EURASIP Journal on Applied Signal Processing 2003:5, 437–448 c 2003 Hindawi Publishing

EURASIP Journal on Applied Signal Processing 2003:5, 437–448 c 2003 Hindawi Publishing Corporation Dynamic Chest Image Analysis: Model-Based Perfusion Analysis in Dynamic Pulmonary Imaging Jianming Liang Turku Centre for Computer Science, DataCity, Lemmink¨ isenkatu 14 A, 20520 Turku, Finland a Email: liang@ ¨ Timo Jarvi Turku Centre for Computer Science, DataCity, Lemmink¨ isenkatu 14 A, 20520 Turku, Finland a Email: jarvi@ Aaro Kiuru Department of Diagnostic Radiology, Turku University, 20520 Turku, Finland Email: Martti Kormano Department of Diagnostic Radiology, Turku University, 20520 Turku, Finland Email: ¨ Erkki Svedstrom Department of Diagnostic Radiology, Turku University, 20520 Turku, Finland Email: Received 31 January 2002 and in revised form 25 October. | EURASIP Journal on Applied Signal Processing 2003 5 437-448 2003 Hindawi Publishing Corporation Dynamic Chest Image Analysis Model-Based Perfusion Analysis in Dynamic Pulmonary Imaging Jianming Liang Turku Centre for Computer Science DataCity Lemminkaisenkatu 14 A 20520 Turku Finland Email liang@ Timo Jarvi Turku Centre for Computer Science DataCity Lemminkaisenkatu 14 A 20520 Turku Finland Email jarvi@ Aaro Kiuru Department of Diagnostic Radiology Turku University 20520 Turku Finland Email Martti Kormano Department of Diagnostic Radiology Turku University 20520 Turku Finland Email Erkki Svedstrom Department of Diagnostic Radiology Turku University 20520 Turku Finland Email Received 31 January 2002 and in revised form 25 October 2002 The Dynamic Chest Image Analysis project aims to develop model-based computer analysis and visualization methods for showing focal and general abnormalities of lung ventilation and perfusion based on a sequence of digital chest fluoroscopy frames collected with the dynamic pulmonary imaging technique. We have proposed and evaluated a multiresolutional method with an explicit ventilation model for ventilation analysis. This paper presents a new model-based method for pulmonary perfusion analysis. According to perfusion properties we first devise a novel mathematical function to form a perfusion model. A simple yet accurate approach is further introduced to extract cardiac systolic and diastolic phases from the heart so that this cardiac information may be utilized to accelerate the perfusion analysis and improve its sensitivity in detecting pulmonary perfusion abnormalities. This makes perfusion analysis not only fast but also robust in computation consequently perfusion analysis becomes computationally feasible without using contrast media. Our clinical case studies with 52 patients show that this technique is effective for pulmonary embolism even .