Variability of coil inductance measurements inside an interleaving structure.

The present article is a continuation of our previous works on the anomalies in the decay rates and the capacitance measurements inside a modified Faraday cage. Here we present the anomalous variations in the measurements of inductance when a coil is placed inside an interleaving structure of metal and organic material. The fluctuation in the inductance values was found to be in the range - 0.007 to 0.018 mH. Additionally, it was observed that the temperature coefficient which was 0.0062 mH/°C initially jumped to two distinct levels, 0.0095 mH/°C and 0.0145 mH/°C, respectively. A multiple factor analysis of our results revealed a very strong correlation (R2 = 95.2%) between the inductance and the combination of the temperature and the relative humidity both measured inside the cage, next to the inductor.

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review.

PURPOSE: To systematically review evidence regarding the association of multiparametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. MATERIALS AND METHODS: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th, 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. RESULTS: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and highrisk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, α=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. CONCLUSION: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.

Towards an in-plane methodology to track breast lesions using mammograms and patient-specific finite-element simulations.

In breast cancer screening or diagnosis, it is usual to combine different images in order to locate a lesion as accurately as possible. These images are generated using a single or several imaging techniques. As x-ray-based mammography is widely used, a breast lesion is located in the same plane of the image (mammogram), but tracking it across mammograms corresponding to different views is a challenging task for medical physicians. Accordingly, simulation tools and methodologies that use patient-specific numerical models can facilitate the task of fusing information from different images. Additionally, these tools need to be as straightforward as possible to facilitate their translation to the clinical area. This paper presents a patient-specific, finite-element-based and semi-automated simulation methodology to track breast lesions across mammograms. A realistic three-dimensional computer model of a patient's breast was generated from magnetic resonance imaging to simulate mammographic compressions in cranio-caudal (CC, head-to-toe) and medio-lateral oblique (MLO, shoulder-to-opposite hip) directions. For each compression being simulated, a virtual mammogram was obtained and posteriorly superimposed to the corresponding real mammogram, by sharing the nipple as a common feature. Two-dimensional rigid-body transformations were applied, and the error distance measured between the centroids of the tumors previously located on each image was 3.84 mm and 2.41 mm for CC and MLO compression, respectively. Considering that the scope of this work is to conceive a methodology translatable to clinical practice, the results indicate that it could be helpful in supporting the tracking of breast lesions.

A patient-specific FE-based methodology to simulate prosthesis insertion during an augmentation mammoplasty.

Breast augmentation surgery is a widespread practice for aesthetic purposes. Current techniques, however, are not able to reliably predict the desired final aspect of the breast after the intervention, whose success relies almost completely on the surgeon's skill. In this way, patient-specific methodologies capable of predicting the outcomes of such interventions are of particular interest. In this paper, a finite element biomechanical model of the breast of a female patient before an augmentation mammoplasty was generated using computer tomography images. Prosthesis insertion during surgery was simulated using the theory of finite elasticity. Hyperelastic constitutive models were considered for breast tissues and silicone implants. The deformed geometry obtained from finite element analysis was compared qualitatively and quantitatively with the real breast shape of the patient lying in supine position, with root-mean-squared errors less than 3mm. The results indicate that the presented methodology is able to reasonably predict the aspect of the breast in an intermediate step of augmentation mammoplasty, and reveal the potential capabilities of finite element simulations for visualization and prediction purposes. However, further work is required before this methodology can be helpful in aesthetic surgery planning.