Application of MRI‑ultrasound Image Fusion Targeted Puncture in Suspicious Breast Lesions

Authors

  • Tianyu She
  • Yuan Li
  • Mei Liu
  • Jie Mei
  • Yina Zhao
  • Hongmin Dang
  • Bei Huang
  • Tao Hou
  • Qiyi Hu
  • Hangyan Qian
  • Yuning Liu
  • Ling Tu
  • Na Jing
  • Yunlong Gao
  • Lei Hua
  • Wei Bai
  • Xiaoxiao Zhao
  • Dan Yun
  • Lining Su
  • Lei Li
  • Kun Nan
  • Xiaoying Tian
  • Yangyan Sun
  • Ruixia Yue
  • Yu Cao
  • Jia Song
  • Lu Xing

DOI:

https://doi.org/10.54691/rfex5j55

Keywords:

MRI‑ultrasound fusion; breast lesions; targeted biopsy; image registration; diagnostic accuracy.

Abstract

Based on the existing studies using MRI-ultrasound (MRI-US) image fusion for targeted biopsies of suspicious breast lesions, this paper summarises their progress and achievements. Combining high-resolution anatomy derived from MRI scans with excellent soft-tissue resolution in ultrasound images, thus achieving an accurate biopsy-targeting method by fusing both sets of information. Methodically search the databases and rigorously screen the contents of the clinical studies. MR-IU fusion has a high detection rate and low false-negative possibility than MR or US alone; also, the procedure is safer with MRI-US fusion. Technical restrictions of the following type: poor image alignment; variability among algorithm outcomes; accessibility issues remain present. Currently, the trend is to expand its usage. Future directions also include integrating artificial intelligence (AI), achieving automatic analysis, protocol standardisation, reduced costs, and increased global accessibility. Breast cancer is an aggressive tumor disease that has become a global accessibility in the last century; there are also many new drugs being developed today.

Downloads

Download data is not yet available.

References

[1] Wang, Y., et al. Comparison of ultrasound and mammography for early diagnosis of breast cancer among Chinese women with suspected breast lesions: A prospective trial. Thoracic Cancer, 13(22), 3145–151. (2022)

[2] Sauer, S. T., et al. The value of second‑look ultrasound and mammography for assessment and biopsy of MRI‑detected breast lesions. Academic Radiology, 32(4), 1818–826. (2025)

[3] Kousaka, J., et al. Targeted sonography using an image fusion technique for evaluation of incidentally detected breast lesions on chest CT: a pilot study. Breast Cancer, 23(2), 301–309. (2016)

[4] Lu, Y., et al. The value and feasibility of the freehand technique in MRI‑guided breast lesion localization: A retrospective cohort study. Medicine, 105(2), e46972. (2026)

[5] Bickelhaupt, S., et al. Independent value of image fusion in unenhanced breast MRI using diffusion‑weighted and morphological T2‑weighted images for lesion characterization in patients with recently detected BI‑RADS 4/5 x‑ray mammography findings. European Radiology, 27(2), 562–569. (2017)

[6] Li, X., et al. MRI Features and Apparent Diffusion Coefficient Histogram‑Based Nomogram for Classifying MRI‑Only Suspicious Breast Lesions. Clinical Breast Cancer. (2025)

[7] Huang, P. Y., et al. Contrast‑enhanced ultrasound‑guided biopsy of suspicious breast lesions on contrast‑enhanced mammography and contrast‑enhanced MRI: a case series. Journal of Medical Ultrasonics, 50(4), 521–529. (2023)

[8] He, P., et al. Deep learning–based computer‑aided diagnosis for breast lesion classification on ultrasound: a prospective multicenter study of radiologists without breast ultrasound expertise. American Journal of Roentgenology, 221(4), 450–459. (2023)

[9] Zhuang, Z., et al. Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi‑model spatial feature fusion. Computer Methods and Programs in Biomedicine, 208, 106221. (2021)

[10] Oztekin, P. S., et al. Comparison of explainable artificial intelligence model and radiologist review performances to detect breast Cancer in 752 patients. Journal of Ultrasound in Medicine, 43(11), 2051–2068. (2024)

[11] Negret, M. A., et al. Portable Electrical Impedance Prescreening for Breast tissue suspicious for malignancy: Model Optimization and Clinical Performance of the Julieta Device in a Multicenter Cross‑Sectional Study in Colombia. Technology in Cancer Research & Treatment, 25, 15330338261422902. (2026)

[12] Lin, S. T., et al. Diagnostic performance of contrast‐enhanced mammography for suspicious findings in dense breasts: a systematic review and meta‑analysis. Cancer Medicine, 13(8), e7128. (2024)

[13] Chou, C. P., et al. Clinical roles of breast 3T MRI, FDG PET/CT, and breast ultrasound for asymptomatic women with an abnormal screening mammogram. Journal of the Chinese Medical Association, 78(12), 719–725. (2015)

[14] Saeed, I., et al. Comparative Evaluation Of Diffusion Weighted MRI And Ultrasonography In The Detection Of Breast Lesions. Journal of Nursing and Allied Health, 3(01), 25–31. (2025)

[15] Tan, X., et al. Effectiveness of ultrasound‐guided vacuum‐assisted excision for treating benign breast lesions. Cancer Innovation, 4(1), e158. (2025)

[16] Ajantha Devi, V., & Nayyar, A. Fusion of deep learning and image processing techniques for breast cancer diagnosis. In Deep Learning for Cancer Diagnosis (pp. 1–25). Springer. (2020)

[17] Dillon, M. F., et al. The accuracy of ultrasound, stereotaxic, and clinical core biopsies in the diagnosis of breast cancer, with an analysis of false‑negative cases. Annals of Surgery, 242(5), 701–707. (2005)

[18] Berg, W. A., et al. Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology, 233(3), 830–834. (2004)

[19] Barentsz, M. W., et al. Same‑day diagnosis based on histology for women suspected of breast cancer: high diagnostic accuracy and favorable impact on the patient. PLOS ONE, 9(7), e103105. (2014)

[20] Scheidhauer, K., Walter, C., & Seemann, M. D. FDG PET and other imaging modalities in the primary diagnosis of suspicious breast lesions. European Journal of Nuclear Medicine and Molecular Imaging, 31(Suppl 1), S70–S79. (2004)

[21] Villa‑Camacho, J. C., Baikpour, M., & Chou, S. H. S. Artificial intelligence for breast US. Journal of Breast Imaging, 5(1), 11–20. (2023)

Downloads

Published

22-04-2026

Issue

Section

Articles