Mechanical imaging of the breast

Abstract

In this paper, we analyze the physical basis for elasticity imaging of the breast by measuring breast skin stress patterns that result from a force sensor array pressed against the breast tissue. Temporal and spatial changes in the stress pattern allow detection of internal structures with different elastic properties and assessment of geometrical and mechanical parameters of these structures. The method entitled mechanical imaging is implemented in the breast mechanical imager (BMI), a compact device consisting of a hand held probe equipped with a pressure sensor array, a compact electronic unit, and a touchscreen laptop computer. Data acquired by the BMI allows calculation of size, shape, consistency/hardness, and mobility of detected lesions. The BMI prototype has been validated in laboratory experiments on tissue models and in an ongoing clinical study. The obtained results prove that the BMI has potential to become a screening and diagnostic tool that could largely supplant clinical breast examination through its higher sensitivity, quantitative record storage, ease-of-use, and inherent low cost.

Fig. 1

General view of the Breast Mechanical Imager. The device comprises: (1) a probe with 2-D pressure sensor array, (2) an electronic unit, and (3) a laptop computer with touch screen capability.

Fig. 2

Breast examination interface for mode 1: (1) momentary pressure pattern of a lesion; (2a) real-time composed frontal and (2b) transversal cross-sections of the lesion; (3) indicator of total applied force to the probe and data collection.

Fig. 3

Procedure model for breast examination by the BMI. Mode 1 shows the total breast examination to detect suspicious sites by means of linear sliding motions. Mode 2 shows a local scan of a suspicious site by means of vertical up/down probe pressings and by means of circular motions with a lubricant.

Fig. 4

Representation of the breast examination results.

Fig. 5

Illustration of the image processing sequence for a pressure pattern recorded during compression of the probe against the breast in vivo. (1) Raw pressure pattern. (2) After temporal and spatial filters. (3) Calculated background. (4) After background subtraction. (5) After thresholding, pixel-wise filter and interpolation. (6) After further interpolation.

Fig. 6

Illustration of a moving object detection algorithm for a pressure pattern recorded during the sliding of the probe over the breast in vivo. (1) Raw image sequence. (2) The same image sequence after noise-removal filter and background/skewing subtraction. (3) Image sequence processed by an algorithm accounting for the detection of a moving object. (4) Image sequence after filtration and interpolation.

Fig. 7

Dependence of the relative strength of the inclusion signal versus the inclusion diameter.

Fig. 8

Parametric study results of the detection capability for manual palpation versus mechanical imaging with the use of the BMI probe (see text).

Fig. 9

Three-dimensional image reconstruction (C) from a 2-D image sequence (B) recorded in the process of circular oscillation of the BMI probe over the embedded structure (A) tissue phantom.

Fig. 10

Algorithm for image compounding along the BMI probe trajectory.

Fig. 11

Inclusion depth influence on the Young’s modulus and cross-sectional area calculations. Inclusion diameter of 10 mm, Young’s modulus of 80 kPa, phantom thickness of 35 mm, and base material Young’s modulus of 5 kPa.