Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging

Abstract

Abstract. Photoacoustic computed tomography (PACT) and ultrasound computed tomography (USCT) are emerging modalities for breast imaging. As in all emerging imaging technologies, computer-simulation studies play a critically important role in developing and optimizing the designs of hardware and image reconstruction methods for PACT and USCT. Using computer-simulations, the parameters of an imaging system can be systematically and comprehensively explored in a way that is generally not possible through experimentation. When conducting such studies, numerical phantoms are employed to represent the physical properties of the patient or object to-be-imaged that influence the measured image data. It is highly desirable to utilize numerical phantoms that are realistic, especially when task-based measures of image quality are to be utilized to guide system design. However, most reported computer-simulation studies of PACT and USCT breast imaging employ simple numerical phantoms that oversimplify the complex anatomical structures in the human female breast. We develop and implement a methodology for generating anatomically realistic numerical breast phantoms from clinical contrast-enhanced magnetic resonance imaging data. The phantoms will depict vascular structures and the volumetric distribution of different tissue types in the breast. By assigning optical and acoustic parameters to different tissue structures, both optical and acoustic breast phantoms will be established for use in PACT and USCT studies.

Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography

“…(37)], and contrast to noise ratio [Eq. (38)] for varying range of regularization parameter for both the standard and proposed Tikhonov method corresponding to PAT phantom [ Fig. 7(g) and 7(h)].…”

“…(c)] was used for evaluating the performance of the proposed method in reconstructing sharp edges. To further evaluate the ability of the proposed method, a realistic numerical breast phantom created from contrast‐enhanced magnetic resonance (MR) imaging data was considered . This phantom facilitates more realistic simulations, as they closely represent patient anatomical structures.…”

“…Contrast to Noise Ratio (CNR) is an another performance metric, which is defined as 16 CNR ¼ l roi À l back ðr 2 roi a roi þ r 2 back a back Þ 1=2 (38) where l denotes the mean and r represents the standard deviation. The "roi" and "back" represent the region of interest and the background correspondingly in the reconstructed image.…”

“…To further evaluate the ability of the proposed method, a realistic numerical breast phantom created from contrast-enhanced magnetic resonance (MR) imaging data was considered. 37,38 This phantom facilitates more realistic simulations, as they closely represent patient anatomical structures. A YZ slice of the right breast of a healthy volunteer, having dimension of 374 9 460 9 712 was shown in Fig.…”

“…Plot of residual norm(35), error norm (kx À x recon k), Pearson correlation (PC)(37), and contrast to noise ratio (CNR)(38) for varying range of regularization parameter for both the standard and proposed Tikhonov method for PAT phantom shown in Figs. 7(g) and 7(h).…”