METHODOLOGY FOR CONVERTING CT MEDICAL IMAGES TO MCNP INPUT USING THE SCAN2MCNP SYSTEM

Transcription

1 2009 International Nuclear Atlantic Conference - INAC 2009 Rio de Janeiro,RJ, Brazil, September27 to October 2, 2009 ASSOCIAÇÃO BRASILEIRA DE ENERGIA NUCLEAR - ABEN ISBN: METHODOLOGY FOR CONVERTING CT MEDICAL IMAGES TO MCNP INPUT USING THE SCAN2MCNP SYSTEM L. S. Boia 1, A. X. Silva 1,2, A. Facure 3, S. C. Cardoso 4, L. A. R. da Rosa 5, R. C. Castro 6 1 [Programa de Engenharia Nuclear]/COPPE 2 [Departamento de Engenharia Nuclear / Escola Politécnica] Universidade Federal do Rio de Janeiro Ilha do Fundão, Caixa Postal 68509, , Rio de Janeiro, RJ, Brasil lboia@con.ufrj.br ademir@con.ufrj.br 3 Comissão Nacional de Energia Nuclear R. Gal. Severiano 90, sala 409, Rio de Janeiro, RJ, Brasil facure@cnen.gov.br 4 Departamento de Física Nuclear Instituto de Física Universidade Federal do Rio de Janeiro Centro de Tecnologia, Bloco A-3º. andar Rio de Janeiro, RJ simone@if.ufrj.br 5 Instituto de Radioproteção e Dosimetria -IRD Av. Salvador Allende, s/nº Recreio dos Bandeirantes Rio de Janeiro lrosa@ird.gov.br 6 Colégio Pedro II Campo de São Cristóvão, , Rio de Janeiro, RJ prof.robinho@gmail.com ABSTRACT This paper develops a methodology for the application software Scan2MCNP, which converts medical images DICOM (Digital Imaging and Communications in Medicine) for MCNP input file. The Scan2MCNP handles, processes and executes the medical images generated by CT equipment, allowing the user to perform the selection and parameterization of the study area in question (tissues and organs). The details of these worked in medical imaging software, therefore, will be converted to equity to the process of language analysis of MCNP radiation transport, through the generation of a code input file. With this file, it s possible to simulate any situation/problem of the type and level of radiation to the proposed treatment chosen by the medical staff responsible for the patient. Within a computational process oriented, the Scan2MCNP can contribute along with other software that has been used recently in the area of medical physics, to improve the levels of quality and precision of radiotherapy treatments. In this work, medical images DICOM of the Anthropomorphic Rando Phantom were used in the process of analysis and development of computer software Scan2MCNP. However, it emphasized that the software is successful in certain situations, depending upon a number of auxiliary procedures and software that can help in the solution of certain problems in the natural radiation treatment or express agility by the team of medical physics.

2 1. INTRODUCTION Nowadays, tridimensional planning computer systems for radiotherapy, like CadPlan, SomaVision and Eclipse, perform them in three dimensions, turning possible conformational radiotherapy in an improved and sophisticated way, directing with more accuracy ionizing radiation to the tumors [1]. Therefore, a maximum preservation of healthy tissues is possible, and collateral damages are reduced. In such context, researches about computational simulation have been having big in medical fields, due to the search of results which help to increase the treatment s accuracy. In Medical and Physics fields, the MCNP code [2] has computational capacity in real patients data manipulation (medical images), generating customized results and aiding to radiotherapy s planning systems used in hospitals and medical clinics. The aim of the present work is a computational methodology s development which arranges all the process of edition, manipulation, parameterization, conversion, simulation and results analysis from medical images (DICOM) which were gotten from tomographs for custom studies of equivalent doses in patients. For this, Scan2MCNP [3], MCNP code and support software were used. The first one simulate particle s path during their lives, from a source to their elimination by either absorption in tissues and organs or escaping to the system. In order to achieve such goal, DICOM images [4] from Alderson Rando anthropomorphic phantom [5] obtained by tomographs [6] were used. 2. DICOM MEDICAL IMAGES All images from CT and MRI devices are configured in order to have their volume elements dimensions in a very small size. Such configuration for clinical for clinical purposes (visualization), because the quality level increases, showing singularities which are useful on tracking of any anomaly in tissues structure and organs in the human body. These high definition on images (512 pixels x 512 pixels matrices), in a large number of slices, are converted to MCNP inputs and generate several problems related to suppressed data from such information for administration in MCNP s memory. Such situations are independent to MCNP source code programming, which sets up limits for loading data for computational simulation. Due to this limitation, the executable input in MCNP cannot perform the simulation, and the source code must be changed in order to run the program. The solution for such issues on MCNP was the conversion from these images to 128 pixels x 128 pixels spatial area and the change of voxel in order to assure electronic equilibrium (Kerma approximation) in the region of interest. It was also performed the development of computational methods for digital image process, using MATLAB software [7], for segment image s regions, aiming to discriminate information for correct interpretations on MCNP. All the process is performed before de conversion one and the Scan2MCNP and MCNP simulations.

3 3. Scan2MCNP SOFTWARE The Scan2MCNP s interface provides straight access to manipulations functions of the selected image matrices (either CT or MRI), according to previously set-up subroutines by the software [3]. Such functions are activated by the available options on the menu. The use of the Scan2MCNP is based on two monitoring windows (Figure 1): the Transcript, which performs the registration of each run operation in the program and the Partition, which informs by grayscale levels the boundaries of organs and tissue regions available on the image. Figure 1: Scan2MCNP s interface and Partition and Transcript windows opened. Scan2MCNP software allows image files manipulation of several extensions, including RAW file extensions, which are oppened by Read IMG File... command for a single image and by Read Multiple IMG Files for several ones. And also the DICOM (medical image internationally standardized) file access are oppened by Read DICOM... command for a single image e Read Multiple DICOMS command for several of them (Figure 2). Figure 2: Scan2MCNP s interface and the OPEN FILE Option.

4 When one or more images are loaded in Scan2MCNP, the Transcript monitoring window shows the loading processes. Intrinsic proprieties of the images can be seen with more details using Image Proprieties command, placed at software menu in Image Options (Figure 3). Figure 3: The Image Proprieties option running at Image Options menu. After the images are loaded and their proprieties are known, the existing regions in DICOM images are identified and indexed to the respective materials (tissues and organs) by a data library on Scan2MCNP (figure 4). For this task, the boundaries option is used, with the option list activated by clicking the mouse s right button (figure 5). Figure 4: Partition Boundaries window: the image identification and index processes.

5 Figure 5: Scan2MCNP s interface and the selected Boundaries option. The Color Scheme resource, available in Scan2MCNP helps in the DICOM images identification, amplifying their signal due to the use of different color spectra formats (RGB). There are 16 RGB spectra available on Scan2MCNP. Such spectra act like masks above images, providing them colored medical images (Figure 6). It s important to emphasize that is a mask, because CT and MRI images, on a computational level, are still in their original standards, in grayscale levels. Figure 6. Color Scheme option and the coloring medical image for visualization.

6 As all images were identified and indexed, the MCNP input s generation is configured by MCNP Options choice, which is found in MCNP option (Figure 7). Figure 7: Activated MCNP Options, window for configuring the input file s format. In File options, a sub-option called Preview MCNP will provide na input file visualization (Figure 8). In this preview, it s possible to check whether the generated file satisfies the previous set-up objectives (figure 9). When the preview is aproved, the recording was executed by Write MCNP... option (Figure 10).

7 Figure 8: The activated FILE options from menu and the Preview MCNP s options. Figure 9: Previous input file.

8 Figure 10: Scan2MCNP The generated input file being saved. 4. THE INPUT FILE (INP) After the conversion from DICOM images into MCNP input, the density, chemical composition of tissues and organs constituent material are provided and each cell s volume are not. In Figure 11, it s possible to see that the generated input has five materials and their respective densities. Figure 11: Input file without information from cells (organs) volumes.

9 In order to solve such problems, the counter of Microsoft Word (version 2003) and Windows XP s scientific calculator were used for voxels counting (Figure 12). The performed counting and the volumes calculation for each cell are listed on Table 1. Figure 12: Geometry s distribution (on input file) of Alderson Rando s slices. Table 1: Number of voxels in each cell and their respective volumes INPUT 128PHM (MCNP) CELL 1 CELL 2 CELL 3 CELL 4 CELL SLICES MATERIAL AND Cortical H2O Muscle B Muscle B Air COLOR Bone cube Rando Rando PART 1 10, ,714 46, ,461 PART 2 10, ,640 56, ,936 PART 3 10, , ,379 PART 4 10, , ,673 VOXELS IN EACH CELL 40,969 11, ,621 1, ,449 EACH CELL S VOLUME (cm 3 ) 1 voxel = cm 3 5, , , ,557 As each cell s volume was found, such information is added to the input file. (Figure 13).

10 Figure 13: Input file with volume s information in each cell. Other non informed quantities in the input file, due to conversion from 512 pixels x 512 pixels into 128 pixels x 128 pixels, are the dimensions of the plans (px, py e pz) from big box and voxel. Such information is obtained from the original DICOM images and the data are calculated again according to 128 pixels x 128 pixels. 5. SYSTEM S GEOMETRY FOR DOSE EVALUATIONS Intending to validate the proposed computational methodology, a study of case was performed using DICOM images from anthropomorphic phantom Alderson Rando. The absorbed doses from different regions of the phantoms were compared to the ones found in literature [8]. The regions of interest in this research were: brain, eye lens, thyroid, upper left lung, bone marrow (left rib). The personal equivalent dose Hp(10) was also determined. The experimental parameters used by the authors and reproduced on computational simulation were: a GBq 137 Cs source, put at 1 meter from the phantom and 1.34 meters from ground. The source was centralized in front of the phantom s chest, where the number five cube groups (Hp(10)) are, and a 16 hours irradiation.

11 From Alderson Rando s tomographic data, MCNP input files were generated, containing head, neck and part of torso and few groups of water cubes, in six different regions, according to figures 14 and 15. Figure 14: Alderson Rando and the location of water cubes: 1 Upper left brain, 2 Eye Lens, 3 thyroid, 4 Upper left lung, 5 Hp(10), 6 Bone marrow (left rib) After the distribution of water cubes inside Adelson Rando phantom, using Photoshop CS3 software [9] and later processed by Scan2MCNP in order to create input files (INP). The values of plans coordinates from big box and voxel were inserted in INP, from original DICOM images. The exclusion sphere from the simulating system, the importance of simulated particles, the source term and its location, tally card F6 or *F8 for dose calculations, each cell volume and the number of histories for executing the program. The slices with the inserted water cubes can be seen in figure 15.

12 Brain upper left Eye lens Thyroid Upper left Lung Hp(10) Bone marrow left rib Figure 15: Location of the inserted water cubes in Phantom Rando s selected regions Six input files were generated intending to isolate and to study each region separately. For this, the F6 card was used in each input file in order calculate the deposited average energy in the regions of interest.

13 Table 2: Absorbed doses obtained in this work and the ones obtained by Hunt [8]. Absorbed Dose (mgy) Organs Alderson This Work Variation (TLD) (a) (%) Upper Left Brain 44± Eye Lens 77± Thyroid 69± Upper Left Lung 67± Hp(10) 98± Bone Marrow (left rib) 52± (a) Hunt [8] It s possible to observe in Table 2 a maximum discrepancy of 10.4% between data from this work and the ones from Hunt [8]. Such discrepancies happened due to a lack of accuracy in coordinates (x, y and z values) from spatial location of water cubes distributed in the phantom. 6. CONCLUSIONS The development a computational methodology for the production of voxel anthropomorphic models from CT and MRI images, for MCNP simulations about radiotherapy in real patients, customizing the simulation process and dosimetric calculation. The aims were reached by the present work. In order to achieve such goal, digital process involving DICOM images were developed intending a satisfactory use of Scan2MCNP for fast and more practical ways to convert voxels models. The absorbed doses from Alderson Rando s images by tomography which were obtained in here showed good agreement with the ones found in literature, which allows the validation of the proposed methodology. ACKNOWLEDGMENTS We wish to thank CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and FAPERJ (Fundação Nacional de Amparo à Pesquisa do Estado do Rio de Janeiro) for the partial financier support that made this work possible.

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