Improvement of Image Quality Using Hybrid Iterative Reconstruction with Noise Power Spectrum Model in Computed Tomography During Hepatic Arteriography

Overview

Journal J Belg Soc Radiol

Publisher Ubiquity Press

Specialty Radiology

Date 2021 Oct 6

PMID 34611577

Authors

Hiroshi Hamasaki

Takashi Shirasaka

Yasuhiro Ushijima

Hiroshi Akamine

Yukihisa Takayama

Yuichiro Kubo

Keisuke Ishimatsu

Akihiro Nishie

Toyoyuki Kato

Affiliations

Soon will be listed here.

Abstract

Objectives: In CT during hepatic arteriography (CTHA), the addition of a noise power spectrum (NPS) model to conventional hybrid iterative reconstruction (HIR) may improve spatial resolution and reduce image noise. This study aims at assessing the image quality provided by HIR with a NPS model at CTHA.

Methods: This institutional review board-approved retrospective analysis included 26 patients with hepatocellular carcinomas (HCCs) who underwent CTHA. In all acquisitions, images were reconstructed with filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR), and AIDR enhanced (eAIDR) with the NPS model. Four radiologists analyzed the signal-to-noise ratio (SNR) of HCC nodules and its associated feeding arteries. The radiologists used a semiquantitative scale (-3 to +3) to rate the subjective image quality comparing both the FBP and eAIDR images with the AIDR images.

Results: The feeding arteries' attenuation was significantly higher in eAIDR compared to AIDR [514.3 ± 121.4 and 448.3 ± 107.3 Hounsﬁeld units (HU), p < 0.05]. The image noise of eAIDR was significantly lower than that of FBP (15.2 ± 2.2 and 28.5 ± 4.8 HU, p < 0.05) and comparable to that of AIDR. The SNR of feeding arteries on eAIDR was significantly higher than on AIDR (34.1 ± 7.9 and 27.4 ± 6.3, p < 0.05). Subjective assessment scores showed that eAIDR provided better visibility of feeding arteries and overall image quality compared to AIDR (p < 0.05). The HCC nodule visibility was not significantly different among the three reconstructions.

Conclusion: In CTHA, eAIDR improved the visibility of feeding arteries associated with HCC nodules without compromising nodule detection.

References

Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Zhou J . Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol. 2019; 29(11):6163-6171. DOI: 10.1007/s00330-019-06170-3. View

Iwazawa J, Ohue S, Mitani T, Abe H, Hashimoto N, Hamuro M . Identifying feeding arteries during TACE of hepatic tumors: comparison of C-arm CT and digital subtraction angiography. AJR Am J Roentgenol. 2009; 192(4):1057-63. DOI: 10.2214/AJR.08.1285. View

Minamishima K, Sugisawa K, Yamada Y, Jinzaki M . Quantitative and qualitative evaluation of hybrid iterative reconstruction, with and without noise power spectrum models: A phantom study. J Appl Clin Med Phys. 2018; 19(3):318-325. PMC: 5978737. DOI: 10.1002/acm2.12304. View

Volders D, Bols A, Haspeslagh M, Coenegrachts K . Model-based iterative reconstruction and adaptive statistical iterative reconstruction techniques in abdominal CT: comparison of image quality in the detection of colorectal liver metastases. Radiology. 2013; 269(2):469-74. DOI: 10.1148/radiol.13130002. View

Golfieri R, Cappelli A, Cucchetti A, Piscaglia F, Carpenzano M, Peri E . Efficacy of selective transarterial chemoembolization in inducing tumor necrosis in small (<5 cm) hepatocellular carcinomas. Hepatology. 2011; 53(5):1580-9. DOI: 10.1002/hep.24246. View

Ko S, Nakajima Y, Kanehiro H, Hisanaga M, Aomatsu Y, Kin T . Significant influence of accompanying chronic hepatitis status on recurrence of hepatocellular carcinoma after hepatectomy. Result of multivariate analysis. Ann Surg. 1996; 224(5):591-5. PMC: 1235434. DOI: 10.1097/00000658-199611000-00001. View

Katsura M, Matsuda I, Akahane M, Sato J, Akai H, Yasaka K . Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol. 2012; 22(8):1613-23. DOI: 10.1007/s00330-012-2452-z. View

Richard S, Husarik D, Yadava G, Murphy S, Samei E . Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms. Med Phys. 2012; 39(7):4115-22. DOI: 10.1118/1.4725171. View

Tatsugami F, Higaki T, Nakamura Y, Yu Z, Zhou J, Lu Y . Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol. 2019; 29(10):5322-5329. DOI: 10.1007/s00330-019-06183-y. View

10.

Kijewski M, Judy P . The noise power spectrum of CT images. Phys Med Biol. 1987; 32(5):565-75. DOI: 10.1088/0031-9155/32/5/003. View

11.

Li K, Tang J, Chen G . Statistical model based iterative reconstruction (MBIR) in clinical CT systems: experimental assessment of noise performance. Med Phys. 2014; 41(4):041906. PMC: 3978426. DOI: 10.1118/1.4867863. View

12.

Adachi E, Maeda T, Matsumata T, Shirabe K, Kinukawa N, Sugimachi K . Risk factors for intrahepatic recurrence in human small hepatocellular carcinoma. Gastroenterology. 1995; 108(3):768-75. DOI: 10.1016/0016-5085(95)90450-6. View

13.

Ueda K, Matsui O, Kawamori Y, Kadoya M, Yoshikawa J, Gabata T . Differentiation of hypervascular hepatic pseudolesions from hepatocellular carcinoma: value of single-level dynamic CT during hepatic arteriography. J Comput Assist Tomogr. 1998; 22(5):703-8. DOI: 10.1097/00004728-199809000-00006. View

14.

Murakami T, Oi H, Hori M, Kim T, Takahashi S, Tomoda K . Helical CT during arterial portography and hepatic arteriography for detecting hypervascular hepatocellular carcinoma. AJR Am J Roentgenol. 1997; 169(1):131-5. DOI: 10.2214/ajr.169.1.9207512. View

15.

Deak Z, Grimm J, Treitl M, Geyer L, Linsenmaier U, Korner M . Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study. Radiology. 2012; 266(1):197-206. DOI: 10.1148/radiol.12112707. View

16.

Kalra M, Maher M, Kamath R, Horiuchi T, Toth T, Halpern E . Sixteen-detector row CT of abdomen and pelvis: study for optimization of Z-axis modulation technique performed in 153 patients. Radiology. 2004; 233(1):241-9. DOI: 10.1148/radiol.2331031505. View