A multicenter observer performance study of 3D JPEG2000 compression of thin-slice CT.
The goal of this study was to determine the compression level at which 3D JPEG2000 compression of thin-slice CTs of the chest and abdomen-pelvis becomes visually perceptible. A secondary goal was to determine if residents in training and non-physicians are substantially different from experienced radiologists in their perception of compression-related changes. This study used multidetector computed tomography 3D datasets with 0.625-1-mm thickness slices of standard chest, abdomen, or pelvis, clipped to 12 bits. The Kakadu v5.2 JPEG2000 compression algorithm was used to compress and decompress the 80 examinations creating four sets of images: lossless, 1.5 bpp (8:1), 1 bpp (12:1), and 0.75 bpp (16:1). Two randomly selected slices from each examination were shown to observers using a flicker mode paradigm in which observers rapidly toggled between two images, the original and a compressed version, with the task of deciding whether differences between them could be detected. Six staff radiologists, four residents, and six PhDs experienced in medical imaging (from three institutions) served as observers. Overall, 77.46% of observers detected differences at 8:1, 94.75% at 12:1, and 98.59% at 16:1 compression levels. Across all compression levels, the staff radiologists noted differences 64.70% of the time, the resident's detected differences 71.91% of the time, and the PhDs detected differences 69.95% of the time. Even mild compression is perceptible with current technology. The ability to detect differences does not equate to diagnostic differences, although perception of compression artifacts could affect diagnostic decision making and diagnostic workflow.
Erickson
Bradley J
BJ
Department of Radiology, Mayo Clinic, Rochester, MN, USA. bje@mayo.edu
Krupinski
Elizabeth
E
Andriole
Katherine P
KP
D000465:Algorithms; D016009:Chi-Square Distribution; D044962:Data Compression; D006801:Humans; D021621:Imaging, Three-Dimensional; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal; D013902:Radiography, Thoracic; D014057:Tomography, X-Ray Computed