Ultrasound Imaging with Flexible Array Transducer for Pancreatic Cancer Radiation Therapy

Abstract

Pancreatic cancer with less than 10% 3-year survival rate is one of deadliest cancer types and greatly benefits from enhanced radiotherapy. Organ motion monitoring helps spare the normal tissue from high radiation and, in turn, enables the dose escalation to the target that has been shown to improve the effectiveness of RT by doubling and tripling post-RT survival rate. The flexible array transducer is a novel and promising solution to address the limitation of conventional US probes. We proposed a novel shape estimation for flexible array transducer using two sequential algorithms: (i) an optical tracking-based system that uses the optical markers coordinates attached to the probe at specific positions to estimate the array shape in real-time and (ii) a fully automatic shape optimization algorithm that automatically searches for the optimal array shape that results in the highest quality reconstructed image. We conducted phantom and in vivo experiments to evaluate the estimated array shapes and the accuracy of reconstructed US images. The proposed method reconstructed US images with low full-width-at-half-maximum (FWHM) of the point scatters, correct aspect ratio of the cyst, and high-matching score with the ground truth. Our results demonstrated that the proposed methods reconstruct high-quality ultrasound images with significantly less defocusing and distortion compared with those without any correction. Specifically, the automatic optimization method reduced the array shape estimation error to less than half-wavelength of transmitted wave, resulting in a high-quality reconstructed image.

Keywords: abdominal motion monitoring; flexible array transducer; gastrointestinal malignancies; pancreatic cancer; ultrasound imaging.

Figure 1

Coordinate system of the flexible array transducer.

Figure 2

Scatter plot of mean absolute errors of the array shape and entropy scores of the corresponding reconstructed images from 1000 random shape assumptions.

Figure 3

An overview of the proposed shape optimization algorithm.

Figure 4

Profile of each scan-line and the corresponding scan converted ultrasound image.

Figure 5

Experimental set-up for evaluating the array shape.

Figure 6

Experimental set-up for the CIRS phantom scan.

Figure 7

X-ray image of the flexible array transducer with the ABDFAN phantom.

Figure 8

Comparison of the estimated array shapes from different methods.

Figure 9

Reconstructed images of the CIRS phantom (a) without array shape correction, (b) with optical-based estimated shape, and (c) with optimized shape, and (d) line plot of lateral FWHM of the point scatters.

Figure 10

Reconstructed images of the ABDFAN phantom (a) without array shape correction, (b) with optical-based estimated shape and (c) optimized shape, (d) ground truth image from the linear array transducer, and (e) cropped image with the same region as the ground truth.

Figure 11

Reconstructed images of the liver scan (a) without array shape correction, (b) with optical-based estimated shape and (c) optimized shape, (d) ground truth image from the linear array transducer, and (e) cropped image with the same region as the ground truth.

Figure 12

Comparison of the segmentations between the flexible array transducer results and the ground truth for the (a) ABDFAN phantom and (b) liver scan. Green regions represent the ground truth, pink regions represent the reconstructed results, white regions represent the common regions, and the red arrows represent the Hausdorff distances.

Figure 13

Reconstructed images with the initial shape, the maximum entropy-optimized shape, and the minimum entropy-optimized shape.