Scholarly record
NIfTI FORMAT FOR USE IN MEDICAL IMAGE ANALYSIS
Abstract
Medical imaging plays a crucial role in the diagnosis and treatment of numerous conditions, allowing physicians to gain insights into the anatomical structures and physiological processes of the body. With the advancement of technology, the need for efficient methods of storing, processing, and analyzing medical images has become increasingly important. The NIfTI (Neuroimaging Informatics Technology Initiative) format has emerged as a standard way of representing image data, particularly in neuroimaging. This article presents the history of the NIfTI format, its applications, and case studies of its implementation. Introduced in 2004, the format was designed to facilitate data exchange between different platforms, simplify image data analysis, and provide flexibility in storing data from various imaging modalities. Its features, such as interoperability, flexibility, and openness, have contributed to its widespread use. Examples of the NIfTI format's utilization include the startup niftiVR and the FSL-MRS toolkit, which demonstrate its versatility and utility in both research and clinical practice. The conclusions highlight the importance of standardization and further development of the NIfTI format to meet the growing demands in the field of medical imaging.
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References10
https://brainder.org/2012/09/23/the-nifti-file-format/ [15.05.2024]
Larobina, M., & Murino, L. (2014). “Medical image file formats”. Journal of Digital Imaging, 27(2), 200-206.
Clarke, W. T., Bell, T. K., Emir, U. E., Mikkelsen, M., Oeltzschner, G., Shamaei, A., Soher, B. J., & Wilson, M. (rok). "NIfTI-MRS: A standard data format for magnetic resonance spectroscopy". Magnetic Resonanse in Medicine. 2022;88:2358–2370.
Kamred Udham Singh, Akshay Kumar, Teekam Singh, & Mangey Ram. "Image-based decision making for reliable and proper diagnosing in NIFTI format using watermarking." Multimedia Tools and Applications, 81 (2022): 39577–39603.
Basheer, S., Singh, K. U., Sharma, V., Bhatia, S., Pande, N., & Kumar, A. (2023). “A robust NIfTI image authentication framework to ensure reliable and safe diagnosis”. PeerJ Comput Sci, 9, e1323.
Li, X., Morgan, P. S., Ashburner, J., Smith, J., & Rorden, C. (2016). The first step for neuroimaging data analysis: DICOM to NIfTI conversion. Journal of Neuroscience Methods, 264, 47-56.
https://niftivr.pl/ [15.05.2024]
Clarke, W. T., Stagg, C. J., & Jbabdi, S. (2021). “FSL-MRS: An end-to-end spectroscopy analysis package”. Magnetic Resonance in Medicine, 85(6), 2950-2964.
Cox RW: AFNI: what a long strange trip it's been. Neuroimage 62(2): 743–7, 2012.
Jenkinson M, Beckmann CF, Behrens TE: Woolrich MW, and Smith SM. FSL. NeuroImage 62(2):782–790, 2012.
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