Multi-scale analysis of lung computed tomography images

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer- Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists

Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights

We propose and evaluate a procedure for the explainability of a breast density deep learning based classifier. A total of 1662 mammography exams labeled according to the BI-RADS categories of breast density was used. We built a residual Convolutional Neural Network, trained it and studied the responses of the model to input changes, such as different distributions of class labels in training and test sets and suitable image pre-processing. The aim was to identify the steps of the analysis with a relevant impact on the classifier performance and on the model explainability. We used the grad-CAM algorithm for CNN to produce saliency maps and computed the Spearman's rank correlation between input images and saliency maps as a measure of explanation accuracy. We found that pre-processing is critical not only for accuracy, precision and recall of a model but also to have a reasonable explanation of the model itself. Our CNN reaches good performances compared to the state-of-art and it considers the dense pattern to make the classification. Saliency maps strongly correlate with the dense pattern. This work is a starting point towards the implementation of a standard framework to evaluate both CNN performances and the explainability of their predictions in medical image classification problems

Predictive models based on Support Vector Machines: whole-brain versus regional analysis of structural MRI in the Alzheimer’s disease

Decision-making systems trained on structural magnetic resonance imaging data of subjects affected by the Alzheimer's disease (AD) and healthy controls (CTRL) are becoming widespread prognostic tools for subjects with mild cognitive impairment (MCI). This study compares the performances of three classification methods based on support vector machines (SVMs), using as initial sets of brain voxels (ie, features): (1) the segmented grey matter (GM); (2) regions of interest (ROIs) by voxel-wise t-test filtering; (3) parceled ROIs, according to prior knowledge. The recursive feature elimination (RFE) is applied in all cases to investigate whether feature reduction improves the classification accuracy. We analyzed more than 600 AD Neuroimaging Initiative (ADNI) subjects, training the SVMs on the AD/CTRL dataset, and evaluating them on a trial MCI dataset. The classification performance, evaluated as the area under the receiver operating characteristic curve (AUC), reaches AUC = (88.9 ± .5)% in 20-fold cross-validation on the AD/CTRL dataset, when the GM is classified as a whole. The highest discrimination accuracy between MCI converters and nonconverters is achieved when the SVM-RFE is applied to the whole GM: with AUC reaching (70.7 ± .9)%, it outperforms both ROI-based approaches in predicting the AD conversion

Noise reduction and spatial resolution in CT imaging with the ASIR iterative reconstruction algorithm at different doses and contrasts – a phantom study

Aims and objectives The aim of this study was to quantitatively assess noise reduction and spatial resolution in computed tomography (CT) imaging with the ASIR (Adaptive Statistical Iterative Reconstruction, GE Healthcare) reconstruction algorithm at different kVp, mAs and contrasts. Methods and materials Acquisitions of the Catphan-504 phantom were performed on a PET/CT scanner (Discovery-710, GE Healthcare). CT images were reconstructed using both filtered back projection (FBP) and ASIR with different percentages of reconstruction (20%, 40%, 60%, 80%, 100%). The image noise was estimated for different values of scanning parameters (i.e. tube-load, kilovoltage, pitch, slice thickness). Then, 3D/2D/1D noise power spectrum was estimated. Also, spatial resolution was assessed by obtaining the modulation transfer function (MTF) for a wide range of scanning parameters values and different contrast objects by the circular Edge Spread Function method (using CTP404 modulus) and the Point Spread Function method (using CTP528 modulus). . Results Image noise decreased (up to 50% as compared to FBP) with increasing the percentage of ASIR reconstruction (behaviour more relevant for higher spatial frequencies). Only for low tube load (<56 mAs) and low contrast objects (polistirene with respect to PMMA) acquisitions, MTF analysis showed that ASIR-reconstructed images were characterized by an appreciable reduction in spatial resolution, when compared to FBP-reconstructed images. Conclusion When compared to FBP, ASIR allows a relevant noise reduction without appreciably affecting image quality, except for very low dose and contrast acquisitions

Dental radiology dosimetric data as routinely collected in an Italian hospital

The work presented here was developed in the framework of the SENTINEL Project and is devoted to the analysis of dental radiology dosimetric data. The procedure of data processing allows the analysis of some important aspects related to the protection of the patient and the staff because of the position of the operators near the patient and their exposure to the radiation scattered by the patient. Dental radiology data was collected in an Italian hospital. Following the Italian quality assurance (QA) protocols and suggestions by the leaders of the SENTINEL Project, X-ray equipment performances have been analysed in terms of: kVp accuracy, exposure time accuracy and precision, tube output, dose reproducibility and linearity, beam collimation, artefacts and light tightness. Referring to these parameters the physical quality index (QI) was analysed. In a single numerical value between 0 and 1, QI summarises the results of quality tests for radiological devices. The actual impact of such a figure (as suggested by international QA protocols or as adopted by local QA routine) on the policy of machine maintenance and replacement is discussed

Imaging spectroscopic performances for a Si based detection system

We present the imaging and spectroscopic capabilities of a system based on a single photon counting chip (PCC) bump-bonded on a Si pixel detector. The system measures the energy spectrum and the flux, produced by a standard mammographic tube. We have also made some images of low contrast details, achieving good results

Semiconductor pixel detectors for digital mammography

Abstract We present some results obtained with silicon and gallium arsenide pixel detectors to be applied in the field of digital mammography. Even though GaAs is suitable for medical imaging applications thanks to its atomic number, which allows a very good detection efficiency, it often contains an high concentrations of traps which decrease the charge collection efficiency (CCE). So we have analysed both electrical and spectroscopic performance of different SI GaAs diodes as a function of concentrations of dopants in the substrate, in order to find a material by which we can obtain a CCE allowing the detection of all the photons that interact in the detector. Nevertheless to be able to detect low contrast details, efficiency and CCE are not the only parameters to be optimized; also the stability of the detection system is fundamental. In the past we have worked with Si pixel detectors; even if its atomic number does not allow a good detection efficiency at standard thickness, it has a very high stability. So keeping in mind the need to increase the Silicon detection efficiency we performed simulations to study the behaviour of the electrical potential in order to find a geometry to avoid the risk of electrical breakdown

Experimental study of Compton scattering reduction in digital mammographic imaging

In mammography, the first cause of image contrast reduction arises from the photons scattered inside the examined organ. The amount of Compton scattering strongly depends on the irradiation area and on the distance between the organ and the X-ray detector. We have experimentally evaluated how these geometrical conditions affect the scattering fraction. Our experimental setup includes a single photon counting device based on a silicon pixel detector as X-ray sensor; a lucite cylinder to simulate the breast tissue, and a lead collimator to define the irradiation area. We have evaluated the contrast and the signal-to-noise ratio for images acquired in different conditions

Experimental study of Compton scattering reduction in digital mammographic imaging

In mammography, the first cause of image contrast reduction arises from the photons scattered inside the examined organ. The amount of Compton scattering strongly depends on the irradiation area and on the distance between the organ and the X-ray detector. We have experimentally evaluated how these geometrical conditions affect the scattering fraction. Our experimental setup includes a single photon counting device based on a silicon pixel detector as X-ray sensor; a lucite cylinder to simulate the breast tissue, and a lead collimator to define the irradiation area. We have evaluated the contrast and the signal-to-noise ratio for images acquired in different conditions