LEADER 03902nam 2200589Ia 450 001 9910437980003321 005 20200520144314.0 010 $a3-642-36441-1 024 7 $a10.1007/978-3-642-36441-9 035 $a(CKB)2670000000371294 035 $a(EBL)1317193 035 $a(OCoLC)847630853 035 $a(SSID)ssj0000904356 035 $a(PQKBManifestationID)11484429 035 $a(PQKBTitleCode)TC0000904356 035 $a(PQKBWorkID)10920515 035 $a(PQKB)11653399 035 $a(DE-He213)978-3-642-36441-9 035 $a(MiAaPQ)EBC1317193 035 $a(PPN)170490890 035 $a(EXLCZ)992670000000371294 100 $a20130314d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$a4D modeling and estimation of respiratory motion for radiation therapy /$fJan Ehrhardt, Cristian Lorenz, editors 205 $a1st ed. 2013. 210 $aBerlin $cSpringer$d2013 215 $a1 online resource (351 p.) 225 0 $aBiological and medical physics, biomedical engineering 300 $aDescription based upon print version of record. 311 $a3-642-44606-X 311 $a3-642-36440-3 320 $aIncludes bibliographical references and index. 327 $a4D Image Acquisition -- Motion Estimation and Modeling -- Modeling of Motion Variability -- Applications of Motion Estimation Algorithms -- Outlook. 330 $aRespiratory motion causes an important uncertainty in radiotherapy planning of the thorax and upper abdomen. The main objective of radiation therapy is to eradicate or shrink tumor cells without damaging the surrounding tissue by delivering a high radiation dose to the tumor region and a dose as low as possible to healthy organ tissues. Meeting this demand remains a challenge especially in case of lung tumors due to breathing-induced tumor and organ motion where motion amplitudes can measure up to several centimeters. Therefore, modeling of respiratory motion has become increasingly important in radiation therapy. With 4D imaging techniques spatiotemporal image sequences can be acquired to investigate dynamic processes in the patient?s body. Furthermore, image registration enables the estimation of the breathing-induced motion and the description of the temporal change in position and shape of the structures of interest by establishing the correspondence between images acquired at different phases of the breathing cycle. In radiation therapy these motion estimations are used to define accurate treatment margins, e.g. to calculate dose distributions and to develop prediction models for gated or robotic radiotherapy. In this book, the increasing role of image registration and motion estimation algorithms for the interpretation of complex 4D medical image sequences is illustrated. Different 4D CT image acquisition techniques and conceptually different motion estimation algorithms are presented. The clinical relevance is demonstrated by means of example applications which are related to the radiation therapy of thoracic and abdominal tumors. The state of the art and perspectives are shown by an insight into the current field of research. The book is addressed to biomedical engineers, medical physicists, researchers and physicians working in the fields of medical image analysis, radiology and radiation therapy. 410 0$aBiological and Medical Physics, Biomedical Engineering,$x1618-7210 606 $aFourth dimension 606 $aRadiotherapy 615 0$aFourth dimension. 615 0$aRadiotherapy. 676 $a616.994240642 701 $aPaques$b Luc E$01752911 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437980003321 996 $a4D modeling and estimation of respiratory motion for radiation therapy$94196091 997 $aUNINA