{what we do}

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development of innovative instrumentation and methods

Improving quantitation for PET and PET/CT scanners

Supported by NIH grants R01-CA42593 (Lewellen), R01-CA74135 (Kinahan), P01-CA42045 (Krohn), and a research grant from General Electric Medical Systems (Kinahan/Lewellen). In addition to the work done here in the IRL, we collaborate with several other groups as well as maintain several other in depth websites outlining additional work.

The UW IRL conducts research synergistically with the he UW Nuclear Medicine clinic for basic, translational, and clinical studies. We provide support for other research activities of the division as well as pursuing related research that is clustered into four areas:

  • Improving quantitation for PET and PET/CT scanners
  • Development of tools to quantitatively measure image quality
  • Optimizing clinical acquisition protocols
  • Investigating alternative scanner geometries

Our work in Improving quantitation for PET and PET/CT scanners falls into four categories: (1) validation of corrections for isotopes with complex decay schemes, (2) development of improved image reconstruction algorithms, (3) improving the accuracy of inclusion of X-ray CT images into attenuation correction for PET/CT scanners, and (4) incorporation of PET images into radiation treatment planning systems.

Radioimmunotherapy with 131 I and 90 Y labeled monoclonal antibodies require accurate biodistribution measurements prior to therapy for reliable dosimetry. The quantitative advantage provided by PET over single photon imaging has inspired considerable interest in the positron emitting isotopes of iodine ( 124 I) and yttrium ( 86 Y). Unfortunately, these isotopes are characterized by prompt cascading gamma ray emissions and photon energies capable of septal penetration and positron-electron pair production, which contribute unwanted backgrounds to the PET image data; without compensation, these backgrounds can produce unacceptable bias in the final image. Figure 10 depicts some of the results on the complex isotope correction scheme development showing the impact of modeling the additional events due to pair production form the high energy gamma   rays in 86Y. Similar correction schemes are being validated for 124 I and 94 Tc.


Figure 10:   Data Spectrum anthropomorphic torso phantom scanned with 86Y in liver and soft tissue regions.   Spine in phantom contains no activity.   The Standard reconstruction (a) yields erroneous activity in spine that was not recorded in 86Y bias corrected image (b).


For conventional positron-emitting isotopes, we are developing image reconstruction techniques that more accurately model the acquisition physics. Specifically we are looking at the impact of more accurately modeling statistical noise and detector resolution blurring. Figure 11 illustrates the effect of including the effect of attenuation on photon statistics. The effect of modeling detector blurring (for a small anima scanner) was illustrated in figure 12 .


Figure 11. Illustration of the different 3D whole-body PET image reconstruction methods from the same patient data. (a) 3DRP. (b) FORE+AWOSEM. The combination of FORE+AWOSEM leads to improvements in image SNR in clinically feasible reconstruction times. In addition we investigated the comparative performances of the 3D reconstruction algorithms for tumor detection with human observers using a volumetric observer tool that uses the same display as our clinical PET oncology imaging (fig. 4).   These results also indicated an advantage for the FORE+AWOSEM over the 3DRP 3D image reconstruction algorithm.


Figure 12. Image resolution improvements with including detector blurring effects.

The advent of dual modality PET/CT scanners has significantly enhanced the physician's armamentarium for the diagnosis and staging of cancer as well as for therapy planning and monitoring response to therapy. The PET/CT scanner platform has new synergies, primarily the use of the X-ray CT image for attenuation and scatter correction of the PET emission data, as well as the use of the CT image as an anatomical prior for the PET image reconstruction. There are, however, relevant scenarios where improving the quantitative accuracy of PET/CT imaging is both important and challenging due to respiratory motion, partial volume effects, or estimation of the attenuation coefficients for high atomic number materials such as bone, metal, or contrast agents. We hypothesize that accurate quantitation can be achieved through the combination of three approaches: (1) combining low-dose X-ray imaging with dual energy CT solely for PET attenuation correction, (2) use of the CT image as an anatomical prior for the PET image reconstruction, and (3) the use of respiratory-gated low-dose CT for attenuation correction. Figure 13 illustrates the effects of motion and contrast agents on PET/CT(AC), while figure 14 demonstrates the potential improvements from including the CT image as an anatomical prior for the PET image reconstruction.


Figure 13. top: CT scan with i.v. contrast agent. Bottom: PET images are reconstructed with CT-based attenuation correction, with and without i.v. contrast agent. PET difference image is superimposed on CT showing combined effects of motion (white arrows) and contrast (yellow arrows) in attenuation correction induced artifacts.


Figure 14. Transverse sections through a CT image volume and reconstructed PET volume images of a test 3D phantom showing four hot contrast objects and three cold contrast objects showing the effect of including anatomical (CT) information with measured data.

Recent publications related to this project:

Harrison, R., S. Dhavala, P. Kumar, Y. Shao, R. Manjeshwar, T. Lewellen, and F. Jansen. Acceleration of SimSET photon history generation. in IEEE Nuclear Science Symposium and Medical Imaging Conference. 2002. Norfolk, VA: IEEE p. 1835-1838.

Harrison, R., D. Somasekhar, N. Prasanth, Y. Shao, and T. Lewellen. Importance Sampling in PET Collimator Simulations. in IEEE Nuclear Science Symposium and Medical Imaging Conference. 2003. Portland, OR. p. in press.

Lee, K., P. Kinahan , J. Fessler, R. Miyaoka, and T. Lewellen. Pragmatic Image Reconstruction for the MiCES Fully-3D Mouse Imaging PET Scanner. in IEEE Nuclear Science Symposium and Medical Imaging Conference. 2003. Portland, OR.: IEEE p. in press.

Lee, K., P. Kinahan, R. Miyaoka, J. Kim, and T. Lewellen, Impact of system design parameters on image figures of merit for a mouse scanner. IEEE Trans. Nucl. Sci., 2004: p. in press.


Development of Tools to Quantitatively Measure Image Quality

With the rapidly increasing use of PET for cancer detection and staging, unanswered questions about the impact of the choice of acquisition, processing, and reconstruction parameters on image quality are becoming more important. To quantitatively assess task-dependent measures of image quality relevant to clinical PET oncology imaging, we have developed volumetric analysis techniques for human and model observer studies. We have applied a multi-target approach (to improve the sensitivity of detection studies) using accurately simulated PET data to compare alternative acquisition, processing, and reconstruction strategies. The ranking of different methods by human observers has been analyzed using (1) a simple non-parametric fraction-found metric and (2) the area under the alternate free-response ROC (AFROC) curve for detection SNR.

We have developed and tested (1) methodologies for human observer studies of lesion detection based on the volumetric display software used in practice for clinical PET imaging, (2) model (numerical) observers based on 3D extensions of the channelized Hotelling observer (CHO) and non-prewhitening matched filter (NPW). Our results indicate that volumetric (3D) model observers behave differently than planar (2D) model observers, and appear to correlate better with volumetric human observer studies ( figure 15 ). These were quantitatively analyzed by alternative free-response ROC (AFROC) analysis and non-parametric methods. These methodologies have been successfully applied to investigations of the impact on lesion detection of the effect of: (1) acquisition mode (2) data pre-processing, (3) image reconstruction, and (4) post-preconstruction smoothing.


Figure 15. Average human observer SNR as calculated from an AFROC analysis compared to SNR(CHO) for targets located in the lungs as a function of the target contrast for three different acquisition protocols.

Recent publications related to this project:

Kinahan, P., J. Kim, C. Lartizien, C. Comtat, and T. Lewellen. A Comparison of Planar Versus Volumetric Numerical Observers for Detection Task Performance in Whole-Body PET Imaging. in IEEE Nuclear Science Symposium and Medical Imaging Conference. 2002. Norfolk, VA p. 1267-1271.

Lartizien C, Kinahan PE, and Comtat C, A Lesion Detection Observer Study Comparing 2D Versus Fully-3D Whole-Body PET Imaging Protocols. Journal of Nuclear Medicine, vol. 45, pp. (to appear), 2004.

Kim J-S, Kinahan PE, Lartizien C, Comtat C, and Lewellen TK, A Comparison of Planar Versus Volumetric Numerical Observers for Detection Task Performance in Whole-Body PET Imaging. IEEE Transactions on Nuclear Science, vol. (accepted), 2004.

Cheng PM, Kinahan PE, Comtat C, Kim J-S, Lartizien C, and Lewellen TK, Effect of scan duration on lesion detectability in PET oncology imaging. In: 2004 IEEE International Symposium on Biomedical Imaging, Arlington, VA, April 15-18, 2004. vol. (to appear), 2004.


Optimizing Clinical Acquisition Protocols

In spite of the rapid growth of PET oncology imaging protocols, there has been no systematic analysis or study performed of the effect of patient scanning protocol on image quality or clinical PET task performance. We have developed a model of the noise effective count rates (NEC) adjusted for injected activity that can be used to choose the dose/weight as a function of body mass index (BMI) to maximize aggregate image quality and also to determine the effect of patient dimensions on aggregate image quality ( figure 16 ).


Figure 16. Model of how the noise effective count rates (NEC) depends on patient morphology and as a function of body mass index (BMI).

Our hypothesis is that the effects of patient morphology scan duration, and tracer uptake levels on image figures of merit can be predicted. We are extending our scanner count rate model to predict how patient morphology, scan duration, and tracer uptake levels affect image quality, as measured by quantitative figures of merit under the assumption that nothing else changes. We are also determining the effect of the reconstruction regularization parameters on image figures of merit.   Our goal is to determine the smallest detectable lesion size (or the detection probability for a range of sizes and tracer accumulation) as a function of the patient morphology, activity at scan start, partial volume corrected tracer uptake, and scan duration. We will also determine if reconstruction parameters that are optimal for numerical observers reduce the minimum size (or range) of detected lesions compared to standard clinical reconstruction parameters. This can clearly effect patient management if, for example, metastases that would otherwise be missed are detected. Finally, these studies will indicate the feasibility of adjusting the activity injected and scan duration to compensate for patient morphology.

Recent publications related to this project:

Beaulieu S, Kinahan PE, Tseng J, Dunnwald LK, Schubert EK, Pham P, Lewellen B, and Mankoff DA, SUV Varies with Time After Injection in 18F-FDG PET of Breast Cancer: Characterization and Method To Adjust for Time Differences. Journal of Nuclear Medicine, vol. 44, pp. 913(abstract), 2003.

Lartizien C, Comtat C, Trebossen R, Kinahan PE, Ferreira N, and Bendriem B, Optimization of the injected dose based on Noise Equivalent Count (NEC) rates for 2D and 3D Whole-Body PET. Journal of Nuclear Medicine, vol. 43, pp. 1268-1278, 2002.

Kinahan PE, Lartizien C, Chander S, Meltzer CC, McCook B, and Torok F, Optimization of Wholebody PET FDG Oncology Scanning Protocols. Journal of Nuclear Medicine, vol. 43, pp. 216P (abstract), 2002.


Investigating Alternative Scanner Geometries

The scanner geometry optimization project is investigating alternatives for 2D and fully-3D PET systems. The approach is to develop a coarse septal-collimation system that will provide an optimization between sensitivity for trues and the acceptance of scatter and random events. We term this approach '2.5D' and are initially investigating collimator optimization for the GE Advance PET scanner. We will then couple these results with simulations of potential system designs where we keep the total volume of scintillator a constant (typically 11 liters for a modern whole body system) and look at the best distribution of that volume (crystal size, ring diameter, and axial extent of the detector array). To determine the distribution of true, scattered, and random events for all of these configurations, SimSET is being used to for the initial investigation. The SimSET results will be used to develop a parametric model that can be used with our analytical simulator, ASIM, which will then be utilized to determine the impact on image quality. This will require many thousands of images to be generated and analyzed using numerical observers to determine the efficiency of detection.

Recent publications related to this project:

Kohlmyer, S., C. Stearns, P. Kinahan , and T. Lewellen. NEMA NU2-2001 performance results for the GE Advance PET system. in 2002 IEEE Nuclear Science Symposium and Medical Imaging Conference. 2002. Norfolk, VA p. 890-894.

Surti S, Badawi RD, Holdsworth C, El Fakhri G, Kinahan PE, and Karp JS, A Multi-Scanner Evaluation of PET Image   Quality Using Phantom Studies. In: 2003 IEEE Nuclear Science Symposium and Medical Imaging Conference, Portland, OR, October 19 - 25, 2003.


construction of analysis and evaluation tools

UW IRL Research tools

Simulation Tools

Our simulation effort is now entering its 17th year of support for development of our public domain simulation software package, SimSET, and applications of simulation to image quality issues in PET imaging - both human and animal systems. The simulation package handles both PET and SPECT imaging systems. Physical effects modeled include: photon transport in objects (e.g., models of humans and animals); scatter and attenuation in collimators and shields, energy deposition in detector systems; and modeling of coherent scatter, positron range, and annihilation photon non-collinearity. The SimSET documentation package is on the web and is provided to the academic community as public domain software (with over 166 users in more than 90 laboratories around the world).   An alternate approach is provided by our analytic wholebody PET simulator (ASIM), which   can accurately simulate the noise properties of sinograms produced by several commercial PET scanners. It does not simulate photon transport in objects to estimate scattered or random coincidences (e.g. SimSET) but instead estimates their effect on sinogram statistics. Attenuation, scatter, randoms, detector efficiencies, and isotope decay are used to calculate the Poisson random deviate for each sinogram element.   Independent realizations of noisy sinograms can be rapidly generated with this approach.   In addition, the SimSET package is now being used with ASIM, where SimSET provides the system characteristics while ASIM provides rapid simulations of many realizations. The synergistic combination of SimSET and ASIM provide us and our collaborators with a particularly powerful set of simulation tools.

The simulation efforts for small animal PET scanners has been critical to the development of the MiCES scanner described earlier in this report. Simulations studies have been conducted to determine the number of events needed for various detection and quantitation tasks. Such studies have also been used to determine the impact of various photon transport processes on final image quality, including positron range, collinearity, parallax, and detector scatter. These studies lead directly to the development of the OSEM_DB reconstruction algorithm for the MiCES scanner (described earlier in this report).



FusionViewer is an open source medical image display package developed by Insightful and the Imaging Research Laboratory. It is designed to improve the physician's ability to interpret the results of combined positron emission tomography (PET) and computed tomography (CT) studies. This software is a display application for facilitating and improving visualization.


Multiviewer is a image volume viewer written in the IDL language ( figure 17 ).  It runs on all platforms IDL currently runs on, including Windows, Linux, Compaq Alpha, and Mac OS X.  With the IDL virtual machine (available since version 6.0 of IDL), Multiviewer can be used without an IDL license.


Figure 17. Example of the multivewer image viewer and image registration control application that   provides general imaging viewing and processing for multiple images. The tool is also being used to view and process PET/CT image pairs from PET/CT combined scanners.

Features of Multiviewer include:

  • Reading of volume image files in Interfile, MetaIO, AVS, and raw file formats, with specification of voxel dimensions
  • Adjustment of level and width interactively through the colorbar
  • Up to 6 independent image panels,
  • Linked cursors and navigation
  • Multiple resizing options
  • Selection of color tables
  • Image fusion via alpha blending, with adjustable alpha and color table selection for the fused image
  • Image checkerboarding for assessment of image registration
  • Image cropping, either rectangular or polygonal cylinder
  • Linked elliptical, polygonal, or freeform ROIs with basic statistics
  • Measurement tool
  • Zoom tool for looking at corresponding parts of a single view across multiple image volumes
  • Hooks for running external image registration routines


Image registration

Registration can be performed using a rigid body package (NEUROSTAT) written by Prof. Satoshi Minoshima in the Division of Nuclear Medicine or by a non-rigid body package developed in the IRL. Both implementations are based on mutual information algorithms. The rigid body tool can usually be run without operator intervention (the operator selects the two data sets and then runs a tool we have written that does the data export, registration, and import of the registered data back into the Advance database). The non-rigid body tool requires operator intervention to define the volumes to be registered, set templates around parts of the anatomy that are different (e.g., arms in/out when registering body CT to body PET), and other QA operations. These operator tasks are performed with tools we have written in IDL.


Figure 18. Registered CT (above) and FDG-PET (below) images aligned using non-rigid registration. Images were obtained from a patient with clinical T2N2a cancer of the left pyriform sinus. One of several ipsilateral FDG-avid zone III nodes is indicated by fiducials. Arrows show FDG-avid primary tumor extending superiorly into the supraglottic region, seen only as a small left-sided mucosal abnormality on CT. On the right, fused axial and coronal CT (grayscale) and FDG-PET (hot-metal scale) images of the same patient show tumor and FDG-avid zone alignment. These images are then used for treatment planning using intensity modulated radiation therapy (IMRT).


IRL Computing Cluster

For extended calculations taking more than a few hours, such as PET scanner simulations, we have a computing cluster of five Apple XServe machines each with dual 1 GHz G4 processors and 2 GB RAM. The servers run the Mac OS X Server operating system, based on Unix, and are connected on a private network via a Gigabit Ethernet switch to a PowerMac 1.25 GHz G4, which functions as a head node. The head node serves as the connection to the outside world and serves to the cluster a shared file system, which is stored on an Apple XServe RAID with 690 GB of disk capacity (expandable to 2.5 TB). The cluster uses the open-source Sun Grid Engine resource management system for job queuing and scheduling, load balancing, and job accounting. In addition, parallel programs written using the MPI (Message-Passing Interface) library specification can be scheduled on the cluster using either the MPICH or LAM environments.


translation of developments to preclinical and clinical applications for improved healthcare

Development of Small Animal PET Systems

Supported by NIH grants R01-EB002117 (Lewellen),   R24-CA88194 (Lewellen),   R21/R33- EB0001563 (Miyaoka), and P01-CA42045 (Krohn).

The Small Animal PET System research cluster has three components:

  • Developing the four-ring micro crystal element scanner (MiCES) to be used for functional and biochemical in vivo imaging of mice and other small animals
  • Testing of a single ring prototype version, QuickPET II
  • Development of next-generation detector technologies for cost-effective construction of small animal PET scanners

The micro crystal element scanner (MiCES)

The micro crystal element scanner (MiCES) will be used for functional and biochemical in vivo imaging of mice and other small animals. Driven by advances in genomics and molecular biology, small animal models, in particular genetically engineered mice, are increasingly recognized as powerful tools to study human disease. In the field of oncology, PET imaging is used for tumor detection (i.e., tumorigenesis and metastasis), disease staging and monitoring, tumor characterization (e.g., hypoxia and estrogen receptor status), response to therapy, and cell trafficking studies. Small animal PET imaging is also being used to study gene expression; to study normal organ uptake; and to study brain function.

The specific aims of the MiCES design were to achieve an image resolution of less than 1 mm and an absolute detection sensitivity of 6%. Each of these design goals is pushing the state of the art for small animal PET imaging systems. The key design component of the system is our micro crystal element (MiCE) detector. Each MiCE detector consists of a 22x22 array of polished 0.8x0.8x10 mm mixed lutetium silicate (MLS) scintillation crystals. The crystals are placed within a grid made of a highly reflective polymer film material (i.e., radiant mirror film, 3M). The grid serves three purposes: 1) it optically isolates the crystals; 2) it functions as a reflective wrap; and 3) it provides structural support for the crystal array. The crystal array is directly mounted to a position sensitive photomultiplier tube (PMT) using an optical coupling compound. The PMT converts and amplifies the light produced by the scintillation crystals into an electrical signal. A sample crystal array and position-sensitive PMT is pictured in figure 1 .


Figure 1. Micro crystal element detector (MiCE) and position sensitive photomultiplier tube next to a dime.

The four ring version of the scanner, named MiCES, is under construction and will be operational by summer of 2004. The system will consist of 72 detector modules housed in 18 detector cassettes. Each detector cassette will house 4 detector modules, as illustrated in figure 2 . MiCES will have ~9.5 cm of axial FOV and ~8 cm transverse FOV. The increase of the in-plane FOV size is due to different data acquisition electronics. To fully sample the imaging FOV, each detector cassette will be axially offset from its neighbor, as illustrated in figure 2 , and the gantry will be continuously rotated during the study.


Figure 2. Neighboring rows of MiCE detectors (detector cassettes 1 and 2) are axially offset to fully sample the imaging FOV when the detector system is rotated.

The UW IRL is designing all of the system electronics with the exception of an amplifier/digitizer board that was developed under a technology sharing agreement between the University of Washington and Concorde Microsystems (Knoxville, Tennessee). A picture of the jointly developed board is shown in figure 3 . Each card supports a detector cassette (i.e., 4 detector modules). The board amplifies and sums the incoming analog signals. The board also assigns a digital time stamp corresponding to the arrival time of each event. The input signals are digitized using very high speed analog to digital converters (ADCs). Processing of the digital data is handled by two powerful field programmable gate array (FPGA) chips located on the board. When a coincidence event is detected, the digitized data is passed to the data acquisition electronics. The data acquisition system is based upon the IEEE 1394a (Firewire) standard and can support coincidence rates in excess of 400 kcps with minimal deadtime.


Figure 3. UW modified CPS analog signal board (ASB).    The 16 analog signals enter at the top and are processed by 4 Concorde ASICs.   The ASIC analog outputs are digitized and integrated by the on-board ADCs and FPGAs.   The ASIC time stamps are transferred to the FPGAs.   Final data is transferred to a DSB.

To enable rotating the detector ring, the gantry is mounted on a mechanical slip ring. All DC voltage signals are supported by the main slip ring. The system also utilizes an optical slip ring to provide high throughput of the digital information.

The data acquisition computer is a Macintosh Xserve running OS X. Data can be transferred to a cluster of five additional Xserves to support image reconstruction and data analysis.

Recent publications related to this project:

Lewellen, T., C. Laymon, R. Miyaoka, M. Janes, B. Park, K. Lee, and P. Kinahan. Development of a data acquisition system for the MiCES small animal PET scanner. in 2002 IEEE Nuclear Science Symposium and Medical Imaging Conference. 2002. Norfolk p. 1066-1070.

Lewellen, T., R. Miyaoka, M. Janes, B. Park, and P. Kinahan System Electronics for the MiCES Small Animal PET Scanner. in IEEE Nuclear Science Symposium and Medical Imaging Conference. 2003. Portland, Or. p. in press.

Miyaoka, R., C. Laymon, M. Janes, K. Lee, P. Kinahan, and T. Lewellen. Recent progress in the development of a micro crystal element (MiCE) PET system. in 2002 IEEE Nuclear Science Symposium and Medical Imaging Conference. 2002. Norfolk p. 1287-1291.

Laymon, C., R. Miyaoka, B. park, and T. Lewellen, Simplified FPGA-Based Data Acquisition System for PET. IEEE Trans. Nucl. Sci., 2003. 50(5): p. 1483-1486


The QuickPET II prototype scanner

A single ring version of the system, pictured in figure 4 , has been operational since June, 2003. It consists of 18 detector modules and has a 12.8 cm ring diameter and a 1.98 cm axial field of view (FOV). The system collects fully 3D data. Lead end shields reduce the animal port to 10 cm. The in-plane imaging FOV is 5.76 cm. The detector ring is mounted on a bearing that allows partial rotation of the detectors (+/- ~20 degrees). This allows the lines of response associated with the gaps between modules to get sampled. The imaging table (Summit Medical Equipment, Oregon) was designed to allow delivery and exhaust of gas anesthesia from a single end of the table. The green tube is for gas delivery while channels in the table allow the exhaust to be collected from the black tube at the end of the table. Three linear stages and one vertical stage allow for accurate table placement. The table has a linear travel range of ~9 cm, enough for whole body scanning of a mouse. An image resolution of 1.1 mm full width at half maximum (FWHM) has been measured for a reconstructed line source.


Figure 4. Single-ring QuickPET II prototype of the MiCES small animal PET system.

Mouse imaging studies have been conducted for a range of radiopharmaceuticals, including FDG, FLT, FES and F-Annexin. Maximum pixel projection images of mice imaged with 18F-flurodeoxyglucose (FDG) are illustrated in figures 5 and 6 . Figure 5 is of a transgenic mouse that develops spontaneous mammary tumors. Figure 6 is of a mouse with induced skin tumors. The image in the blue box, figure 6 , is a transverse slice through the mouse's heart. The bright ring is the mouse's left ventricle. The faint ring is the mouse's right ventricle. This image illustrates the extremely high spatial resolution of the scanner.


Figure 5. Sample FDG image of a mouse with mammary tumors.


Figure 6. Maximum pixel projection of mouse with induced skin tumors.  
Image in the blue box is the transverse slice through the heart represented by the blue dotted line.

Along with building the MiCES scanner, the UW IRL is investigating statistical (i.e., iterative) image reconstruction techniques to improve image quality. A pragmatic iterative reconstruction utilizing a factorized system model of the MiCES scanner is being developed. This work focuses on the characterization and incorporation of accurate models of the MiCES scanner in the reconstruction method. The current implementation includes a model of the system's detector response function. Images of a mouse heart reconstructed with a standard analytic technique (i.e., filtered back projection, FBP); a standard statistical technique (i.e., ordered subsets expectation maximization, OSEM); and our statistical method that includes a model of the detector blurring response function (OSEM+DB) are illustrated in figure 7 . Our OSEM+DB reconstruction improves spatial resolution without significantly increasing the noise texture relative to standard OSEM. In addition to including a model of the detector response characteristics, we are working on models for 18F and 11C positron range to incorporate in our statistical reconstruction.


Figure 7. Transverse sections of an image volume of a mouse heart FDG uptake reconstructed with: (a) FBP, (b) OSEM and (c) OSEM+DB.

Recent publications related to this project:

Miyaoka, R., M. Janes, B. park, K. Lee, P. Kinahan , and T. Lewellen. Toward the Development of a Micro Crystal Element Scanner (MiCES): quickPET II. in IEEE Nuclear Science Symposium and Medical Imaging Conference. 2003. Portland, OR: IEEE p. in press.

Miyaoka, R., M. Janes, and T. Lewellen. Optimization of Mounting Large Crystal Arrays to Photomultiplier Tubes. in IEEE Nuclear Science Symposium and Medical Imaging Conference. 2003. Portland, OR.: IEEE p. in press.

Lee, K., P. Kinahan , J. Fessler, R. Miyaoka, and T. Lewellen. Pragmatic Image Reconstruction for the MiCES Fully-3D Mouse Imaging PET Scanner. in IEEE Nuclear Science Symposium and Medical Imaging Conference. 2003. Portland, OR.: IEEE p. in press.

Lee, K., P. Kinahan, R. Miyaoka, J. Kim, and T. Lewellen, Impact of system design parameters on image figures of merit for a mouse scanner. IEEE Trans. Nucl. Sci., 2004: p. in press.


Micro Detector Systems Research

To further improve our capabilities for small animal imaging, two research projects are underway to improve the basic detector technology. A fundamental goal for all of our development is to design cost effective detector designs that can easily be adapted to the basic MiCES scanner gantry and electronics. We have taken care to design the gantry and electronics so that new detector module designs can be integrated with a minimum of modifications.

A refinement of the MiCE module is to add depth-of-interaction (DOI) capability (dMiCE) as well higher sensitivity (longer crystals). Other designs we are testing focus on reading out light from both ends of the crystal or using different crystals stacked in layers and using differences in risetime to identify the layer of interaction. Both of these approaches require more electronic channels and light sensors, increasing the cost of implementation. Our basic approach is to use a single-ended light collection system and control the sharing of light between adjacent crystals to provide the DOI information. This approach is inherently less expensive than the other designs. The basic approach is shown in Figure 8 and preliminary results are depicted in figure 9 . To make a practical version of dMiCE with small cross section crystals (e.g., 1x1x25 mm) we are developing an approach with laser cutting patterns into the same 3M reflective polymer used for the MiCE module. We can then fabricate the modules as a laminate of layers of crystals and properly cut reflector. Currently we are working on the ideal pattern versus crystal size and testing a variety of glues and laminate techniques to assure adequate light collection.


Figure 8.   UW DOI detector concept. (a) DOI detector unit.   (b)   Ratio plots.   A significant amount of light is shared when an event is detected near the entrance face of the detector unit.   Less sharing occurs for interactions near the PMT interface.


Figure 9.   Ratio peak and FWHM values of ratio plot versus DOI for a GSO crystal pair with unpolished surfaces and the coupling scheme of Figure 1.   The black lines are estimates of DOI uncertainty for each depth position.

The second detector design is based on small, continuous blocks of scintillator (cMiCE). This design would dramatically reduce the cost of module construction by eliminating most of the crystal and polishing costs. The challenge is in obtaining good spatial resolution uniformly throughout the crystal. Conventional weighted summing techniques are inadequate. Our group has developed a statistical positioning approach that greatly improves the ability to obtain high spatial resolution throughout the crystal volume. Currently, simulation work is in progress to explore optimal arrangement of photomultiplier tube elements. Test crystals (25x25 and 48x48 mm) are being fabricated with a variety of thicknesses. Initial simulation results indicate that using new technology flat PMTs provide excellent performance with intrinsic resolutions of 1 mm with crystal thicknesses of 8 and 10 mm. The challenge is to achieve such performance in real detectors with crystal thicknesses of up to 20 mm.

The laboratory has recently been expanded with a new 64 channel ADC system and motorized x-y-z stages to allow proper experimental evaluation of the cMiCE and dMiCE detector modules. We will develop both detector modules to determine which design will provide the best price/performance ratio for our next small animal PET scanner.

Recent publications related to this project:

Miyaoka, R., T. Lewellen, H. Yu, and D. McDaniel, A depth of interaction PET detector module. J. Nucl. Med, 1998. 39(5): p. 170P.

Miyaoka, R.S. and T.K. Lewellen, Effect of Detector Scatter on the Decoding Accuracy of a DOI Detector Module. IEEE Trans. Nucl. Sci., 2000. 47(4): p. 1614-19.

Joung, J., R. Miyaoka, and T. Lewellen, cMiCE: A High Resolution Animal PET Using Continuous LSO with a Statistics Based Positioning Scheme. Nucl. Inst. Methods, 2002: p. in press



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