000 04140nam a22006017a 4500
001 sulb-eb0024719
003 BD-SySUS
005 20160413122453.0
007 cr nn 008mamaa
008 130531s2013 gw | s |||| 0|eng d
020 _a9783642364419
_9978-3-642-36441-9
024 7 _a10.1007/978-3-642-36441-9
_2doi
050 4 _aQC474-496.9
050 4 _aR895-920
072 7 _aMMPH
_2bicssc
072 7 _aPNRL
_2bicssc
072 7 _aSCI058000
_2bisacsh
082 0 4 _a610.153
_223
245 1 0 _a4D Modeling and Estimation of Respiratory Motion for Radiation Therapy
_h[electronic resource] /
_cedited by Jan Ehrhardt, Cristian Lorenz.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXX, 341 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aBiological and Medical Physics, Biomedical Engineering,
_x1618-7210
505 0 _a4D Image Acquisition -- Motion Estimation and Modeling -- Modeling of Motion Variability -- Applications of Motion Estimation Algorithms -- Outlook.
520 _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.
650 0 _aPhysics.
650 0 _aNuclear medicine.
650 0 _aRespiratory organs
_xDiseases.
650 0 _aBiophysics.
650 0 _aBiological physics.
650 0 _aMedical physics.
650 0 _aRadiation.
650 0 _aBiomedical engineering.
650 1 4 _aPhysics.
650 2 4 _aMedical and Radiation Physics.
650 2 4 _aBiophysics and Biological Physics.
650 2 4 _aPneumology/Respiratory System.
650 2 4 _aBiomedical Engineering.
650 2 4 _aNuclear Medicine.
700 1 _aEhrhardt, Jan.
_eeditor.
700 1 _aLorenz, Cristian.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642364402
830 0 _aBiological and Medical Physics, Biomedical Engineering,
_x1618-7210
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-36441-9
912 _aZDB-2-PHA
942 _2Dewey Decimal Classification
_ceBooks
999 _c46811
_d46811