臺灣博碩士論文加值系統

中文摘要
一、研究目的：
肺癌長期位居所有癌症致死率的首位，而低劑量電腦斷層掃描（LDCT）已被證實能夠早期發現肺癌。本研究透過回溯性分析，對於接受固定參數LDCT檢查的患者影像，應用三種不同的影像重建技術進行重建，以評估各技術對影像品質的影響，目的是提供判讀之影像重建方法有新的選擇。
二、材料與方法：
病人選擇：依IRB內容，回溯本院進行低劑量胸腔CT掃描之病人。
儀器及參數：使用Aquilion ONE 640切片CT掃描儀（Canon Medical Systems），採用固定的120kV和30mA作為掃描參數，比較三種重建技術(AIDR 3D、FIRST、AiCE)後的影像中肺結節之雜訊、SNR和CNR。
統計分析：藉由Levene檢定來考量運用變異數分析（ANOVA）或Welch和Brown Forsythe檢定及事後分析來比較三種重建技術的雜訊、SNR及CNR值。
三、結果與結論：
統計分析顯示，三種重建技術在影像品質上存在顯著差異。在SNR及CNR值方面，AiCE和FIRST相比AIDR 3D展現了更佳的性能；且在雜訊上與AIDR 3D相比也有顯著差異的抑制。目前醫院的放射科醫師主要使用AIDR 3D進行低劑量胸腔CT的影像報告，根據本研究結果，FIRST及AiCE成像表現皆優於AIDR 3D，若將FIRST與AiCE影像相比，兩者則無顯著差異。若考量組像時間，則AiCE與AIDR 3D較接近，FIRST則須多花更多時間才能組像完成。且AiCE成像品質上比AIDR 3D更佳，因此綜合考量下，建議臨床上可考慮使用AiCE作為重建技術，以作為影像重組之替代選擇。
關鍵詞：低劑量電腦斷層、肺結節、影像品質、組像方式、影像重建

ABSTRACT

Objective:
Lung cancer has long been the leading cause of cancer-related deaths, and low-dose computed tomography (LDCT) has been proven to facilitate the early detection of lung cancer. This study, through a retrospective analysis of patient images obtained with fixed-parameter LDCT examinations, applied three different image reconstruction techniques for reconstruction to assess their impact on image quality. The aim is to provide new options for image reconstruction methods.

Materials and Methods:
Patient Selection: According to the IRB content, patients who underwent low-dose chest CT scans at our institution were retrospectively selected.
Equipment and Parameters: Utilizing the Aquilion ONE 640-slice CT scanner (Canon Medical Systems) with fixed scanning parameters of 120kV and 30mA, this study compared the noise, signal-to-noise ratio (SNR), and contrast-to-noise (CNR) of nodules in images reconstructed with three techniques (AIDR 3D, FIRST, and, AiCE).
Statistical Analysis: Based on the Levene's test, consider using analysis of variance (ANOVA) or Welch and Brown-Forsythe tests, along with post-hoc analyses, to compare the noise, SNR, and CNR values of the three reconstruction techniques.

Results and Conclusion:
Statistical analysis revealed there were significant differences in image quality among the three reconstruction techniques. In terms of SNR and CNR values, AiCE and FIRST demonstrated superior performance to AIDR 3D; they also showed significant noise suppression when compared to AIDR 3D. Currently, radiologists at our hospital primarily use AIDR 3D for reporting images from low-dose chest CT scans. According to the results of this study, both FIRST and AiCE exhibited better imaging quality than AIDR 3D. When comparing FIRST with AiCE, there were no significant differences between these two. Considering the time required for image reconstruction, AiCE is similar to AIDR 3D, while FIRST requires more time to complete. Given that the image quality of AiCE is much better than AIDR 3D, it is suggested that AiCE be considered as a reconstruction technique in clinical settings, serving as an alternative choice for image reconstitution.
Keywords: Low-Dose Computed Tomography, Lung Nodules, Image Quality, Reconstruction Techniques, Image Reconstruction

目錄
致謝 I
中文摘要 III
ABSTRACT V
目錄 VIII
表目錄 X
圖目錄 XI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
第二章 文獻探討 4
2.1 肺結節簡介 4
2.2 肺結節診斷 4
2.3 文獻回顧 5
第三章 材料與方法 8
3.1 研究對象 8
3.2 電腦斷層掃描參數 8
3.3 重建組像技術 9
3.3.1 AIDR 3D 9
3.3.2 FIRST 11
3.3.3 AiCE 12
3.4 定量分析 14
3.5 定性分析 17
3.6 統計分析 20
第四章 結果 21
4.1 受試者 21
4.2 統計分析結果 22
4.3 定性分析結果 29
第五章 討論 31
第六章 結論 34
參考文獻 35

 
表目錄
表 3.1五分制影像評分標準 15
表 4.1雜訊使用Levene檢定結果 19
表 4.2雜訊ANOVA結果 19
表 4.3雜訊使用Post Hoc檢定LSD法之結果 19
表 4.4 SNR使用Levene檢定結果 21
表 4.5 SNR使用Welch和Brown Forsythe檢定結果 21
表 4.6 SNR使用Post Hoc檢定Games-Howell檢定之結果 21
表 4.7 CNR使用Levene檢定結果 23
表 4.8 CNR使用Welch和Brown Forsythe檢定結果 23
表 4.9 CNR使用Post Hoc檢定Games-Howell檢定之結果 23
表 4.10第一位醫師評分結果 24
表 4.11第二位醫師評分結果 24
表 4.12第三位醫師評分結果 25

 
圖目錄
圖 3.1 Aquilion ONE電腦斷層儀器 8
圖 3.2 AIDR 3D應用於肺部成像示意圖 9
圖 3.3 FIRST應用於肺部成像示意圖 10
圖 3.4 AiCE應用於肺部成像示意圖 11
圖 3.5 ROI圈選數值呈現範例 12
圖 3.6 ROI圈選示意圖 13
圖 4.1三種組像方式之雜訊盒鬚圖 18
圖 4.2三種組像方式之SNR盒鬚圖 20
圖 4.3三種組像方式之CNR盒鬚圖 22

1.Ministry of Health and Welfare. 肺癌早期偵測計畫. Ministry of Health and Welfare 2022 [cited 2023 January 1]; Available from: https://www.hpa.gov.tw/Pages/List.aspx?nodeid=4619.
2.American Thoracic Society. Lung Cancer Screening Implementation Guide. Lung Cancer Screening Implementation Guide [cited 2019 April 11]; Available from: https://www.thoracic.org/members/assemblies/assemblies/thoracic-oncology/the-screen-lung-cancer-screening-and-prevention/lung-cancer-screening-implementation-guide.php.
3.Bonney, A., et al., Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality. Cochrane Database Syst Rev, 2022. 8(8): p. Cd013829.
4.Slatore, C.G., et al., Smoking behaviors among patients receiving computed tomography for lung cancer screening. Systematic review in support of the U.S. preventive services task force. Ann Am Thorac Soc, 2014. 11(4): p. 619-27.
5.Goldstraw, P., et al., The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol, 2016. 11(1): p. 39-51.
6.Greffier, J., et al., Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study. Quant Imaging Med Surg, 2022. 12(1): p. 229-243.
7.MacMahon, H., et al., Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology, 2017. 284(1): p. 228-243.
8.Steven E Weinberger, M., MACP, FRCPShaunagh McDermott, MD. Diagnostic evaluation of the incidental pulmonary nodule. UpToDate 2024 Jan 16, 2024. [cited 2024 March 1]; Available from: https://www.uptodate.com/contents/diagnostic-evaluation-of-the-incidental-pulmonary-nodule?search=lung%20nodule&source=search_result&selectedTitle=1%7E150&usage_type=default&display_rank=1.
9.Detterbeck, F.C., et al., The Eighth Edition Lung Cancer Stage Classification. Chest, 2017. 151(1): p. 193-203.
10.Asamura, H., et al., The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for the Revision of the N Descriptors in the Forthcoming 8th Edition of the TNM Classification for Lung Cancer. J Thorac Oncol, 2015. 10(12): p. 1675-84.
11.Mazzone, P.J. and L. Lam, Evaluating the Patient With a Pulmonary Nodule: A Review. Jama, 2022. 327(3): p. 264-273.
12.Tækker, M., et al., Diagnostic accuracy of low-dose and ultra-low-dose CT in detection of chest pathology: a systematic review. Clin Imaging, 2021. 74: p. 139-148.
13.Jiang, B., et al., Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT. Radiology, 2022. 303(1): p. 202-212.
14.Hong, S.G., et al., Effect of Hybrid Kernel and Iterative Reconstruction on Objective and Subjective Analysis of Lung Nodule Calcification in Low-Dose Chest CT. Korean J Radiol, 2018. 19(5): p. 888-896.
15.SYSTEMS, C.M. AIDR 3D Iterative Reconstruction Dose Reduction. AIDR 3D; Available from: https://us.medical.canon/products/computed-tomography/aidr-3d/.
16.Greffier, J., et al., CT iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol, 2020. 30(1): p. 487-500.
17.SYSTEMS, C.M. Forward projected model-based Iterative
Reconstruction SoluTion “FIRST”. Jan 2018 [cited 2024 Jan 01]; Available from: https://us.medical.canon/products/computed-tomography/aidr-3d/.
18.Kurokawa, R., et al., Forward-projected Model-based Iterative Reconstruction SoluTion in Temporal Bone Computed Tomography: A Comparison Study of All Reconstruction Modes. J Comput Assist Tomogr, 2021. 45(2): p. 308-314.
19.Greffier, J., et al., Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study. Med Phys, 2021. 48(10): p. 5743-5755.
20.CANON MEDICAL SYSTEMS. AiCE Deep Learning Reconstruction: Bringing the power of Ultra-High Resolution CT to routine imaging. AiCE Deep Learning Reconstruction Jan 2019 [cited 2024 Jan 01]; Available from: https://global.medical.canon/publication/ct/2019WP_AiCE_Deep_Learning.
21.Ma, Y., et al., Multi-instance learning based lung nodule system for assessment of CT quality after small-field-of-view reconstruction. Sci Rep, 2024. 14(1): p. 3109.
22.Lee, H.N., J.I. Kim, and S.Y. Shin, Measurement accuracy of lung nodule volumetry in a phantom study: Effect of axial-volume scan and iterative reconstruction algorithm. Medicine (Baltimore), 2020. 99(23): p. e20543.
23.Punwani, S., et al., Paediatric CT: the effects of increasing image noise on pulmonary nodule detection. Pediatr Radiol, 2008. 38(2): p. 192-201.
24.Oshima, Y., et al., Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods. Eur J Radiol, 2023. 166: p. 110969.
25.Ma, Y., et al., Preoperative CT-guided localization of pulmonary nodules with low-dose radiation. Quant Imaging Med Surg, 2023. 13(7): p. 4295-4304.
26.Chamberlin, J.H., et al., Ultra-high-resolution photon-counting detector computed tomography of the lungs: Phantom and clinical assessment of radiation dose and image quality. Clin Imaging, 2023. 104: p. 110008.
27.Bongartz G. European guidelines on quality criteria for computed tomography. [cited 2021 August 26]; Available from: http://www.drs.dk/guidelines/ct/quality/htmlindex.htm.
28.Hassani, C., et al., Forward-Projected Model-Based Iterative Reconstruction in Screening Low-Dose Chest CT: Comparison With Adaptive Iterative Dose Reduction 3D. AJR Am J Roentgenol, 2018. 211(3): p. 548-556.
29.Singh, R., et al., Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT. American Journal of Roentgenology, 2020. 214(3): p. 566-573.
30.Michallek, F., et al., Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study. Eur Radiol, 2022. 32(7): p. 4587-4595.
31.Leon, S., et al., Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images. Phys Med Biol, 2021. 66(12).
32.Bernard, A., et al., Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality. Quant Imaging Med Surg, 2021. 11(1): p. 392-401.
33.Hata, A., et al., Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation. AJR Am J Roentgenol, 2020. 215(6): p. 1321-1328.