Experiences on quantitative cardiac PET analysis

Quantitative analysis of PET images is a useful as well as essential practice to perform an objective measurement of a physiological process. It allows to study diseases, evaluating treatment response and comparing patients data by quantify images. The analysis consists in estimating the quantity of radionuclide tracer uptaken by tissues. We focus on quantitative analysis of dynamic PET studies to evaluate the diseases of coronary artery and myocardium perfusion. We report experiences on quantitative cardiac PET analysis by using a commercial and largely used software to evaluate viable myocardium through Patlak method. We report also results obtained on PET images provided by clinical departments of the Magna Graecia University Medical School of Catanzaro.

[1]  P. Kaufmann,et al.  Myocardial blood flow measurement by PET: technical aspects and clinical applications. , 2005, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[2]  C S Patlak,et al.  Graphical Evaluation of Blood-to-Brain Transfer Constants from Multiple-Time Uptake Data , 1983, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[3]  P. Wolf,et al.  Heart disease and stroke statistics--2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. , 2006, Circulation.

[4]  Sonia Pujol,et al.  The 3DSlicer open-source platform for segmentation, registration, quantitative imaging and 3D visualization of biomedical image data , 2013 .

[5]  Roger N Gunn,et al.  Positron Emission Tomography Compartmental Models: A Basis Pursuit Strategy for Kinetic Modeling , 2002, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[6]  Albert Gjedde,et al.  Influence of GLP-1 on Myocardial Glucose Metabolism in Healthy Men during Normo- or Hypoglycemia , 2014, PloS one.

[7]  Jens Sandahl Christiansen,et al.  Low myocardial glucose uptake in Turner syndrome is unaffected by growth hormone: a randomized, placebo‐controlled FDG‐PET study , 2015, Clinical endocrinology.

[8]  Giuseppe Tradigo,et al.  AutoSPET: An SPM plugin to automatize neuroimages PET analysis , 2013, Interdisciplinary Sciences: Computational Life Sciences.

[9]  Giuseppe Tradigo,et al.  A framework for the atrial fibrillation prediction in electrophysiological studies , 2015, Comput. Methods Programs Biomed..

[10]  B.J. Parker,et al.  Graph-based Mumford-Shah segmentation of dynamic PET with application to input function estimation , 2005, IEEE Transactions on Nuclear Science.

[12]  A. Lammertsma,et al.  Monitoring response to therapy in cancer using [18F]-2-fluoro-2-deoxy-d-glucose and positron emission tomography: an overview of different analytical methods , 2000, European Journal of Nuclear Medicine.

[13]  Pierangelo Veltri.  Algorithms and tools for analysis and management of mass spectrometry data , 2008, Briefings Bioinform..

[14]  Alicia Fernández,et al.  An open tool for input function estimation and quantification of dynamic PET FDG brain scans , 2016, International Journal of Computer Assisted Radiology and Surgery.

[15]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. , 2002, Circulation.

[16]  C. Gibson,et al.  Evaluation of myocardial perfusion , 2009 .

[17]  Guido Germano,et al.  The role of PET quantification in cardiovascular imaging , 2014, Clinical and Translational Imaging.