4D deep learning for real-time volumetric optical coherence elastography.

PURPOSE: Elasticity of soft tissue provides valuable information to physicians during treatment and diagnosis of diseases. A number of approaches have been proposed to estimate tissue stiffness from the shear wave velocity. Optical coherence elastography offers a particularly high spatial and temporal resolution. However, current approaches typically acquire data at different positions sequentially, making it slow and less practical for clinical application. METHODS: We propose a new approach for elastography estimations using a fast imaging device to acquire small image volumes at rates of 831 Hz. The resulting sequence of phase image volumes is fed into a 4D convolutional neural network which handles both spatial and temporal data processing. We evaluate the approach on a set of image data acquired for gelatin phantoms of known elasticity. RESULTS: Using the neural network, the gelatin concentration of unseen samples was predicted with a mean error of 0.65 ± 0.81 percentage points from 90 subsequent volumes of phase data only. We achieve a data acquisition and data processing time of under 12 ms and 22 ms, respectively. CONCLUSIONS: We demonstrate direct volumetric optical coherence elastography from phase image data. The approach does not rely on particular stimulation or sampling sequences and allows the estimation of elastic tissue properties of up to 40 Hz.

Needle tip force estimation by deep learning from raw spectral OCT data.

PURPOSE: Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be used to image tissue. Here, we study how to calibrate OCT to sense forces, e.g., during robotic needle placement. METHODS: We investigate whether using raw spectral OCT data without a typical image reconstruction can improve a deep learning-based calibration between optical signal and forces. For this purpose, we consider three different needles with a new, more robust design which are calibrated using convolutional neural networks (CNNs). We compare training the CNNs with the raw OCT signal and the reconstructed depth profiles. RESULTS: We find that using raw data as an input for the largest CNN model outperforms the use of reconstructed data with a mean absolute error of 5.81 mN compared to 8.04 mN. CONCLUSIONS: We find that deep learning with raw spectral OCT data can improve learning for the task of force estimation. Our needle design and calibration approach constitute a very accurate fiber-optical sensor for measuring forces at the needle tip.