Sumedha P. Sinha

Automated Ultrasound Scanning on a Dual-Modality Breast Imaging System Coverage and Motion Issues and Solutions

We are developing an automated ultrasound imaging‐mammography system wherein a digital mammography unit has been augmented with a motorized ultrasound transducer carriage above a special compression paddle. Challenges of this system are acquiring complete coverage of the breast and minimizing motion. We assessed these problems and investigated methods to increase coverage and stabilize the compressed breast.

Multi-modality 3D breast imaging with X-Ray tomosynthesis and automated ultrasound

This study evaluated the utility of 3D automated ultrasound in conjunction with 3D digital X-ray tomosynthesis for breast cancer detection and assessment, to better localize and characterize lesions in the breast. Tomosynthesis image volumes and automated ultrasound image volumes were acquired in the same geometry and in the same view for 27 patients. 3 MQSA certified radiologists independently reviewed the image volumes, visually correlating the images from the two modalities with in-house software. More sophisticated software was used on a smaller set of 10 cases, which enabled the radiologist to draw a 3D box around the suspicious lesion in one image set and isolate an anatomically correlated, similarly boxed region in the other modality image set. In the primary study, correlation was found to be moderately useful to the readers. In the additional study, using improved software, the median usefulness rating increased and confidence in localizing and identifying the suspicious mass increased in more than half the cases. As automated scanning and reading software techniques advance, superior results are expected.

Large Area MEMS Based Ultrasound Device for Cancer Detection.

We present image results obtained using a prototype ultrasound array which demonstrates the fundamental architecture for a large area MEMS based ultrasound device for detection of breast cancer. The prototype array consists of a tiling of capacitive Micro-Machined Ultrasound Transducers (cMUTs) which have been flip-chip attached to a rigid organic substrate. The pitch on the cMUT elements is 185 um and the operating frequency is nominally 9 MHz. The spatial resolution of the new probe is comparable to production PZT probes, however the sensitivity is reduced by conditions that should be correctable. Simulated opposed-view image registration and Speed of Sound volume reconstruction results for ultrasound in the mammographic geometry are also presented.

Machine learning for noise removal on breast ultrasound images

This study assessed the utility of machine learning for isolating noise and artifacts in breast ultrasound images. Such corrupt image regions (ROIs) can be automatically excluded when registering images acquired from different angles. Artifacts included posterior acoustic shadowing and enhancement arising from cancers and cysts respectively. Images were obtained on a breast-mimicking phantom containing multiple cysts and lesions with variable speed of sound and attenuation properties. In vivo breast images of cysts and cancers were also available. Results show that the classifiers were able to identify the regions of corrupt data accurately.

Image Registration for Detection and Quantification of Change on Digital Tomosynthesis Mammographic Volumes

OBJECTIVE The purpose of this study was to achieve 3D registration of digital tomosynthesis mammographic volumes using mutual information. CONCLUSION Registration of digital breast tomosynthesis mammographic volumes was achieved with an average error of 1.8 +/- 1.4 mm.

Breast ultrasound image improvement by pixel compounding of compression sequence

Pixel compounding is a technique that synthesizes the information of an image sequence involving slow decorrelation of the speckle to form a detail-recovered and speckle reduced image. To avoid extra data acquisition time and patient exposure, reuse of the existing data is desirable. In the procedure of elasticity imaging, a set of B-mode images with slight changes due to deformation is produced, which provides an ideal input for the pixel compounding. The improvement in image quality is evaluated quantitatively using a figure-of-merit (FOM) that indicates the quality of boundary information recovery and the contrast-to-noise ratio (CNR) over the phantom images. The increase in average CNR is from 0.4 in the original images to 0.8 in the pixel compounded images. The improvement in average FOM is from 0.15 to more than 0.5 on a scale of 0 to 1. In vivo results with a breast cyst, a fibroadenoma, and a breast cancer are also presented and the image quality improvement is subjectively evaluated. The results suggest that B-mode breast images from compression procedures are suitable data for pixel compounding, and that a speckle-reduced and detail-recovered or detail-maintained image can be produced. The improved imaging may provide alternative or better information for detection and diagnosis. A similar approach could be extended to elasticity imaging with other modalities.

Non-rigid registration of three-dimensional (3D) grayscale and Doppler ultrasound breast images

The purpose of this study is to evaluate the accuracy of image volume based registration (IVBaR) of 3D ultrasound (US) image volumes of the whole breast acquired at different times. Successful IVBaR could aid in detection of tumor changes in response to neoadjuvant chemotherapy and potentially be useful for routine breast cancer screening and diagnosis. IVBaR was successful in 9 of 10 reproducibility studies, 11 of 15 image pairs collected before and after approximately 45 days of chemotherapy. Doppler study yielded volume of blood flow to the region surrounding the lesion and its change when reducing breast compression. The color flow vessels provided independent measures for validation of registration of the grayscale portion of those images.

Image processing and registration of opposed view 3d breast ultrasound

We are studying opposed view ultrasonic imaging (OVI) of the breast in the mammographic geometry, with probable future automation and alignment with X-ray tomosynthesis. OVI through a filament mesh paddle results in improved spatial resolution, contrast, and signal-to-noise ratio. We expect these images will be of a quality that justifies their use for screening purposes, especially for subjects with dense breasts. A previous study assessed machine learning for isolating image artifacts, which included posterior acoustic shadowing from cancers and enhancement arising from cysts. The image volumes were acquired on a custom breast-mimicking phantom containing multiple cysts and solid masses. This paper reports that 3D non-linear registration of opposed view image volumes was robust for the segmented image volumes with noisy areas excluded.

Sub-pixel compounding from elasticity imaging data

Sub-pixel compounding is a technique that synthesizes the information of an image sequence to form a betterresolved and speckle reduced image. To avoid extra data acquisition time and patient exposure, reuse of the existing data is highly desired. In elasticity imaging, a set of images with slight changes due to deformation is produced, which provides an ideal input for the sub-pixel compounding process. In this paper, a brief review of the resolution enhancement techniques in ultrasound imaging will be provided, and then, a diffusion-regularized, least square approach is presented for sub-pixel compounding image reconstruction. Based on the results, we suggest that (1) B-mode images from elastic imaging are suitable data for sub-pixel compounding and a speckle noise reduced higher-resolution image is a co-product of elasticity imaging; (2) for breast diagnosis, resolution improvement is of strong interest since better depiction of the interior and exterior structures of a tumor provides important detection and diagnostic information; (3) a similar approach could be extended to elasticity imaging with other modalities.

SU‐FF‐J‐116: Image Registration for Change Detection and Quatification in Multimodality Breast Tomosynthesis and Ultrasound

Purpose: To evaluate tomosynthesis‐to‐tomosynthesis and ultrasound‐to‐ultrasound image based registration (IBaR) of whole breast image volumes acquired at different times. These two modalities are probably the most immediately promising for routine breast cancer screening and diagnosis. Successful IBaR should aid more rapid and detailed detectio of change in response to treatment or tumor growth over time. Method and Materials: A system combining automated whole breast ultrasound (ABU) and digital tomosynthesis mammography(DTM) is being utilized in several studies of breast mass classification and assessment of response to neoadjuvant chemotherapy. Much attention was given to making the breast stable for the duration of each exam with the breast compressed between mammographic‐style plates and with acoustic coupling for reproducible, large area scanning through the plates. Four DTM pairs and many ABU pairs were acquired after reasonable stability and coverage techniques were developed. Registration was performed with MIAMI Fuse™ nonrigid registration based on the mutual information cost function and thin plate spline interpolation.Results: Registration was successful on 5 of 8 recent ABU image volume pairs with a mean registration error (MRE) of 3±2 mm and on all 4 DTM pairs, MRE = 4 ± 2 mm, as determined from visually selected homologous points. The lowest DTM mean error was obtained from studies 1 year apart, with minimal change in the breast. Conclusion: Both ABU and DTM offer angle dependent artifacts, with registration being perhaps more difficult in ABU. The variable breast distortion during compression offers similar difficulties, probably greater than actual breast change. No special effort was directed toward reproducible positioning. These results suggest that, usually, it should be possible to display in the same slice of two separate studies, a breast mass of > 5 mm, or its preceding tissues. Conflict of Interest: Work performed in cooperation with xxx, Inc.