Citation: | PAN Z J, Liu L, Li Q Y, et al. Deep Learning Image Reconstruction to Improve Computed Tomography Image Quality of the Phantom with Standard Liver Density[J]. CT Theory and Applications, xxxx, x(x): 1-10. DOI: 10.15953/j.ctta.2024.056. (in Chinese). |
Objective: This study aimed to compare the quality of reconstructed images by deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) techniques at different scan doses using a phantom with liver density. Methods: The Gammex computed tomography (CT) phantom with a standard liver-density insert (ρew=1.06) was scanned at six different radiation doses (CTDIvol; 30, 20, 15, 10, 7.5, and 4.5 mGy). Images obtained at each dose were reconstructed using DLIR and ASIR-V. Image quality was analyzed through the imQuest software. The quality of reconstructed images by DLIR at 4.5 mGy (lowest radiation dose) and ASIR-V at 15 mGy (recommended scan dose) were compared using the Bland–Altman method. Results: Across the six doses, DLIR significantly outperformed ASIR-V in key metrics, such as noise (P<0.001), signal-to-noise ratio (SNR) (P<0.001), contrast-to-noise ratio (CNR) (P<0.001), and detectability index (d') (P<0.001). Bland–Altman analysis indicated that the quality of reconstructed images by DLIR at 4.5 mGy was significantly better to those by ASIR-V at 15 mGy. The noise level of DLIR images at 4.5 mGy was 17.41±0.32, which is significantly lower than that of ASIR-V at 15 mGy (21.17±0.67) (P<0.001). At 4.5 mGy, DLIR SNR, CNR, and d' were 3.21±0.24, 3.42±0.35, and 8.81±0.63, respectively, which are significantly higher than that of ASIR-V at 15 mGy (2.69±0.14, 2.87±0.11, and 5.61±1.28, respectively) (P=0.006, 0.029, and 0.005 respectively). Conclusion: In CT scan of focal liver-density lesions using a phantom, DLIR significantly improved the SNR, CNR, and d' values and reduced image noise compared to ASIR-V. DLIR was able to achieve better quality image reconstruction at 4.5 mGy than the conventional ASIR-V reconstruction at 15 mGy.
[1] |
MARRERO J A, AHN J, RAJENDER REDDY K, et al. ACG clinical guideline: The diagnosis and management of focal liver lesions[J]. The American Journal of Gastroenterology, 2014, 109(9): 1328−1347,1348. DOI: 10.1038/ajg.2014.213.
|
[2] |
CHOI H, CHANG W, KIM J H, et al. Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: A phantom study[J]. European Radiology, 2022, 32(2): 1247−1255. DOI: 10.1007/s00330-021-08199-9.
|
[3] |
LEE H J, KIM J S, LEE J K, et al. Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: A non-inferiority study[J]. European Journal of Radiology, 2023, 159: 110659. DOI: 10.1016/j.ejrad.2022.110659.
|
[4] |
van STIPHOUT J A, DRIESSEN J, KOETZIER L R, et al. The effect of deep learning reconstruction on abdominal CT densitometry and image quality: S systematic review and meta-analysis[J/OL]. European Radiology, 2022, 32(5). [2023-12-27]. https://pubmed.ncbi.nlm.nih.gov/34913104/. DOI: 10.1007/s00330-021-08438-z.
|
[5] |
GREFFIER J, PEREIRA F, MACRI F, et al. CT dose reduction using automatic exposure control and iterative reconstruction: A chest paediatric phantoms study[J]. Physica Medica: PM: An International Journal Devoted to the Applications of Physics to Medicine and Biology: Official Journal of the Italian Association of Biomedical Physics (AIFB), 2016, 32(4): 582−589. DOI: 10.1016/j.ejmp.2016.03.007.
|
[6] |
CHEN L H, JIN C, LI J Y, et al. Image quality comparison of two adaptive statistical iterative reconstruction (asir, asir-v) algorithms and filtered back projection in routine liver CT[J]. The British Journal of Radiology, 2018, 91(1088): 20170655. DOI: 10.1259/bjr.20170655.
|
[7] |
GEYER L, SCHOEPF U J, MEINEL F G, et al. State of the art: Iterative ct reconstruction techniques[J]. Radiology, 2015, 276(2): 339−357. DOI: 10.1148/radiol.2015132766.
|
[8] |
GREFFIER J, HAMARD A, PEREIRA F, et al. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: A phantom study[J]. European Radiology, 2020, 30(7): 3951−3959. DOI: 10.1007/s00330-020-06724-w.
|
[9] |
KOETZIER L R, MASTRODICASA D, SZCZYKUTOWICZ T P, et al. Deep learning image reconstruction for CT: Technical principles and clinical prospects[J]. Radiology, 2023, 306(3): e221257. DOI: 10.1148/radiol.221257.
|
[10] |
SZCZYKUTOWICZ T P, NETT B, CHERKEZYAN L, et al. Protocol optimization considerations for implementing deep learning CT reconstruction[J]. American Journal of Roentgenology, 2021, 216(6): 1668−1677. DOI: 10.2214/AJR.20.23397.
|
[11] |
MOHAMMADINEJAD P, MILETO A, YU L, et al. CT noise-reduction methods for lower-dose scanning: Strengths and weaknesses of iterative reconstruction algorithms and new techniques[J]. Radiographics: A Review Publication of the Radiological Society of North America, Inc, 2021, 41(5): 1493−1508. DOI: 10.1148/rg.2021200196.
|
[12] |
JENSEN C T, GUPTA S, SALEH M, et al. Reduced-dose deep learning reconstruction for abdominal CT of liver metastases[J]. Radiology, 2022, 303(1): 90−98. DOI: 10.1148/radiol.211838.
|
[13] |
KANAL K M, BUTLER P F, SENGUPTA D, et al. U. S. diagnostic reference levels and achievable doses for 10 adult ct examinations[J]. Radiology, 2017, 284(1): 120−133. DOI: 10.1148/radiol.2017161911.
|
[14] |
ZHONG J, XIA Y, CHEN Y, et al. Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: A phantom study[J]. European Radiology, 2022, 33(2): 812−824. DOI: 10.1007/s00330-022-09119-1.
|
[15] |
TSURUSAKI M, SOFUE K, HORI M, et al. Dual-energy computed tomography of the liver: Uses in clinical practices and applications[J]. Diagnostics (Basel, Switzerland), 2021, 11(2): 161. DOI: 10.3390/diagnostics11020161.
|
[16] |
SAMEI E, BAKALYAR D, BOEDEKER K L, et al. Performance evaluation of computed tomography systems: Summary of aapm task group 233[J]. Medical Physics, 2019, 46(11): e735−e756. DOI: 10.1002/mp.13763.
|
[17] |
HAN W K, NA J C, PARK S Y. Low-dose ct angiography using asir-v for potential living renal donors: A prospective analysis of image quality and diagnostic accuracy[J]. European Radiology, 2020, 30(2): 798−805. DOI: 10.1007/s00330-019-06423-1.
|
[18] |
LARBI A, ORLIAC C, FRANDON J, et al. Detection and characterization of focal liver lesions with ultra-low dose computed tomography in neoplastic patients[J]. Diagnostic and Interventional Imaging, 2018, 99(5): 311−320. DOI: 10.1016/j.diii.2017.11.003.
|
[19] |
MILETO A, GUIMARAES L S, MCCOLLOUGH C H, et al. State of the art in abdominal CT: The limits of iterative reconstruction algorithms[J]. Radiology, 2019, 293(3): 491−503. DOI: 10.1148/radiol.2019191422.
|
[20] |
HSIEH J, LIU E, NETT B, et al. A new era of image reconstruction: TruefidelityTM. technical white paper on deep learning image reconstruction[J/OL]. gehealthcare. com, 2019.
|
[21] |
CAO L, LIU X, LI J, et al. A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions[J]. The British Journal of Radiology, 2021, 94(1118): 20201086. DOI: 10.1259/bjr.20201086.
|
[22] |
GREFFIER J, FRANDON J, LARBI A, et al. CT iterative reconstruction algorithms: A task-based image quality assessment[J]. European Radiology, 2020, 30(1): 487−500. DOI: 10.1007/s00330-019-06359-6.
|
[23] |
CARUSO D, DE SANTIS D, DEL GAUDIO A, et al. Low-dose liver CT: Image quality and diagnostic accuracy of deep learning image reconstruction algorithm[J]. European Radiology, 2023, 34(4): 2384−2393. DOI: 10.1007/s00330-023-10171-8.
|
[24] |
JENSEN C T, LIU X, TAMM E P, et al. Image quality assessment of abdominal CT by use of new deep learning image reconstruction: Initial experience[J]. American Journal of Roentgenology, 2020, 215(1): 50−57. DOI: 10.2214/AJR.19.22332.
|
[25] |
CHEN Y, ZHONG J, WANG L, et al. Multivendor comparison of quantification accuracy of iodine concentration and attenuation measurements by dual-energy CT: A phantom study[J]. American Journal of Roentgenology, 2022, 219(5): 827−839. DOI: 10.2214/AJR.22.27753.
|
[26] |
SINGH R, DIGUMARTHY S R, MUSE V V, et al. Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT[J/OL]. American Journal of Roentgenology, 2020. [2024-06-25]. DOI: 10.2214/AJR.19.21809.
|
[27] |
NAM J G, AHN C, CHOI H, et al. Image quality of ultralow-dose chest CT using deep learning techniques: Potential superiority of vendor-agnostic post-processing over vendor-specific techniques[J]. European Radiology, 2021, 31(7): 5139−5147. DOI: 10.1007/s00330-020-07537-7.
|
[1] | YU Ming, HU Jiayuan, SONG Yan, LI Saying. Analysis of Spiral CT Phenotypes and Features of Primary Intestinal Lymphoma[J]. CT Theory and Applications, 2022, 31(4): 449-458. DOI: 10.15953/j.ctta.2022.090 |
[2] | SU Da-jun, ZHA Yun-fei. Application of ASIR Techniques in Hip with Low Tube Current 64 Row CT[J]. CT Theory and Applications, 2014, 23(6): 1011-1017. |
[3] | WANG Guo-shi, WEI Ming-gui, LI Shao-wei, SHA Yan-zhi. Comparison between Two Materials of Bone Graft in Guided Bone Regeneration by CT Image Analysis[J]. CT Theory and Applications, 2014, 23(5): 843-850. |
[4] | YANG Xiao-sheng, CHEN Qin, CHEN Xi-feng. Spiral CT Diagnosis of Acute Pancreatitis Classification Method and its Clinical Significance[J]. CT Theory and Applications, 2012, 21(4): 735-740. |
[5] | MAO Guo-min, JIANG Zhi-rui. An Analysis of the Development and Present Situation of "CT Theory and Applications"[J]. CT Theory and Applications, 2011, 20(4): 573-582. |
[6] | CHEN Xing-ming, LIANG Zhen-hua. The Diagnostic Value of 16-Slice Spiral CT Reconstruction in Talus Fractures[J]. CT Theory and Applications, 2010, 19(1): 87-92. |
[7] | LI Jiang-shan, LI Shao-dong, CHENG Guang-jun, XU Kai. Application of Multi-slice Spiral CT in the Diagnosis of Moyamoya Disease[J]. CT Theory and Applications, 2005, 14(4): 33-37. |
[8] | PENG Lei, XU Chun-lin, WEI Li-li. Spiral CT 3D Reconstructions for Pelvic Application in Bone Fracture[J]. CT Theory and Applications, 2002, 11(3): 39-41. |
[9] | HUANG Li-ling, WANG Xu-wen. Applications of More-phase Spiral CT in Small Hepatic Hemangioma[J]. CT Theory and Applications, 2002, 11(3): 29-30. |
[10] | Guo Sujin, Huan Yi. Spiral CT: Renal Disease Applications[J]. CT Theory and Applications, 2000, 9(1): 26-28. |