Phantomless calibration of CT scans for hip fracture risk prediction in silico: Comparison with phantom-based calibration.

Finite element models built from quantitative computed tomography images rely on element-wise mapping of material properties starting from Hounsfield Units (HU), which can be converted into mineral densities upon calibration. While calibration is preferably carried out by scanning a phantom with known-density components, conducting phantom-based calibration may not always be possible. In such cases, a phantomless procedure, where the scanned subject's tissues are used as a phantom, is an interesting alternative. The aim of this study was to compare a phantom-based and a phantomless calibration method on 41 postmenopausal women. The proposed phantomless calibration utilized air, adipose, and muscle tissues, with reference equivalent mineral density values of -797, -95, and 38 mg/cm3, extracted from a previously performed phantom-based calibration. A 9-slice volume of interest (VOI) centred between the femoral head and knee rotation centres was chosen. Reference HU values for air, adipose, and muscle tissues were extracted by identifying HU distribution peaks within the VOI, and patient-specific calibration was performed using linear regression. Comparison of FE models calibrated with the two methods showed average relative differences of 1.99% for Young's modulus1.30% for tensile and 1.34% for compressive principal strains. Excellent correlations (R2 > 0.99) were identified for superficial maximum tensile and minimum compressive strains. Maximum normalised root mean square relative error (RMSRE) values settled at 4.02% for Young's modulus, 2.99% for tensile, and 3.22% for compressive principal strains, respectively. The good agreement found between the two methods supports the adoption of the proposed methodology when phantomless calibration is needed.

Development and validation of a semi-automated and unsupervised method for femur segmentation from CT.

Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 ± 0.01 mm) and the difference union ratio (0.06 ± 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 ± 0.01 mm and 0.03 ± 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods.

Deep Learning Method for Estimation of Morphological Parameters Based on CT Scans.

In this study, we propose a Convolutional Neural Network (CNN) with an assembly of non-linear fully connected layers for estimating body height and weight using a limited amount of data. This method can predict the parameters within acceptable clinical limits for most of the cases even when trained with limited data.

Statistical Properties of a Virtual Cohort for In Silico Trials Generated with a Statistical Anatomy Atlas.

Osteoporosis-related hip fragility fractures are a catastrophic event for patient lives but are not frequently observed in prospective studies, and therefore phase III clinical trials using fractures as primary clinical endpoint require thousands of patients enrolled for several years to reach statistical significance. A novel answer to the large number of subjects needed to reach the desired evidence level is offered by In Silico Trials, that is, the simulation of a clinical trial on a large cohort of virtual patients, monitoring the biomarkers of interest. In this work we investigated if statistical aliasing from a custom anatomy atlas could be used to expand the patient cohort while retaining the original biomechanical characteristics. We used a pair-matched cohort of 94 post-menopausal women (at the time of the CT scan, 47 fractured and 47 not fractured) to create a statistical anatomy atlas through principal component analysis, and up-sampled the atlas in order to obtain over 1000 synthetic patient models. We applied the biomechanical computed tomography pipeline to the resulting virtual cohort and compared its fracture risk distribution with that of the original physical cohort. While the distribution of femoral strength values in the non-fractured sub-group was nearly identical to that of the original physical cohort, that of the fractured sub-group was lower than in the physical cohort. Nonetheless, by using the classification threshold used for the original population, the synthetic population was still divided into two parts of approximatively equal number.

Moldless Printing of Silicone Lenses With Embedded Nanostructured Optical Filters.

Optical lenses are among the oldest technological innovations (3000 years ago) and they have enabled a multitude of applications in healthcare and in our daily lives. The primary function of optical lenses has changed little over time; they serve mainly as a light-collection (e.g. reflected, transmitted, diffracted) element, and the wavelength and/or intensity of the collected light is usually manipulated by coupling with various external optical filter elements or coatings. This generally results in losses associated with multiple interfacial reflections, and increases the complexity of design and construction. In this work we introduce a change in this paradigm, by integrating both light-shaping and image magnification into a single lens element using a moldless procedure that takes advantage of the physical and optical properties of mesoporous silicon (PSi) photonic crystal nanostructures. Casting of a liquid poly(dimethyl) siloxane (PDMS) pre-polymer solution onto a PSi film generates a droplet with contact angle that is readily controlled by the silicon nanostructure, and adhesion of the cured polymer to the PSi photonic crystal allows preparation of lightweight (10 mg) freestanding lenses (4.7 mm focal length) with an embedded optical component (e.g. optical rugate filter, resonant cavity, distributed Bragg reflector). Our fabrication process shows excellent reliability (yield 95%) and low cost and we expect our lens to have implications in a wide range of applications. As a proof-of-concept, using a single monolithic lens/filter element we demonstrate: fluorescence imaging of isolated human cancer cells with rejection of the blue excitation light, through a lens that is self-adhered to a commercial smartphone; shaping the emission spectrum of a white light emitting diode (LED) to tune the color from red through blue; and selection of a narrow wavelength band (bandwidth 5 nm) from a fluorescent molecular probe.

Bioresorbable Materials on the Rise: From Electronic Components and Physical Sensors to In Vivo Monitoring Systems.

Over the last decade, scientists have dreamed about the development of a bioresorbable technology that exploits a new class of electrical, optical, and sensing components able to operate in physiological conditions for a prescribed time and then disappear, being made of materials that fully dissolve in vivo with biologically benign byproducts upon external stimulation. The final goal is to engineer these components into transient implantable systems that directly interact with organs, tissues, and biofluids in real-time, retrieve clinical parameters, and provide therapeutic actions tailored to the disease and patient clinical evolution, and then biodegrade without the need for device-retrieving surgery that may cause tissue lesion or infection. Here, the major results achieved in bioresorbable technology are critically reviewed, with a bottom-up approach that starts from a rational analysis of dissolution chemistry and kinetics, and biocompatibility of bioresorbable materials, then moves to in vivo performance and stability of electrical and optical bioresorbable components, and eventually focuses on the integration of such components into bioresorbable systems for clinically relevant applications. Finally, the technology readiness levels (TRLs) achieved for the different bioresorbable devices and systems are assessed, hence the open challenges are analyzed and future directions for advancing the technology are envisaged.

Decoration of Porous Silicon with Gold Nanoparticles via Layer-by-Layer Nanoassembly for Interferometric and Hybrid Photonic/Plasmonic (Bio)sensing.

Gold nanoparticle layers (AuNPLs) enable the coupling of morphological, optical, and electrical properties of gold nanoparticles (AuNPs) with tailored and specific surface topography, making them exploitable in many bioapplications (e.g., biosensing, drug delivery, and photothermal therapy). Herein, we report the formation of AuNPLs on porous silicon (PSi) interferometers and distributed Bragg reflectors (DBRs) for (bio)sensing applications via layer-by-layer (LbL) nanoassembling of a positively charged polyelectrolyte, namely, poly(allylamine hydrochloride) (PAH), and negatively charged citrate-capped AuNPs. Decoration of PSi interferometers with AuNPLs enhances the Fabry-Pérot fringe contrast due to increased surface reflectivity, resulting in an augmented sensitivity for both bulk and surface refractive index sensing, namely, about 4.5-fold using NaCl aqueous solutions to infiltrate the pores and 2.6-fold for unspecific bovine serum albumin (BSA) adsorption on the pore surface, respectively. Sensitivity enhancing, about 2.5-fold, is also confirmed for affinity and selective biosensing of streptavidin using a biotinylated polymer, namely, negatively charged poly(methacrylic acid) (b-PMAA). Further, decoration of PSi DBR with AuNPLs envisages building up a hybrid photonic/plasmonic optical sensing platform. Both photonic (DBR stop-band) and plasmonic (localized surface plasmon resonance, LSPR) peaks of the hybrid structure are sensitive to changes of bulk (using glucose aqueous solutions) and surface (due to BSA unspecific adsorption) refractive index. To the best of our knowledge, this is the first report about the formation of AuNPLs via LbL nanoassembly on PSi for (i) the enhancing of the interferometric performance in (bio)sensing applications and (ii) the building up of hybrid photonic/plasmonic platforms for sensing and perspective biosensing applications.

Room-Temperature Low-Threshold Lasing from Monolithically Integrated Nanostructured Porous Silicon Hybrid Microcavities.

Silicon photonics would strongly benefit from monolithically integrated low-threshold silicon-based laser operating at room temperature, representing today the main challenge toward low-cost and power-efficient electronic-photonic integrated circuits. Here we demonstrate low-threshold lasing from fully transparent nanostructured porous silicon (PSi) monolithic microcavities (MCs) infiltrated with a polyfluorene derivative, namely, poly(9,9-di- n-octylfluorenyl-2,7-diyl) (PFO). The PFO-infiltrated PSiMCs support single-mode blue lasing at the resonance wavelength of 466 nm, with a line width of ∼1.3 nm and lasing threshold of 5 nJ (15 µJ/cm2), a value that is at the state of the art of PFO lasers. Furthermore, time-resolved photoluminescence shows a significant shortening (∼57%) of PFO emission lifetime in the PSiMCs, with respect to nonresonant PSi reference structures, confirming a dramatic variation of the radiative decay rate due to a Purcell effect. Our results, given also that blue lasing is a worst case for silicon photonics, are highly appealing for the development of low-cost, low-threshold silicon-based lasers with wavelengths tunable from visible to the near-infrared region by simple infiltration of suitable emitting polymers in monolithically integrated nanostructured PSiMCs.