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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jacr.2005.10.001 Byline: Stephen R. Baker, Maureen Barry, Hamaira Chaudhry, Basil Hubbi Keywords: Women; residency; leadership; barriers to entry and advancement Abstract: In consideration of the fact that women constitute only 25% of radiology residents, even though they constitute 45% of medical students, this study was conducted to determine if the trend of women choosing radiology as a career differs from that for other medical specialties and if there are differences on the basis of the gender of program directors or geographic location. The authors also wished to determine if constraints exist that prevent women from advancing into positions of leadership in radiology. Author Affiliation: University of Medicine and Dentistry of New Jersey, Department of Radiology, Newark, NJ

In this study, we performed dual-modality optical coherence tomography (OCT) characterization (volumetric OCT imaging and quantitative optical coherence elastography) on human breast tissue specimens. We trained and validated a U-Net for automatic image segmentation. Our results demonstrated that U-Net segmentation can be used to assist clinical diagnosis for breast cancer, and is a powerful enabling tool to advance our understanding of the characteristics for breast tissue. Based on the results obtained from U-Net segmentation of 3D OCT images, we demonstrated significant morphological heterogeneity in small breast specimens acquired through diagnostic biopsy. We also found that breast specimens affected by different pathologies had different structural characteristics. By correlating U-Net analysis of structural OCT images with mechanical measurement provided by quantitative optical coherence elastography, we showed that the change of mechanical properties in breast tissue is not directly due to the change in the amount of dense or porous tissue.

We investigate a spatial coordinate correction (SCC) method to track motion with high accuracy for optical coherence elastography (OCE). Through SCC, we refer the displacement field tracked by optical coherence tomography (OCT) in the loaded sample to individual material points defined in a fixed coordinate system. SCC allows OCE to perform spatially and temporally unambiguous tracking of displacement and enables accurate mechanical characterization of biological tissue for cancer diagnosis and tumor margin assessment. In this study, we validated the effectiveness of motion tracking based on SCC using experimental OCE data obtained from ex vivo human breast tissues.

We developed a miniature quantitative optical coherence elastography (qOCE) instrument with an integrated Fabry-Perot force sensor, for in situ elasticity measurement of biological tissue. The technique has great potential for biomechanics modeling and clinical diagnosis. We designed the fiber-optic qOCE probe that was used to exert a compressive force to deform tissue at the tip of the probe. Using the space-division multiplexed optical coherence tomography (OCT) signal detected by a spectral domain OCT engine, we were able to quantify the probe deformation that was proportional to the force applied, and to quantify the tissue deformation corresponding to the external stimulus. Simultaneous measurement of force and displacement allowed us to extract Young’s modulus of biological tissue. We experimentally calibrated our qOCE instrument, and validated its effectiveness on tissue mimicking phantoms and biological tissues.

Speckle decorrelation analysis of optical coherence tomography (OCT) signal has been used in motion tracking. In our previous study, we demonstrated that cross-correlation coefficient (XCC) between Ascans had an explicit functional dependency on the magnitude of lateral displacement (δx). In this study, we evaluated the sensitivity of speckle motion tracking using the derivative of function XCC(δx) on variable δx. We demonstrated the magnitude of the derivative can be maximized. In other words, the sensitivity of OCT speckle tracking can be optimized by using signals with appropriate amount of decorrelation for XCC calculation. Based on this finding, we developed an adaptive speckle decorrelation analysis strategy to achieve motion tracking with optimized sensitivity. Briefly, we used subsequently acquired Ascans and Ascans obtained with larger time intervals to obtain multiple values of XCC and chose the XCC value that maximized motion tracking sensitivity for displacement calculation. Instantaneous motion speed can be calculated by dividing the obtained displacement with time interval between Ascans involved in XCC calculation. We implemented the above-described algorithm in real-time using graphic processing unit (GPU) and demonstrated its effectiveness in reconstructing distortion-free OCT images using data obtained from a manually scanned OCT probe. The adaptive speckle tracking method was validated in manually scanned OCT imaging, on phantom as well as in vivo skin tissue.