide new diagnostic information, and allow for decreased radiation dose. The purpose of this review is to familiarize radiologists with the uses of SDCT in liver imaging.

CONCLUSION

SDCT has a variety of post-processing techniques, which can be used in advanced liver imaging and can significantly add value in clinical practice.

Iodine load reduction in dual-energy spectral CT portal venography with low energy images combined with adaptive statistical iterative reconstruction

OBJECTIVE

To study the application of using low energy images combined with adaptive statistical iterative reconstruction (ASiR) in dual-energy spectral CT portal venography (CTPV) to reduce iodine load.

METHODS

41 patients for CTPV were prospectively and randomly divided into two groups. Group A ( n = 21) used conventional 120 kVp scanning protocol with contrast dose at 0.6 gI/kg while group B ( n = 20) used dual-energy spectral imaging with reduced contrast dose at 0.3 gI/kg. The 120 kVp images in Group A and 50 keV images in Group B were reconstructed with 40% ASiR. The contrast-to-noise ratio of portal vein was calculated. The image quality and the numbers of intrahepatic portal vein branches were evaluated by two experienced radiologists using a 5-point scoring system.

RESULTS

Group B reduced iodine load by 52% compared to Group A (17.21 ± 3.30 gI vs 35.80 ± 6.18 gI, p < 0.001). All images in both groups were acceptable for diagnosis. CT values and standard deviations in portal veins of Group B were higher than Group A (all p < 0.05); There were no statistical differences in contrast-to-noise ratio, image quality score and the number of observed portal vein branches between the two groups (all p > 0.05), and the two observers had excellent agreement in image quality assessment (all κ > 0.75).

CONCLUSION

The use of 50 keV images in dual-energy spectral CTPV with ASiR reduces total iodine load by 52% while maintaining good image quality.

ADVANCES IN KNOWLEDGE

Spectral CT images combined with ASiR can be used in low contrast dose CTPV portal venography to maintain image quality and reduce contrast dose.

Effects of low-dose energy spectrum scanning combined with adaptive statistical iterative reconstruction on the quality of imaging in Budd-Chiari syndrome

OBJECTIVE

To assess the quality and diagnostic accuracy of monochromatic images combined with adaptive statistical iterative reconstruction (ASIR) performed via spectral computed tomography (CT) in patients with Budd-Chiari syndrome (BCS).

METHODS

Sixty-two patients with BCS underwent pectral CT with upper abdominal two-phase contrast-enhanced scanning to generate a 60keV monochromatic energy level combined with ASIR (ranging from 0% -100%) during the portal venous phase (PVP) and the hepatic venous phase (HVP). One-way ANOVA was used to compare vessel-to-liver contrast-to-noise ratio (CNR) for the portal vein (PV), hepatic vein (HV), and inferior vena cava (IVC). Subjective evaluations of the images in the three groups were conducted by image quality assessors and compared via Kruskal-Wallis H test.

RESULTS

The CNR values of the PV trunk, HV, IVC, liver parenchyma and pancreas were within ASIR (ranging from 0% - 100%) weight, and the difference were statistically significant (p <0.05). The highest overall image score was distributed at 50% ASIR weight value. Higher CNR values of HV, hepatic parenchyma and pancreas were obtained in the IVC type than in mixed and HV types (respective p values = 0.035, 0.019 and 0.042). Higher CNR values of the IVC were obtained in the HV type than in mixed and IVC types (p = 0.032). The CNR value of the IVC in the mixed type was less than that of the HV type (p = 0.028). The CNR values of the HV and liver parenchyma in mixed type were lower than those of the IVC type (p = 0.016 and 0.038, respectively). The CNR value of pancreas in IVC type was higher than that of the HV type (p = 0.037). The diagnostic value of CNR in patients with the IVC type was higher than that in patients with mixed and HV type, while the diagnostic value of CNR was found to be the lowest for the HV type (p = 0.043).

CONCLUSION

A monochromatic energy level of 60 keV with 50% ASIR can significantly improve image quality in cases of BCS.