Department of Diagnostic Radiology

Yale University School of Medicine, New Haven, CT 06510

Yale University School of Medicine

Department of Diagnostic Radiology, BML 332

333 Cedar Street

New Haven, CT 06510

key words: segmentation, human model, anthropomorphic phanton, x-ray CT

One of the earliest computerized anthropomorphic phantoms was developed for estimating doses to various human organs from internal or external sources of radioactivity and served to calculate the S-factors for internal dose calculations in nuclear medicine [1]. This mathematical phantom models internal structures as either ellipsoids, cylinders, or rectangular volumes. For internal dosimetry purposes, such human model approximations serve quite sufficiently and have the advantage of allowing very fast calculation of the intersection of ray lines with the analytical surfaces which delineate the organs. A version of this mathematical phantom has been updated to include female organs [2]. There are additionally versions1 of the phantom which are used for dedicated cardiac studies where the structures adjacent to the heart and the heart itself have been more realistically modeled [3].

Computer models have also been applied to better understand the image formation process in diagnostic radiology [4-7], particularly for analyzing scatter and attenuation problems in nuclear medicine [8-14]. Since much higher statistics are necessary to model imaging simulations (compared to dosimetry simulations), speed of computing individual gamma ray histories becomes of paramount importance for imaging physics calculations. The software phantoms modeled in these imaging simulations have sometimes been limited to simple point, rod, and slab shapes of sources and attenuating media. Such simple geometries are useful in studying more fundamental issues of scatter and attenuation; but, clinically realistic distributions cannot be adequately evaluated by such simple geometries. The intricate protuberances and convolutions of human internal structures are important in evaluating imaging techniques; and as the resolution of imaging equipment improves, it is essential to enhance our computer models.

In the field of oncology, internal and external radiotherapy sources have become more sophisticated in their design and applications. The calculations involved in clinical therapy planning have become more sophisticated [15-18]. These new therapy techniques can be more effectively investigated with higher resolution, computerized realistic human models.

In order to make 3-dimensional anatomical data suitable for use in any of these radiologic calculations, we must be able to delineate the surfaces and internal volumes which define the various structures of the body. These segmented volumes can then be indexed to activity distributions or other physical characteristics (density or elemental composition). We have constructed an anatomically correct human geometry for use in these types of radiologic calculations where each organ (structure) is segmented and its internal volume is referenced by an index number.

The segmented image information is stored in two independent files. A variable size file is created for each transverse slice and contains the x,y coordinates of each of the contours drawn on that slice. The slice number is retained in the name of the file. These contours serve as the input to the filling routine, which creates a fixed size organ index image. The organ index image is a 512 by 512 byte matrix filled with integer values which delineate the internal structures (organs) of the body. The organ index image is therefore, in effect, the original CT transverse slice in which the Hounsfield numbers are replaced by integers corresponding to the organ index value. The assignment of integers to the organs are shown in Table 1.

The total storage capacity of the files are: original CT images = 29 Megabyte, x,y contours = 1 Megabyte, organ index matrices 20 Megabytes, and are available for public access through our Imaging Science Research Laboratory.2

New imaging devices can be investigated using "in vivo" simulations. The tumor detection capabilities of a novel coincidence counting probe system has been investigating using the anthropomorphic phantom described here [22]. Early design changes can be realized before studies are conducted in living models. One of the advantages of developing this very realistic human model is that such simulations can decrease the necessity of conducting experimental studies using animal models - particularly primates.

Dose calculations for internal and external radiation sources using this phantom can give new insights in the field of health physics and therapy. We hope to extend the application of this phantom to therapy related simulations.

[2] G Williams, M Zankl, W Abmayr, R Veit, G Drexler: "The Calculation of Dose from External Photon Exposures using Reference and Realistic Human Phantoms and Monte Carlo Methods", Phys. Med. Bio.,31, 449-452, (1986).

[3] H. Wang, RJ Jaszczak, RE Coleman: "Solid Geometry-Based Object Model for Monte Carlo Simulated Emission and Transmission Tomographic Imaging Systems." IEEE Transactions on Medical Imaging,11, 361-372, (1992).

[4] G Barnea, CE Dick: "Monte Carlo studies of x-ray scatterings in transmission diagnostic radiology." Med. Phys.,16,: 490-5, (1986).

[5] HP Chan, K Doi: "Physical characteristics of scattered radiation in diagnostic radiology: Monte Carlo simulation studies." Med. Phys.,12, 152-65, (1985).

[6] H Kanamori, N Nakamori, K Inone: "Effects of Scattered x-rays on CT images." Phys. Med. Bio.,30, 239-49, (1985).

[7] DR Dance, GJ Day: "The computation of scatter in mammography by Monte Carlo methods." Phys. Med. Bio.,29, 237-47, (1984).

[8] J Logan, HJ Bernstein: "A Monte Carlo simulation of Compton scattering in positron emission tomography." J. Comput. Assist. Tomogr.,7, 316-20, (1983).

[9] CE Floyd, RS Jaszczak, CC Harris, RE Coleman: "Energy and spatial distribution of multiple order Compton scatter in SPECT. A Monte Carlo investigation." Phys. Med. Bio.,29, 1217-30, (1984).

[10] CE Floyd, RJ Jaszczak, KL Greer, RE Coleman: "Deconvolution of Compton scatter in SPECT." J. Nucl. Med.,26, 403-8, (1985).

[11] CE Floyd, RJ Jaszczak, KL Greer, RE Coleman: "Inverse Monte Carlo as a Unified Reconstruction Algorithm for ECT." J. Nucl. Med.,27, 1577-85, (1986).

[12] CE Floyd, RJ Jaszczak, KL Greer, RE Coleman: "Brain Phantom: high resolution imaging with SPECT and I-123." Radiology,164, 279-81, (1987).

[13] JW Beck, RJ Jaszczak, RE Coleman, CF Starmer, LW Nolte. "Analysis of SPECT Including Scatter and Attenuation Using Sophisticated Monte Carlo Modeling Methods." IEEE Transactions on Nuclear Science,29, 506-511, (1982).

[14] D Acchiappati, N Cerullo, R Guzzardi: "Assessment of the Scatter Fraction Evaluation Methodology using Monte-Carlo Simulation Techniques." European Journal of Nuclear Medicine,15, 683-686,(1989).

[15] M Saxner, A Trepp, A Ahnesjo: "A pencil beam model for photon dose calculation." Med. Phys.,19, p 263-273, (1992).

[16] AS Meigooni, R Nath: "Tissue inhomogeneity correction for brachytherapy sources in a heterogeneous phantom with cylindrical symmetry." Med. Phys.,19, 401-407, (1992).

[17] R Mohan, G Mageras, B Baldwin, L Brewster, G Kutcher, S Leibel, C Burman, C. Long, Z Fuks: "Clinically relevant optimization of 3-D conformal treatments." Med. Phys.,19, 933-944, (1992).

[18] D Nigg, P Randolph, F Wheeler: "Demonstration of three-dimensional deterministic radiation transport theory dose distribution analysis for boron neutron capture therapy." Med. Phys.,18, 43-53, (1991).

[19] IG Zubal, CH Harell. "Voxel based Monte Carlo Calculations of Nuclear Medicine Images and Applied Variance Reduction Techniques." Image and Vision Computing,10, 342-348, (1992).

[20] IG Zubal, CR Harrell, PD Esser. "Monte Carlo determination of emerging energy spectra for diagnostically realistic radiopharmaceutical distributions." Nuclear Instruments and Methods in Physics Research, A299, 544-547,(1990).

[21] GS Hademenos, MA King, M Ljungberg, IG Zubal, CR Harrell. "A Scatter Correction Method for T1201 Images: A Monte Carlo Investigation." 1992 IEEE Nuclear & Science Symposium and Medical Imaging Conference Oct. 25-31, 1992, Orlando FL, Volume 2, pp. 1213-1216.

[22] JR Saffer, HH Barrett, HB Barber , JM Woolfenden. "Surgical Probe Design for a coincidence Imaging System Without a Collimator" Proceeding of the 12th International Conference on Information Processing in Medical Imaging,, Springer- Verlag, Ed Colchester and Hawkes, 8-22, (1991).

FIGURE 1: Anterior and lateral projections of the 3-dimensional segmented human phantom. The skin and fat (index number =1) are highlighted to show the outline of the patient as well as internal bones (corresponding to index numbers: 4,5,6,7, and 8).

FIGURE 2: Anterior and lateral projections highlighting the skin and fat (index number=1) as well as the brain (2), lungs (10), bladder and ureters (15), and bone (34).

FIGURE 3: Anterior and lateral projections highlighting the skin and fat (index number=1), and myocardium of the heart (11).

Table 1

Organ Numbers for the Torso 0 - Void outside of phantom 18 - Small intestine 1 - Skin/body fat 19 - Colon/Large intestines 2 - Brain 20 - Pancreas 3 - Spinal chord 21 - Adrenals 4 - Skull 22 - Fat 5 - Spine 23 - Blood pool (all vessels) 6 - Rib cage and sternum 24 - Gas volume (bowel 7 - Pelvis 25 - Fluid volume (bowel) 8 - Long bones 26 - Bone marrow 9 - Skeletal muscle 27 - Lymph nodes 10 - Lungs 28 - Thyroid 11 - Heart 29 - Trachea 12 - Liver 30 - Diaphram 13 - Gallbladder 31 - Spleen 14 - Left and right kidney 32 - Urine 15 - Bladder 33 - Feces (colon contents) 16 - Esophagus 34 - Testes 17 - Stomach 35 - Prostate 36 - Liver lesionFigure 1

Figure 2

Figure 3

2 In order to gain access to the phantom data, please send your request to Dr. George Zubal.

e-mail:Zubal@BioMed.Med.Yale.Edu.