Recently it was shown that soft tissue can be differentiated with spectral unmixing and detection methods that utilize multi-band information obtained from a High-Resolution Ultrasonic Transmission Tomography (HUTT) system. In this study, we focus on tissue differentiation using the spectral target detection method based on Constrained Energy Minimization (CEM). We have developed a new tissue differentiation method called “CEM filter bank”. Statistical inference on the output of each CEM filter of a filter bank is used to make a decision based on the maximum statistical significance rather than the magnitude of each CEM filter output. We validate this method through 3-D inter/intra-phantom soft tissue classification where target profiles obtained from an arbitrary single slice are used for differentiation in multiple tomographic slices. Also spectral coherence between target and object profiles of an identical tissue at different slices and phantoms is evaluated by conventional cross-correlation analysis. The performance of the proposed classifier is assessed using Receiver Operating Characteristic (ROC) analysis. Finally we apply our method to classify tiny structures inside a beef kidney such as Styrofoam balls (~1mm), chicken tissue (~5mm), and vessel-duct structures.
The precise knowledge of the electromechanical properties of an ultrasonic transmit-receive system can be used to optimize the excitation waveform in transmission-mode tomographic imaging. Although a linear system hypothesis is often postulated to model the dynamic transformation of the excitation waveform delivered at the transducer of the transmitter (input) into the received waveform at the receiver (output), linearity may not be appropriate in order to account for the actual dynamic characteristics of the system. In this work, we use a nonlinear system modeling/identification method to find a mathematical model of the nonlinear dynamic transformation between the excitation and received signals. The method employs the Laguerre-Volterra Network that has been successfully applied to modeling dynamic physiological systems. The obtained nonlinear model can be used to derive an optimal excitation waveform that produces the maximum peak value of the received signal for given power of the excitation signal. Using experimental data from a commercial ultrasonic array, we show how an optimal excitation signal can be derived from the obtained nonlinear model that maximizes the peak received value. We also demonstrate that the optimally designed excitation waveform offers significant performance improvement over conventional pulse waveforms (~35 times greater peak value).
In diagnostic ultrasound, tissue differentiation is essential to detect lesions or cancerous tissues from normal tissue. The attenuation characteristics of various tissues will be different at different frequencies, since the propagating ultrasonic pulse undergoes frequency-dependent attenuation, that is characteristic of the material it traverses. These vectors of attenuation values at different frequency bands represent multi-band characteristics of individual pixels (termed “multispectral”) that can be used for tissue differentiation akin to color. In this study, we have developed tissue differentiation methods that utilize the multispectral signatures of different materials in multi-band images produced by a newly built high-resolution ultrasonic transmission tomography (HUTT) system. The HUTT system obtains 3-D multi-band sinograms through FFT analysis of the first arriving pulse (snippet). A filtered backprojection algorithm is utilized to reconstruct a stack of multi-band attenuation images that contain multispectral signatures for each pixel and represent a multispectral augmentation of the 2-D conventional tomographic slice. To differentiate each tissue type according to its characteristic multispectral signature, we adopt the methods of spectral unmixing and spectral target detection. We demonstrate the feasibility of tissue differentiation using multi-band/multispectral signatures of different tissue objects in initial data collected from soft animal tissue phantoms.
A novel system for High-resolution Ultrasonic Transmission Tomography (HUTT) is presented. The critical innovation of the HUTT system includes the use of sub-millimeter transducer elements for both transmitter and receiver arrays and multi-band analysis of the first-arrival pulse. The first-arrival pulse is detected and extracted from the received signal (i.e., snippet) at each azimuthal and angular location of a mechanical tomographic scanner in transmission mode. Each extracted snippet is processed to yield a “multispectral” vector of attenuation values at multiple frequency bands. Other acoustic attributes of the object (such as time-of-flight or wavelet decomposition coefficients) can also be obtained through snippet analysis. These vectors form a 3-D sinogram representing a multispectral augmentation of the conventional 2-D sinogram. A filtered backprojection algorithm is used to reconstruct a stack of multispectral images for each 2-D tomographic slice that may allow tissue characterization and improved image segmentation. We present illustrative examples of 2-D images formed at various frequency bands to demonstrate the high-resolution capability of the system and the potential of multispectral analysis. It is shown that spherical objects with diameter down to 0.3mm can be detected. Reconstruction of 3-D images has been achieved using multiple 2-D slices with sub-mm elevation differences.
A feasibility study was conducted to segment 1.5T fMRIs into gray matter and large veins using individual pixel intensity and temporal phase delay as two correlated parameters in gradient echo images. The time-course of each pixel in gradient echo images acquired during visual stimulation with a checkerboard flashing at 8Hz was correlated to the stimulation 'on'-'off' sequence to identify activated pixels. The temporal delay of each activated pixels was estimated by fitting its time-course to a reference sinusoidal function. The mean signal intensity difference of the activated pixels was computed by subtracting the average of the 'on' images from the average of the 'off' images. After replacing each activated pixel with 2D features (i.e., intensity and time-delay), a clustering method based on a K-means algorithm was employed to classify vein and tissue pixels. Good demarcation between large veins and activated gray matter was achieved with this method.
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