Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography.

BACKGROUND: Breast Background Parenchymal Enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in Contrast-Enhanced Mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described in Breast Imaging Reporting and Data System (BI-RADS). However, BPE classification re- mains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast Contrast-Enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM. Materials & Methods: A BPE level classification tool based on Deep Learning (DL) has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using dif- ferent metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated. Results: The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2-71.9) with 98.8% of classification errors between adjacent classes. For binary classifi- cation, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements. Conclusion: The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.

Damage Detection in a Polymer Matrix Composite from 4D Displacement Field Measurements.

Standard Digital Volume Correlation (DVC) approaches enable quantitative analyses of specimen deformation to be performed by measuring displacement fields between discrete states. Such frameworks are thus limited by the number of scans (due to acquisition duration). Considering only one projection per loading step, Projection-based Digital Volume Correlation (P-DVC) allows 4D (i.e., space and time) full-field measurements to be carried out over entire loading histories. The sought displacement field is decomposed over a basis of separated variables, namely, temporal and spatial modes. In the present work, the spatial modes are constructed via scan-wise DVC, and only the temporal amplitudes are sought via P-DVC. The proposed method is applied to a glass fiber mat reinforced polymer specimen containing a machined notch, subjected to in situ cyclic tension and imaged via X-ray Computed Tomography. The P-DVC enhanced DVC method employed herein enables for the quantification of damage growth over the entire loading history up to failure.

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography.

BACKGROUND: The recent development of deep neural network models for the analysis of breast images has been a breakthrough in computer-aided diagnostics (CAD). Contrast-enhanced mammography (CEM) is a recent mammography modality providing anatomical and functional imaging of the breast. Despite the clinical benefits it could bring, only a few research studies have been conducted around deep-learning (DL) based CAD for CEM, especially because the access to large databases is still limited. This study presents the development and evaluation of a CEM-CAD for enhancing lesion detection and breast classification. MATERIALS & METHODS: A deep learning enhanced cancer detection model based on a YOLO architecture has been optimized and trained on a large CEM dataset of 1673 patients (7443 images) with biopsy-proven lesions from various hospitals and acquisition systems. The evaluation was conducted using metrics derived from the free receiver operating characteristic (FROC) for the lesion detection and the receiver operating characteristic (ROC) to evaluate the overall breast classification performance. The performances were evaluated for different types of image input and for each patient background parenchymal enhancement (BPE) level. RESULTS: The optimized model achieved an area under the curve (AUROC) of 0.964 for breast classification. Using both low-energy and recombined image as inputs for the DL model shows greater performance than using only the recombined image. For the lesion detection, the model was able to detect 90% of all cancers with a false positive (non-cancer) rate of 0.128 per image. This study demonstrates a high impact of BPE on classification and detection performance. CONCLUSION: The developed CEM CAD outperforms previously published papers and its performance is comparable to radiologist-reported classification and detection capability.

Deformable registration with intensity correction for CESM monitoring response to Neoadjuvant Chemotherapy.

This paper proposes a robust longitudinal registration method for Contrast Enhanced Spectral Mammography in monitoring neoadjuvant chemotherapy. Because breast texture intensity changes with the treatment, a non-rigid registration procedure with local intensity compensations is developed. The approach allows registering the low energy images of the exams acquired before and after the chemotherapy. The measured motion is then applied to the corresponding recombined images. The difference of registered images, called residual, makes vanishing the breast texture that did not changed between the two exams. Consequently, this registered residual allows identifying local density and iodine changes, especially in the lesion area. The method is validated with a synthetic NAC case where ground truths are available. Then the procedure is applied to 51 patients with 208 CESM image pairs acquired before and after the chemotherapy treatment. The proposed registration converged in all 208 cases. The intensity-compensated registration approach is evaluated with different mathematical metrics and through the repositioning of clinical landmarks (RMSE: 5.9 mm) and outperforms state-of-the-art registration techniques.

Projection-based dynamic tomography.

Objective. This paper proposes a 4D dynamic tomography framework that allows a moving sample to be imaged in a tomograph as well as the associated space-time kinematics to be measured with nothing more than a single conventional x-ray scan.Approach. The method exploits the consistency of the projection/reconstruction operations through a multi-scale procedure. The procedure is composed of two main parts solved alternatively: a motion-compensated reconstruction algorithm and a projection-based measurement procedure that estimates the displacement field directly on each projection.Main results. The method is applied to two studies: a numerical simulation of breathing from chest computed tomography (4D-CT) and a clinical cone-beam CT of a breathing patient acquired for image guidance of radiotherapy. The reconstructed volume, initially blurred by the motion, is cleaned from motion artifacts.Significance. Applying the proposed approach results in an improved reconstruction quality showing sharper edges and finer details.

Dynamic Tomographic Reconstruction of Deforming Volumes.

The motion of a sample while being scanned in a tomograph prevents its proper volume reconstruction. In the present study, a procedure is proposed that aims at estimating both the kinematics of the sample and its standard 3D imaging from a standard acquisition protocol (no more projection than for a rigid specimen). The proposed procedure is a staggered two-step algorithm where the volume is first reconstructed using a "Dynamic Reconstruction" technique, a variant of Algebraic Reconstruction Technique (ART) compensating for a "frozen" determination of the motion, followed by a Projection-based Digital Volume Correlation (P-DVC) algorithm that estimates the space/time displacement field, with a "frozen" microstructure and shape of the sample. Additionally, this procedure is combined with a multi-scale approach that is essential for a proper separation between motion and microstructure. A proof-of-concept of the validity and performance of this approach is proposed based on two virtual examples. The studied cases involve a small number of projections, large strains, up to 25%, and noise.

Separation of superimposed images with subpixel shift.

The problem of the separation of superimposed images is considered in the particular case of a steady background and a foreground that is composed of different patterns separated in space, each with a compact support. Each pattern of the foreground may move in time independently. A single pair of these superimposed images is assumed to be available, and the displacement amplitude is typically smaller than the pixel size. Further, assuming that the background is smoothly varying in space, an original algorithm is proposed. To illustrate the performance of the method, a real test case of X-ray tomographic radiographs with moving patterns due to dust particles or surface scratches of optical elements along the beam is considered. Finally an automatic and simple treatment is proposed to erase the effects of such features.

On the use of flat-fields for tomographic reconstruction.

Seeking for quantitative tomographic images, it is of utmost importance to limit reconstruction artifacts. Detector imperfections, inhomogeneity of the incident beam, as classically observed in synchrotron beamlines, and their variations in time are a major cause of reconstruction bias such as `ring artifacts'. The present study aims at proposing a faithful estimate of the incident beam local intensity for each acquired projection during a scan, without revisiting the process of data acquisition itself. Actual flat-fields (acquired without specimen in the beam) and sinogram borders (when the specimen is present), which are not masked during the scan, are exploited to construct a suited instantaneous detector-wide flat-field. The proposed treatment is fast and simple. Its performance is assessed on a real scan acquired at ESRF ID19 beamline. Different criteria are used including residuals, i.e. difference between projections of reconstruction and actual projections. All confirm the benefit of the proposed procedure.