Dynamic contrast breast MRI is becoming an important adjunct in screening women at high risk for breast cancer,
determining extent of disease (staging) and monitoring response to therapy. In dynamic contrast breast MRI,
regions of rapid contrast uptake indicate increases in vascularity which can be associated with abnormal tissue,
sometimes significant for malignant disease. To show these areas of enhancement, subtractions between the pre and
post contrast images and maximum intensity projections (MIPs) are computed. Many projections are obscured by
normally enhancing anatomy (heart, aorta, pulmonary vessels). Identification of these structures allows their
removal from MIPs, which improves image quality, diagnostic utility and the conspicuity of the enhancing regions.
In this study, a fully automated classifier is presented which uses the spatial location of enhancing regions to
separate those that occur inside the chest wall from those occurring in the tissue of interest (breast, axilla, chest
wall). The classifier was trained on 21 studies each acquired at a different institution (699 clusters of pixels), and
tested on 7 studies (231 clusters of pixels) that were not part of the training set. Multiple cost functions for training
were examined. The measurements for the peak performance of the classifier were sensitivity 97.0%, specificity
99.4%, PPV 99.9%, NPV 78.8%.
Breast MR is being employed to detect, diagnose, and stage breast cancer. With a breast MR study, areas that exhibit
rapid uptake of contrast followed by washout behavior have been shown to be indicative of malignant tissue. The most
common way to display this temporal information is with a time versus percent enhancement curve that plots the
enhancement of the tissue for each series relative to a baseline or pre contrast series. The generation of time curves is
commonly done using manual methods, but could easily be automated by a computer to reduce user variability. The
information obtained by the time curve can then be used for computer assisted analysis of suspicious areas. We
compare two methods for the automated detection of such time curves for 42 malignant lesions. The first method is a
previously published technique which finds the highest enhancing 3x3 area of a lesion to generate a curve. The second
method is a new hierarchical search that examines end time point behavior in combination with enhancement to find an
optimal curve location. The two methods for curve generation are examined in their ability to produce a washout type
curve, which has a greater likelihood of being malignant than curves that continue to enhance. The time curves found
using highest percent enhancement method showed washout in 52 percent (22/42) of lesions. Using the hierarchical
search algorithm, 90 percent (38/42) of lesions showed washout type curves
Wavelet compression has been shown to give exceptional subjective image quality with high compression ratios for medical imaging. In an effort to effect real-time wavelet compression of digitized ultrasound video for low bandwidth networks, Fourier domain subsampling may demonstrate reduced computational overhead compared to convolution methods. The anticipated benefit is dependent on: the size of the mother wavelet used, data dimensions along each axis, and available Fourier processing power. The process of wavelet compression is comptutationally expensive, requiring multiple convolutions with similar mother wavelets at different resolutions. In contrast, Fourier domain subsampling states that if an image is downsampled by a factor of two,the spatial frequencies of the image all increase by a factor of two. This allows the use of only one forward FFT on the data at run time, and only one inverse FFT at the time of each filter application, significantly reducing the computational load. A wavelet transform in the third dimensions takes advantage of the high correlation between adjacent frames in ultrasound video. Our presentation will demonstrate a comparison of benchmarks for both wavelet transform methods and analyze the advantage with respect to mother wavelet size.
KEYWORDS: Video, Video compression, Wavelets, 3D video compression, Ultrasonography, Image compression, Transformers, 3D video streaming, Diagnostics, Discrete wavelet transforms
We present a wavelet-based video codec based on a 3D wavelet transformer, a uniform quantizer/dequantizer and an arithmetic encoder/decoder. The wavelet transformer uses biorthogonal Antonini wavelets in the two spatial dimensions and Haar wavelets in the time dimensions. Multiple levels of decomposition are supported. The codec has been applied to pre-scan-converted ultrasound image data and does not produce the type of blocking artifacts that occur in MPEG- compressed video. The PSNR at a given compression rate increases with the number of levels of decomposition: for our data at 50:1 compression, the PSNR increases from 18.4 dB at one level to 24.0 dB at four levels of decomposition. Our 3D wavelet-based video codec provides the high compression rates required to transmit diagnostic ultrasound video over existing low bandwidth links without introducing the blocking artifacts which have been demonstrated to diminish clinical utility.
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