MedIGrid: a Medical Imaging application for computational Grids

M. Bertero 1 P. Bonetto1 L. Carracciuolo2 L. D'Amore4 A. Formiconi3

M. R. Guarracino2 G. Laccetti4 A. Murli2;4 G. Oliva2

1Department of Computer and Information Science, University of Genoa, Italy2Institute for High Performance Computing and Networking - CNR, Naples, Italy

3Department of Clinical Phisiopatology, University of Florence, Italy4Department of Mathematics and Applications, University of Naples Federico II, Italy

Abstract

In the last decades, diagnosing medical images hasheavily relied on digital imaging. As a consequence,huge amounts of data produced by modern medical in-struments need to be processed, organized, and visual-ized in a suitable response time. Many e�orts have beendevoted to the development of digital Picture Archivingand Communications Systems (PACS) which archiveand distribute image information across a hospital andprovide web access to avoid the expensive deploymentof a large number of such systems. On the other hand,this approach does not solve problems related to theincreasing demand of high performace computing andstorage facilities, which cannot be placed within a hos-pital.

In this work we describe MedIGrid, an applicationthat enables nuclear doctors to transparently use highperformance computers and storage systems for thePET/SPECT (Positron Emission Tomography/SinglePhoton Emission Computed Tomography) image pro-cessing, management, visualization and analysis.

MedIGrid is the result of the joint e�orts of a groupof researchers committed to the development of a dis-tributed application to test and deploy new reconstruc-tion methods in clinical environments. The outcomesof this work include a set of platform independent soft-ware tools to read medical images, control the executionof computing intensive tomographic algorithms, and ex-plore the reconstructed tomographic volumes.

In the following we describe how the collaborationamong di�erent research groups has contributed to theintegration of the application into a single framework.The results of our work will be discussed.

Keywords distributed computing, medical imaging,grid-aware application, middleware tools, image pro-cessing and visualization.

1 Introduction

In the last decades, new imaging systems, suchas computer tomography, ultrasound, digital radiog-raphy, magnetic resonance imaging, and tomographicradioisotope imaging, have revolutionized medical di-agnosing providing the clinician with new informationabout the interior of the human body that has neverbefore been available. Therefore, visualization and on-line processing of medical images have signi�cantly in-creased.

Many e�orts have been devoted to the develop-ment and deployment of the so-called electronic Pic-ture Archiving and Communications Systems (PACS)which archive and distribute any kind of informationrelated to the huge amount of data acquired by medicalinstruments, such as quantitative results or interpreta-tions of the specialist, across the hospitals [5, 6].

However, several diÆculties are associated with theactual deployment of such systems. PACS worksta-tions are expensive, they run proprietary software, andthen have limited computing and storage capabilities.Moreover, a large number of workstations is usuallyneeded to satisfy the requirements of the hospital.

The use of proprietary software may have a strongin uence on the diagnosis. Indeed, since the algorithmsinvolved in image reconstruction are kept secret by themanufacturer, the results of the same analysis may varyamong di�erent instruments: it is often not possible toknow which one is used, due to copyrights. Further-more, it is not possible to exchange data among in-struments since their format is also proprietary. Suchlimits slow down the impact of algorithms advances onsoftware products, since only the producer of the equip-ment decides which changes and optimization the nextsoftware release will address its software and it doesnot allow to compare di�erent software and data sets.

The limited computing capabilities of the control

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workstation restrict the number of usable reconstruc-tion techniques to the ones with the lowest computa-tion complexity. Last, once data are stacked up in 2Dimages, they are deleted due to limited storage capa-bilities: the entire information in the volume data setis lost forever, barring its future use.

PACS workstations cannot be easily deployed in sev-eral locations because they are fairly expensive andthey require dedicated hardware for image display.Nuclear medicine workstations are typically too com-plex, they often require considerable software setup andmaintenance and they only partially solve the outlinedproblems.

Delocalization of acquisition instruments fromprocessing power and storage facilities seems a viableway to overcome such diÆculties. Indeed, end-users ofsuch applications are not distributed systems experts;this led to hide as much as possible the diÆcultiesrelated to the use of high performance geographicallydistributed platforms and needed to manage, catalogueand process this huge amounts of data.

In the last ten years the world wide web hasbeen accepted by the scienti�c community as a toolto distribute and access services with existing orspeci�cally developed protocols. In the meantime suchservices and protocols aren't any longer capable ofproviding the coordination and sharing capabilitiesneeded by the development of applications that usedistributed resources. In this context the use oftechnologies for computational grids [11] can solveproblems related to the authentication, discovery,dynamic allocation and all the other aspect connectedto remote resources access. Such technology providesthe software infrastructure for scienti�c computingneeded for transversely sharing information, knowl-edge and competence among disciplines, institutionsand nations.

In the present work we describe MedIGrid, a dis-tributed application in which we have integrated thesoftware components needed to develop a grid collabo-rative applications useful to nuclear doctors.

MedIGrid provides the following bene�ts:

� it promotes the creation of a virtual organizationsharing resources on a computational grid;

� it enables transparent use of remote resources;

� it promotes the development of open source scien-ti�c software, in a easy and fast way;

� it makes the distribution and installation of soft-ware on remote resources an easy task;

� it supports the distant collaboration between doc-tors.

We further show how MedIGrid takes advantages ofthe grid middleware software, enabling new ways fordoctors to conduct diagnosis and obtain coordinatedresource sharing and problem solving in dynamic,multi-institutional virtual organizations [12].

This work is organized as follows: Section 2 moti-vates the MedIGrid structure explaining the challengesfaced along the implementation of such applications;Section 3 details software architecture of the systemexplaining the interaction among the various compo-nents; Section 4 describes the advantages that can beobtained from the solution of those problems; Section5 overviews the state of the art and describes relatedresearch projects and products, highlighting the di�er-ences with MedIGrid; Section 6 presents future possibledevelopment and, �nally, in Section 7 conclusions aredrawn.

2 MedIGrid Overview

In this section we describe MedIGrid and how wesolved the problem of delocalizing the acquisitionequipment from the computing and storage elements.

Let us start from a case study. Suppose that the ra-diologist has to edit an analysis to complete his medicalreport: he needs to access data sets produced by themedical instruments and decide the kind of reconstruc-tion needed; he may also want to consult a distant col-league to setup the reconstruction and share the resultswith him.

The situation is described in Figure 1: when a pa-tient is under analysis, scanned data are saved on astorage server. At the end of this phase, the doc-tor visualizes the raw data and decides, with the helpof a graphical user interface (GUI), the reconstructionmethod to use along with its related parameters suchas reconstruction volume boundaries. This informationis stored in a metadata �le. He can now authenticatehimself within MedIGrid. Authentication is based onan encrypted certi�cate stored on the machine fromwhich he is accessing the data. With a single password,certi�cate is decrypted and used to create a proxy cre-dential with limited time validity. Such credential willbe used for all subsequent operations involved in theprocess. MedIGrid application will now transfer theraw data, and the �le containing the metadata, fromthe storage server to the high performance computingsystem. This �le transfer uses SSL encryption to pro-

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Figure 1. MedIGrid Application

tect data privacy. Once the data are copied, a check-sum is performed to ensure no corruption occurred. Atthis point a job is queued to the local scheduler: itspurpose is to execute the reconstruction, transfer thedata back to the storage server, and return to the doc-tor whether the job completed successfully or, in casean error occurred, what is the state of the computation.It is now possible to access the reconstructed data andanalyze its content with the help of a GUI.

3 Software Architecture

In this section we describe in detail both the middle-ware and the application software needed in the imple-mentation of MedIGrid. In the following, the variouscomponents of the application and their interaction aredescribed.

3.1 Globus

The Globus Toolkit [9] has been choosen as middle-ware software upon which to build the application. Inparticular we used the following services:

� GASS (Globus Access to Secondary Storage Sys-tem) [3] allows the application to access datastored on any remote �lesystem specifying the po-sition and the transfer protocol with the URL syn-tax prot://hostname:port/path.

� GSI (Grid Security Infrastructure) [2] allows se-cure authentication and communication over anopen network, mutual authentication across theorganizations bounds and single sign-on authenti-cation with X.509 certi�cates.

� GRAM (Globus Resource Allocation Manager)[10] manages requests for resources for remoteapplication execution, allocates the required re-sources, and monitors the jobs during the execu-tion.

3.2 Repository

The repository is the system that manages both rawdata as acquired by the medical equipments and thedata that was already processed. It is implementedintegrating the Globus services for the authenticationand the data transfer with MedIMan.

3.3 MedIMan

The application manager is composed of a set of pro-cedures, developed at ICAR-CNR section of Naples,coordinates all grid operations: authentication, I/Oservers activations, �le transfers, data consistencychecks, reconstruction jobs submissions. When in-voked, they generate a credential that is needed for au-thentication on all the systems involved in the process,and activate the I/O servers for the secure data transferfrom the repository to the parallel computer runningthe reconstruction software. Security in the transfer isimplemented using SSL tunnelling [13]. Once the datatransfer is completed, they verify data consistency andsubmit the reconstruction script to the local queuingsystem. This script executes MedITomo and transfersthe reconstructed data back to the repository. In caseof errors in the reconstruction process an e-mail mes-sage is sent to the system administrator.

3.4 MedITomo

MedITomo, initially developed at the Dept. of Fi-siopatologia Clinica, Univerity of Florence, is the soft-ware library of computational routines that apply tothe reconstruction of SPECT images from projectiondata. The reconstruction algorithms in the packageare based on the Conjugate Gradient (CG) and onthe Ordered Subset Expetation Maximization (OSEM)method. The routines are written in FORTRAN andin ANSI C. The parallel software [1], developed at De-partment of Mathematics and Applications, Universityof Naples Federico II, in collaboration with ICAR-CNRsection of Naples, is based on the standard messagepassing interface, MPI.

In the following we give an overview of the softwarelibrary. The library is organized in packages: besidethe computational routines, each package contains theheader �le for setting the input parameters, the input

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data �le and the output data �le. The reconstructionalgorithms are supplied as the following subroutines:

2d+1tomo fan cg CG based reconstruction algo-rithm for a fan-beam geometry of the collimatorfor data collection.

2d+1 fan tv+cg CG based reconstruction algo-rithm for a fan-beam geometry of the collimatorfor data collection. The reconstruction techniquehas been regularized by using a TV regularizationfunctional.

2d+1tomo fan emosn EM based reconstruction al-gorithm for a fan-beam geometry of the collimatordata collection.

3dtomo par cg CG based reconstruction algo-rithm for a parallel geomety of the collimatorfor data collection. The underlying mathematicalmodel is the so called fully 3D.

3dtomo fan cg CG based 3D reconstruction algo-rithm for a fan beam geomety of the collimator fordata collection.

3dtomo fan cg+tv CG based 3D reconstructionalgorithm for a fan beam geomety of the collimatorfor data collection. The reconstruction algorithmhas been regularized by using the TV regulariza-tion functional.

3dtomo fan emosn+tv EMOS based 3D recon-struction algorithm for a fan beam geomety of thecollimator for data collection. The reconstructionalgorithm has been regularized by using the TVregularization functional.

The pre�x 3d or 2d+1 of each Package means thatthe underling mathematical model is the fully 3D orthe "approximation 2D+1" [4].

3.5 Graphical Plugins

The development line we chose to design our toolsmeant to address and overcome the gap that exists be-tween the medical and the research community. Theunderlying goal of our work was to produce softwarethat represents a mean of interaction between the frontend and the end users: it has to be easily, cheaply andwidely accessible to the medical community, and, atthe same time, it has to be constantly upgradable andadaptable according to the feedback and the concreteneeds of the latter, thus providing a way of continuouscontribution to research.

In the context of programming, these ideas are for-mulated in terms of portability, extendibility, man-tainance, as well as robustness, modularity, testability.A careful evaluation of these prerequisites as well asthe current available products and programming envi-ronments led us to the choice of ImageJ as our startingpoint. ImageJ [19] is a public domain Java image pro-cessing program developed by Wayne Rasband at theNational Institutes of Mental Health. It consists ofa collection of tools and algorithms to read, display,edit, analyze, process, save and print images in vari-ous di�erent formats. Its main features are platformindependence, free availability of the source code, andan open architecture that provides extendibility in amodular way via Java plugins. As of today there areover 90 plugins available for download from the ImageJWeb site and the program has already reached a cer-tain richness and versatility as well as popularity in thescienti�c community so to represent a solid foundationupon which to build our work. According to the Im-ageJ philosophy we have organized our tools in terms ofthe several plugins. Some of them, meant to facilitatethe user in his routinary use of ImageJ, represent anextension and optimization of tools already providedwith the package - the �rst three are related to �lereading, whereas the two last ones to the displaying ofthe images:

GetPetOp loads a DICOM study acquired by aPET GE. A study consist of a collection of .dcm�les, one for each transaxial slice, with all �les re-lated to the same data being located in a commondirectory and having a name that ful�lls speci�csimple rules.

File Opener loads several images simultaneouslyby multiply selecting them in a "File open" dialogbox and subsequently opening them according tothe "open" modality.

Raw File Opener is similar to File Opener: itloads several images simultaneously by multiplyselecting them from a "File open" dialog box andsubsequently opening them according to the "Im-port / Raw" modality. Hence, it asks for the pa-rameters required for the speci�c format.

OrtView o�ers an alternative way to display animage stack: given a volume and the coordinatesof a point within that volume, it shows the threeorthogonal planes passing through that very point.The user can interact with the image window inorder to change the focus location and apply someprocessing to the volume such as axial smoothingand interpolation.

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Figure 2. A snapshot of GridReconstruction

Figure 3. A snapshot of OrtView

Mip computes a sequence of lateral projection ofa volume, usually consisting of transaxial planes,according to the Maximal Intensity Projection(MIP) method: this scheme requires the volumeto be rotated in a stepwise way, with the lateralprojections being added together and then nor-malized at each step. The resulting images areinserted into a new stack that can be regularlydisplayed from within the main ImageJ kernel.

A speci�c contribute to ImageJ has been developed, byadding two new ImageJ plugins, LocalReconstructionand GridReconstruction, in order to create a softwareinterface between ImageJ and our tomographic recon-struction procedures [8].

Those procedures have been developed in Fortran,without any graphical interface: our plugins let the

Figure 4. A snapshot of Mip

user employ two di�erent reconstruction methods with-out leaving the ImageJ graphical environment. In sub-stance, they can show a stack of lateral projections,and allow the user to choose the upper and lower lim-its (within which the volume has to be reconstructed)and to select the required reconstruction parameters.Furthermore, LocalReconstruction has been designedto launch a reconstruction procedure on the local ma-chine, whereas GridReconstruction can be used to starta remote reconstruction process, possibly taking advan-tage of the distributed environment.

4 MedIGrid features

The scenario we described has all the characteristicsof a distributed application, where members of a virtualorganization share their competences and resources. Inthis application such competences have provided themeans to develop computational kernels for image re-construction, port the latters on parallel computer, de-sign graphical user interfaces and visualization tools,and integrate all these modules into a distributed com-puting environment.

The shared resources are the medical equipment,a storage server and a parallel computer: thePET/SPECT instrument is owned and managed by thePoliclinico Universitario Careggi in Florence, the stor-age equipment by ICAR-CNR in Naples, and the Be-owulf class supercomputer by the Department of Math-ematics and Applications at the University of NaplesFederico II.All this appears to the user as a single commodity re-source. Nevertheless, each component of the systemcan still be used for its original purpose. During a usersession, resources are dynamically and transparentlyacquired and released to �t to the current situation.

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The application is composed of a dynamic set of pro-cesses, that are executed on computers belonging tothe virtual organization, and uses various di�erent re-sources.

The application modularity has allowed the use ofservices available through the web and eveloped inother scienti�c applications, with similar requirements,thus promoting a community of scienti�c software de-velopers, that collaborate in the development of theapplication.

Another important point is that only Open Sourcesoftware is used, which allows to quickly debug singleapplication components, since the entire organizationhas access to the source code. Furthermore, it is easyto distribute software, and its installation is not an is-sue. Last, in such an environment distant collaborationamong doctors is natural since the application is per-vasively accessible from any location.The approach proposed also provides a way to over-came the limits in the diagnosis process outlined insection 1. Indeed, in an open environment the resultsin the image reconstruction only depend on the algo-rithm adopted, which is freely chosen by the doctor,and its implementation is provided by the virtual or-ganization. If a new algorithm is conceived, it can beimplemented and used by everyone in the community.The increased computing power incourages the usersto use more complex algorithms and the standard onestake shorter wall-clock execution times. The addedstorage capabilities make it possible to store data forfuture reuse, thus providing a more powerful tool anda deeper insight, as discussed in section 6.

5 Related work

The MedIGrid application represents a new tool formedical diagnosis providing new insight into what canbe done in a distributed collaborative environment.Overcoming the limits of the diagnosis process revealsnew frontiers as well as new challenges and problems,the solution of which could lead an even more pow-erful tool: considering that the realization of virtualorganizations has up to day become possible with defacto standard software tools what will happen whenmore and more institutions decide to take part to sucha community? What are the added features that awider community can provide? What are the problemsrelated to the management of a complex environmentin which a variety of di�erent human, hardware andsoftware resources meet?

Those questions have been only partialy answeredby completed and ongoing projects, and some of those

Figure 5. Waldo application

aspects challenge research communities not strictly re-lated to medical problems. Indeed, many national andinternational projects are facing the fascinating prob-lems related to the use, allocation, scheduling and map-ping issues in those dynamic, multi-institutional, vir-tual organizations. In the next session an overview ofsome of those projects is given.

WALDO (Wide Area Large Data Objects) [11, 16]is an application to store medical data. As shown inFigure 5, it is based on a distributed database capableto manage large quantities of data, which are visualizedon a remote workstation with a browser. Mechanismssuch as network cache are used to access data fromspeci�c applications.

Since data are asynchronously produced by di�er-ent sources, the WALDO architecture provides mech-anisms to exibly handle data storage, security andaccess integrity. Moreover, a data catalog is also pro-vided.

The basic components are data collection tools, im-age processing and reconstruction tools for di�erentmedical analysis, software to manage issues related tosecurity and protection and application oriented graph-ical user interfaces to access data. Such a GUI prividesthe means to pervasively access the data.

Kaiser [15] is based on this early project and its aimis to use on-line instruments as data source. In particu-lar, cardio-hagiographical data are collected by a scan-ner, sent to storage servers, and �nally redistributedby WALDO to be accessed from other hospitals. Thedi�erence with MedIGrid is that image processing andreconstruction tools are not integrated in the environ-ment, in order to use them in the di�erent hospitalswhere data are analysed.

Within NPACI alliance there is Telescience [20], a

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project devoted to the investigation of tomographic ap-plications. As stated on the website, the project ismerging technologies for remote control, grid comput-ing, and federated digital libraries of multiscale, cell-structure data. The objective is to provide a com-plete teleinstrumentation solution that will connectscientists desktops to remote instruments, distributeddatabases, high-performance analysis environments,and experiment planning. Products of the project areGlobus-enabled tomography (GTOMO) codes for sim-ple back projection and iterative restoration, whichis being used by the NASA Information Power Gridand further developed by Argonne National Labora-tory (synchrotron x-ray tomography), and the Tele-science Portal, to remotely access and control instru-ments, manage data, and control batch jobs with asingle login and password. Key features of the por-tal include personalized user information, collaborationtools such as chat and shared white boards, automaticstorage of data with the Storage Resource Broker, andjob tracking tools. Our project di�ers with Telesciencein that it is designed in order to let doctors share theprogresses made by new reconstruction and processingtechniques.

6 Future work

The research carried out has highlithed many waysto complete or to improve the implemented application(Figure 6). The �rst consists in providing the appli-cation with a tool for its pervasive use. This can beachieved with a web portal. A user equipped with aninternet access and a web browser can use the portalto request image reconstructions, monitor the state ofsubmitted jobs, visualize reconstructed images to com-plete his medical reports. With the integration of webtechnologies, visualization software and grid softwaretools, it is possible to implement a collaborative diag-nostic tool which enables a pervasive access to MedI-Grid. The implementation represents a challenge fromthe point of view of security, authentication, privacyand web-grid interface [18].

A catalogue of both raw and reconstructed data pro-duced by a variety of acquisition devices, as well asmetadata regarding the patient, medical reports, diag-nosis produced over the years, represent a rich startingset of data on which to carry out statistical studies, andcompare old and new reconstruction techniques. Themanagement of such data is a challenging task, in par-ticular from the point of view of eÆcient distributionand storage.

To increase application scalability dynamic resourcediscovery and allocation is needed [7], which allows to

Figure 6. Future implementation

reserve resources on the grid in the most economic way.

Navigation and visualization tools [14] need to begrid-enabled both with the use of techniques like datastaging, caching and asyncronous striping, that helphiding variable latency and transfer speeds, and withalgorithms capable to adapt to the dinamic behaviourof the computing environment. Such methods improvedistant collaboration and make data steering possible.

7 Conclusions

MedIGrid, a grid enabled application for medical di-agnosis has been described. It has been developed withthe aim of facilitating and optimizing reconstruction,display and analysis of medical images. Feedback fromthe medical community has proven these tools to beuseful in a clinical environment. A further point ofinterest of the tools we have developed is their freelyavalaibility.

The bene�ts of using a grid computing paradigmhighlights the advantages of the application of thisparadigm to medical application showed.

From these points we conclude that future workmust be oriented towards making scienti�c materialsas widely accessibly to the community as possible: be-side developing code in an Open Source environment,this also means optimizing the performance and thesimplicity of use. In this way it will represent a pos-sible key contact point between the basic science andthe medical communities.

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8 Acknowledgments

This work was partially founded by Italian NationalResearch Council through Agenzia 2000 grant Grid

Computing and Applications.

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