Coherence Metrics for Reader-Independent Differentiation of Cystic From Solid Breast Masses in Ultrasound Images

Far-focus compound ultrasound imaging with lag-one coherence-based zero-cross factor

BACKGROUND

Ultrasound imaging has been widely used in clinical examination because of portability, safety, and low cost. However, there are still some main challenges of imaging quality that remain in conventional ultrasound systems.

OBJECTIVE

Improving image quality of SA-based methods using an improved imaging mode named far-focus compound (FSC) imaging.

METHODS

A far-focus compound (FSC) imaging based on full-aperture transmission and full-aperture reception is proposed in this paper. In transmission, it uses the full aperture to transmit the focused beam to ensure image resolution and emission of sound field energy. In reception, the full aperture is used to receive the reflected beam to ensure the image quality. A lag-one coherence-based zero-cross factor (LOCZF) is then implemented in FSC for improvement of contrast ratio (CR). The LOCZF uses lag-one coherence as zero-cross factorâs adaptive coefficient. Comparisons were made with several other weighting techniques by performing simulations and experiments for performance evaluation.

RESULTS

Results confirm that LOCZF applied to FSC offers a good image contrast and simultaneously the speckle pattern. For simulated cysts, CR improvement of LOCZF reaches 194.1%. For experimental cysts, CR improvement of LOCZF reaches 220%. From the in-vivo result, compared with FSC, CR improvement of LOCZF reaches 112.7%.

CONCLUSION

Proved gCNR performance. In addition, the LOCZF method shows good performance in experiments. The proposed method can be used as an effective weighting technique for improvement of image quality in ultrasound imaging.

Spatial Coherence Approaches to Distinguish Suspicious Mass Contents in Fundamental and Harmonic Breast Ultrasound Images

When compared to fundamental B-mode imaging, coherence-based beamforming, and harmonic imaging are independently known to reduce acoustic clutter, distinguish solid from fluid content in indeterminate breast masses, and thereby reduce unnecessary biopsies during a breast cancer diagnosis. However, a systematic investigation of independent and combined coherence beamforming and harmonic imaging approaches is necessary for the clinical deployment of the most optimal approach. Therefore, we compare the performance of fundamental and harmonic images created with short-lag spatial coherence (SLSC), M-weighted SLSC (M-SLSC), SLSC combined with robust principal component analysis with no M-weighting (r-SLSC), and r-SLSC with M-weighting (R-SLSC), relative to traditional fundamental and harmonic B-mode images, when distinguishing solid from fluid breast masses. Raw channel data acquired from 40 total breast masses (28 solid, 7 fluid, 5 mixed) were beamformed and analyzed. The contrast of fluid masses was better with fundamental rather than harmonic coherence imaging, due to the lower spatial coherence within the fluid masses in the fundamental coherence images. Relative to SLSC imaging, M-SLSC, r-SLSC, and R-SLSC imaging provided similar contrast across multiple masses (with the exception of clinically challenging complicated cysts) and minimized the range of generalized contrast-to-noise ratios (gCNRs) of fluid masses, yet required additional computational resources. Among the eight coherence imaging modes compared, fundamental SLSC imaging best identified fluid versus solid breast mass contents, outperforming fundamental and harmonic B-mode imaging. With fundamental SLSC images, the specificity and sensitivity to identify fluid masses using the reader-independent metrics of contrast difference, mean lag one coherence (LOC), and gCNR were 0.86 and 1, 1 and 0.89, and 1 and 1, respectively. Results demonstrate that fundamental SLSC imaging and gCNR (or LOC if no coherence image or background region of interest is introduced) have the greatest potential to impact clinical decisions and improve the diagnostic certainty of breast mass contents. These observations are additionally anticipated to extend to masses in other organs.

Ultrasound as a Method for Early Diagnosis of Breast Pathology

INTRODUCTION

Ultrasound is a non-invasive, low-cost technique that does not use ionising radiation and provides a "real-time" image, and for these reasons, this method is ideal in several situations.

PURPOSE

To demonstrate breast ultrasound evaluation as a first-line diagnostic method and to evaluate the variation of breast characteristics with age.

MATERIAL AND METHODS

A total of 105 women with a mean age of 30 years participated and were divided into three age groups: 18-39, 40-59, and 60-79 years, excluding participants subject to mastectomy. After completing the informed consent, all participants answered personal and sociodemographic questions, such as personal and family history, menstrual cycle, pregnancy, ultrasound, and mammography, among others. They were then submitted to a bilateral breast ultrasound examination. Subsequently, all the images and their data were analysed, and a technical report of the examination was given to all the participants.

RESULTS

A total of 105 women with a mean age of 30 years participated, 58 of whom underwent the examination for the first time. In 31, changes (of which only 7 were known) were diagnosed. It was verified that, according to age group, the density of the breast stroma varied; older women have less breast density.

CONCLUSIONS

Ultrasound is a good method for breast evaluation and can be considered important for the early evaluation of breast pathology and follow-up of the pathology.

Knowledge Tensor-Aided Breast Ultrasound Image Assistant Inference Framework

Breast cancer is one of the most prevalent cancers in women nowadays, and medical intervention at an early stage of cancer can significantly improve the prognosis of patients. Breast ultrasound (BUS) is a widely used tool for the early screening of breast cancer in primary care hospitals but it relies heavily on the ability and experience of physicians. Accordingly, we propose a knowledge tensor-based Breast Imaging Reporting and Data System (BI-RADS)-score-assisted generalized inference model, which uses the BI-RADS score of senior physicians as the gold standard to construct a knowledge tensor model to infer the benignity and malignancy of breast tumors and axes the diagnostic results against those of junior physicians to provide an aid for breast ultrasound diagnosis. The experimental results showed that the diagnostic AUC of the knowledge tensor constructed using the BI-RADS characteristics labeled by senior radiologists achieved 0.983 (95% confidential interval (CI) = 0.975-0.992) for benign and malignant breast cancer, while the diagnostic performance of the knowledge tensor constructed using the BI-RADS characteristics labeled by junior radiologists was only 0.849 (95% CI = 0.823-0.876). With the knowledge tensor fusion, the AUC is improved to 0.887 (95% CI = 0.864-0.909). Therefore, our proposed knowledge tensor can effectively help reduce the misclassification of BI-RADS characteristics by senior radiologists and, thus, improve the diagnostic performance of breast-ultrasound-assisted diagnosis.