A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to ...A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good sparsifying transform, the TIWT is comprehensively investigated and compared with Total Variation (TV), using six under-sampling patterns through simulation. Both trajectory and random mask based under-sampling of MRI data are reconstructed to demonstrate a comprehensive coverage of tests. Notably, the TIWT in CS reconstruction performs well for all varieties of under-sampling patterns tested, even for cases where TV does not improve the mean squared error. This improved Image Quality (IQ) gives confidence in applying this transform to more CS applications which will contribute to an even greater speed-up of a CS MRI scan. High vs low resolution time of flight MRI CS re-constructions are also analyzed showing how partial Fourier acquisitions must be carefully addressed in CS to prevent loss of IQ. In the spirit of reproducible research, novel software is introduced here as FastTestCS. It is a helpful tool to quickly develop and perform tests with many CS customizations. Easy integration and testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI datasets are shown to be efficiently distributed as a scalable solution for large studies. Comparisons in reconstruction computation time are made between the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a significant time savings factor of 60×. The addition of FastTestCS is proven to be a fast, flexible, portable and reproducible simulation aid for CS research.展开更多
Baseline wander is a common noise in electrocardiogram (ECG) results.To effectively correct the baseline and to preserve more underlying components of an ECG signal,we propose a simple and novel filtering method based...Baseline wander is a common noise in electrocardiogram (ECG) results.To effectively correct the baseline and to preserve more underlying components of an ECG signal,we propose a simple and novel filtering method based on a statistical weighted moving average filter.Supposed a and b are theminimum and maximum of all sample values within a moving window,respectively.First,the whole region [a,b] is divided into M equal sub-regions without overlap.Second,three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region,denoted as [a 0,b 0 ] for simplicity.Third,for every sample point in the moving window,its weight is set to 1 if its value falls in [a 0,b 0 ];otherwise,its weight is 0.Last,all sample points with weight 1 are averaged to estimate the baseline.The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org.The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and wavelet package translation did.展开更多
文摘A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good sparsifying transform, the TIWT is comprehensively investigated and compared with Total Variation (TV), using six under-sampling patterns through simulation. Both trajectory and random mask based under-sampling of MRI data are reconstructed to demonstrate a comprehensive coverage of tests. Notably, the TIWT in CS reconstruction performs well for all varieties of under-sampling patterns tested, even for cases where TV does not improve the mean squared error. This improved Image Quality (IQ) gives confidence in applying this transform to more CS applications which will contribute to an even greater speed-up of a CS MRI scan. High vs low resolution time of flight MRI CS re-constructions are also analyzed showing how partial Fourier acquisitions must be carefully addressed in CS to prevent loss of IQ. In the spirit of reproducible research, novel software is introduced here as FastTestCS. It is a helpful tool to quickly develop and perform tests with many CS customizations. Easy integration and testing for the TIWT and TV minimization are exemplified. Simulations of 3D MRI datasets are shown to be efficiently distributed as a scalable solution for large studies. Comparisons in reconstruction computation time are made between the Wavelab toolbox and Gnu Scientific Library in FastTestCS that show a significant time savings factor of 60×. The addition of FastTestCS is proven to be a fast, flexible, portable and reproducible simulation aid for CS research.
基金supported by the Science and Technology Project of Guangdong Province (No.2009B060700124)the Science and Technology Project of Guangzhou Municipality,Guangdong Province,China (No.2010Y1-C801)
文摘Baseline wander is a common noise in electrocardiogram (ECG) results.To effectively correct the baseline and to preserve more underlying components of an ECG signal,we propose a simple and novel filtering method based on a statistical weighted moving average filter.Supposed a and b are theminimum and maximum of all sample values within a moving window,respectively.First,the whole region [a,b] is divided into M equal sub-regions without overlap.Second,three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region,denoted as [a 0,b 0 ] for simplicity.Third,for every sample point in the moving window,its weight is set to 1 if its value falls in [a 0,b 0 ];otherwise,its weight is 0.Last,all sample points with weight 1 are averaged to estimate the baseline.The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org.The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and wavelet package translation did.
