TwinTree Insert

References


Chapter 15


Aichner FT, Felber SR, Muller RN, Rinck PA (eds.): Three-dimensional magnetic resonance imaging. An integrated clinical update on 3D-imaging and 3D-postprocessing. Oxford: Blackwell Scientific Publications 1994.

Alaux A, Rinck PA. Multispectral analysis of magnetic resonance imaging: a comparison between supervised and unsupervised classification techniques. in: Higer HP, Bielke G (eds). Tissue characterization in MR imaging. Berlin: Springer 1990. 165-169.

Bezdek JC, Hall LO, Clarke LP. Review of MR segmentation techniques using pattern recognition. Med Phys 1993; 20: 1033.

Bielke G, Meves M, Meindl S, Brückner A, Rinck P, von Seelen W, Pfannenstiel P: A systematic approach to optimization of pulse sequences in NMR-imaging by computer simulations. In: Esser PD, Johnston RE (eds.): The Technology of NMR. New York. The Society of Nuclear Medicine Computer and Instrumentation Councils. 1984. 109-117.

Bobman SA, Riederer SJ, Lee JN, Suddarth SA, Wang HZ, Drayer BP, MacFall JR. Cerebral magnetic resonance image synthesis. AJNR Am J Neuroradiol 1985; 6: 265-269.

Clarke LP, Velthuizen RP, Phuphanich S, Schellenberg JD, Arrington JA, Silbiger M. MRI: Stability of three supervised segmentation techniques. Magn Res Imag 1993; 11: 95.

Eklund A, Nichols TE, Knutsson H. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. PNAS 2016; 113, 28: 7900-7905.

Ferrari E, Bosco P, Calderoni S, Oliva P, Palumbo L, Spera G, Fantacci ME, Retico A. Dealing with confounders and outliers in classification medical studies: The Autism Spectrum Disorders case study. Artif Intell Med. 2020; 108: 101926. doi: 10.1016/j.artmed.2020.101926.

Fischer HW, Rinck PA, Van Haverbeke Y, Muller RN. Nuclear relaxation of human brain gray and white matter: analysis of field dependence and implications for MRI. Magn Reson Med 1990; 16: 317-334.

Frisby JP. Seeing: Illusion, brain and mind. Oxford: Oxford University Press. 1979.

Gauriau R, Bizzo BC, Kitamura FC, et al. A Deep Learning-based model for detecting abnormalities on brain MR images for triaging: preliminary results from a multisite experience. Radiol Artif Intell. 21 April 2021; 3(4):e200184.doi: 10.1148/ryai.2021200184.

Godtliebsen F. A study of image improvement techniques applied to NMR images. Doctoral thesis. Trondheim: The Norwegian Institute of Technology, Division of Mathematical Sciences. 1989.

Gonzalez RC, Wintz P. Digital image-process­ing. 3rd ed. Upper Saddle River, NJ (U.S.A.): Pearson Prentice Hall. 2008.

Kaushal A, Altman R, Langlotz C. Geographic distribution of US cohorts used to train deep learning algorithms. JAMA. 2020; 324 (12): 1212–1213. doi:10.1001/ jama.2020.12067

Lloret Iglesias L, Sanz Bellón P, Pérez del Barrio A, Menéndez Fernández — Miranda P, Rodríguez González D, Vega JA, González Mandly AA, Parra Blanco JA. A primer on deep learning and convolutional neural networks for clinicians. Insights Imaging. 2021; 12: 117

Lundervold A, Myhr G, Bosnes V, Myrheim J. Automatic recognition of pathological tissues in the central nervous system using MRI contrast agents and pattern recognition techniques. Oslo: Norwegian Computing Center, Report 858, 1992.

Maintz JB, Viergever MA: A survey of medical image registration. Med Image Anal 1998; 2: 1-36.

Montagnon M, Cerny M, Cadrin-Chênevert A, et al. Deep learning workflow in radiology: a primer. Insights Imaging. 2020; 11:22. doi.org/10.1186/s13244-019-0832-5

Odeblad E, Lindström G. Some preliminary observations on the proton magnetic resonance in biological samples. Acta Radiol 1955; 43: 469-476

Riederer SJ, Suddarth SA, Bobman SA, Lee JN, Wang HZ, MacFall JR. Automated MR image synthesis: feasibility studies. Radiology. 1984; 153: 203-206.

Rinck PA, Meindl S, Higer HP, Bieler EU, Pfannenstiel P. MRI of brain tumors: discrimination and attempt of typing by CPMG sequences and in vivo T2-measurements. Radiology 1985; 157: 103.

Rinck PA, Petersen SB, Heidelberger E, Lauterbur PC. NMR ventilation imaging of the lungs using perfluorinated gases. Proceedings. The Society of Magnetic Resonance in Medicine. Second Annual Meeting. San Francisco 1983, 302-303, and: Magn Reson Med 1984; 1: 237 [Direct Access]; and: Rinck PA, Petersen SB, Lauterbur PC. NMR-Imaging von fluorhaltigen Substanzen. 19-Fluor Ventilations- und Perfusionsdarstellungen. Fortschr Röntgenstr 1984; 140: 239-243 [Direct Access]

Rinck PA. Some reflections on artificial intelligence in medicine. Rinckside 2018; 29,5: 11-13 [Direct Access]; and: Rinck PA. Artificial intelligence meets validity. Rinckside 2019; 30,5: 13-15 [Direct Access].

Rinck PA. All is not what it seems in the messy world of research. Don’t play it again, Sam. Rinckside 2021; 32,6: 17-18 [Direct Access]; and: Rinck PA. Mapping the biological world. Rinckside 2017; 28,7: 13-15 [Direct Access].

Roberts M, Driggs D, Thorpe M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell. 2021; 3: 199–217. doi.org/10.1038/s42256-021-00307-0

Russ JC, Brent NF. The image-processing handbook. 7th ed. Boca Raton (U.S.A.): CRC Press. 2017.

Skalej M, Higer HP, Meves M, Brückner A, Bielke G, Meindl S, Rinck PA, Pfannenstiel P. T2-Analyse normaler und pathologischer Strukturen des Kopfes. Digit Bilddiag 1985; 5: 112.

Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks. arXiv:1312.6199. 2014; 1-10.

Tiede U, Bomans M, Höhne KH, Pommert A, Riemer M, Schiemann T, Schubert R. A computerized three-dimensional atlas of the human skull and brain. In: Aichner FT, Felber SR, Muller RN, Rinck PA (eds.): Three-dimensional magnetic resonance imaging. An integrated clinical update on 3D-imaging and 3D-postprocessing. Oxford: Blackwell Scientific Publications 1994, 61-74.

Torheim G, Rinck PA, Jones RA, Kværness J. A simulator for teaching MR image contrast behavior. Magn Res Materials 1994; 2: 515-522. [Direct Access/Abstract].

Torheim G, Rinck PA. MR Image Expert - interactively teaching contrast behavior in magnetic resonance imaging. In: Lemke HU, Vannier MW, Inamura K, Farman AG (eds.): Computer Assisted Radiology, CAR '96. Amsterdam: Excerpta Medica 1996, 619.

Wu E, Wu K, Daneshjou R, Ouyang D, Ho DE, Zou J. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med 2021; 27: 582–584. doi.org/10.1038/s41591-021-01312-x