den Harder AM, Willemink MJ, de Jong PA et al (2016) New horizons in cardiac CT. Clin Radiol 71:758–767

Article  Google Scholar 

Schuijf JD, Lima JAC, Boedeker KL et al (2022) CT imaging with ultra-high-resolution: opportunities for cardiovascular imaging in clinical practice. J Cardiovasc Comput Tomogr 16:388–396

Article  PubMed  Google Scholar 

Takagi H, Tanaka R, Nagata K et al (2018) Diagnostic performance of coronary CT angiography with ultra-high-resolution CT: comparison with invasive coronary angiography. Eur J Radiol 101:30–37

Article  PubMed  Google Scholar 

Si-Mohamed SA, Boccalini S, Lacombe H et al (2022) Coronary CT angiography with photon-counting CT: first-in-human results. Radiology 303:303–313

Article  PubMed  Google Scholar 

Mergen V, Sartoretti T, Cundari G et al (2023) The importance of temporal resolution for ultra-high-resolution coronary angiography: evidence from photon-counting detector CT. Invest Radiol 58:767–774

Article  CAS  PubMed  Google Scholar 

Ohno Y, Takenaka D, Kanda T et al (2012) Adaptive iterative dose reduction using 3D processing for reduced- and low-dose pulmonary CT: comparison with standard-dose CT for image noise reduction and radiological findings. AJR Am J Roentgenol 199:W477–W485

Article  PubMed  Google Scholar 

Ohno Y, Koyama H, Seki S, Kishida Y, Yoshikawa T (2019) Radiation dose reduction techniques for chest CT: principles and clinical results. Eur J Radiol 111:93–103

Article  PubMed  Google Scholar 

Halliburton SS, Tanabe Y, Partovi S, Rajiah P (2017) The role of advanced reconstruction algorithms in cardiac CT. Cardiovasc Diagn Ther 7:527–538

Article  PubMed  PubMed Central  Google Scholar 

Mohammadinejad P, Mileto A, Yu L et al (2021) CT noise-reduction methods for lower-dose scanning: strengths and weaknesses of iterative reconstruction algorithms and new techniques. Radiographics 41:1493–1508

Article  PubMed  Google Scholar 

Latina J, Shabani M, Kapoor K et al (2021) Ultra-high-resolution coronary CT angiography for assessment of patients with severe coronary artery calcification: initial experience. Radiol Cardiothorac Imaging 3:e210053

Article  PubMed  PubMed Central  Google Scholar 

Nishii T, Funama Y, Kato S et al (2022) Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study. Jpn J Radiol 40:279–288

Article  PubMed  Google Scholar 

Koetzier LR, Mastrodicasa D, Szczykutowicz TP et al (2023) Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology 306:e221257

Article  PubMed  Google Scholar 

Hamabuchi N, Ohno Y, Kimata H et al (2023) Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images. Jpn J Radiol 41:1373–1388

Article  PubMed  PubMed Central  Google Scholar 

Orii M, Sone M, Osaki T et al (2023) Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience. BMC Med Imaging 23:171

Article  PubMed  PubMed Central  Google Scholar 

Takafuji M, Kitagawa K, Mizutani S et al (2023) Super-resolution deep learning reconstruction for improved image quality of coronary CT angiography. Radiol Cardiothorac Imaging 5:e230085

Article  PubMed  PubMed Central  Google Scholar 

Kawai H, Motoyama S, Sarai M et al (2024) Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study. Eur Radiol 34:2647–2657

Article  PubMed  Google Scholar 

Funama Y, Utsunomiya D, Hirata K et al (2017) Improved estimation of coronary plaque and luminal attenuation using a vendor-specific model-based iterative reconstruction algorithm in contrast-enhanced CT coronary angiography. Acad Radiol 24:1070–1078

Article  PubMed  PubMed Central  Google Scholar 

Nagayama Y, Emoto T, Kato Y et al (2023) Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography. Eur Radiol 33:8488–8500

Article  PubMed  Google Scholar 

Hernandez AM, Shin DW, Abbey CK et al (2020) Validation of synthesized normal-resolution image data generated from high-resolution acquisitions on a commercial CT scanner. Med Phys 47:4775–4785

Article  PubMed  Google Scholar 

Cury RC, Abbara S, Achenbach S et al (2016) CAD-RADS(TM) coronary artery disease—reporting and data system. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Cardiovasc Comput Tomogr 10:269–281

Article  PubMed  Google Scholar 

Cury RC, Leipsic J, Abbara S et al (2022) CAD-RADS™ 2.0—2022 coronary artery disease—reporting and data system an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI). Radiol Cardiothorac Imaging 4:e220183

Article  PubMed  PubMed Central  Google Scholar 

Koweek L, Achenbach S, Berman DS et al (2023) Standardized medical terminology for cardiac computed tomography 2023 update: an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), North American Society for Cardiovascular Imaging (NASCI), and Radiological Society of North America (RSNA) with endorsement by the Asian Society of Cardiovascular Imaging (ASCI), the European Association of Cardiovascular Imaging (EACI), and the European Society of Cardiovascular Radiology (ESCR). Radiol Cardiothorac Imaging 5:e230167

Article  PubMed  PubMed Central  Google Scholar 

Renker M, Steinbach R, Schoepf UJ et al (2023) Comparison of first-generation and third-generation dual-source computed tomography for detecting coronary artery disease in patients evaluated for transcatheter aortic valve replacement. J Thorac Imaging 38:165–173

Article  PubMed  Google Scholar 

Tatsugami F, Higaki T, Nakamura Y et al (2019) Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 29:5322–5329

Article  PubMed  Google Scholar 

Matsukiyo R, Ohno Y, Matsuyama T et al (2021) Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions. Jpn J Radiol 39:186–197

Article  PubMed  Google Scholar 

Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310

Article  CAS  PubMed  Google Scholar 

Svanholm H, Starklint H, Gundersen HJ, Fabricius J, Barlebo H, Olsen S (1989) Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic. APMIS 97:689–698

Article  CAS  PubMed  Google Scholar