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008 140715s2013 gw | s |||| 0|eng d
020 _a9783642364419
_9978-3-642-36441-9
024 7 _a10.1007/978-3-642-36441-9
_2doi
035 _ato000484716
040 _aSpringer
_cSpringer
_dRU-ToGU
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
100 1 _aEhrhardt, Jan.
_eeditor.
_9417501
245 1 0 _a4D Modeling and Estimation of Respiratory Motion for Radiation Therapy
_helectronic resource
_cedited by Jan Ehrhardt, Cristian Lorenz.
260 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXX, 341 p. 111 illus., 75 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
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.
_9566227
650 0 _aNuclear medicine.
_9566268
650 0 _aPneumology.
_9304224
650 0 _aBiomedical engineering.
_9302214
650 1 4 _aPhysics.
_9566228
650 2 4 _aMedical and Radiation Physics.
_9410466
650 2 4 _aBiophysics and Biological Physics.
_9410468
650 2 4 _aPneumology/Respiratory System.
_9304226
650 2 4 _aBiomedical Engineering.
_9302214
650 2 4 _aNuclear Medicine.
_9566269
700 1 _aLorenz, Cristian.
_eeditor.
_9417502
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aBiological and Medical Physics, Biomedical Engineering,
_9303857
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-36441-9
912 _aZDB-2-PHA
999 _c358425