Wavelet compression on detection of brain lesions with magnetic resonance imaging.
The purpose of this report is to assess clinically acceptable compression ratios on the detection of brain lesions at magnetic resonance imaging (MRI). Four consecutive T2-weighted and the corresponding T1-weighted images obtained in 20 patients were studied for 109 anatomic sites including 50 with lesions and 59 without lesions. The images were obtained on a 1.5-T MR unit with a pixel size of 0.9 to 1.2 x 0.47 mm and a section thickness of 5 mm. The image data were compressed by wavelet-based algorithm at ratios of 20:1, 40:1, and 60:1. Three radiologists reviewed these images on an interactive workstation and rated the presence or absence of a lesion with a 50 point scale for each anatomic site. The authors also evaluated the influence of pixel size on the quality of image compression. At receiver operating characteristic (ROC) analysis, no statistically significant difference was detected at a compression ratio of 20:1. A significant difference was observed with 40:1 compressed images for one reader (P = .023), and with 60:1 for all readers (P = .001 to .012). A root mean squared error (RMSE) was higher in 0.94- x 0.94-mm pixel size images than in 0.94- x 0.47-mm pixel size images at any compression ratio, indicating compression tolerance is lower for the larger pixel size images. The RMSE, subjective image quality, and error images of 10:1 compressed 0.94- x 0.94-mm pixel size images were comparable with those of 20:1 compressed 0.94- x 0.47-mm pixel size images. Wavelet compression can be acceptable clinically at ratios as high as 20:1 for brain MR images when a pixel size at image acquisition is around 1.0 x 0.5 mm, and as high as 10:1 for those with a pixel size around 1.0 x 1.0 mm.
Terae
S
S
Department of Radiology, Hokkaido University School of Medicine, Sapporo, Japan.
Miyasaka
K
K
Kudoh
K
K
Nambu
T
T
Shimizu
T
T
Kaneko
K
K
Yoshikawa
H
H
Kishimoto
R
R
Omatsu
T
T
Fujita
N
N
D000465:Algorithms; D001927:Brain Diseases / Q000175:diagnosis* / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000556:secondary; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D012372:ROC Curve; D011873:Radiology Information Systems; D014057:Tomography, X-Ray Computed