Real-Time Imaging System for the OpenPET

40 IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 59, NO. 1, FEBRUARY 2012

Real-Time Imaging System for the OpenPETHideaki Tashima, Eiji Yoshida, Shoko Kinouchi, Fumihiko Nishikido, Naoko Inadama, Hideo Murayama,

Mikio Suga, Hideaki Haneishi, and Taiga Yamaya

Abstract—The OpenPET and its real-time imaging capabilityhave great potential for real-time tumor tracking in medical pro-cedures such as biopsy and radiation therapy. For the real-timeimaging system, we intend to use the one-pass list-mode dy-namic row-action maximum likelihood algorithm (DRAMA)and implement it using general-purpose computing on graphicsprocessing units (GPGPU) techniques. However, it is difficult tomake consistent reconstructions in real-time because the amountof list-mode data acquired in PET scans may be large dependingon the level of radioactivity, and the reconstruction speed dependson the amount of the list-mode data. In this study, we developeda system to control the data used in the reconstruction step whileretaining quantitative performance. In the proposed system, thedata transfer control system limits the event counts to be usedin the reconstruction step according to the reconstruction speed,and the reconstructed images are properly intensified by usingthe ratio of the used counts to the total counts. We implementedthe system on a small OpenPET prototype system and evalu-ated the performance in terms of the real-time tracking abilityby displaying reconstructed images in which the intensity wascompensated. The intensity of the displayed images correlatedproperly with the original count rate and a frame rate of 2 framesper second was achieved with average delay time of 2.1 s.

Index Terms—Image reconstruction, positron emission tomog-raphy, real-time tumor tracking, system architecture.

I. INTRODUCTION

W E have proposed the OpenPET, an open type positronemission tomography (PET) having a ring gap be-

tween two detector rings, which allows easy access to thepatient during PET scanning [1]–[6]. Real-time imagingusing OpenPET has great potential. For example, real-timePET-guided medical procedures such as biopsy [7], [8] and

Manuscript received January 12, 2011; revised June 03, 2011 and August 05,2011; accepted September 16, 2011. Date of publication November 08, 2011;date of current version February 10, 2012. This work was supported by theGrant-in-Aid for Scientists Research (A) of Kakenhi (22240065), by the NIRSPresident Grant Creative Scientific Research, and by a research grant from theAssociation for Nuclear Technology in Medicine.H. Tashima, E. Yoshida, F. Nishikido, N. Inadama, and H.Murayama are with

the National Institute of Radiological Sciences, Chiba 263-8555, Japan (e-mail:[email protected]; [email protected]; [email protected]; [email protected];[email protected]).S. Kinouchi is with the Graduate School of Engineering, Chiba University,

Chiba 263-8522, Japan, and also with the National Institute of Radiological Sci-ences, Chiba 263-8555, Japan (e-mail: [email protected]).M. Suga is with the Graduate School of Engineering, Chiba University, Chiba

263-8522, Japan (e-mail: [email protected]).H. Haneishi is with the Research Center for Frontier Medical Engineering,

Chiba University, Chiba 263-8522, Japan (e-mail: [email protected]).T. Yamaya is with the National Institute of Radiological Sciences, Chiba

263-8555, Japan, and also with the Research Center for Frontier Medical Engi-neering, Chiba University, Chiba 263-8522, Japan (e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TNS.2011.2169988

Fig. 1. Small OpenPET prototype. (a) Schematic design. (b) Photograph.

radiation therapy are feasible by visualizing and tracking atarget tumor labeled with radioactive tracer. Another feasibleapplication is for in-situ dose monitoring known as in-beamPET [9]–[14]. It should be noted that tumor-tracking is a muchmore challenging application in terms of the real-time require-ment. Our ultimate goal is to develop a real-time PET-guidedtumor-tracking radiation therapy. Real-time tracking is desir-able, especially for mobile organs such as the lungs whichmove with respiration. The main technology to deal with mo-tion during radiation therapy is beam gating using a surrogatefor the tumor position such as an optical marker on the patient’schest [15], an RF transponder implanted in the tumor [16],or a gold fiducial marker implanted in the tumor [17]–[26].Using these surrogates is invasive and indirect. The implantedmarker does not directly indicate the tumor location and size.Since the direct visualization of the tumor area during radiationtherapy is desired, we believe that PET imaging is promisingfor real-time tumor tracking.There are three major challenges for real-time tumor tracking

by PET: 1) providing an open space for the surgery with full3-D imaging capability, 2) getting high sensitivity, and 3) im-plementing real-time imaging. The OpenPET can achieve thefirst and second items. In contrast to dual-head positron cam-eras [9]–[14], the OpenPET has a practical capability for 3-Dimaging ability and high sensitivity. Further, it has been shownthat even if there is a gap between rings, sensitivity is still highcompared with commercial PET scanners [6]. However, real-time imaging of PET is still a challenging task because of itscomputational burden in image reconstruction. To the best ofour knowledge, no one has ever actually demonstrated real-timeimaging for PET. Therefore, the aim of this study is to demon-strate real-time OpenPET imaging by proposing a new systemarchitecture and implementing it on a prototype system.

0018-9499/$26.00 © 2011 IEEE

TASHIMA et al.: REAL-TIME IMAGING SYSTEM FOR THE OPENPET 41

To prove the concept of the OpenPET geometry, we aredeveloping a small OpenPET prototype (Fig. 1), which has twodetector rings (110 mm diameter and 42 mm axial length) com-posed of eight block-detectors [5], [6]. Each block-detector has14 14 4 LGSO crystals. Although the OpenPET imagingof the in-gap area uses only oblique lines of response (LORs),the depth-of-interaction detector [27] suppresses the spatialresolution deterioration in the field of view (FOV) [5], [6]. Forreal-time visualization, we intend to use the recently developedfast PET reconstruction algorithm, one-pass list-mode dynamicrow-action maximum likelihood algorithm (DRAMA) [28],[29], and general-purpose computing on graphics processingunits (GPGPU) techniques [30]–[32]. GPGPU techniques cansignificantly reduce the computational time. However, it isstill difficult to achieve real-time visualization if the amount oflist-mode data is huge for the real-time processing. In this paper,we propose a system architecture to control the amount of datato be processed for real-time reconstruction. We implement thearchitecture on the small OpenPET prototype and evaluate theperformance in terms of real-time processing ability.

II. METHOD

A. Proposed Architecture

The OpenPET system detects coincidence gamma ray eventdata, converts them into a list-mode data file format using anelectronic circuit, and places them in storage. Conventionally,images are reconstructed after all data are acquired. To performreal-time imaging, we propose a system architecture withoutlarge changes from the current system design.Fig. 2 shows the proposed architecture and data flow in the

real-time imaging system. The data flow and process of its com-ponent systems are as follows.

1) The data acquisition (DAQ) board, which forms partof the acquisition system, converts event data into coin-cidence event data.2) The DAQ software manages the DAQ board and storesthe list-mode data in the high-speed storage.3) The data transfer control (DTC) system monitors thestorage.4) At every preset time interval, the DTC system checkswhether new list-mode data have been added to storageand, if so, transfers a part or all of the new data to anotherpart of the high-speed storage as a new file to be read bythe real-time reconstruction (RTR) system according to theprocessing capacity of the RTR system. We note that thedestination high-speed storage can be either on the samedevice or on a different device such as a random-accessmemory (RAM) disk.5) Then the DTC system calls the RTR system with theargument of the transfer ratio. This is a ratio of the trans-ferred counts to the total counts in the time interval bywhich the RTR system can compensate the image inten-sity after the reconstruction. Here, the DTC system checksthe RTR system status before calling. If a previous processin the RTR system is still running, the DTC system skipsthe call.

Fig. 2. Architecture of the real-time imaging system.

6) The RTR system reads the transferred new list-modedata and reconstructs the image. The image intensity iscompensated according to the transfer ratio.7) After reconstruction is done, the RTR system stores thereconstructed image in the storage.8) The DTC system checks the exit status of the RTRsystem for reconstruction of the next new list-mode data.9) The real-time view (RTV) system monitors the high-speed storage and reads the reconstructed image immedi-ately after the reconstruction and displays it on a PC screen.9’) Alternatively, because monitoring the storage can beslow especially if the number of files in the directory isvery large, the DTC checks the result of the reconstructionand tells the RTV system to update the image for quickerupdates.10) The RTV system reads the reconstructed data and dis-plays the image on the PC screen.

B. Implementation

We implemented the RTV, DTC, and RTR systems on a singlePC having 3.33-GHz Intel® Core™ i7 CPU, 24 GB memory(2.5 GB for system memory and 21.5 GB for RAM disk), anda NVIDIA® Tesla® C1060 graphics processing unit (GPU)card, which was connected to the small OpenPET prototype,DAQ board. The DAQ board was controlled by DAQ softwareinstalled on the PC which transfers the list-mode event dataacquired by the OpenPET to the computer. The RTV systemmonitored changed in storage, read the most recent file, anddisplayed certain slices in the image. The DTC system alsomonitored changes in storage but only read the new part of thefile from the previous time interval because we assumed that the


Fig. 3. Parallelization schemes: event parallelization divided list mode eventsinto subgroups and each thread processed LORs in an assigned subgroup. LORparallelization further divided LORs according to the number of slices crossingthe LOR and summed them up later.

Fig. 4. Tracking ability demonstration setup. (a), (b) Schematic illustration oftop and side view. (c) Photograph of the demonstration setup. Na point source(1MBq) was set between the ring gap and moved up and down with a 30 s cycle.

Fig. 5. Comparison of reconstruction speed with different parallelizationmethods at low count number.

file was being read by the DTC system while the DAQ systemwas writing it. Here, the DTC system limited the amount ofdata to be read according to the reconstruction speed capability.The RTR system was implemented with the one-pass list-modeDRAMA using GPGPU techniques on compute unified devicearchitecture (CUDA) [26] version 2.3. Themost time consumingprocess in the RTR system was ray tracing for forward andback projection of lines of response (LORs). In the list-modeDRAMA, list-modedatawere divided into subsets and the imagewas updated by the forward and back projection of the list-modedata in each subset. Therefore, forward and back projection of

the LORs was parallelized in the GPU implementation. Here,we implemented the algorithm in two different parallelizationschemes for the forward projection as shown in Fig. 3: 1) eventparallelization and 2) LOR parallelization. In event paralleliza-tion, list-mode data in the subset were divided into subgroupsand each GPU thread processed list-mode data in a subgroup. InLOR parallelization, ray tracing of the LOR was further dividedinto small tasks to be processed in each GPU thread according tothe number of slices crossing the LOR. Each task calculated thesummation of the product of the voxel value and the detector re-sponse function in the assigned slice for the forward projection.The all summations in the assigned sliceswere summed up alongthe LOR later on to complete the ray tracing of the LOR.We notethat, for the back projection, LOR parallelization was the onlychoice because a temporary image buffer equivalent to the sizeof the reconstructed image was required for each subgroupwhich prevents increasing the number of threads.

C. Performance Evaluation

1) Reconstruction Speed: Reconstruction speed of the twoparallelization schemes in the list-mode DRAMA implemen-tation was evaluated as a function of the number of list-modeevents. The reconstructed image sizes were 76 76 84 and38 38 42 voxels, with voxel sizes of 1.5 1.5 1.5 mmand 3.0 3.0 3.0 mm , respectively. Performance for a lownumber of events was measured to investigate the minimum re-construction time.2) Tracking Ability Demonstration: The system performance

in terms of the real-time tracking ability was demonstrated bya point source tracking experiment. In the demonstration, aNa point source (1 MBq) was put on a moving stage with a

long bar so that the source was placed between the gap of thesmall OpenPET prototype (Fig. 4). The number of counts to beused in the GPU-implemented list-mode DRAMA was limitedbelow 5000 and voxel size was set to 3.0 3.0 3.0 mm forbetter frame rate performance. The reconstructed image sizewas 38 38 42 voxels.3) Frame Rate Performance and Quantitative Accuracy:

The frame rate performance and quantitative accuracy wereevaluated using simulated data for various count rates. We mea-sured a line source phantom using the small OpenPET prototypeand the simulation data were generated based on the measureddata. To simulate various count rates, the generated list-modedata were transferred to the high-speed storage, which the DTCmonitors, while controlling the transfer speed. For evaluationof the quantitative accuracy, the region of interest (ROI) wasset to cover the entire phantom and the total value in the ROIwas recorded in each image update. The simulation data wereput into the real-time imaging system and we measured theframe rate by counting the number of displayed images and theROI value while changing the count rate of the radioactivityfor three conditions: 1) limit control and compensation, 2) nolimit control, and 3) limit control and no compensation. In thesimulation, the number of list-mode data counts transferred andused in the RTR system is limited to 5000 with limit control.The preset time interval of the DTC system was 0.5 s. Thesensitivity performance of the small OpenPET prototype andDAQ system was excluded in this simulation.


Fig. 6. Examples of images captured during the tracking demonstration. Op-tical camera images are shown with reconstructed images in which slices con-taining the maximum-valued voxel were selected. Actual frame rate was 2 fpsin the demonstration.

Fig. 7. Displayed positions of the point source in the images obtained by theoptical camera and by the OpenPET during the tracking demonstration. Fromthe sine curve fitting, the delay was determined as 2.1 s.

III. RESULTS

A. Reconstruction Speed

Fig. 5 shows the reconstruction speed of the RTR system.The LOR parallelization is much faster than the event paral-lelization in the low count case. In the conventional way whereall data are stored before the reconstruction step, the differencebetween the two parallelization methods is less important. Inthe real-time system, however, the difference between the twoschemes is crucial because the reconstruction speed is requiredto be less than 1 s for updating the image at least every second.The event parallelization did not reduce the calculation time inthe low count case (less than 10 000 counts) while the LOR par-allelization reduced to less than 0.5 s. Therefore, we employedthe LOR parallelization for the real-time imaging system.

B. Tracking Ability Demonstration

In the demonstration, we displayed transaxial, coronal, andsagittal slices of the reconstructed OpenPET images and moviesof a moving point source captured by optical cameras on thePC screen simultaneously (Fig. 6). The sagittal images are ro-tated in 90 from the standard direction to be synchronizedwith the camera 2 images displayed on right side. The pointsource moved up and down with a 30 s cycle sine curve and thereal-time viewer showed the reconstructed images with a framerate of 2 frames per second (fps). Fig. 7 shows displayed pointsource position on the screen. From the sine curve fitting, an av-erage delay time of 2.1 s was obtained and the mean error was2.0 mm.

C. Frame Rate Performance and Quantitative Accuracy

Fig. 8 shows the frame rate performance and Fig. 9 shows thequantitative accuracy. These performances were measured for100 s at each count rate and then averaged. The standard devia-tion of the ROI value variation during the measurement at eachcount rate was also calculated and showed as error bars.Withoutlimit control, the frame rate dropped when the generated countrate exceeded about 15 000 cps. The frame rates changed in thesteps for the case without limit control. This was because thetimings of data transfer and reconstruction launch were cyclicto some extent. The list-mode data were transferred after ac-cumulating a certain number of counts. The DTC checked the


Fig. 8. Frame rate performance against count rate.

Fig. 9. ROI value of displayed image against count rate. The error bars indicatestandard deviation of the ROI value during measurement.

status of the RTR system before calling, but skipped the call ifthe previous process was still running. On the other hand, theframe rate was stable with limit control even if the count rateincreased significantly. In terms of quantitative accuracy, it isdesirable for the total voxel value in the ROI to be proportionalto the generated radioactivity. Fig. 9 shows that the limit controlled to a loss of accuracy but this loss was successfully compen-sated by using the transfer ratio of the DTC although the stan-dard deviation was slightly increased.

IV. DISCUSSION

We expected that the main bottlenecks in the real-timeimaging system were reconstruction speed and stability. Theproposed architecture resolved them by introducing the RTRand DTC systems. The reconstruction speed of the RTR systemwas accelerated by using the one-pass list-mode DRAMA andit was stabilized by the DTC system which controls maximumdata usage. Other expected bottlenecks were communicationspeed among the RTR, DTC, and RTV systems and among thereal-time imaging system, DAQ system, and the OpenPET.The communication speed among the former was acceleratedby implementing them on the same computer and using theRAM disk. The random access performance of the RAM disk

was 40–100 times faster than a conventional hard disk drive.Communication speed among the latter was satisfactory com-pared with the reconstruction speed. However, the evaluationof the delay showed that the total delay of the current systemwas 2.1 s. The clear breakdown of the delay was 0.5 s for dataaccumulation and 0.5 s for reconstruction and the rest could notbe analyzed yet. We expect that it includes delay in the DAQsystem, which we plan to improve in the next version of theOpenPET prototype.The final goal of this research is to develop the real-time

imaging system for the OpenPET having the size to allow scan-ning of a human body for tumor tracking during surgery. To-ward this goal, the number of slices or image width in the re-constructed image with the FOV of around 550 mm in diam-eter needs to be about three times larger than that of the currentprototype system (3.0 mm voxel for FOV of 114 mm in diam-eter) even if the voxel size is set to 5.0 mm. Because the recon-struction speed is almost directly proportional to the numberof events and the number of voxels along the LORs, it willtake several seconds for one frame and delay will further in-crease if we directly use the current system. The implicationof the delay during radiation therapy is undesirable doses out-side of the treatment area caused by misalignment due to suddenmotion, which increases risk of radiation hazard. For example,1 s delay of detecting unexpected motion during pencil beamscanning irradiation of heavy-ion cancer treatment, where res-piratory gated irradiation method by use of an external opticalmarker is normally employed [33], can cause 1 Gy additionaldose to the volume of about 30–70 cc in the worst-case scenario.Note that a periodic motion can be compensated by using mo-tion estimation algorithms such as the unscented Kalman filter[34] and the combination with additional devices such as opticalmarker on chest is greatly helpful to prevent undesired radia-tion dose by sudden motion. Also, the information of the motionphase detected by the optical marker can be useful prior infor-mation in developing new image reconstruction algorithms. Inany case, more improvement is necessary to be practical. There-fore, we are trying to increase the reconstruction speed by usingmultiple GPUs [35] and optimizing their memory access. Fur-ther, we are considering applying ROI reconstruction [36], [37]to reconstruct only the region including the targeted tumor sothat the reconstruction speed is accelerated.In this study, we showed the tracking ability of the real-time

imaging system by using a point source and a line source. Inactual situations, however, there are more complicated struc-tures in the FOV. For actual use, we need to reconstruct themand extract the tumor region from the reconstructed images.The radioactivity concentration in the tumor is also of concern.Whether enough list-mode event counts can be acquired by theOpenPET depends on the injected radioactivity, tumor size, andits uptake relative to background. The best currently availabletracer of tumors is F-labeled fludeoxyglucose ( F-FDG).Typically, the F-FDG standard uptake value (SUV) of atumor is around 5, which is 10 times greater than normal tissuein the lung [38], [39]. If we inject 370 MBq of F-FDG into apatient with body weight of 60 kg to track a 3 cm tumor with theSUV of 5 in the lung, expected dose concentration on the tumoris about 0.3 MBq. Because the sensitivity of the OpenPET


Fig. 10. Reconstructed images by the 3-D list-mode DRAMA from simulatedlist-mode data with various event counts. List-mode data were generated byforward projection and randomization. The numerical phantom consisted of acylinder background with the diameter of 60 mm and axial length of 20 mm andthe hot spot sphere with the diameter of 6 mm. Background to hot spot ratio was1:5. Transaxial and coronal slices including hot spot are shown.

is expected to be about 6.6% [5], [6] and transmittance ofthe human body with an average attenuation coefficient of0.05 cm and cross-section length of 27 cm is about 26% forannihilation photons, the count rate of the list-mode event fromthe tumor only is about 5000 cps. We note that the geometricalsensitivity of the OpenPET does not change when enlarged tothe human scale while maintaining the solid angle. Therefore,the radioactivity of the point source used in our demonstrationis rather strong but not too unrealistic. Of course there are alot of background events surrounding the tissue. If backgroundevents exist, many more counts are required to detect a hot spot.Fig. 10 shows reconstructed images of a hot spot phantom withbackground from simulated list-mode data with various counts.With a small number of list-mode data, the hot spot was notresolved and the result showed that at least 50 000 counts wererequired for tumor tracking by the current system. Even so, itis expected that we can achieve a good frame rate and noiseproperty by limiting the ROI to the area in which the tumormay move and selecting only the list-mode data contributing tothe ROI and incorporating motion information extracted fromthe reconstruction history. In actual situations, we can acquire4-D images reconstructed with slightly longer scan time priorto tumor tracking. Then we can detect the tumor, define theROI as the possible area in which the tumor may move, andacquire motion information as a priori information. For the 4-Dimage reconstruction, we will develop a fast maximum a priorireconstruction algorithm incorporating motion information andevaluate it using the XCAT phantom which is a realistic torsophantom [40]–[42].Although further development is necessary to reach our final

goal, the developed system showed the clear possibility forreal-time imaging and real-time tumor tracking during radiationtherapy.

V. CONCLUSION

We proposed the system architecture for the real-timeimaging system of the small OpenPET prototype. The pointsource tracking demonstration and computer simulationshowed that the system has a good possibility for real-timetumor tracking while maintaining quantitative accuracy.

ACKNOWLEDGMENT

The authors would like to thank Dr. A. Tsuji for his advice ontumor-tracking applications, Mr. H. Mashino for his technicalsupport on the data acquisition system, and Dr. T. Inaniwa forhis advice on heavy-ion cancer therapy.

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Real-Time Imaging System for the OpenPET

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