ANALYSIS OF SOFTWARE SOLUTIONS FOR THE MANIPULATION AND PROCESSING OF IMAGING NEUROLOGICAL NUCLEAR MEDICINE STUDIES
Keywords:
nuclear medicine, neurology, reconstruction, filters, diagnostic methodsAbstract
Nowadays nuclear medicine studies take an important place in diagnosticevaluation ofpatients with
neurological desieses. Unlikeradiological methods, that give us insight in morfology, nuclear medicine studiescan be
used for functionalexamination of nervous system. Usingnuclear medicine methods we can test efficiency, transport,
binding to receptors or efectivness of new experimentalneurologicalmedications.
Methods: Program IBM SPSS Statistics 22.0v for MacOS was used for statisticanalyses. Vriables were
dividedintosixgroups. Firstgroupcontains of DICOM readers, general imaging, DICOM applications, nuclear
medicine software, PACS work stations and radiation protection programs. According to data, parametric and
nonparametrictests, as methods of descriptivestatistic, were used for statisticanalysis.
Results: The studyincluded 88 programs: 33 DICM readers (31,4%), 5 general imaging (4,8%), 9 DICOM
applications (8,6%), 30 nuclear medicine studies (28,6%), 9 PACS work stations (8,6%) and 2 radiation protection
pograms (1,9%). From all analysed programs 36 (34,3%) were for Windows, 1 (1%) for Linux, 6 (5,7%) for
MacOS, 5 (4,8) were based on multiplatform intrgration for Windows and Linux, and 32 (30,5%) for Windows,
Linux and Mac OS integrated multiplatforms. From all software solutions that were analysed, PACS integration had
29 (27,6%) programs and 53 (50,5%) did not have PACS integration.DICOM integration had 77 (73,3%) programs
and 5 (4,8%) programs did not have DICOM integration. HL7 integration had 37 (35,2%) programs and 45 (42,9%)
did not have HL7 integration. 52 (49.5%) programs did not have a special purpose, while 30 (28.6%) had a special
purpose. Reconstructions performed during image reconstruction are filtered back projection ( FBP ) 1 (16.7%),
OSEM3D 1 (16.7%) and Flash3D 1 (16.7%). The filters that were used during the reconstruction were the following
and Hanning filter 1 (16.7%), Gauss filter 3 (50.0%), unknown was 1 (16.7%), Chang and Butterworth 1 (16.7%).
76 (72.4%) programs had formats for selecting different export from the system, while 6 (5.7%) did not have this
possibility . 42 (40%) programs had film export formats, while 35 (33.3%) programs did not, 4 (3.8%) programs
were unknown. 34 (32.4%) programs had support for multiple monitors , while 45 (42.9%) programs did not, while 3 (2.9%) programs did not have support. 74 (70.5%) programs had a cine function, while 8 (7.6%) did not have a
cine function. 69 (65.7%) had a reference line, while 13 (12.4%) did not have a reference line. Of the total number
of programs, 81 (100%) are free, 53 (50.5%) programs are free, while 25 (23.8%) programs are shareware, and the
status of 1 (1.0%) program is unknown.
Conclusion : Iterative reconstruction is one of the most frequently used reconstructions in nuclear medicine for
neurological diagnostic. It is combined with Gaussian, Hanning or Butterworth algorithms for study processing. In
the future , nuclear medicine studies will probably be integrated into the Cloud environment , where only the
hardware components of processing workstations can be a limiting factor in neurological nuclear medicine
reconstructions.
References
Akdemir, Ü. Ö., & Atay Kapucu, L. Ö. (2016). Nuclear Medicine Imaging in Pediatric Neurology. Molecular Imaging And Radionuclide Therapy, 25(1), 1-10. doi:10.4274/mirt.49389
Barbaras, L., Tal, I., Palmer, M. R., Parker, J. A., & Kolodny, G. M. (2007). Shareware program for nuclear medicine and PET/CT PACS display and processing. AJR Am J Roentgenol, 188(6), W565-568. doi:10.2214/ajr.06.1058
Barra, F. R., Barra, R. R., & Sobrinho, A. B. (2010). Freeware medical image viewers: can we rely only on them? Radiologia Brasileira, 43(5), 313-318.
Bruyant, P. P. (2002). Analytic and iterative reconstruction algorithms in SPECT. Journal Of Nuclear Medicine: Official Publication, Society Of Nuclear Medicine, 43(10), 1343-1358.
Costa, D. C., Pilowsky, L. S., & Ell, P. J. (1999). Nuclear medicine in neurology and psychiatry. Lancet (London, England), 354(9184), 1107-1111.
Escott, E. J., & Rubinstein, D. (2003). Free DICOM image viewing and processing software for your desktop computer: what's available and what it can do for you. RadioGraphics, 23(5), 1341-1357. doi:10.1148/rg.235035047
Fatima, N., & Zaman, M. U. (2017). Nuclear Medicine Imaging in Emergency Room and Priority Clinical Decision Making. J Coll Physicians Surg Pak, 27(4), 197-199. doi:2588
Frey, E. C., Humm, J. L., & Ljungberg, M. Accuracy and Precision of Radioactivity Quantification in Nuclear Medicine Images. Semin Nucl Med, 42(3), 208-218. doi:10.1053/j.semnuclmed.2011.11.003
Granov, A. M., Tyutin, L. A., & Stanzhevskii, A. A. (2012). The Application of Nuclear Medicine Imaging in Neurology, Neurosurgery and Psychiatry. Annals of the Russian academy of medical sciences, 67(9), 13-18. doi:10.15690/vramn.v67i9.401
Grosser, O. S., Kupitz, D., Ruf, J., Czuczwara, D., Steffen, I. G., Furth, C., . . . Amthauer, H. (2015). Optimization of SPECT-CT Hybrid Imaging Using Iterative Image Reconstruction for Low-Dose CT: A Phantom Study. PLoS One, 10(9), e0138658. doi:10.1371/journal.pone.0138658
Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., . . . Turkbey, B. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications, 11(1), 4080. doi:10.1038/s41467-020-17971-2
Langer, S., Charboneau, N., & French, T. (2010). DCMTB: a virtual appliance DICOM toolbox. J Digit Imaging, 23(6), 681-688. doi:10.1007/s10278-009-9230-8
Larobina, M., & Murino, L. (2014). Medical Image File Formats. J Digit Imaging, 27(2), 200-206. doi:10.1007/s10278-013-9657-9
Lo Presti, G., Carbone, M., Ciriaci, D., Aramini, D., Ferrari, M., & Ferrari, V. (2015). Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room. J Digit Imaging, 28(5), 518-527. doi:10.1007/s10278-015-9786-4
Luo, Y., Jiang, L., & Zhuang, T. G. (2009). A grid-based model for integration of distributed medical databases. J Digit Imaging, 22(6), 579-588. doi:10.1007/s10278-007-9077-9
Park, J. E. (2022). Artificial Intelligence in Neuro-Oncologic Imaging: A Brief Review for Clinical Use Cases and Future Perspectives. Brain Tumor Res Treat, 10(2), 69-75. doi:10.14791/btrt.2021.0031
Seifert, R., Weber, M., Kocakavuk, E., Rischpler, C., & Kersting, D. (2021). Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives. Semin Nucl Med, 51(2), 170-177. doi:https://doi.org/10.1053/j.semnuclmed.2020.08.003
Zaharchuk, G., & Davidzon, G. (2021). Artificial Intelligence for Optimization and Interpretation of PET/CT and PET/MR Images. Semin Nucl Med, 51(2), 134-142. doi:https://doi.org/10.1053/j.semnuclmed.2020.10.001
