AI-enhanced CT reconstruction enables strong noise suppression and dose reduction, but aggressive denoising can distort diagnostically relevant texture, lowering reader confidence—the “texture preservation paradox.” Clinical evidence linking texture assessment with validation across AI approaches remains fragmented.
Objective
To systematically evaluate AI-enhanced CT reconstruction techniques with emphasis on texture preservation and to summarize clinical validation evidence supporting dose optimization.
Methods
This systematic review followed PRISMA guidelines and searched PubMed, IEEE Xplore, Scopus, and Web of Science (January 2020 to November 2025) for peer-reviewed studies evaluating AI-enhanced CT reconstruction with texture-related image quality assessment and clinical validation.
Results
Fifteen clinical studies involving 1847 patients were included in the qualitative synthesis. AI-enhanced CT reconstruction showed consistently improved image quality compared with conventional reconstruction, with representative reports of higher PSNR and SSIM, reduced noise, and improved lesion conspicuity. Multiple studies emphasized preservation of diagnostically relevant texture, addressing the limitation that conventional metrics do not fully capture perceptual fidelity. Reader-based validation indicated improved diagnostic confidence, better inter-reader consistency, and maintained diagnostic acceptability at reduced radiation dose.
Conclusions
AI-enhanced CT reconstruction shows promising clinical utility for improving image quality and supporting dose optimization while preserving texture characteristics. However, heterogeneity in study design and evaluation metrics warrants cautious interpretation and highlights the need for standardized assessment.
PesapaneFCodariMSardanelliF. Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp2018; 2: 35.
2.
HosnyAParmarCQuackenbushJ, et al.Artificial intelligence in radiology. Nat Rev Cancer2018; 18: 500–510.
3.
MazurowskiMABudaMSahaA, et al.Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging JMRI2019; 49: 939–954.
4.
McColloughCHYuLKoflerJM, et al.Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology2015; 276: 499–506.
5.
BeisterMKolditzDKalenderWA. Iterative reconstruction methods in X-ray CT. Phys Medica PM Int J Devoted Appl Phys Med Biol Off J Ital Assoc Biomed Phys AIFB2012; 28: 94–108.
6.
WangTXiaWLuJ, et al.A review of deep learning CT reconstruction from incomplete projection data. IEEE Trans Radiat Plasma Med Sci2024; 8: 138–152.
7.
SinghSKalraMKGilmanMD, et al.Adaptive statistical iterative reconstruction technique for radiation dose reduction in chest CT: A pilot study. Radiology2011; 259: 565–573.
8.
KoetzierLRMastrodicasaDSzczykutowiczTP, et al.Deep learning image reconstruction for CT: Technical principles and clinical prospects. Radiology2023; 306: e221257.
9.
JiangJ-MMiaoLLiangX, et al.The value of deep learning image reconstruction in improving the quality of low-dose chest CT images. Diagnostics2022; 12: 2560.
10.
SzczykutowiczTPToiaGVDhanantwariA, et al.A review of deep learning CT reconstruction: Concepts, limitations, and promise in clinical practice. Curr Radiol Rep2022; 10: 101–115.
11.
ZhuBLiuJZCauleySF, et al.Image reconstruction by domain-transform manifold learning. Nature2018; 555: 487–492.
12.
MoherDLiberatiATetzlaffJ, et al.Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med2009; 6: e1000097.
13.
KakACSlaneyM. Principles of Computerized Tomographic Imaging. New York, NY: IEEE press, 1988. https://ieeexplore.ieee.org/book/9127264 (1988, accessed 26 October 2025).
14.
ThibaultJBSauerKDBoumanCA, et al.A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys2007; 34: 4526–4544.
15.
ChenHLiQZhouL, et al.Deep learning-based algorithms for low-dose CT imaging: A review. Eur J Radiol2024; 172: 111355.
FairEProfioMKulkarniN, et al.Image quality evaluation in dual-energy CT of the chest, abdomen, and pelvis in obese patients with deep learning image reconstruction. J Comput Assist Tomogr2022; 46: 604.
18.
PrabhatKCZengRFarhangiMM, et al.Deep neural networks-based denoising models for CT imaging and their efficacy. In: Medical imaging 2021: physics of medical imaging. Bellingham, WA, USA, SPIE, 2021, pp. 105–117.
19.
SantanaGOCouto R deMLoureiroRM, et al.Economic evaluations and equity in the use of artificial intelligence in imaging exams for medical diagnosis in people with skin, neurological, and pulmonary diseases: Protocol for a systematic review. JMIR Res Protoc2023; 12: e48544.
20.
TamuraAMukaidaEOtaY, et al.Deep learning reconstruction allows low-dose imaging while maintaining image quality: Comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT. Quant Imaging Med Surg2022; 12: 2977–2984.
21.
BradySL. Implementation of AI image reconstruction in CT—how is it validated and what dose reductions can be achieved. Br J Radiol2023; 96: 20220915.
22.
Bani-AhmadMEnglandAMcLaughlinL, et al.Potential of artificial intelligence for radiation dose reduction in computed tomography -A scoping review. Radiogr Lond Engl1995 2025; 31: 102968.
23.
JingBLiaoGLiJ, et al.CT Super resolution using generative adversarial network. In: 2025 5th international conference on neural networks, information and communication engineering (NNICE), Guangzhou, China, IEEE, 2025, pp. 473–478.
24.
OostveenLJBoedekerKLShinDW, et al.Perceptual thresholds for differences in CT noise texture. J Med Imaging2024; 11: 035501.
25.
NjølstadTJensenKDybwadA, et al.Low-contrast detectability and potential for radiation dose reduction using deep learning image reconstruction—A 20-reader study on a semi-anthropomorphic liver phantom. Eur J Radiol Open2022; 9: 100418.
26.
XingR. Deep Learning Based CT Image Reconstruction. Master's thesis, Piscataway, NJ, USA, University of Washington, 2020.
27.
WajerRDabrowski-TumanskiPWajerA, et al.Enhancing image quality in dental-maxillofacial CBCT: The impact of iterative reconstruction and AI on noise reduction—a systematic review. J Clin Med2025; 14: 4214.
28.
WangZBovikACSheikhHR, et al.Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process2004; 13: 600–612.
29.
TivnanMKikkertIDWuD, et al.Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model. Sci Rep2025; 15: 26002.
30.
HanMShimHBaekJ. Perceptual CT loss: Implementing CT image specific perceptual loss for CNN-based low-dose CT Denoiser. IEEE Access2022; 10: 62412–62422.
31.
RahmanHKhanARSadiqT, et al.A systematic literature review of 3D deep learning techniques in computed tomography reconstruction. Tomography2023; 9: 2158–2189.
32.
GreffierJMolinerGDabliD, et al.Task-based image quality assessment comparing classical and iterative cone beam CT images on halcyon. Diagnostics2023; 13: 448.
33.
LvYYeJYinYL, et al.A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules. Clin Radiol2022; 77: e741–e748.
CastiglioneDGPorrecaAFalsaperlaD, et al.Inter-reader agreement in LR-TRA application and NLR association in HCC patients treated with endovascular vs. Ablative Procedures Cancers2025; 17: 492.
36.
OedingJFKrychAJPearleAD, et al.Medical imaging applications developed using artificial intelligence demonstrate high internal validity yet are limited in scope and lack external validation. Arthrosc J Arthrosc Relat Surg2025; 41: 455–472.
37.
PalazzoGMangiliPDeantoniC, et al.Real-world validation of artificial intelligence-based computed tomography auto-contouring for prostate cancer radiotherapy planning. Phys Imaging Radiat Oncol2023; 28: 100501.
38.
LubnerMGPickhardtPJToiaGV, et al.Clinical consequences of deep learning image reconstruction at CT. Br J Radiol2025; 98: tqaf152.
39.
MasturzoLBarcaPDe MasiL, et al.Voxelwise characterization of noise for a clinical photon-counting CT scanner with a model-based iterative reconstruction algorithm. Eur Radiol Exp2025; 9: 2.
40.
KetolaJHJInkinenSIMäkeläT, et al.Automatic chest computed tomography image noise quantification using deep learning. Phys Med2024; 117: 103186.
41.
JiangXFangCHuP, et al.Fast and effective single-scan dual-energy cone-beam CT reconstruction and decomposition Denoising based on dual-energy vectorization. Med Phys2021; 48: 4843–4856.
42.
MuramatsuSSatoK. Quantitative analysis of emphysema in ultra-high-resolution CT by using deep learning reconstruction: Comparison with hybrid iterative reconstruction. Nihon Hoshasen Gijutsu Gakkai Zasshi2020; 76: 1163–1172.
43.
GohlaGÖrgelAKloseU, et al.Comparison of ADMIRE, SAFIRE, and filtered back projection in standard and low-dose non-enhanced head CT. Diagnostics2025; 15: 1541.
44.
HeYZengLYuW, et al.Noise suppression–guided image filtering for low-SNR CT reconstruction. Med Biol Eng Comput2020; 58: 2621–2629.
45.
SandhuMKushwahaSAroraT. A comprehensive review of GAN-based Denoising models for low-dose computed tomography images. Int J Image Graph2025; 25: 2550030.
46.
AbdelwanisMSimseklerMCEGaborAF, et al.Artificial intelligence adoption challenges from healthcare providers’ perspectives: A comprehensive review and future directions. Saf Sci2026; 193: 107028.
47.
ChunSHSuhYJHanK, et al.Deep learning-based reconstruction on cardiac CT yields distinct Radiomic features compared to iterative and filtered back projection reconstructions. Sci Rep2022; 12: 15171.
48.
NagayamaYSakabeDGotoM, et al.Deep learning-based reconstruction for lower-dose pediatric CT: Technical principles, image characteristics, and clinical implementations. RadioGraphics2021; 41: 1936–1953.
GoddardLVeltenCTangJ, et al.Evaluation of multiple-vendor AI Autocontouring solutions. Radiat Oncol Lond Engl2024; 19: 69.
51.
SunCSalimiYAngelikiN, et al.An efficient dual-domain deep learning network for sparse-view CT reconstruction. Comput Methods Prog Biomed2024; 256: 108376.
52.
DongCLiTZXuK, et al.Characterizing browser-based medical imaging AI with Serverless edge computing: towards addressing clinical data security constraints. Proc SPIE– Int Soc Opt Eng2023; 12469: 1246907.
53.
TanguayWAcarPFineB, et al.Assessment of radiology artificial intelligence software: A validation and evaluation framework. Can Assoc Radiol J J Assoc Can Radiol2023; 74: 326–333.
54.
GuanHYapP-TBozokiA, et al.Federated learning for medical image analysis: A survey. Pattern Recognit2024; 151: 110424.
