Including the effect of motion artifacts in noise and performance analysis of dual-energy contrast-enhanced mammography

Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.

The detective quantum efficiency of cadmium telluride photon-counting x-ray detectors in breast imaging applications

PURPOSE

In breast imaging applications, cadmium telluride (CdTe) photon counting x-ray detectors (PCDs) may reduce radiation dose and enable single-shot multi-energy x-ray imaging. The purpose of this work is to determine the upper limits of the detective quantum efficiency (DQE) of CdTe PCDs for x-ray mammography and to compare them with the published DQEs of energy-integrating detectors (EIDs) and other PCDs.

METHODS

We calibrated and validated a Monte Carlo (MC) model of the DQE of CdTe PCDs using an XCounter CdTe PCD. Our model accounted for charge sharing, electronic noise, and charge summation logic. We used a 28 kVp Mo/Mo spectrum hardened by 3.9 cm of Lucite to optimize the detector thickness and energy threshold for pixel sizes of 50, 85, and 100 μ m with and without inter-pixel charge summation logic. The figure of merit used for optimization was the integral of the DQE, which is equivalent to the detectability index for a delta function task function, which represents a high-frequency task.

RESULTS

For an electronic noise level equal to that of the XCounter, the optimal DQE(0) without charge summing was 0.74. Charge summing for charge-sharing correction reduced DQE(0) by 14% due to an increase in electronic noise. Reducing the electronic noise to ∼0.5 keV per pixel in combination with charge summing resulted in DQE(0) ≈ 0.78 for 85  μ m pixels, which is approximately equal to that of a-Se and slot-scanning silicon-strip PCDs. At higher spatial frequencies, and for matched pixel sizes, the DQE was inferior to that of a-Se EIDs and superior to that of slot-scanning silicon-strip PCDs in the scan direction but inferior in the slit direction.

CONCLUSIONS

(1) CdTe PCDs have the potential to provide a zero-frequency DQE equal to that of a-Se EIDs and slot-scanning silicon-strip PCDs, but this will require electronic noise levels ∼0.5 keV per pixel. (2) At mid-to-high spatial frequencies the DQE of CdTe PCDs may be (a) inferior to that of a-Se EIDs and slot-scanning silicon-strip PCDs in the slit direction, and (b) superior to slot-scanning silicon-strip PCDs in the scan direction.

Cascaded systems analysis of anatomic noise in digital mammography and dual-energy digital mammography

In x-ray based imaging of the breast, contrast between fibroglandular (Fg) tissue and adipose (Ad) tissue is a source of anatomic noise. The goal of this work was to validate by simulation and experiment a mathematical framework for modelling the Fg component of anatomic noise in digital mammograpy (DM) and dual-energy (DE) DM. Our mathematical framework unifies and generalizes existing approaches. We compared mathematical predictions directly with empirical measurements of the anatomic noise power spectrum of the CIRS BR3D structured breast phantom using two clinical mammography systems and four beam qualities. Our simulation and experimental results showed agreement with mathematical predictions. As a demonstration of utility, we used our mathematical framework in a theoretical spectral optimization of DM for the task of detecting breast masses. Our theoretical optimization showed that the optimal tube voltage for DM may be higher than that based on predictions that do not account for anatomic noise, in agreement with recent theoretical findings. Additionally, our theoretical optimization predicts that filtering tungsten-anode x-ray spectra with rhodium has little influence on lesion detectability, in contrast with previous findings. The mathematical methods validated in this work can be incorporated easily into cascaded systems analysis of breast imaging systems and will be useful when optimizating novel techniques for x-ray-based imaging of the breast.

High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose Contrast-Enhanced Digital Mammography

Contrast-enhanced digital mammography (CEDM) is an alternative to conventional X-ray mammography for imaging dense breasts. However, conventional approaches to CEDM require a double exposure of the patient, implying higher dose, and risk of incorrect image registration due to motion artifacts. A novel approach is presented, based on hyperspectral imaging, where a detector combining positional and high-resolution spectral information (in this case based on Cadmium Telluride) is used. This allows simultaneous acquisition of the two images required for CEDM. The approach was tested on a custom breast-equivalent phantom containing iodinated contrast agent (Niopam 150®). Two algorithms were used to obtain images of the contrast agent distribution: K-edge subtraction (KES), providing images of the distribution of the contrast agent with the background structures removed, and a dual-energy (DE) algorithm, providing an iodine-equivalent image and a water-equivalent image. The high energy resolution of the detector allowed the selection of two close-by energies, maximising the signal in KES images, and enhancing the visibility of details with the low surface concentration of contrast agent. DE performed consistently better than KES in terms of contrast-to-noise ratio of the details; moreover, it allowed a correct reconstruction of the surface concentration of the contrast agent in the iodine image. Comparison with CEDM with a conventional detector proved the superior performance of hyperspectral CEDM in terms of the image quality/dose tradeoff.

Optimization of contrast-enhanced breast imaging: Analysis using a cascaded linear system model

PURPOSE

Contrast-enhanced (CE) breast imaging involves the injection contrast agents (i.e., iodine) to increase conspicuity of malignant lesions. CE imaging may be used in conjunction with digital mammography (DM) or digital breast tomosynthesis (DBT) and has shown promise in improving diagnostic specificity. Both CE-DM and CE-DBT techniques require optimization as clinical diagnostic tools. Physical factors including x-ray spectra, subtraction technique, and the signal from iodine contrast, must be considered to provide the greatest object detectability and image quality. We developed a cascaded linear system model (CLSM) for the optimization of CE-DM and CE-DBT employing dual energy (DE) subtraction or temporal (TE) subtraction.

METHODS

We have previously developed a CLSM for DBT implemented with an a-Se flat panel imager (FPI) and filtered backprojection (FBP) reconstruction algorithm. The model is used to track image quality metrics - modulation transfer function (MTF) and noise power spectrum (NPS) - at each stage of the imaging chain. In this study, the CLSM is extended for CE breast imaging. The effect of x-ray spectrum (varied by changing tube potential and the filter) and DE and TE subtraction techniques on breast structural noise was measured was studied and included as a deterministic source of noise in the CLSM. From the two-dimensional (2D) and three-dimensional (3D) MTF and NPS, the ideal observer signal-to-noise ratio (SNR), also known as the detectability index (d'), may be calculated. Using d' as a FOM, we discuss the optimization of CE imaging for the task of iodinated contrast object detection within structured backgrounds.

RESULTS

Increasing x-ray energy was determined to decrease the magnitude of structural noise and not its correlation. By performing DE subtraction, the magnitude of the structural noise was further reduced at the expense of increased stochastic (quantum and electronic) noise. TE subtraction exhibited essentially no residual structural noise at the expense of increased quantum noise, even over that of the DE case. For DE subtraction, optimization of dose weighting to the HE view (f_h ) results in the minimization of quantum noise. Both subtraction weighting factor (w_Sub ) and the iodine contrast signal were dependent on the LE and HE x-ray spectra. To best detect a 5 mm Gaussian lesion with 5 mg/ml of iodine within a 4 cm thick breast, it was found that the high energy (HE) view should be acquired with a tube potential of 47 kVp (W/Ti spectrum) and the low energy (LE) view with a potential of 23 kVp (W/Rh spectrum). Due to the complete removal of structural noise, TE subtraction produced much higher d' than DE subtraction both as a function of mean glandular dose and iodine concentration.

CONCLUSIONS

We have shown the effect of increasing x-ray energy as well as projection domain subtraction on breast structural noise. Further, we have exhibited the utility of the CLSM for DE and TE subtraction CE imaging in the optimization of imaging parameters such as x-ray energy, f_h , and w_Sub as well as guiding the understanding of their effects on image contrast and noise.

Dual-energy contrast-enhanced spectral mammography (CESM)

Dual-energy contrast-enhanced mammography is one of the latest developments in breast care. Imaging with contrast agents in breast cancer was already known from previous magnetic resonance imaging and computed tomography studies. However, high costs, limited availability-or high radiation dose-led to the development of contrast-enhanced spectral mammography (CESM). We reviewed the current literature, present our experience, discuss the advantages and drawbacks of CESM and look at the future of this innovative technique.

Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis

PURPOSE

The use of contrast agents in breast imaging has the capability of enhancing nodule detectability and providing physiological information. Accordingly, there has been a growing trend toward using iodine as a contrast medium in digital mammography (DM) and digital breast tomosynthesis (DBT). Widespread use raises concerns about the best way to use iodine in DM and DBT, and thus a comparison is necessary to evaluate typical iodine-enhanced imaging methods. This study used a task-based observer model to determine the optimal imaging approach by analyzing six imaging paradigms in terms of their ability to resolve iodine at a given dose: unsubtracted mammography and tomosynthesis, temporal subtraction mammography and tomosynthesis, and dual energy subtraction mammography and tomosynthesis.

METHODS

Imaging performance was characterized using a detectability index d', derived from the system task transfer function (TTF), an imaging task, iodine signal difference, and the noise power spectrum (NPS). The task modeled a 10 mm diameter lesion containing iodine concentrations between 2.1 mg/cc and 8.6 mg/cc. TTF was obtained using an edge phantom, and the NPS was measured over several exposure levels, energies, and target-filter combinations. Using a structured CIRS phantom, d' was generated as a function of dose and iodine concentration.

RESULTS

For all iodine concentrations and dose, temporal subtraction techniques for mammography and tomosynthesis yielded the highest d', while dual energy techniques for both modalities demonstrated the next best performance. Unsubtracted imaging resulted in the lowest d' values for both modalities, with unsubtracted mammography performing the worst out of all six paradigms.

CONCLUSIONS

At any dose, temporal subtraction imaging provides the greatest detectability, with temporally subtracted DBT performing the highest. The authors attribute the successful performance to excellent cancellation of inplane structures and improved signal difference in the lesion.

Evaluating noise reduction techniques while considering anatomical noise in dual-energy contrast-enhanced mammography

PURPOSE

The authors describe modifications to previously developed cascaded systems analysis to include the anatomical noise in evaluation of dual-energy noise reduction techniques. Previous models have ignored the anatomical noise in theoretical analysis of noise reduction techniques. The inclusion of anatomical noise leads to more accurate estimation of potential noise reduction improvements and optimization.

METHODS

The model is applied to dual-energy contrast-enhanced mammography. The effect of linear noise reduction filters on the anatomical noise is taken into account using cascaded systems analysis. The noise model is included in the ideal observer detectability for performance evaluation of the noise reduction techniques.

RESULTS

Dual-energy image noise with and without including the effect of anatomical noise in noise reduction technique analysis is reported. The theoretical model is compared with clinical images from a previous dual-energy contrast enhanced mammography clinical study and good agreement is observed. The results suggest that the inclusion of anatomical noise in the evaluation and comparison of noise reduction techniques is highly warranted for more accurate analysis.

CONCLUSIONS

This work establishes a useful extension to dual-energy cascaded systems analysis for maximizing image quality using noise reduction techniques. The extension includes the effect of linear image filtering, such as that used for noise reduction, on anatomical noise. The results suggest that the inclusion of anatomical noise in the evaluation of noise reduction techniques can lead to more accurate optimization, noise, and performance estimations.