Purpose: Dual-energy (DE) contrast-enhanced digital mammography (CEDM) uses an iodinated contrast agent in combination with digital mammography (DM) to evaluate lesions on the basis of tumor angiogenesis. In DE imaging, low-energy (LE) and high-energy (HE) images are acquired after contrast administration and their logarithms are subtracted to cancel the appearance of normal breast tissue. Often there is incomplete signal cancellation in the subtracted images, creating a background "clutter" that can impair lesion detection. This is the second component of a two-part report on anatomical noise in CEDM. In Part I the authors characterized the anatomical noise for single-energy (SE) temporal subtraction CEDM by a power law, with model parameters α and β. In this work the authors quantify the anatomical noise in DE CEDM clinical images and compare this with the noise in SE CEDM. The influence on the anatomical noise of the presence of iodine in the breast, the timing of imaging postcontrast administration, and the x-ray energy used for acquisition are each evaluated.

Methods: The power law parameters, α and β, were measured from unprocessed LE and HE images and from DE subtracted images to quantify the anatomical noise. A total of 98 DE CEDM cases acquired in a previous clinical pilot study were assessed. Conventional DM images from 75 of the women were evaluated for comparison with DE CEDM. The influence of the imaging technique on anatomical noise was determined from an analysis of differences between the power law parameters as measured in DM, LE, HE, and DE subtracted images for each subject.

Results: In DE CEDM, weighted image subtraction lowers β to about 1.1 from 3.2 and 3.1 in LE and HE unprocessed images, respectively. The presence of iodine has a small but significant effect in LE images, reducing β by about 0.07 compared to DM, with α unchanged. Increasing the x-ray energy, from that typical in DM to a HE beam, significantly decreases α by about 2×10(-5) mm2, and lowers β by about 0.14 compared to LE images. A comparison of SE and DE CEDM at 4 min postcontrast shows equivalent power law parameters in unprocessed images, and lower α and β by about 3×10(-5) mm2 and 0.50, respectively, in DE versus SE subtracted images.

Conclusions: Image subtraction in both SE and DE CEDM reduces β by over a factor of 2, while maintaining α below that in DM. Given the equivalent α between SE and DE unprocessed CEDM images, and the smaller anatomical noise in the DE subtracted images, the DE approach may have an advantage over SE CEDM. It will be necessary to test this potential advantage in future lesion detectability experiments, which account for realistic lesion signals. The authors' results suggest that LE images could be used in place of DM images in CEDM exam interpretation.

Download full-text PDF

Source
http://dx.doi.org/10.1118/1.4812681DOI Listing

Publication Analysis

Top Keywords

anatomical noise
32
subtracted images
16
power law
16
images
15
cedm
13
digital mammography
12
noise cedm
12
law parameters
12
unprocessed images
12
anatomical
8

Similar Publications

Skin cancer is considered globally as the most fatal disease. Most likely all the patients who received wrong diagnosis and low-quality treatment die early. Though if it is detected in the early stages the patient has fairly good chance and the aforementioned diseases can be cured.

View Article and Find Full Text PDF

Thanks to affordable 3D printers, creating complex designs like anatomically accurate dummy heads is now accessible. This study introduces dummy heads with 3D-printed skulls and silicone skins to explore crosstalk cancellation in bone conduction (BC). Crosstalk occurs when BC sounds from a transducer on one side of the head reach the cochlea on the opposite side.

View Article and Find Full Text PDF

Magnetic resonance imaging (MRI) is an invaluable method of choice for anatomical and functional in vivo imaging of the brain. Still, accurate delineation of the brain structures remains a crucial task of MR image evaluation. This study presents a novel analytical algorithm developed in MATLAB for the automatic segmentation of cerebrospinal fluid (CSF) spaces in preclinical non-contrast MR images of the mouse brain.

View Article and Find Full Text PDF

Introduction: Ultra-high-field magnetic resonance (MR) systems (7 T and 9.4 T) offer the ability to probe human brain metabolism with enhanced precision. Here, we present the preliminary findings from 3D MR spectroscopic imaging (MRSI) of the human brain conducted with the world's first 10.

View Article and Find Full Text PDF

A lightweight generative model for interpretable subject-level prediction.

Med Image Anal

December 2024

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland; Department of Computer Science, Aalto University, Finland.

Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject's diagnosis, from medical images depicting its anatomical-functional effects. Methods based on discriminative modeling excel at making accurate predictions, but are challenged in their ability to explain their decisions in anatomically meaningful terms. In this paper, we propose a simple technique for single-subject prediction that is inherently interpretable.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!