Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoising

• URL: http://arxiv.org/abs/2505.05812v1
• Date: Fri, 09 May 2025 06:15:28 GMT
• Title: Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoising
• Authors: Ashkan Pakzad, Robert Turnbull, Simon J. Mutch, Thomas A. Leatham, Darren Lockie, Jane Fox, Beena Kumar, Daniel Häsermann, Christopher J. Hall, Anton Maksimenko, Benedicta D. Arhatari, Yakov I. Nesterets, Amir Entezam, Seyedamir T. Taba, Patrick C. Brennan, Timur E. Gureyev, Harry M. Quiney,
• Abstract summary: Phase-contrast computed tomography (PCT) has been shown to produce higher-quality images at lower doses.<n>Deep learning-based image denoising can further reduce the radiation dose by a factor of 16 or more, without any loss of image quality.
• Score: 5.618529985480756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast cancer is the most frequently diagnosed human cancer in the United States at present. Early detection is crucial for its successful treatment. X-ray mammography and digital breast tomosynthesis are currently the main methods for breast cancer screening. However, both have known limitations in terms of their sensitivity and specificity to breast cancers, while also frequently causing patient discomfort due to the requirement for breast compression. Breast computed tomography is a promising alternative, however, to obtain high-quality images, the X-ray dose needs to be sufficiently high. As the breast is highly radiosensitive, dose reduction is particularly important. Phase-contrast computed tomography (PCT) has been shown to produce higher-quality images at lower doses and has no need for breast compression. It is demonstrated in the present study that, when imaging full fresh mastectomy samples with PCT, deep learning-based image denoising can further reduce the radiation dose by a factor of 16 or more, without any loss of image quality. The image quality has been assessed both in terms of objective metrics, such as spatial resolution and contrast-to-noise ratio, as well as in an observer study by experienced medical imaging specialists and radiologists. This work was carried out in preparation for live patient PCT breast cancer imaging, initially at specialized synchrotron facilities.

Related papers

• A European Multi-Center Breast Cancer MRI Dataset [32.81686559528397]
  The ODELIA consortium has made this multi-centre dataset publicly available to assist in developing AI tools for the detection of breast cancer on MRI.<n>Recent guidelines by the European Society of Breast Imaging recommend breast MRI as a supplemental screening tool for women with dense breast tissue.<n>The ODELIA consortium has made this multi-centre dataset publicly available to assist in developing AI tools for the detection of breast cancer on MRI.
  arXiv  Detail & Related papers  (2025-05-31T08:45:02Z)
• Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation [71.91773485443125]
  We show that the best AUC is achieved by the CDI$s$ - optimized modality, outperforming the best gold-standard modality by 0.0044. Notably, the optimized CDI$s$ modality also achieves AUC values over 0.02 higher than the Unoptimized CDI$s$ value.
  arXiv  Detail & Related papers  (2024-05-13T16:07:58Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
  Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:40:56Z)
• Blind Signal Separation for Fast Ultrasound Computed Tomography [6.25118865553438]
  FastUSCT is a method to acquire a high-quality image faster than traditional methods for USCT. It transmits multiple ultrasound waves at the same time to reduce the imaging time. It separates the overlapping waves recorded by the receiving elements into each wave with UNet.
  arXiv  Detail & Related papers  (2023-04-27T12:14:03Z)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT [7.712142153700843]
  We transform the cutting-edge Denoising Diffusion Probabilistic Model (DDPM) into a parallel framework for sub-volume-based sparse-view breast CT image reconstruction. Our experimental findings reveal that this method delivers competitive reconstruction performance at half to one-third of the standard radiation doses.
  arXiv  Detail & Related papers  (2023-03-22T18:55:43Z)
• High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection [48.88813637974911]
  Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. High-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms.
  arXiv  Detail & Related papers  (2022-09-20T15:57:12Z)
• Improving Specificity in Mammography Using Cross-correlation between Wavelet and Fourier Transform [0.0]
  The incidence of breast cancer remains high around the world, but the mortality rate has been continuously reduced. We will investigate an approach that applies the discrete wavelet transform and Fourier transform to parse the images.
  arXiv  Detail & Related papers  (2022-01-20T00:49:33Z)
• Deep Integrated Pipeline of Segmentation Leading to Classification for Automated Detection of Breast Cancer from Breast Ultrasound Images [0.0]
  The proposed framework integrates ultrasonography image preprocessing with Simple Linear Iterative Clustering (SLIC) to tackle the complex artifact of Breast Ultrasonography Images. The proposed automated pipeline can be effectively implemented to assist medical practitioners in making more accurate and timely diagnoses of breast cancer.
  arXiv  Detail & Related papers  (2021-10-26T20:42:39Z)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• Segmentation of Infrared Breast Images Using MultiResUnet Neural Network [0.0]
  We are investigating infrared (IR) thermography as a noninvasive adjunct to mammography for breast cancer screening. Autoencoder-like convolutional and deconvolutional neural networks (C-DCNN) had been applied to automatically segment the breast area in IR images in previous studies. In this study, we applied a state-of-the-art deep-learning segmentation model, MultiResUnet, which consists of an encoder part to capture features and a decoder part for precise localization.
  arXiv  Detail & Related papers  (2020-10-31T22:15:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.