Method to improve the localization accuracy and contrast recovery of lesions in separately acquired X-ray and diffuse optical tomographic breast imaging

Abstract

Near-infrared diffuse optical tomography (DOT) has the potential to improve the accuracy of breast cancer diagnosis and aid in monitoring the response of breast tumors to chemotherapy by providing hemoglobin-based functional imaging. The use of structural lesion priors derived from clinical breast imaging methods, such as mammography, can improve recovery of tumor optical contrast; however, accurate lesion prior placement is essential to take full advantage of prior-guided DOT image reconstruction. Simultaneous optical and anatomical imaging may not always be possible or desired, which can make the accurate registration of the lesion prior challenging. In this paper, we present a three-step lesion prior scanning approach to facilitate improved accuracy in lesion localization based on the optical contrast quantified by the total hemoglobin concentration (HbT) for non-simultaneous multimodal DOT and digital breast tomosynthesis (DBT) imaging. In three challenging breast cancer patient cases, where no clear optical contrast was present initially, we have demonstrated consistent improvement in the recovered HbT lesion contrast by utilizing this method.

Functional hemodynamic imaging markers for the prediction of pathological outcomes in breast cancer patients treated with neoadjuvant chemotherapy

“…Compared to standalone breast DOT that was often used previously for the same purpose, this multimodal imaging approach allows us to utilize clinical 3D DBT, contemporaneously acquired with the same patient positioning protocol as in CC-view X-ray mammography, as structural priors to enhance the contrast recovery of breast lesions. By implementing a semi-automated three-step lesion scanning image registration method developed by us previously, 54 we ensured accurate tumor localization and consistent DOT images across time points for longitudinal data analysis. Moreover, our imaging protocol (shown in Fig.…”

“…53 To account for shifts in breast position and non-elastic deformation between the two separate compressions for optical and X-ray data acquisition, the 3D location of the lesion used for the three-composition prior-guided reconstruction was further adjusted using the three-step lesion scanning method previously described. 54 In short, potential lesion locations were placed in grid locations in the mesh, with the scanning range shrinking with each step, to pinpoint the most plausible location by maximizing the recovery of lesion contrast.…”

The Solus Instrument: Optical Characterization of the First Hand-Held Probe for Multimodal Imaging (Ultrasound and Multi-Wavelength Time-Resolved Diffuse Optical Tomography)

“…In fact, a well-known shortcoming of diffuse optical imaging is its limited spatial resolution, due to the highly diffusive nature of light in biological tissues in the red and infrared spectral range. The exploitation of a priori anatomical constraints obtained through B-mode, X-ray or MRI images already led to a distinctly more accurate quantification of the optical properties, and in turn of tissue composition, with respect to DOT data alone, thus improving the diagnostic potential of the technique [18][19][20][21][22][23][24][25][26][27].…”

Micro-CT guided deep neural network for 3D reconstructions in widefield diffuse optical tomography

“…However, 3D reconstruction of OPs from widefield DOT data suffers from poor spatial resolution. 3,4 Moreover, successful 3D reconstruction requires solving the infamous inverse problem, which involves a time-consuming, manual, expertcentric approach to parameter optimization. 5 Therefore, there has been a growing interest in employing Deep Neural Networks (DNNs) to solve the inverse problem for higher time efficiency.…”

“…6 Furthermore, to overcome the low spatial resolution associated with widefield DOT, the use of complementary imaging modalities, such as ultrasound and MRI, has always been a popular choice (mainly via incorporating a Laplacian regularization kernel in the inverse problem formulation). 4,7 Hence, to have the best of both worlds, efforts are being made to design end-to-end DNNs for DOT reconstruction encompassing features from a complementary modality in the network training process. Notably, a network, coined as Z-Net, has been recently proposed that extracted spatial features from DCE MRI to guide the reconstructions in 2D spectral DOT.…”