[1] |
Raskob GE, Angchaisuksiri P, Blanco AN, et al. Thrombosis: a major contributor to global disease burden [J]. Arterioscler Thromb Vasc Biol, 2014, 34(11): 2363-71.
|
[2] |
Qanadli SD, Hajjam ME, Mesurolle B, et al. Pulmonary embolism detection: prospective evaluation of dual-section helical CT versus selective pulmonary arteriography in 157 patients[J]. Radiology, 2000, 217(2):447-455.
|
[3] |
Stein PD, Fowler SE, Goodman LR, et al. Multidetector computed tomography for acute pulmonary embolism [J]. N Engl J Med, 2006, 354(22): 2317-27.
|
[4] |
Patel S, Kazerooni EA, Cascade PN. Pulmonary embolism: optimization of small pulmonary artery visualization at multi-detector row CT[J]. Radiology, 2003, 227(2):455-460.
|
[5] |
Keller K, Hobohm L, Ebner M, et al. Trends in thrombolytic treatment and outcomes of acute pulmonary embolism in Germany[J]. Eur Heart J, 2020, 41(4):522-529.
|
[6] |
Lehnert P, Lange T, Møller CH, et al. Acute pulmonary embolism in a national danish cohort: increasing incidence and decreasing mortality [J]. Thromb Haemost, 2018, 118(3): 539-46.
|
[7] |
Jiménez D, de Miguel-Díez J, Guijarro R, et al. Trends in the management and outcomes of acute pulmonary embolism: analysis from the RIETE registry[J]. J Am Coll Cardiol, 2016, 67(2):162-170.
|
[8] |
Kligerman SJ, Mitchell JW, Sechrist JW, et al. Radiologist performance in the detection of pulmonary embolism: features that favor correct interpretation and risk factors for errors[J]. J Thorac Imaging, 2018, 33(6):350-357.
|
[9] |
Tourassi GD, Floyd CE, Sostman HD, et al. Artificial neural network for diagnosis of acute pulmonary embolism: effect of case and observer selection[J]. Radiology, 1995, 194(3):889-893.
|
[10] |
Al'Aref SJ, Anchouche K, Singh G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging[J]. Eur Heart J, 2019, 40(24):1975-1986.
|
[11] |
Dey D, Slomka PJ, Leeson P, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review[J]. J Am Coll Cardiol, 2019, 73(11):1317-1335.
|
[12] |
Retson TA, Besser AH, Sall S, et al. Machine learning and deep neural networks in thoracic and cardiovascular imaging[J]. J Thorac Imaging, 2019, 34(3):192-201.
|
[13] |
Litjens G, Ciompi F, Wolterink JM, et al. State-of-the-art deep learning in cardiovascular image analysis[J]. JACC Cardiovasc Imaging, 2019, 12(8 Pt 1):1549-1565.
|
[14] |
Lenfant M, Chevallier O, Comby PO, et al. Deep learning versus iterative reconstruction for CT pulmonary angiography in the emergency setting: improved image quality and reduced radiation dose[J]. Diagnostics (Basel), 2020, 10(8):558.
|
[15] |
Chen H, Zhang Y, Kalra MK, et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE Trans Med Imaging, 2017, 36(12):2524-2535.
|
[16] |
Du W, Chen H, Wu Z, et al. Stacked competitive networks for noise reduction in low-dose CT[J]. PLoS One, 2017, 12(12):e0190069.
|
[17] |
Kang E, Chang W, Yoo J, et al. Deep convolutional framelet denosing for low-dose CT via wavelet residual network[J]. IEEE Trans Med Imaging, 2018, 37(6):1358-1369.
|
[18] |
Kandathil A, Chamarthy M. Pulmonary vascular anatomy & anatomical variants[J]. Cardiovasc Diagn Ther, 2018, 8(3):201-207.
|
[19] |
Zhou C, Chan HP, Sahiner B, et al. Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications[J]. Med Phys, 2007, 34(12):4567-4577.
|
[20] |
Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, et al. Pulmonary artery-vein classification in CT images using deep learning[J]. IEEE Trans Med Imaging, 2018, 37(11):2428-2440.
|
[21] |
Hu J, Wang H, Wang J, et al. SA-Net: a scale-attention network for medical image segmentation [J]. PLoS One, 2021, 16(4): e0247388.
|
[22] |
Nam JG, Witanto JN, Park SJ, et al. Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps[J]. Eur Radiol, 2021, 31(12):9012-9021.
|
[23] |
Zhang C, Sun M, Wei Y, et al. Automatic segmentation of arterial tree from 3D computed tomographic pulmonary angiography (CTPA) scans[J]. Comput Assist Surg (Abingdon), 2019, 24(sup2):79-86.
|
[24] |
Masutani Y, MacMahon H, Doi K. Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis[J]. IEEE Trans Med Imaging, 2002, 21(12):1517-1523.
|
[25] |
Maizlin ZV, Vos PM, Godoy MC, et al. Computer-aided detection of pulmonary embolism on CT angiography: initial experience [J]. J Thorac Imaging, 2007, 22(4): 324-329.
|
[26] |
Engelke C, Schmidt S, Bakai A, et al. Computer-assisted detection of pulmonary embolism: performance evaluation in consensus with experienced and inexperienced chest radiologists[J]. Eur Radiol, 2008, 18(2):298-307.
|
[27] |
Bouma H, Sonnemans JJ, Vilanova A, et al. Automatic detection of pulmonary embolism in CTA images[J]. IEEE Trans Med Imaging, 2009, 28(8):1223-1230.
|
[28] |
Lee CW, Seo JB, Song JW, et al. Evaluation of computer-aided detection and dual energy software in detection of peripheral pulmonary embolism on dual-energy pulmonary CT angiography[J]. Eur Radiol, 2011, 21(1):54-62.
|
[29] |
Kligerman SJ, Lahiji K, Galvin JR, et al. Missed pulmonary emboli on CT angiography: assessment with pulmonary embolism-computer-aided detection [J]. AJR Am J Roentgenol, 2014, 202(1): 65-73.
|
[30] |
Zhou C, Chan HP, Sahiner B, et al. Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): performance evaluation with independent data sets[J]. Med Phys, 2009, 36(8):3385-3396.
|
[31] |
Özkan H, Osman O, Şahin S, et al. A novel method for pulmonary embolism detection in CTA images[J]. Comput Methods Programs Biomed, 2014, 113(3):757-766.
|
[32] |
Schoepf UJ, Schneider AC, Das M, et al. Pulmonary embolism: computer-aided detection at multidetector row spiral computed tomography[J]. J Thorac Imaging, 2007, 22(4):319-323.
|
[33] |
Wittenberg R, Peters JF, Sonnemans JJ, et al. Computer-assisted detection of pulmonary embolism: evaluation of pulmonary CT angiograms performed in an on-call setting [J]. Eur Radiol, 2010, 20(4): 801-806.
|
[34] |
Blackmon KN, Florin C, Bogoni L, et al. Computer-aided detection of pulmonary embolism at CT pulmonary angiography: can it improve performance of inexperienced readers?[J]. Eur Radiol, 2011, 21(6):1214-1223.
|
[35] |
Weikert T, Winkel DJ, Bremerich J, et al. Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm[J]. Eur Radiol, 2020, 30(12):6545-6553.
|
[36] |
Tajbakhsh N, Shin JY, Gotway MB, et al. Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation[J]. Med Image Anal, 2019, 58:101541.
|
[37] |
Schmuelling L, Franzeck FC, Nickel CH, et al. Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: no significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation[J]. Eur J Radiol, 2021, 141:109816.
|
[38] |
Buls N, Watté N, Nieboer K, et al. Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism[J]. Phys Med, 2021, 83:154-160.
|
[39] |
Huang SC, Kothari T, Banerjee I, et al. PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging[J]. NPJ Digit Med, 2020, 3:61.
|
[40] |
Huang SC, Pareek A, Zamanian R, et al. Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection[J]. Sci Rep, 2020, 10(1):22147.
|
[41] |
Liu W, Liu M, Guo X, et al. Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning[J]. Eur Radiol, 2020, 30(6):3567-3575.
|
[42] |
Huhtanen H, Nyman M, Mohsen T, et al. Automated detection of pulmonary embolism from CT-angiograms using deep learning[J]. BMC Med Imaging, 2022, 22(1):43.
|
[43] |
Cheikh AB, Gorincour G, Nivet H, et al. How artificial intelligence improves radiological interpretation in suspected pulmonary embolism[J]. Eur Radiol, 2022, 32(9):5831-5842.
|
[44] |
Batra K, Xi Y, Al-Hreish KM, et al. Detection of incidental pulmonary embolism on conventional contrast-enhanced chest CT: comparison of an artificial intelligence algorithm and clinical reports[J]. AJR Am J Roentgenol, 2022, 219(6):895-902.
|
[45] |
Apfaltrer P, Henzler T, Meyer M, et al. Correlation of CT angiographic pulmonary artery obstruction scores with right ventricular dysfunction and clinical outcome in patients with acute pulmonary embolism[J]. Eur J Radiol, 2012, 81(10):2867-2871.
|
[46] |
Vedovati MC, Becattini C, Agnelli G, et al. Multidetector CT scan for acute pulmonary embolism: embolic burden and clinical outcome[J]. Chest, 2012, 142(6):1417-1424.
|
[47] |
Lerche M, Bailis N, Akritidou M, et al. Pulmonary vessel obstruction does not correlate with severity of pulmonary embolism[J]. J Clin Med, 2019, 8(5):584.
|
[48] |
Jia D, Li XL, Zhang Q, et al. A decision tree built with parameters obtained by computed tomographic pulmonary angiography is useful for predicting adverse outcomes in non-high-risk acute pulmonary embolism patients[J]. Respir Res, 2019, 20(1):187.
|
[49] |
Shen C, Yu N, Wen L, et al. Risk stratification of acute pulmonary embolism based on the clot volume and right ventricular dysfunction on CT pulmonary angiography[J]. Clin Respir J, 2019, 13(11):674-682.
|
[50] |
Ende-Verhaar YM, Cannegieter SC, Vonk Noordegraaf A, et al. Incidence of chronic thromboembolic pulmonary hypertension after acute pulmonary embolism: a contemporary view of the published literature[J]. Eur Respir J, 2017, 49(2):1601792.
|
[51] |
Rogberg AN, Gopalan D, Westerlund E, et al. Do radiologists detect chronic thromboembolic disease on computed tomography?[J]. Acta Radiol, 2019, 60(11):1576-1583.
|
[52] |
Vainio T, Mäkelä T, Savolainen S, et al. Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study[J]. Eur Radiol Exp, 2021, 5(1):45.
|
[53] |
Dissaux B, Le Floch PY, Robin P, et al. Pulmonary perfusion by iodine subtraction maps CT angiography in acute pulmonary embolism: comparison with pulmonary perfusion SPECT (PASEP trial)[J]. Eur Radiol, 2020, 30(9):4857-4864.
|
[54] |
Singh R, Nie RZ, Homayounieh F, et al. Quantitative lobar pulmonary perfusion assessment on dual-energy CT pulmonary angiography: applications in pulmonary embolism[J]. Eur Radiol, 2020, 30(5):2535-2542.
|
[55] |
杨浩宇,刘敏. 慢性血栓栓塞性肺动脉高压的影像诊断与评估[J].中国实用内科杂志,2022,42(12):982-986.
|
[56] |
谢万木,刘敏,杨宏伟等. 慢性血栓栓塞性肺动脉高压患者CT肺血管造影的特征[J].中华医学杂志,2020,100(26):2012-2017.
|
[57] |
Koike H, Sueyoshi E, Sakamoto I, et al. Comparative clinical and predictive value of lung perfusion blood volume CT, lung perfusion SPECT and catheter pulmonary angiography images in patients with chronic thromboembolic pulmonary hypertension before and after balloon pulmonary angioplasty[J]. Eur Radiol, 2018, 28(12):5091-5099.
|
[58] |
Meinel FG, Nance JW, Schoepf UJ, et al. Predictive value of computed tomography in acute pulmonary embolism: systematic review and meta-analysis[J]. Am J Med, 2015, 128(7):747-759.e2.
|
[59] |
Huang L, Li J, Huang M, et al. Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models[J]. Eur Radiol, 2020, 30(3):1369-1377.
|
[60] |
Yang L, Gu D, Wei J, et al. A Radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J]. Liver Cancer, 2019, 8(5):373-386.
|
[61] |
Chu H, Liu Z, Liang W, et al. Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma[J]. Eur Radiol, 2021, 31(4):2368-2376.
|
[62] |
Sun F, Chen Y, Chen X, et al. CT-based radiomics for predicting brain metastases as the first failure in patients with curatively resected locally advanced non-small cell lung cancer[J]. Eur J Radiol, 2021, 134:109411.
|