STIR is an open source software for Emission Tomography data manipulation and image reconstruction, covering both PET and SPECT. In this work recent additions to the STIR code base are highlighted, namely the ability to read General Electric (GE) Raw Data Format 9 (RDF9) files, incorporation of GPU operators for forward and back projection, as well as work towards quantitative imaging for both PET and SPECT

Biguri, A

Deidda, D

Ovtchinnikov, E

Schramm, G

Stearns, CW

Su, KH

Thielemans, K

Twyman, R

Wadhwa, P

English

UCL Discovery

Recent Progress in STIR 5.0Ander Biguri, Palak Wadhwa, Daniel Deidda, Member, IEEE, Georg Schramm,Kuan-Hao Su, Charles W. Stearns, Fellow, IEEE, Robert Twyman, Student Member, IEEE,Evgueni Ovtchinnikov and Kris Thielemans, Senior Member, IEEEAbstract—STIR is an open source software for Emission To-mography data manipulation and image reconstruction, coveringboth PET and SPECT. In this work recent additions to the STIRcode base are highlighted, namely the ability to read GeneralElectric (GE) Raw Data Format 9 (RDF9) files, incorporation ofGPU operators for forward and back projection, as well as worktowards quantitative imaging for both PET and SPECT.Index Terms—STIR, PET-CT, PET-MR, file formats, imagereconstruction, GPUI. INTRODUCTIONSTIR (Software for Tomographic Image Reconstruction)[1]is an open source software (OSS) for PET imaging that con-tains utilities for data manipulation and image reconstruction.The code base of STIR is in constant update and currentlySTIR has reached version 4.1. This work highlights three of themany features have been added recently into STIR: GE RDF9data format readers, GPU projectors and calibration factors forquantitative imaging.II. METHODSA. GE RDF9 Data FormatPET data from scanners from GE Healthcare are storedin the GE-specific Raw Data Format (RDF) format. SinceRDF9, this format is based on the Hierarchical Data FormatHDF5. In previous work [2] we reported on the extension ofSTIR to be able to process data from the GE Signa PET/MR.This capability has now been extended towards GE PET/CTscanners.Data are exported from the scanner in DICOM format. RDFfiles can be extracted using the pet-rd-tools OSS [3].STIR uses the HDF5 library for accessing the data fields, in-cluding scanner information, acquisition timing etc. STIR 4.1 isable to read (uncompressed) list mode data and (uncompressed)Manuscript received November 13, 2021.This work is supported by the UKEPSRC under grants EP/T026693/1, EP/M022587/1 and EP/P022200/1, by GEHealthcare and by the UK National Physical Laboratory through the NationalMeasurement System and the UK STFC. We gratefully acknowledge assistancefrom Michael Spohr (GEHC) for the phantom data.A. Biguri, R. Twyman, D. Deidda and K. Thielemans are with the Instituteof Nuclear Medicine, University College London, UKK. Thielemans is also with the Centre for Medical Image Computing,University College London, UKP. Wadhwa was with the Biomedical Imaging Science Department, Univer-sity of Leeds, UKK. H. Su and C. W. Stearns are with Molecular Imaging and ComputedTomography Engineering, GE Healthcare, USAD. Deidda is with the National Physical Laboratory, UKG. Schramm is with the Department of Imaging and Pathology, Division ofNuclear Medicine, KU Leuven, BelgiumE. Ovtchinnikov is with the Scientific Technology Facilities Council, UKsinogram data, as well as the normalisation file. In addition,singles per crystal can also be read. The latter are used forconstructing a randoms estimate, as GE scanners normally nolonger acquire delayed coincidences. At the time of writing,dead-time information (while present in the raw data file) isnot yet read or processed.STIR also provides tools to convert CT DICOM images tomu-maps suitable for attenuation and scatter correction. Workis in progress to read GE PET Image for Attenuation (PIFA)files suitable for MRAC.The modular nature of STIR allows the user to read datadirectly from the RDF9 file without extra conversion tools. AllSTIR processes including scatter estimation, image reconstruc-tion and listmode binning or reconstruction therefore directlywork on RDF9 data.B. CalibrationWith the aim of improving support to quantitative imag-ing, especially in dosimetry application, a new derived class,BinNormalisationWithCalibration was added. Thisclass allows the application of a calibration factor that maybe stored in the raw data information in a generic way. Thestrategy used to read such information however, needs tobe different according to modality and vendor. For example,for SPECT sinograms the calibration factor can be read inthe DICOM header, while for PET data from the SiemensmMR it is found in the normalisation header. In addition,since not all SPECT scanners are quantitative the option ofsetting a calibration factor was added in the reconstructionparameter file. An important step to allow the propagation ofthis information from the data to the normalisation class isthe extension of the object ExamInfo which now contains acalibration factor member and its set/get functions.The class BinNormalisationWithCalibration alsoallows to apply the branching ratio factor to PET data. Thisinformation depends on the radionuclide used in that specificacquisition, for this reason a database was created for themost used radionuclides. The database, in JSON format, isinstalled as a configuration file and contains information aboutbranching ratio, isotope name, half-life, and modality, whichare taken from standardised data on the ”laboratoire nationalHenri Becquerel” (LNHB) website, with the relative uncertain-ties.Therefore, when reading the data, STIR will automat-ically associate the isotope name with its characteris-tics. This is particularly useful for decay correction. TheBinNormalisationSPECT, applies the decay factors perdetector head position, whereas a slight modification still needsto be introduced for static scanners (including PET).C. GPU ProjectorsPET acquisition modelling is generally computed asyestimated = ε aGλ+ bwith ε the detection efficiency, a attenuation, G a projectionmatrix, b a background term incorporating randoms and scatterand λ the current image estimate. The size of the matrix G pre-vents storing it in memory, even for modern computers. STIRallows for two different strategies: computation of the matrixwith storing in memory exploiting symmetries and on-the-flyprojection. STIR already contained several methods for thesestrategies, including a matrix calculation based on Siddon’smethod (with optionally multiply rays). These implementationare executed on CPU using multithreading via OpenMP.With the advent of graphical processing units (GPUs) andtheir cheap yet powerful parallel capabilities, the ability toperform massively parallel computations is more commonplacein personal computers and this has been exploited to createPET projector operators[4]. STIR 4.1 added the capability tocall the NiftyPET[5] GPU projector for the Siemens mMR.In addition, STIR now implements the projectors availableas parallelproj[6], which uses Joseph’s method [7]. Thecode will parallelize on all GPUs visible in the node where itis running. All geometry information is computed by STIR andthis projector can therefore by used for any scanner supportedby STIR. However, some modifications are still required forthe recent block-detector geometry additions [8].III. RESULTSIn this section, we illustrate some of these new capabilitiesusing data of a torso-like phantom scanned on a GE DiscoveryMI 3-ring, and stored in RDF9 data format. The body-shapedphantom has a “liver” and “lung” inserts, as well as thecapability to add several spheres in the lung. In the currentacquisition, the lungs were filled with (cold) polystyrene balls.The activity ratios for background: liver: lesion were 1 : 2 : 4.Data were acquired in listmode for 3600s. 660M prompts wereacquired with a randoms-to-true ratio of 0.30.Fig. 1 shows example non-TOF reconstructions using Sid-don’s ray tracing on a CPU and Joseph’s ray tracing on aGPU. The images were reconstructed by unlisting listmodedata, calculating randoms from single events, correcting forgeometric and detector normalization factors and computingsingle scatter estimations. The reconstruction incorporated ran-doms, normalisation, scatter and attenuation correction, allcomputed in STIR. The images are 53 × 305 × 305 voxelsof 2.76 × 2.2 × 2.2 mm per voxel. ROI mean values relativeto the background mean values for background : liver : lung :spine were 1 : 1.998 : 0.056 : 0.008. Reconstruction has beenperformed using OSEM, but other reconstruction algorithmsare available in STIR.Example of reconstruction computational times for the samescanner and image size are reported in Table III for CPU (AMDRyzen 9 3950X) with ray tracing and for GPU using an NvidiaRTX 2080. GPU times are currently slower in comparison forOSEM because, due to limitations in the STIR design, GPUprojectors compute full sinograms, even when only a subset isrequired, thus computation is being wasted.Fig. 1. OSEM reconstruction using 17 subsets and 140 subiterations (no post-filtering has been applied) for both CPU ray tracing functionality (5 rays perbin) (top) and the GPU accelerated Joseph’s projector (bottom).TABLE IMLEM (14 ITERATIONS) AND OSEM (17 SUBSETS 238 SUBITERATIONS)TOTAL RECONSTRUCTION TIME (IN SECONDS) ON CPU (WITH OPENMP)VS GPU.OSEM MLEMCPU 8 threads 685 655CPU 16 threads 400 337CPU 28 threads 321 248GPU 531 74IV. DISCUSSION AND FUTURE WORKThis work presents some of the new features in STIR that areof general interest for PET and SPECT researchers on imagereconstruction. Other items include speeding-up the reading ofGATE ROOT files and various small improvements to makethe connection between GATE and STIR easier, see [9].STIR forms the basis of the PET support in the SynergisticImage Reconstruction Framework (SIRF) [10]. Most of the newfeatures added to STIR are automatically available in SIRF.SIRF 3.0 added wrapping to the RDF9 features as well asparallelproj.A new version of the RDF data format has been recentlyreleased by GE, which is based on the same technologyas RDF9. Among other things, RDF10 includes support forseamless inline decompression (with appropriate GE plug-ins),as well as direct random access to blocks of list data based ontime stamps. Next steps will add support for the RDF10 format.GPU usage on subset algorithms is currently wasteful andthus future work will focus on minimizing the unnecessaryoverhead. The GPU code in parallelproj also support listmodeand time of flight projections, but this functionality remains tobe exported to STIR.REFERENCES[1] K. Thielemans et al., “STIR: Software for tomographic imagereconstruction release 2,” Phys. Med. Biol., vol. 57, no. 4,pp. 867–883, Jan. 2012, Publisher: IOP Publishing, ISSN:0031-9155. DOI: 10.1088/0031-9155/57/4/867.[2] P. Wadhwa et al., “Implementation of image reconstruction forGE SIGNA PET/MR PET data in the STIR library,” in 2018IEEE (NSS/MIC), ISSN: 2577-0829, Nov. 2018, pp. 1–3. DOI:10.1109/NSSMIC.2018.8824341.[3] B. Thomas et al., UCL/pet-rd-tools: V2.0.1, Oct. 26, 2020.DOI: 10.5281/zenodo.4136503.[4] P. Després and X. Jia, “A review of GPU-based medical imagereconstruction,” Physica Medica, vol. 42, pp. 76–92, Oct. 1,2017, ISSN: 1120-1797. DOI: 10.1016/j.ejmp.2017.07.024.[5] P. J. Markiewicz et al., “NiftyPET: A high-throughput softwareplatform for high quantitative accuracy and precision PETimaging and analysis,” Neuroinform, vol. 16, no. 1, pp. 95–115,Jan. 1, 2018, ISSN: 1559-0089. DOI: 10.1007/s12021- 017-9352-y.[6] G. Schramm, Parallelproj, github.com/gschramm/parallelproj,May 4, 2021.[7] P. M. Joseph, “An improved algorithm for reprojecting raysthrough pixel images,” IEEE Transactions on Medical Imag-ing, vol. 1, no. 3, pp. 192–196, 1982. DOI: 10.1109/TMI.1982.4307572.[8] P. Khateri et al., “Implementation of cylindrical PET scannerswith block detector geometry in STIR,” EJNMMI Physics,vol. 6, no. 1, p. 15, Jul. 29, 2019, ISSN: 2197-7364. DOI:10.1186/s40658-019-0248-9.[9] R. Twyman et al., “A demonstration of STIR-GATE-connection,” in submitted, 2021 IEEE NSS/MIC, 2021.[10] E. Ovtchinnikov et al., “SIRF: Synergistic image recon-struction framework,” Comput. Phys. Commun., vol. 249,p. 107 087, 2020, ISSN: 0010-4655. DOI: https://doi.org/10.1016/j.cpc.2019.107087.

Recent Progress in STIR 5.0

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Recent Progress in STIR 5.0

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