Inverse planning for functional image-guided intensity-modulated radiation therapy.
Radiation therapy is an image-guided process whose success critically depends on the imaging modality used for treatment planning and the level of integration of the available imaging information. In this work, we establish a dose optimization framework for incorporating metabolic information from functional imaging modalities into the intensity-modulated radiation therapy (IMRT) inverse planning process and to demonstrate the technical feasibility of planning deliberately non-uniform dose distributions in accordance with functional imaging data. For this purpose, a metabolic map from functional images is discretized into a number of abnormality levels (ALs) and then fused with CT images. To escalate dose to the metabolically abnormal regions, we assume, for a given spatial point, a linear relation between the AL and the prescribed dose. But the formalism developed here is independent of the assumption and any other relation between AL and prescription is applicable. For a given AL and prescription relation, it is only necessary to prescribe the dose to the lowest AL in the target and the desired doses to other regions with higher AL values are scaled accordingly. To accomplish differential sparing of a sensitive structure when its functional importance (FI) distribution is known, we individualize the tolerance doses of the voxels within the structure according to their Fl levels. An iterative inverse planning algorithm in voxel domain is used to optimize the system with in homogeneous dose prescription. To model intra-structural trade-off, a mechanism is introduced through the use of voxel-dependent weighting factors, in addition to the conventional structure specific weighting factors which model the inter-structural trade-off. The system is used to plan a phantom case with a few hypothetical functional distributions and a brain tumour treatment with incorporation of magnetic resonance spectroscopic imaging data. The results indicated that it is technically feasible to produce deliberately non-uniform dose distributions according to the functional imaging requirements. Integration of functional imaging information into radiation therapy dose optimization allows for consideration of patient-specific biologic information and provides a significant opportunity to truly individualize radiation treatment. This should enhance our capability to safely and intelligently escalate dose and lays the technical foundation for future clinical studies of the efficacy of functional imaging-guided IMRT.
Xing
Lei
L
Department of Radiation Oncology, Stanford University School of Medicine, CA 94305-5304, USA. lei@reyes.stanford.edu
Cotrutz
Cristian
C
Hunjan
Sandeep
S
Boyer
Arthur L
AL
Adalsteinsson
Elfar
E
Spielman
Daniel
D
D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D005240:Feasibility Studies; D005910:Glioma / Q000000981:diagnostic imaging / Q000532:radiotherapy; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D019047:Phantoms, Imaging; D011786:Quality Control; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011874:Radiometry / Q000295:instrumentation / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000295:instrumentation / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*