Harmonic chirp imaging method for ultrasound contrast agent

Coded excitation is currently used in medical ultrasound to increase signal-to-noise ratio (SNR) and penetration depth. We propose a chirp excitation method\ud for contrast agents using the second harmonic component of the response. This method is based on a compression filter that selectively compresses and extracts the second harmonic component from the received echo signal. Simulations have shown a clear increase in response for chirp excitation\ud over pulse excitation with the same peak amplitude. This was confirmed by two-dimensional (2-D) optical observations of bubble response with a fast framing camera. To evaluate the harmonic compression method, we applied it to\ud simulated bubble echoes, to measured propagation harmonics, and to B-mode scans of a flow phantom and compared it to regular pulse excitation imaging. An increase of approximately 10 dB in SNR was found for chirp excitation. The\ud compression method was found to perform well in terms of resolution. Axial resolution was in all cases within 10% of the axial resolution from pulse excitation. Range side-lobe levels were 30 dB below the main lobe for the simulated bubble echoes and measured propagation harmonics. However,\ud side-lobes were visible in the B-mode contrast images

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue