We present the first parallel medical application for the analysis of dynamic positron emission tomography (PET) images together with a practical performance model. The parallel application may improve the diagnosis for a patient (e. g. in epilepsy surgery) because it enables the fast computation of parametric images on a pixed level as opposed to the traditionally used region of interest (ROI) approach which is applied to determine an average parametric value for a particular anatomic region of the brain. We derive the performance model from the application context and show its relation to abstract machine models. We demonstrate the accuracy of the model to predict the runtime of the application on a network of workstations and use it to determine an optimal value in the message frequency-size relationship

@inproceedings{KAOFITCFAP99,
	author	 = {Frank Munz and Thomas Ludwig and Sibylle Ziegler and Peter Bartenstein and Markus Schwaiger and Arndt Bode},
	title	 = {{Kinetic analysis of functional images: The case for a practical approach to performance prediction}},
	year	 = {1999},
	booktitle	 = {{High Performance Computing}},
	editor	 = {Constantine Polychronopoulos and Kazuki Fukuda and Shinji Tomita},
	publisher	 = {Springer},
	address	 = {Berlin / Heidelberg, Germany},
	series	 = {Lecture Notes in Computer Science},
	number	 = {1615},
	pages	 = {169--180},
	conference	 = {ISHPC-99},
	location	 = {Kyoto, Japan},
	doi	 = {http://dx.doi.org/10.1007/BFb0094920},
	abstract	 = {We present the first parallel medical application for the analysis of dynamic positron emission tomography (PET) images together with a practical performance model. The parallel application may improve the diagnosis for a patient (e. g. in epilepsy surgery) because it enables the fast computation of parametric images on a pixed level as opposed to the traditionally used region of interest (ROI) approach which is applied to determine an average parametric value for a particular anatomic region of the brain. We derive the performance model from the application context and show its relation to abstract machine models. We demonstrate the accuracy of the model to predict the runtime of the application on a network of workstations and use it to determine an optimal value in the message frequency-size relationship},
	url	 = {http://www.springerlink.com/content/85n414l237h28546/fulltext.pdf},
}