Recently, there has been an increased interest in quantitative MR parameters to improve diagnosis and treatment. Parameter mapping requires multiple images acquired with different timings usually resulting in long acquisition times. While acquisition time can be reduced by acquiring undersampled data, obtaining accurate estimates of parameters from undersampled data is a challenging problem, in particular for structures with high spatial frequency content. In this work, Principal Component Analysis (PCA) is combined with a model-based algorithm to reconstruct maps of selected principal component coefficients from highly undersampled radial MRI data. This novel approach linearizes the cost function of the optimization problem yielding a more accurate and reliable estimation of MR parameter maps. The proposed algorithm - REconstruction of Principal COmponent coefficient Maps (REPCOM) using Compressed Sensing - is demonstrated in phantoms and in vivo and compared to two other algorithms previously developed for undersampled data.

Key Points Question Can in silico imaging trials play a role in the evaluation of new medical imaging systems? Findings This diagnostic study used computer-simulated imaging of 2986 synthetic image–based virtual patients to compare digital mammography and digital breast tomosynthesis and found an improved lesion detection performance favoring tomosynthesis for all breast sizes and lesion types. The increased performance for tomosynthesis was consistent with results from a comparative trial using human patients and radiologists. Meaning The study’s findings suggest that in silico imaging trials and imaging system computer simulation tools can in some cases be considered viable sources of evidence for the regulatory evaluation of imaging devices.

Purpose: Physical phantoms are central to the evaluation of 2D and 3D breast-imaging systems. Currently, available physical phantoms have limitations including unrealistic uniform background structure, large expense, or excessive fabrication time. The purpose of this work is to outline a method for rapidly creating realistic, inexpensive physical anthropomorphic phantoms for use in full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT). Methods: The phantom was first modeled using analytical expressions and then discretized into vox-els of a specified size. The interior of the breast was divided into glandular and adipose tissue classes using Voronoi segmentation, and additional structures like blood vessels, chest muscle, and ligaments were added. The physical phantom was then fabricated from the virtual model in a slice by slice fashion through inkjet printing, using parchment paper and a radiopaque ink containing 33% (I 33%) or 25% (I 25%) iohexol by volume. Three types of parchment paper (P1, P2, and P3) were examined. The phantom materials were characterized in terms of their effective linear attenuation coefficients (l eff) using full-field digital mammography (FFDM) and their energy-dependent linear attenuation coefficients (l(E)) using a spectroscopic energy discriminating detector system. The printing method was further validated on the basis of accuracy, print consistency, and the reproducibility of ink batches. Results: The l eff of two types of parchment paper were close to that of adipose tissue, with l eff = 0.61 AE 0.05 cm À1 for P1, 0.61 AE 0.04 cm À1 for P2, and 0.57 AE 0.03 cm À1 for adipose tissue. The addition of the iodinated ink increased the effective attenuation to that of glandular tissue, with l eff = 0.89 AE 0.06 cm À1 for P1 + I 25% and 0.94 AE 0.06 cm À1 for P1 + I 33% compared to 0.90 AE 0.03 cm À1 for glandular tissue. Spectroscopic measurements showed a good match between the parchment paper and reference values for adipose and glandular tissues across photon energies. Good accuracy was found between the model and the printed phantom by comparing a FFDM of the virtual model simulated through Monte Carlo with a real FFDM of the fully printed phantom. High consistency was found over multiple prints, with 3% variability in mean ink signal across various samples. Reproducibility of ink consistency was very high with <1% variation signal from multiple batches of ink. Imaging of the phantom using FFDM and DBT systems showed promising utility for 2D and 3D imaging. Conclusions: A novel, realistic breast phantom can be created using an analytically defined breast model and readily available materials. The work provides a method to fabricate any virtual phantom in a manner that is accurate, inexpensive, easily accessible, and can be made with different materials or breast models.

Purpose: Physical phantoms are central to the evaluation of 2D and 3D breast-imaging systems. Currently, available physical phantoms have limitations including unrealistic uniform background structure, large expense, or excessive fabrication time. The purpose of this work is to outline a method for rapidly creating realistic, inexpensive physical anthropomorphic phantoms for use in fullfield digital mammography (FFDM) and digital breast tomosynthesis (DBT). Methods: The phantom was first modeled using analytical expressions and then discretized into voxels of a specified size. The interior of the breast was divided into glandular and adipose tissue classes using Voronoi segmentation, and additional structures like blood vessels, chest muscle, and ligaments were added. The physical phantom was then fabricated from the virtual model in a slice by slice fashion through inkjet printing, using parchment paper and a radiopaque ink containing 33% (I 33% ) or 25% (I 25% ) iohexol by volume. Three types of parchment paper (P1, P2, and P3) were examined. The phantom materials were characterized in terms of their effective linear attenuation coefficients (l eff ) using full-field digital mammography (FFDM) and their energy-dependent linear attenuation coefficients (l(E)) using a spectroscopic energy discriminating detector system. The printing method was further validated on the basis of accuracy, print consistency, and the reproducibility of ink batches. Results: The l eff of two types of parchment paper were close to that of adipose tissue, with l eff = 0.61 AE 0.05 cm À1 for P1, 0.61 AE 0.04 cm À1 for P2, and 0.57 AE 0.03 cm À1 for adipose tissue. The addition of the iodinated ink increased the effective attenuation to that of glandular tissue, with l eff = 0.89 AE 0.06 cm À1 for P1 + I 25% and 0.94 AE 0.06 cm À1 for P1 + I 33% compared to 0.90 AE 0.03 cm À1 for glandular tissue. Spectroscopic measurements showed a good match between the parchment paper and reference values for adipose and glandular tissues across photon energies. Good accuracy was found between the model and the printed phantom by comparing a FFDM of the virtual model simulated through Monte Carlo with a real FFDM of the fully printed phantom. High consistency was found over multiple prints, with 3% variability in mean ink signal across various samples. Reproducibility of ink consistency was very high with <1% variation signal from multiple batches of ink. Imaging of the phantom using FFDM and DBT systems showed promising utility for 2D and 3D imaging. Conclusions: A novel, realistic breast phantom can be created using an analytically defined breast model and readily available materials. The work provides a method to fabricate any virtual phantom in a manner that is accurate, inexpensive, easily accessible, and can be made with different materials or breast models.

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The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.

Over the past decades, significant improvements have been made in the field of computational human phantoms (CHPs) and their applications in biomedical engineering. Their sophistication has dramatically increased. The very first CHPs were composed of simple geometric volumes, e.g., cylinders and spheres, while current CHPs have a high resolution, cover a substantial range of the patient population, have high anatomical accuracy, are poseable, morphable, and are augmented with various details to perform functionalized computations. Advances in imaging techniques and semi-automated segmentation tools allow fast and personalized development of CHPs. These advances open the door to quickly develop personalized CHPs, inherently including the disease of the patient. Because many of these CHPs are increasingly providing data for regulatory submissions of various medical devices, the validity, anatomical accuracy, and availability to cover the entire patient population is of utmost importance. The article is organized into two main sections: the first section reviews the different modeling techniques used to create CHPs, whereas the second section discusses various applications of CHPs in biomedical engineering. Each topic gives an overview, a brief history, recent developments, and an outlook into the future.

The resemblance between human faces has been shown to be a possible cue in recognizing the relatedness between parents and children, and more recently, between siblings. However, the general inclusive fitness theory proposes that kin-selective behaviours are also relevant to more distant relatives, which requires the detection of larger kinship bonds. We conducted an experiment to explore the use of facial clues by 'strangers', i.e. evaluators from a different family, to associate humans of varying degrees of relatedness. We hypothesized that the visual capacity to detect relatedness should be weaker with lower degrees of relatedness. We showed that human adults are capable of (although not very efficient at) assessing the relatedness of unrelated individuals from photographs and that visible facial cues vary according to the degree of relatedness. This sensitivity exists even for kin pair members that are more than a generation apart and have never lived together. Collectively, our findings are in agreement with emerging knowledge on the role played by facial resemblance as a kinship cue. But we have progressed further to show how the capacity to distinguish between related and non-related pairs applies to situations relevant to indirect fitness.