Los Alamos National Laboratory is operated by t h e University of California for t h e United States Department of Energy under contract W-7405-ENG-36.

TITLE: COMPUTATIONAL RADIOCOGY AND IMAGING WITH THE MCNP CODE

AUTHOR(S): Guy P. Estes and William M. Taylor

SUBMITTED TO: 1st World Congress on Computational Medicine, Public Health, and Biotechnology

University of Texas System - Center for High Performance Computing

DECLAIMER Austin, Texas - April 24-28, 1994

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsi- bility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Refer- ence herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recam- mendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

By acceptance of this article. the publisher recognizes that the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution. or to allow others to do so, for U.S. Government purposes.

The Los Alamos National Laboratory requests that the publisher identify this article as work performed under the auspices of t h e U S . Department of Energy

DISCLAIMER

Portions of this document may be illegible in electronic image products. Images are produced from the best available original document.

I

Dr. Matthew Witten - 2 - April 25, 1995

COMPUTATIONAL RADIOLOGY AND IMAGING WITH THE MCNP MONTE CARLO CODE

Guy P. Estes Radiation Transport Group Los Alamos National Laboratory Los Alamos, NM 87545 USA gpe@lanl.gov

William M. Taylor Radiation Transport Group Los Alamos National Laboratory Los Alamos, NM 87545 USA

ABSTRACT

MCNP, a 3D coupled neutron/photon/electron Monte Carlo radiation transport code, is currently used in medical applications such as cancer radiation treatment planning, interpretation of diagnostic radiation images, and treatment beam optimization. This paper will discuss MCNP’s current uses and capabilities, as well as envisioned improve- ments that would further enhance MCNP role in computational medicine. It will be demonstrated that the methodology exists to simulate medical images (e.g. SPECT). Techniques will be discussed that would enable the construction of 3D computational geometry models of individual patients for use in patient-specific studies that would improve the quality of care for patients.

I. INTRODUCTION

MCNPTM [l], a Los Alamos National Laboratory Monte Carlo radiation transport code, is currently used in the medical community for a variety of purposes including treatment planning, diagnostics, beam design, tomographic studies, and radiation pro- tection. The current widespread medical use of MCNP after its general public distribu- tion in about 1980 attests to the code’s general versatility and usefulness, particularly since its development to date has been influenced little by medical applications.

This paper will give a brief introduction to MCNP, describe some of its current applica- tions at various institutions in the United States, and discuss some of its current capa- bilities that can and are being applied directly to medical imaging, treatment planning, and visualization. The paper will conclude by discussing relatively straightforward en- hancements that would greatly facilitate its usefulness in the medical community, and that could eventually lead to its routine use in clinical diagnostics and treament plan- ning. Throughout this paper references will be made to the common themes such as the uses and/or needs for patient-specific body geometry from CT/MRI scans, analy- sis of medical image data, and quantative assay of radioisotopes in the body, to name a few.

footnote:

Dr. Matthew Witten - 3 - April 25, 1995 I

MCNP is a trademark of the Regents of the University of California, Los Alamos National Laboratory.

11. THE MCNP MONTE CARLO CODE

MCNP is a general, coupled neutron/photon/electron Monte Carlo code developed and maintained by the Radiation Transport Group at Los Alamos National Laboratory. It has been used extensively for radiation shielding studies, reactor analysis, detector design, physics experiment interpretation, oil and gas well logging, radiation protection studies, accelerator design, etc. over the years. It is estimated to have hundreds of person-years of development effort to its credit, and traces it roots back to the World War I1 Manhattan Project and scientists such as Fermi, von Neumann, Ulam and others.

MCNP is a fully three-dimensional (3-D) geometry, continuous energy physics code capable of modeling complex geometries, specifying material regions such as organs by the intersections of analytic surface contours. An example of the geometric complexity possible is shown in Fig. [l], where a partial MCNP model of the MIRD human phantom [2] is presented. This figure is a SABRINA [3] 3-D "picture" of the MCNP model generated by ray-tracing techniques, where the flesh and some organs have been omitted from the plot for clarity. This model will be used later to demonstrate the capabilities of MCNP for medical imaging studies.

MCNP contains many "user-friendly" standard input features such as numerous vari- ance reduction options, generalized source specifications, plotting capabilities , and tally options for output definition. For example, one can places numerous sources uniform in volume in irregular shapes such as the phantom bones as discussed later very easily by user input to the standard code. Tally results can plotted periodically as a calculation progresses so that the user can monitor the statistical behavior of the results, etc.

MCNP is designed to run a large variety of coupled neutron/photon/electron prob- lems. One can start a neutron source, produce neutron-induced photons which in turn produce electrons, which in turn produce electron-induced photons, and so forth. Problems that start photons or electrons produce photon- or electron-induced elec- trons and photons, respectively. At present MCNP cannot produce photoneutrons, although this has been considered. The ITS Version 1.0 [xx] electron physics currently exists in MCNP, but ITS 3.0 physics is planned for the near future.

MCNP has a Boltzmann-Fokker-Planck (BFP) capability [xx] that can be used for the transport of charged particles other than electrons. MCNP can also be coupled with LAHET [xx] to perform calculations for charged particles such as protons and pions up to 40 TeV.

Dr. Matthew Witten - 4 - April 25, 1995

111. CURRENT MEDICAL USES OF MCNP

Currently, MCNP is widely used for a variety of medical studies such as cancer treat- ment planning, radiation diagnostics, treatment beam design, computed tomographic (CT) studies, and radiation protection. This paper will not attempt to present an exhaustive bibliography of its current uses, but rather will point out various appli- cations that are familiar to the authors and/or others at Los Alamos by virtue of collaborations either on specific projects or on the use of MCNP itself.

1II.a. Neutron Capture Therapy

MCNP as it currently exists is perhaps best suited for use in the neutron capture therapy (NCT) arena since few if any modifications are needed to the basic code in order to perform relevant calculations. Researchers at MIT and Tufts/New England Medical Center [xx] have used it extensively for studies of reactor beam collimation, filtering, and quality, as well as for NCT treatment studies where CT and MRI(xx?) images of patients were used to generate materials for MCNP models. Brookhaven National Laborarory (BNL) [xx], the State University of New York at Stoney Brook [xx], and the University of Missouri [xx] have also used it for reactor beam design for NCT at the BNL reactor. Ohio State University also uses MCNP for--NCT studies [XXI.

1II.b. Brachytherapy and SPECT

MIT has also used MCNP in brachytherapy [xx] and single photon emission com- puted tomography (SPECT) studies [xx]. MCNP as it currently exists is well-suited for brachytherapy studies, but requires some automation for SPECT because of the modeling and tallying needed for imaging detectors.

1II.c. X-Ray Therapy

At UCLA, MCNP is being used to do treatment planning for x-ray cancer therapy. Researchers there are working on using computed tomography (CT) scans of patients to set up patient-specific MCNP geometries for use in treatment planning calculations [xx]. They are also using parallel virtual memory (PVM) technology [xx] to utilize a large number of widely separated workstations around campus in order to improve computational efficiency.

1II.d. PET Simulation

Researchers at Case Western Reserve University/University Hospitals of Cleveland [xx] have pioneered the use of MCNP in positron emission tomography (PET). They are using newly developed "supertrack" metholology [xx] for MCNP that allows rigor-

Dr. Matthew Witten - 5 - April 25, 1995

ous first principles calculations of photon detector response that has been performed approximately [xx] up until this time for special situations.

1II.e. Fast Neutron Therapy

At the University of Washington, MCNP is being used for fast neutron therapy studies [xx] that use a proton acclerator to produce the neutrons. They are also studying the enhancement of fast neutron therapy with NCT by introducing boron into skin tumors, thereby utilizing thermal neutrons that can be made available, or are available, in conjunction with fast neutrons.

1II.f. Simulations of Radiography and CT Scans

By virtue of the nature of Monte Carlo, MCNP can be used to simulate x-rays by defining appropriate tally volumes or surfaces to represent the x-ray film. In practice, it is difficult to achieve the resolution of actual x-rays because of the limitations of finite size tallies to represent the detail obtainable from x-ray film.

SABRINA has had the capability for some time to simulate an unscattered x-ray "picture" of MCNP models in a more physically realistic manner. The increased realism in SABRINA is due to the fact that the density of the "rays)) is uniform and that this density can be varied to achieve the fine-grained look of actual x-ray pictures.

Neutron CT scans have been simulated at MIT [xx] by using the output of MCNP calculations as input to a tomographic reconstruction package. It goes without saying that this could be done with photon CT scans as well. This subject will be discussed in more detail later in Section IV.

1II.f. High Energy Charged Particles with LAHET

1II.h. General Comments

It should be noted that MCNP is not generally used now for routine clinical studies that require a number of reasonable-runtime computations for a given treatment plan formulation. It is also not designed at present to efficiently compute the multi-detector configurations of modalities such as PET. However, as will be noted later, there are a number of enhancements that could be made relatively easily, based on previous expe- rience at Los Alamos. In addition, more difficult enhancements could be implemented utilizing the experience base in Monte Carlo at Los Alamos.

IV. IMAGING CAPABILITIES OF MCNP

Images such as those produced by Anger cameras, uniformly redundant arrays, or coded apertures can in principle be generated by Monte Carlo. A pinhole "gamma camera" capability exists at Los Alamos (although not in the production version of

Dr. Matthew Witten - 6 - April 25, 1995

MCNP at present) and has been used to simulate the image one would obtain from a pinhole collimator. Two examples of this imaging will be presented in the following sections. It should be noted that these images are for "perfect" pinholes (i.e. a single pinhole with an infininitely small radius in an opaque medium and without umbra/penumbra effects). However, finite hole size and umbra/penumbra effects can also be modeled. In principle, multiple pinhole images could also be generated with MCNP, although no work has been done to date in this area.

1V.a. SPECT-type Image of Internal Radioisotopes

The first example of this imaging capability is illustrated in Fig. [2] using the MIRD human phantom model of Fig. [l]. Photon-emitting radioisotopes were assumed to be present in the major bones (skull, spine, arms, pelvis, and legs) of the model, and were imaged through the perfect pinhole to obtain the radiation contour image shown in Fig. [xx]. The bones with sources stand out clearly in the figure, and the effect of scattered radiation in flesh and other bones can be seen in the interstitial regions. Since unscattered radiation can be segregated quite easily in Monte Carlo (as shown in Fig. [xx]), this technique could be used to computationally estimate the scattered components for actual measured medical images, subtract them from the measured image, and thereby produce more unambiguous images for medical diagnoses. This will be discussed further in Section V in connection with creating patient-specific MCNP models from CT and MRI images.

1V.b. Assay of Internal Radioisotope Concentrations

In the treatment of cancers, it would seem to be important to be able to determine the amount of a radioisotope that had been introduced into a patient, both for assurance that the dose administered is consistent with the planning and for use in correlat- ing remission results with actual administered dose. Since it is likely that different people have different metabolic rates for intake of radioisotopes as well as different physical sizes (i.e. both in organs of interest as well as general body characteristics from a shielding viewpoint), patient-specific assays would produce both better care for the patient and more opportunity for understanding dose/response relationships in treatment.

A tumor scenario was developed using the lungs of the MIRD phantom to demonstrate how this might work. A 1 cm radius cell was introduced into each lung to simulate a tumor region. A photon source with an arbitrary strength (photons/sec) was placed uniformly in each of these cells. Ten times that source strength was also distributed uniformly in the remaining volume of the lung to simulate the physical situation where not all of the administered radioisotope was taken up by the tumor so that there is significant "background" around the tumor "foreground" (i.e. the signal to be

Dr. Matthew Witten - 7 - April 25, 1995

assayed). Fig. [xx] shows the calculated image of the phantom torso obtained. The tumors appear as hot spots with a uniform background appearing in the rest of the lungs as well as some interstitial scatttering in other parts of the phantom (i.e. in the neck at top for example). If a patient-specific body geometry was available, then one could perform various calculations of tumor and lung radioisotope concentrations in order to determine an absolute quanity of the radioisotope and the tumor/lung concentration ratio that would be needed to match a given measured image.

1V.c. Radiography and CT Image Simulation

It was noted in Section 1II.f above that it is possible to simulate radiography and CT images with MCNP. At present, this is a manipulation-intensive process that involves the creation of an array of detectors or tallies that emulate the film, phospor, or detectors, and then transmitting the appropriate source through some geometrical model to the detectors. The efficiency of this for x-rays is of course limited by the number of tally ”pixels” needed to produce a useful image. For CT scans, the multiple calculations for the many different source/detector angles must be processed brute- force through a tomographic reconstruction program in the same way that one would process the actual angular scan data.

1V.d. PET Simulation

This subject also belongs in this section, but will not be covered further here since it was discussed in 1II.a. above.

V. ENVISIONED ENHANCEMENTS TO MCNP FOR MEDICAL STUDIES

At present the use of MCNP as a routine clinical tool is not practical in most cases since the computer and calendar time generally needed for the calculations and model- ing is excessive even though in many cases the level of patient care could be improved. Improved computational efficiency is the key to more widespread use of Monte Carlo which would in turn lead to improved medical diagnostics and treatment of patients. This improved computational efficiency could take the form of software enhancements, more efficient computers, better use of existing computers, etc. as discussed below. MCNP enhancements such as tally and imaging capabilities that are tailored to med- ical community needs are also needed to minimize user setup time and output data analysis.

V.a. Improved Transport Efficiency

We think it remarkable that MCNP is used as widely as it is in the medical community in spite of the fact that essentially none of its development to date has been influenced by the needs of the medical community. This occured primarily because it was not publically released until about 1980, and because Los Alamos did not follow its use

Dr. Matthew Witten - 8 - April 25, 1995

in the medical arena until several years ago. MCNP, in its present form, is a very general code that was designed for a variety of non-medical applications. It therefore has a considerable overhead due to features not needed for most medical studies. In particular, Los Alamos studies have shown that the tracking of particles through the analytically defined surfaces used in MCNP increased the running time by at least a factor of 10 over that which would be required when tracking through the regular rectangular mesh that would result from CT/MRI-derived geometry. Work is planned to address this issue.

V.b. PVM, Multitasking, and Computers

As noted above, PVM is being used to improve computational speed by utilizing multiple low-cost workstations in parallel. This is especially effective when this can be done during low demand times using machines that otherwise would be idle. Los Alamos is also working on load balancing for PVM which would allow MCNP to more effectively utilize available machine time with less pertubation on the machine "owners".

Los Alamos currently operates a cluster of eight IBM RISC 6000 machines which are about 15 times more cost-effective than a CRAY YM-P. These machines can be multi-tasked with PVM and are used for production work.

Finally, modestly or massively parallel computers are likely the wave of the future. MCNP is being installed on the T3D 128 CPU machine at Los Alamos and in principle can run on all processors at once.

V.C. MCNP Input/Output Features

As noted earlier, since MCNP development has not been influenced by the medical community, it is cumbersome to do certain types of calculations such as those with multiple detectors, x-ray film/phosphor output, imaging detectors, etc. In principle, these types of input/output features are possible as indicated throughout the paper, but need to be tailored to the unique requirements of the medical community.

SUMMARY AND CONCLUSIONS

The intent of this paper has been to demonstrate that many types of medical studies are possible with MCNP basically as it exists today. In addition, a number of other types of applications are believed possible, with relatively modest effort, based on the Monte Carlo expertise that resides at Los Alamos both in people, one-of-a-kind patches, and other codes. It is the belief of the authors that eventually affordable and copious computer time will be available in amounts sufficient, when coupled with enhancements such as the above, to use MCNP in routine clinical applications for many scenarios.

Dr. Matthew Witten - 9 - April 25, 1995

Admitedly, the use of MCNP (or Monte Carlo in general) is not warranted in in- stances where approximate methods provide adequate treatment and are much faster. However, there known deficiencies in approximate treatment planning methods (e.g. near tissue/bone interfaces) that could negatively impact the treatment that patients receive. This typically manifests itself as a lower dose delivered to a tumor in order to not overdose normal tissue. There are apparently cancer patients who inexplicably do not survive after treatment, even though other patients with similiar cancers and treatments do suvive.

The key to using Monte Carlo like MCNP in routine clinical applications lies in im- proved computational efficiency in conjunction with available and affordable CPU time. Computational efficiency in MCNP can be improved by at least a factor of 10 by providing for particle tracking thru the regular rectangular mesh that would result from CT/MRI-derived geometry models. Advances in personal workstations, cluster- ing of powerful and cheap workstations, the affordability of modestly and massively parallel machines, and multi-tasking promise to revolutionize the use of Monte Carlo in routine clinical applications.

Sincerely,

Guy P. Estes, Ph.D. Radiation Transport Group

GPE:xxx

Cy: R. C. Little, X-6, MS B226 X-6 files CRM0(2),MS A150

0

0 c1

0 0

0

0

0

5

P

0

0 n I

I

0 .O 0 0 0 o s

9 1 0

I

9 0

0 0

0 0

0 0 m I I 2 9 0

0

0 0 0

0 n 0 0 0 n 9 9 ? 8 8

$ i 0

t

8 8 P 8 8 t 8 8 8 8 8

i 4 i i i i