Our group provides clinical image processing services to various institutes at NIH. We develop or adapt image
processing programs for a variety of applications. However, each program requires a human operator to select a specific
set of images and execute the program, as well as store the results appropriately for later use. To improve efficiency, we
design a parallelized clinical image processing engine (CIPE) to streamline and parallelize our service. The engine takes
DICOM images from a PACS server, sorts and distributes the images to different applications, multithreads the
execution of applications, and collects results from the applications. The engine consists of four modules: a listener, a
router, a job manager and a data manager. A template filter in XML format is defined to specify the image specification
for each application. A MySQL database is created to store and manage the incoming DICOM images and application
results. The engine achieves two important goals: reduce the amount of time and manpower required to process medical
images, and reduce the turnaround time for responding. We tested our engine on three different applications with 12
datasets and demonstrated that the engine improved the efficiency dramatically.
Dynamic Contrast Enhanced MRI (DCE-MRI) is one method for drug and tumor assessment. Selecting a consistent
arterial input function (AIF) is necessary to calculate tissue and tumor pharmacokinetic parameters in DCE-MRI. This
paper presents an automatic and robust method to select the AIF. The first stage is artery detection and segmentation,
where knowledge about artery structure and dynamic signal intensity temporal properties of DCE-MRI is employed. The
second stage is AIF model fitting and selection. A tri-exponential model is fitted for every candidate AIF using the
Levenberg-Marquardt method, and the best fitted AIF is selected. Our method has been applied in DCE-MRIs of four
different body parts: breast, brain, liver and prostate. The success rates in artery segmentation for 19 cases are
89.6%±15.9%. The pharmacokinetic parameters computed from the automatically selected AIFs are highly correlated
with those from manually determined AIFs (R2=0.946, P(T<=t)=0.09). Our imaging-based tri-exponential AIF model
demonstrated significant improvement over a previously proposed bi-exponential model.
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