Dosimetry for Beta-Emitter Radionuclides by Means of Monte Carlo Simulations

Nowadays, there are interests as well as active investigations devoted to the study and application of radiolabeled molecules able to selectively target and irradiate tumoral cells during nuclear medicine procedures. With this kind of pharmaceuticals, spatial activity distribution with extremely non-uniform characteristics may be assessed in patients. Actually, this feature constitutes precisely themain advantage in view ofmaximizing the discrimination between affected and healthy tissue. The mentioned situation constitutes the main motivation for the present work. In this sense, the chapter is focused on nuclear medicine dosimetry pointing out the main features about how to implement Monte Carlo (MC) approaches to this aim. Nowadays, from a general point of view, therapies with radiopharmaceuticals using beta-emitter radionuclides are growing significantly and very fast. Beta-emitters can be emitters of β− or β+ radiation. Commonly, β+ emitters, like 18F are used for imaging techniques, whereas β− are mainly used with therapeutic purposes, to deliver high dose rate on tumors. Therefore, β− emitters are usually those of more interest for dosimetry. During nuclear medicine procedures, radiopharmaceutical activity distribution may be determined by means of different modalities. Nowadays it is mainly assessed using imaging techniques but otherwise it is also possible to infer it [Stabin (2008)]. This information is then incorporated in the treatment planning system in order to obtain an estimation of the dose distribution. More specifically, patient-specific dose distribution owing to alpha, beta and/or gamma emitters can be calculated starting from activity distribution by means of either direct MC simulation or analytical methods. On the other hand, patient-specific dosimetry requires anatomical information, which shall be further considered as input for establishing mass distribution during MC computations. Patient anatomical information can be suitably extracted from typical non-invasive imaging techniques, like computed tomography (CT) or magnetic resonance imaging (MRI). Many studies have been performed by means of MC applications in Nuclear Medicine up today, both in the imaging field, and regarding dosimetry calculations [F. Botta & Valente (2011), Zubal & Harrel (1992), H. Yoriyaz & dos Santos (2001), M. Ljungberg & Strand (2002)]. I troduction 11

[1]  Sung-Joon Ye,et al.  Benchmark of PENELOPE code for low-energy photon transport: dose comparisons with MCNP4 and EGS4. , 2004, Physics in medicine and biology.

[2]  V A Semenenko,et al.  Lyman–Kutcher–Burman NTCP model parameters for radiation pneumonitis and xerostomia based on combined analysis of published clinical data , 2008, Physics in medicine and biology.

[3]  W. V. Prestwich,et al.  Beta dose point kernels for radionuclides of potential use in radioimmunotherapy. , 1989, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[4]  J. Balter,et al.  Erratum: Analysis of radiation-induced liver disease using the Lyman NTCP model (International Journal of Radiation Oncology Biology Physics (2002) 53 (810-821)) , 2002 .

[5]  J. Sempau,et al.  PENELOPE-2006: A Code System for Monte Carlo Simulation of Electron and Photon Transport , 2009 .

[6]  X Allen Li,et al.  Extending the linear-quadratic model for large fraction doses pertinent to stereotactic radiotherapy. , 2004, Physics in medicine and biology.

[7]  Charles R. Harrell,et al.  Voxel based Monte Carlo calculations of nuclear medicine images and applied variance reduction techniques , 1992, Image Vis. Comput..

[8]  F. Salvat,et al.  Improved electron transport mechanics in the PENELOPE Monte-Carlo model , 2001 .

[9]  W E Bolch,et al.  MIRD pamphlet No. 17: the dosimetry of nonuniform activity distributions--radionuclide S values at the voxel level. Medical Internal Radiation Dose Committee. , 1999, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[10]  A Mairani,et al.  Calculation of electron and isotopes dose point kernels with FLUKA Monte Carlo code for dosimetry in nuclear medicine therapy. , 2011, Medical physics.

[11]  J. Sempau,et al.  Monte Carlo simulation of electron beams from an accelerator head using PENELOPE , 2001, Physics in Medicine and Biology.

[12]  Paul J Keall,et al.  A new formula for normal tissue complication probability (NTCP) as a function of equivalent uniform dose (EUD) , 2008, Physics in medicine and biology.

[13]  D. Mihailidis,et al.  Fundamentals of nuclear medicine dosimetry , 2008 .

[14]  Pedro Andreo,et al.  Positron flight in human tissues and its influence on PET image spatial resolution , 2003, European Journal of Nuclear Medicine and Molecular Imaging.

[15]  R K Sachs,et al.  A convenient extension of the linear-quadratic model to include redistribution and reoxygenation. , 1995, International journal of radiation oncology, biology, physics.

[16]  Daniel Normolle,et al.  Analysis of radiation-induced liver disease using the Lyman NTCP model. , 2002, International journal of radiation oncology, biology, physics.

[17]  Josep Sempau,et al.  Electron beam quality correction factors for plane-parallel ionization chambers: Monte Carlo calculations using the PENELOPE system. , 2004, Physics in medicine and biology.

[18]  M G Stabin,et al.  Monte Carlo MCNP-4B-based absorbed dose distribution estimates for patient-specific dosimetry. , 2001, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[19]  J. Fernández-Varea,et al.  Characterization of a high-dose-rate 90Sr-90Y source for intravascular brachytherapy by using the Monte Carlo code PENELOPE. , 2002, Physics in medicine and biology.

[20]  D. J. Strom,et al.  Microdosimetric properties of ionizing electrons in water: a test of the PENELOPE code system. , 2002, Physics in medicine and biology.

[21]  Xiaowei Liu,et al.  A 3-dimensional absorbed dose calculation method based on quantitative SPECT for radionuclide therapy: evaluation for (131)I using monte carlo simulation. , 2002, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[22]  Xiao-dong Zhu,et al.  Prediction of radiation-induced liver disease by Lyman normal-tissue complication probability model in three-dimensional conformal radiation therapy for primary liver carcinoma. , 2006, International journal of radiation oncology, biology, physics.