Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning
ilustraciones, fotografías, gráficas
- Autores:
-
Prieto González, Leonar Steven
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82118
- Palabra clave:
- 610 - Medicina y salud
Imagen de Difusión por Resonancia Magnética
Procesamiento de Imagen Asistido por Computador
Diffusion Magnetic Resonance Imaging
Image Processing, Computer-Assisted
Imágenes por resonancia magnética
IRM
difusión tisular
movimiento incoherente intravóxel
aprendizaje automático
ADC
DWI
IVIM
Magnetic Resonance Imaging
MRI
tissue diffusion
Intravoxel Incoherent Motion
Machine Learning
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning |
| dc.title.translated.eng.fl_str_mv |
Analysis of nuclear magnetic resonance imaging diffusion models using machine learning |
| title |
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning |
| spellingShingle |
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning 610 - Medicina y salud Imagen de Difusión por Resonancia Magnética Procesamiento de Imagen Asistido por Computador Diffusion Magnetic Resonance Imaging Image Processing, Computer-Assisted Imágenes por resonancia magnética IRM difusión tisular movimiento incoherente intravóxel aprendizaje automático ADC DWI IVIM Magnetic Resonance Imaging MRI tissue diffusion Intravoxel Incoherent Motion Machine Learning |
| title_short |
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning |
| title_full |
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning |
| title_fullStr |
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning |
| title_full_unstemmed |
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning |
| title_sort |
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning |
| dc.creator.fl_str_mv |
Prieto González, Leonar Steven |
| dc.contributor.advisor.none.fl_str_mv |
Agulles Pedros, Luis |
| dc.contributor.author.none.fl_str_mv |
Prieto González, Leonar Steven |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Física Médica |
| dc.subject.ddc.spa.fl_str_mv |
610 - Medicina y salud |
| topic |
610 - Medicina y salud Imagen de Difusión por Resonancia Magnética Procesamiento de Imagen Asistido por Computador Diffusion Magnetic Resonance Imaging Image Processing, Computer-Assisted Imágenes por resonancia magnética IRM difusión tisular movimiento incoherente intravóxel aprendizaje automático ADC DWI IVIM Magnetic Resonance Imaging MRI tissue diffusion Intravoxel Incoherent Motion Machine Learning |
| dc.subject.decs.spa.fl_str_mv |
Imagen de Difusión por Resonancia Magnética Procesamiento de Imagen Asistido por Computador |
| dc.subject.decs.eng.fl_str_mv |
Diffusion Magnetic Resonance Imaging Image Processing, Computer-Assisted |
| dc.subject.proposal.spa.fl_str_mv |
Imágenes por resonancia magnética IRM difusión tisular movimiento incoherente intravóxel aprendizaje automático |
| dc.subject.proposal.eng.fl_str_mv |
ADC DWI IVIM Magnetic Resonance Imaging MRI tissue diffusion Intravoxel Incoherent Motion Machine Learning |
| description |
ilustraciones, fotografías, gráficas |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-08-25T20:20:41Z |
| dc.date.available.none.fl_str_mv |
2022-08-25T20:20:41Z |
| dc.date.issued.none.fl_str_mv |
2022-05 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/82118 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/82118 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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Valerij G. Kiselev. Fundamentals of diffusion mri physics. NMR in biomedicine, 30, 3 2017. Denis Le Bihan. What can we see with ivim mri? NeuroImage, 187:56–67, 2 2019. Mara Cercignani and Mark A. Horsfield. The physical basis of diffusion-weighted mri. Journal of the neurological sciences, 186 Suppl 1, 5 2001. Radiopaedia.org. Diffusion-weighted imaging — radiology reference article —, 2021. Laura Andrea Pastor Luque and Luis Agulles Pedros. Modelación de difusión en irm, 2020. Denis Le Bihan. Apparent diffusion coefficient and beyond: What diffusion mr imaging can tell us about tissue structure. https://doi.org/10.1148/radiol.13130420, 268:318–322, 8 2013. Carmelo Messina, Rodolfo Bignone, Alberto Bruno, Antonio Bruno, Federico Bruno, Marco Calandri, Damiano Caruso, Pietro Coppolino, Riccardo De Robertis, Francesco Gentili, Irene Grazzini, Raffaele Natella, Paola Scalise, Antonio Barile, Roberto Grassi, and Domenico Albano. Diffusion-weighted imaging in oncology: An update. Cancers, 12:1–28, 6 2020. Saef Izzy, Daniel B. Rubin, Firas S. Ahmed, Feras Akbik, Simone Renault, Katelyn W. Sylvester, Henrikas Vaitkevicius, Jennifer A. Smallwood, Michael M. Givertz, and Steven K. Feske. Cerebrovascular accidents during mechanical circulatory support: New predictors of ischemic and hemorrhagic strokes and outcome. Stroke, 49:1197– 1203, 2018. Ashok Srinivasan, R. Dvorak, K. Perni, S. Rohrer, and S. K. Mukherji. Differentiation of benign and malignant pathology in the head and neck using 3t apparent diffusion coefficient values: Early experience. American Journal of Neuroradiology, 29:40–44, 1 2008. Young Jin Choi, In Sook Lee, You Seon Song, Jeung Il Kim, Kyung Un Choi, and Jong Woon Song. Diagnostic performance of diffusion-weighted (dwi) and dynamic contrast-enhanced (dce) mri for the differentiation of benign from malignant soft-tissue tumors. Journal of magnetic resonance imaging : JMRI, 50:798–809, 9 2019. Lluís Agulles Pedros, R. Acosta, Peter Blümler, and Hans Spiess. Diffusion influence on gas magnetic resonance imaging. Universitas Scientiarum, 13:281–289, 09 2008. E. O. Stejskal and J. E. Tanner. Spin diffusion measurements: Spin echoes in the presence of a time-dependent field gradient. The Journal of Chemical Physics, 42:288, 7 1965. D. Le Bihan, E. Breton, D. Lallemand, M. L. Aubin, J. Vignaud, and M. Laval-Jeantet. Separation of diffusion and perfusion in intravoxel incoherent motion mr imaging. https://doi.org/10.1148/radiology.168.2.3393671, 168:497–505, 8 1986. Olaf Dietrich, Andreas Biffar, Andrea Baur-Melnyk, and Maximilian F. Reiser. Tech nical aspects of mr diffusion imaging of the body. European Journal of Radiology, 76:314–322, 2010. Fumiyuki Yamasaki, Kaoru Kurisu, Kenichi Satoh, Kazunori Arita, Kazuhiko Sugi yama, Megu Ohtaki, Junko Takaba, Atushi Tominaga, Ryosuke Hanaya, Hiroyuki Yoshioka, Seiji Hama, Yoko Ito, Yoshinori Kajiwara, Kaita Yahara, Taiichi Saito, and Muhamad Thohar Arifin. Apparent diffusion coefficient of human brain tumors at mr imaging. Radiology, 235:985–991, 6 2005. Signe Swerkersson, Oscar Grundberg, Karl Kolbeck, Andreas Carlberg, Sven Nyr ¨ en, ´ and Mikael Skorpil. Optimizing diffusion-weighted magnetic resonance imaging for evaluation of lung tumors: A comparison of respiratory triggered and free breathing techniques. European Journal of Radiology Open, 5:189, 1 2018. Francesco Giganti, Elena Orsenigo, Antonio Esposito, Damiano Chiari, Annalaura Salerno, Alessandro Ambrosi, Luca Albarello, Elena Mazza, Carlo Staudacher, Ales sandro Del Maschio, and Francesco De Cobelli. Prognostic role of diffusion-weighted mr imaging for resectable gastric cancer. Radiology, 276:444–452, 8 2015. Sungmin Woo, Chong Hyun Suh, Sang Youn Kim, Jeong Yeon Cho, Seung Hyup Kim, and Min Hoan Moon. Head-to-head comparison between biparametric and multiparametric mri for the diagnosis of prostate cancer: A systematic review and meta-analysis. AJR. American journal of roentgenology, 211:W226–W241, 11 2018. Weigen Yao, Jie Liu, Jiaju Zheng, Pengcong Lu, Shufang Zou, and Ying Xu. Study on diagnostic value of quantitative parameters of intravoxel incoherent motion diffusion-weighted imaging (ivim-dwi) in prostate cancer. American Journal of Translational Research, 13:3696, 2021. Harri Merisaari, Pekka Taimen, Rakesh Shiradkar, Otto Ettala, Marko Pesola, Jani Saunavaara, Peter J. Bostrom, Anant Madabhushi, Hannu J. Aronen, and Ivan Jam- ¨ bor. Repeatability of radiomics and machine learning for dwi: Short-term repeatability study of 112 patients with prostate cancer. Magnetic resonance in medicine, 83:2293–2309, 6 2020. Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I, and Dipy Contributors. Dipy, a library for the analysis of diffusion mri data. frontiers in neuroinformatics. 8, 2014. Rafael Neto Henriques, Ariel Rokem, Eleftherios Garyfallidis, Samuel St-Jean, Eric Thomas Peterson, and Marta Morgado Correia. [re] optimization of a free water elimination two-compartment model for diffusion tensor imaging. bioRxiv, page 108795, 2 2017. Brian Hansen and Sune Nørhøj Jespersen. Data for evaluation of fast kurtosis strategies, b-value optimization and exploration of diffusion mri contrast. Scientific Data 2016 3:1, 3:1–5, 8 2016. Eric Peterson. Ivim dataset. figshare. dataset., 2016. Tim B. Dyrby, William F.C. Baare, Daniel C. Alexander, Jacob Jelsing, Ellen Garde, ´ and Lise V. Søgaard. An ex vivo imaging pipeline for producing high-quality and high-resolution diffusion-weighted imaging datasets. Human brain mapping, 32:544– 563, 4 2011. Andrés Felipe Giraldo Román and Luis Agulles Pedrós. Análisis en imágenes de resonancia magnética pesadas por difusión. Master’s thesis, Universidad Nacional de Colombia, 2014. National Institute of Biomedical Imaging and Bioengineering (NIBIB). Imagen por resonancia magnetica (irm). J. J. (Jun John) Sakurai. Advanced quantum mechanics. Addison-Wesley Pub. Co, 1 edition, 1967. David J. Griffiths and Darrell F. Schroeter. Introduction to Quantum Mechanics. Cambridge University Press, 3 edition, 8 2018. E. Mark Haacke, Robert W. Brown, Michael R. Thompson, and Ramesh Venkatesan. Magnetic Resonance Imaging - Physical Principles and Sequence Design. John Wiley & Sons, 1 edition, 1999. Joseph Larmor. Ix. a dynamical theory of the electric and luminiferous medium.— part iii. relations with material media. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 190:205–300, 12 1897. Robin A. De Graaf. In vivo NMR spectroscopy: Principles and techniques. John Wiley & Sons, 2 edition, 2007. F Reif. Fundamentals of statistical and thermal physics. McGraw-Hill, Inc, 1 edition, 1965. Evert J Blink. Basic mri physics, 2010. Malcolm H Levitt. Spin Dynamics Basics of Nuclear Magnetic Resonance Second edition. Ltd John Wiley & Sons, second edition edition, 2008. Ray Freeman. Spin choreography : basic steps in high resolution nmr. page 391, 1999. F. Bloch. Nuclear induction. Physical Review, 70:460, 10 1946. Jean Philibert. One and a half century of diffusion: Fick, einstein, before and beyond. 2005. Adolf Fick. Ueber diffusion. Annalen der Physik, 170:59–86, 1855. J Crank. The mathematics of diffusion. Clerendon Press, 2 edition, 1975. Ángel Franco García. Difusión. ley de Fick, 2010. Jaume Gili and Julio Alonso. Introducción biofísica a la resonancia magnética en neuroimagen, 2004. H. C. Torrey. Bloch equations with diffusion terms. Physical Review, 104:563, 11 1956. V M Kenkre, Eiichi Fukushima, and D Sheltraw. Simple solutions of the torrey-bloch equations in the nmr study of molecular diffusion. JOURNAL OF MAGNETIC RESONANCE, 128:62–69, 1997. E. L. Hahn. Spin echoes. Physical Review, 80:580, 11 1950. H. Y. Carr and E. M. Purcell. Effects of diffusion on free precession in nuclear magnetic resonance experiments. Physical Review, 94:630, 5 1954 Jonathan H. Burdette, Allen D. Elster, and Peter E. Ricci. Acute cerebral infarction: quantification of spin-density and t2 shine-through phenomena on diffusion weighted mr images. Radiology, 212:333–339, 1999. D. Le Bihan, E. Breton, D. Lallemand, M. L. Aubin, J. Vignaud, and M. Laval-Jeantet. Separation of diffusion and perfusion in intravoxel incoherent motion mr imaging. Radiology, 168:497–505, 1988. D. (Denis) Le Bihan, Mami Lima, Christian Federau, and Eric E. Sigmund. Intravoxel incoherent motion (IVIM) MRI : principles and applications. Jenny Stanford Publishing, 1 edition, 2018. Andrew J. Steven, Jiachen Zhuo, and Elias R. Melhem. Diffusion kurtosis imaging: An emerging technique for evaluating the microstructural environment of the brain. http://dx.doi.org/10.2214/AJR.13.11365, 202, 12 2013. Mami Iima and Denis Le Bihan. Clinical intravoxel incoherent motion and diffusion mr imaging: Past, present, and future. Radiology, 278:13–32, 1 2016. Steren Chabert and Paola Scifo. Diffusion signal in magnetic resonance imaging: Origin and interpretation in neurosciences. Biological Research, 40:385–400, 2007. Jens H. Jensen, Joseph A. Helpern, Anita Ramani, Hanzhang Lu, and Kyle Kaczynski. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic resonance in medicine, 53:1432–1440, 2005. Robert V. Mulkern, Steven J. Haker, and Stephan E. Maier. On high b diffusion imaging in the human brain: ruminations and experimental insights. Magnetic resonance imaging, 27:1151, 10 2009. Dmitriy A. Yablonskiy, G. Larry Bretthorst, and Joseph J.H. Ackerman. Statistical model for diffusion attenuated mr signal. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 50:664, 10 2003. Kevin M. Bennett, Kathleen M. Schmainda, Raoqiong Bennett, Daniel B. Rowe, Hanbing Lu, and James S. Hyde. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magnetic resonance in medicine, 50:727–734, 10 2003 Qiang Zeng, Feina Shi, Jianmin Zhang, Chenhan Ling, Fei Dong, and Biao Jiang. A modified tri-exponential model for multi-b-value diffusion-weighted imaging: A method to detect the strictly diffusion-limited compartment in brain. Frontiers in Neuroscience, 12:102, 2 2018. Matt G. Hall and Thomas R. Barrick. From diffusion-weighted mri to anomalous diffusion imaging. Magnetic resonance in medicine, 59:447–455, 2008. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. The MIT Press, 1 edition, 2012. C. E. Brodley and M. A. Friedl. Identifying mislabeled training data. Journal Of Artificial Intelligence Research, 11:131–167, 6 2011. Joaqu´ın Amat Rodrigo. Machine learning w python & scikit-learn. Scikit learn 1.0.2 documentation. Cross-validation: evaluating estimator performance, 2022. Markus Ojala@tkk Fi and Gemma C Garriga. Permutation tests for studying classifier performance markus ojala. Journal of Machine Learning Research, 11:1833–1863, 2010. David Powers and Ailab. Evaluation: From precision, recall and f-measure to roc, informedness, markedness & correlation. J. Mach. Learn. Technol, 2:2229–3981 S. Madeh Piryonesi and Tamer E. El-Diraby. Data analytics in asset management: Cost-effective prediction of the pavement condition index. Journal of Infrastructure Systems, 26:04019036, 12 2019. Ana Rocío Rocío Del Valle Benavides and Juan Manuel Muñoz Pichardo. Curvas roc (receiver-operating-characteristic) y sus aplicaciones, 2017 Kelly H. Zou, A. James O’Malley, and Laura Mauri. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation, 115:654– 657, 2 2007. Google Developers. Machine learning crash course. Christopher D Brown and Herbert T Davis. Receiver operating characteristics curves and related decision measures: A tutorial. 2005. Morris H. DeGroot and Mark J. Schervish. Probability and Statistics, volume 1. Fourth edition edition, 2011. A. Mood, F. Graybill, and D Boes. Introduction to the Theory of Statistics. McGraw-Hill, 3rd edition, 1974. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011. Nadege Corbin and Martina F Callaghan. Imperfect spoiling in variable flip angle t 1 mapping at 7t: Quantifying and minimizing impact. Magn Reson Med, 00:1–16, 2021. Saurav Basu, Thomas Fletcher, and Ross Whitaker. Rician noise removal in diffusion tensor mri. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4190 LNCS - I:117–125, 2006 |
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Derechos reservados al autor, 2022 |
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http://purl.org/coar/access_right/c_abf2 |
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Reconocimiento 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
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Reconocimiento 4.0 Internacional Derechos reservados al autor, 2022 http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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x, 76 páginas |
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application/pdf |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Física Médica |
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Departamento de Física |
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Facultad de Ciencias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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Reconocimiento 4.0 InternacionalDerechos reservados al autor, 2022http://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Agulles Pedros, Luis2c8db60360320997af52663ab8100dbbPrieto González, Leonar Steven50a9aaea95dec94ffb1f688ee39fa16dGrupo de Física Médica2022-08-25T20:20:41Z2022-08-25T20:20:41Z2022-05https://repositorio.unal.edu.co/handle/unal/82118Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, gráficasEn este trabajo se presenta la caracterización de las curvas de atenuación por difusión vóxel a vóxel de 4 conjuntos de imágenes (uno de próstata, dos de cerebro humano y uno de cerebro ex vivo de una neoplasia benigna). Esta caracterización incluye la determinación de los valores de difusión (D), pseudo-difusión (D∗), perfusión (f) y curtosis (K) usando los métodos de ADC (mono-exponencial) e IVIM (bi-exponencial) con y sin curtosis. Estos valores son utilizados como referencia para entrenar y probar la validez de varios algoritmos de machine learning (ML) que permiten disminuir el tiempo en la caracterización de la atenuación. Para decidir si en un vóxel existe difusión se implementan algoritmos de clasificación (Extra-Tree Classifier (ETC), Regresión logística (LR), C-Support vector (SVC), Extra-Gradient Boost (XGB) y perceptron multicapa (MLP)), evaluados mediante la precisión y el test AUC. Mientras que para estimar los parámetros característicos se implementan métodos de regresión (Regresión lineal (LinR), regr. polinómica (Poly), XGB, Ridge, Lasso, Random Forest (RF), ElasticNet y support-vector machine (SVM)) que son evaluados mediante diferentes métricas de regresión, particularmente, la raíz del error cuadrático medio de la validación cruzada (RMSE CV). El objetivo de este trabajo es aplicar estas herramientas de ML para el análisis de difusión por imágenes de resonancia magnética. Se obtuvieron como mejores clasificadores el ETC y el MLP con una precisión del 94.1 % y 91.7 % respectivamente. Para la estimación de parámetros el mejor algoritmo fue RF; D posee un RMSECV del 8.39 %, D∗ del 3.57 %, f con 4.52 % y K con 3.53 %. Aunque estos resultados pueden ser considerados satisfactorios, es posible que otros algoritmos que no se tuvieron en cuenta en este trabajo puedan reportar un mejor desempeño. El tiempo promedio que los algoritmos de ML tardan en caracterizar 100.000 vóxeles es 18, 998 ± 0, 135 s mientras que mediante métodos convencionales es de 4408 ± 351 s.In this work we present the characterization of the voxel-by-voxel diffusion attenuation curves of 4 sets of images (one from the prostate, two from the human brain and one from the ex vivo brain of a mini pig). This characterization includes the determination of the values of diffusion (D), pseudo-diffusion (D∗), perfusion (f), and kurtosis (K); using ADC (mono-exponential) and IVIM (bi-exponential) considering kurtosis when convenient. These values are used as a reference to train and test the validity of several machine learning (ML) algorithms that allow to reduce the CPU time to characterize the attenuation curves. To decide if there is diffusion in a voxel, classification algorithms are implemented; (Extra-Tree Classifier (ETC), Logistic Regression (LR), C-Support Vector (SVC), Extra-Gradient Boost (XGB) and Multilayer Perceptron (MLP)), were evaluated by precision and the AUC tests. On the other hand, regression methods; (Linear Regres sion (LinR), Polynomial Regr. (Poly), XGB, Ridge, Lasso, Random Forest (RF), Elastic Net and Support-Vector Machines (SVM)) are implemented to estimate the characteristic parameters and are evaluated using different regression metrics, particularly, root mean square error of cross-validation (RMSECV). The main objective of this work is to apply the se ML tools for diffusion analysis in magnetic resonance images. The ETC and the MLP showed the best classifiers with accuracies of 94.1 % and 91.7 %, respectively. For parame ters estimation, the best algorithm was RF; D has an RMSECV of 8.39 %, D∗ of 3.57 %, f of 4.52 % and K of 3.53 %. Although these results can be considered satisfactory, it is possi ble that other algorithms that were not taken into account in this work may report better performance.The average time that ML algorithms take to characterize 100.000 voxels is 18,998 ± 0,135 s while using conventional methods it is 4408 ± 351 s.MaestríaMagíster en Física MédicaRadiología e Imágenes Diagnósticasx, 76 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Física MédicaDepartamento de FísicaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y saludImagen de Difusión por Resonancia MagnéticaProcesamiento de Imagen Asistido por ComputadorDiffusion Magnetic Resonance ImagingImage Processing, Computer-AssistedImágenes por resonancia magnéticaIRMdifusión tisularmovimiento incoherente intravóxelaprendizaje automáticoADCDWIIVIMMagnetic Resonance ImagingMRItissue diffusionIntravoxel Incoherent MotionMachine LearningAnálisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learningAnalysis of nuclear magnetic resonance imaging diffusion models using machine learningTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMValerij G. 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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4190 LNCS - I:117–125, 2006EstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.unal.edu.co/bitstream/unal/82118/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51ORIGINAL1018452370.2022.pdf1018452370.2022.pdfTesis de Maestría en Física Médicaapplication/pdf2758761https://repositorio.unal.edu.co/bitstream/unal/82118/2/1018452370.2022.pdf67fded89eaf93045e7fb58897159bba6MD52THUMBNAIL1018452370.2022.pdf.jpg1018452370.2022.pdf.jpgGenerated Thumbnailimage/jpeg5930https://repositorio.unal.edu.co/bitstream/unal/82118/3/1018452370.2022.pdf.jpgf2d88d6a490b6bca18ba81ed8e00aec5MD53unal/82118oai:repositorio.unal.edu.co:unal/821182023-08-08 23:03:51.494Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |