Clinical study of contrast-enhanced digital mammography and the evaluation of blood and lymphatic microvessel density.

OBJECTIVE: To correlate image parameters in contrast-enhanced digital mammography (CEDM) with blood and lymphatic microvessel density (MVD). METHODS: 18 Breast Imaging-Reporting and Data System (BI-RADS)-4 to BI-RADS-5 patients were subjected to CEDM. Craniocaudal views were acquired, two views (low and high energy) before iodine contrast medium (CM) injection and four views (high energy) 1-5 min afterwards. Processing included registration and two subtraction modalities, traditional single-energy temporal (high-energy) and "dual-energy temporal with a matrix", proposed to improve lesion conspicuity. Images were calibrated into iodine thickness, and iodine uptake, contrast, time-intensity and time-contrast kinetic curves were quantified. Image indicators were compared with MVD evaluated by anti-CD105 and anti-podoplanin (D2-40) immunohistochemistry. RESULTS: 11 lesions were cancerous and 7 were benign. CEDM subtraction strongly increased conspicuity of lesions enhanced by iodine uptake. A strong correlation was observed between lymphatic vessels and blood vessels; all benign lesions had <30 blood microvessels per field, and all cancers had more than this value. MVD showed no correlation with iodine uptake, nor with contrast. The most frequent curve was early uptake followed by plateau for uptake and contrast in benign and malignant lesions. The positive-predictive value of uptake dynamics was 73% and that of contrast was 64%. CONCLUSION: CEDM increased lesion visibility and showed additional features compared with conventional mammography. Lack of correlation between image parameters and MVD is probably due to tumour tissue heterogeneity, mammography projective nature and/or dependence of extracellular iodine irrigation on tissue composition. ADVANCES IN KNOWLEDGE: Quantitative analysis of CEDM images was performed. Image parameters and MVD showed no correlation. Probably, this is indication of the complex dependence of CM perfusion on tumour microenvironment.

Geant4-based Monte Carlo simulations on GPU for medical applications.

Monte Carlo simulation (MCS) plays a key role in medical applications, especially for emission tomography and radiotherapy. However MCS is also associated with long calculation times that prevent its use in routine clinical practice. Recently, graphics processing units (GPU) became in many domains a low cost alternative for the acquisition of high computational power. The objective of this work was to develop an efficient framework for the implementation of MCS on GPU architectures. Geant4 was chosen as the MCS engine given the large variety of physics processes available for targeting different medical imaging and radiotherapy applications. In addition, Geant4 is the MCS engine behind GATE which is actually the most popular medical applications' simulation platform. We propose the definition of a global strategy and associated structures for such a GPU based simulation implementation. Different photon and electron physics effects are resolved on the fly directly on GPU without any approximations with respect to Geant4. Validations have shown equivalence in the underlying photon and electron physics processes between the Geant4 and the GPU codes with a speedup factor of 80-90. More clinically realistic simulations in emission and transmission imaging led to acceleration factors of 400-800 respectively compared to corresponding GATE simulations.

Validation of a digital mammographic unit model for an objective and highly automated clinical image quality assessment.

In mammography, image quality assessment has to be directly related to breast cancer indicator (e.g. microcalcifications) detectability. Recently, we proposed an X-ray source/digital detector (XRS/DD) model leading to such an assessment. This model simulates very realistic contrast-detail phantom (CDMAM) images leading to gold disc (representing microcalcifications) detectability thresholds that are very close to those of real images taken under the simulated acquisition conditions. The detection step was performed with a mathematical observer. The aim of this contribution is to include human observers into the disc detection process in real and virtual images to validate the simulation framework based on the XRS/DD model. Mathematical criteria (contrast-detail curves, image quality factor, etc.) are used to assess and to compare, from the statistical point of view, the cancer indicator detectability in real and virtual images. The quantitative results given in this paper show that the images simulated by the XRS/DD model are useful for image quality assessment in the case of all studied exposure conditions using either human or automated scoring. Also, this paper confirms that with the XRS/DD model the image quality assessment can be automated and the whole time of the procedure can be drastically reduced. Compared to standard quality assessment methods, the number of images to be acquired is divided by a factor of eight.

Computation of realistic virtual phantom images for an objective lesion detectability assessment in digital mammography.

Image quality assessment is required for an optimal use of mammographic units. On the one hand, there are objective image quality assessment methods based on the measurement of technical parameters such as modulation transfer function (MTF), noise power spectrum (NPS) or detection quantum efficiency (DQE) describing performances of digital detectors. These parameters are, however, without direct relationship with lesion detectability in clinical practice. On the other hand, there are image quality assessment methods involving time consuming procedures, but presenting a direct relationship with lesion detectability. This contribution describes an X-ray source/digital detector model leading to the simulation of virtual contrast-detail phantom (CDMAM) images. The virtual image computation method requires the acquisition of only few real images and allows for an objective image quality assessment presenting a direct relationship with lesion detectability. The transfer function of the proposed model takes as input physical parameters (MTF* and noise) measured under clinical conditions on mammographic units. As presented in this contribution, MTF* is a modified MTF taking into account the effects due to X-ray scatter in the breast and magnification. Results obtained with the structural similarity index prove that the simulated images are quite realistic in terms of contrast and noise. Tests using contrast detail curves highlight the fact that the simulated and real images lead to very similar data quality in terms of lesion detectability. Finally, various statistical tests show that quality factors computed for both the simulated images and the real images are very close for the two data sets.