title: A data compression method for image time series.
abstract: Image time series for functional studies frequently vary relatively little from one time point to the next. We describe here a method of lossless file compression that calculates the change in signal intensity as a char (one byte), as opposed to a short or long integer (usually two or four bytes), thus resulting in a compressed file size just over half the size of the original (a 2:1 compression ratio). The calculations and format are exceedingly simple; the compression time is comparable to the time required for a file copy, and is thus transparent to most users. The approach works equally well on any integer-based scanning modality, such as PET, CT, or DSA. Our purpose in this communication is to advocate it as a potential standard, to promote free interchange of scan data.
authors.affiliation: UCLA Brain Mapping Division, Ahmanson-Lovelace Brain Mapping Center, Los Angeles, California. 90095-7085, USA. mscohen@ucla.edu
authors.forename: M S
authors.initials: MS
authors.lastname: Cohen
mesh_terms: D000465:Algorithms; D016477:Artifacts; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012985:Software Design