Ca2Lib: Simple and Accurate LiDAR-RGB Calibration Using Small Common Markers.

Modern visual perception techniques often rely on multiple heterogeneous sensors to achieve accurate and robust estimates. Knowledge of their relative positions is a mandatory prerequisite to accomplish sensor fusion. Typically, this result is obtained through a calibration procedure that correlates the sensors' measurements. In this context, we focus on LiDAR and RGB sensors that exhibit complementary capabilities. Given the sparsity of LiDAR measurements, current state-of-the-art calibration techniques often rely on complex or large calibration targets to resolve the relative pose estimation. As such, the geometric properties of the targets may hinder the calibration procedure in those cases where an ad hoc environment cannot be guaranteed. This paper addresses the problem of LiDAR-RGB calibration using common calibration patterns (i.e., A3 chessboard) with minimal human intervention. Our approach exploits the flatness of the target to find associations between the sensors' measurements, leading to robust features and retrieval of the solution through nonlinear optimization. The results of quantitative and comparative experiments with other state-of-the-art approaches show that our simple schema performs on par or better than existing methods that rely on complex calibration targets.

Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach.

Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1 , and IR-bSSFP-B1 , the latter two designed to be more specific for B1+ parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary-based and the NN approaches. In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR-FISP MRF sequence exploiting both simulations and in vivo acquisitions. Results demonstrate that the NN approach outperforms the dictionary-based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T1 , T2 , and M0 maps that are in good agreement with those obtained with the original dictionary approach. k-SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.

Coral micro- and macro-morphological skeletal properties in response to life-long acclimatization at CO2 vents in Papua New Guinea.

This study investigates the effects of long-term exposure to OA on skeletal parameters of four tropical zooxanthellate corals naturally living at CO2 seeps and adjacent control sites from two locations (Dobu and Upa Upasina) in the Papua New Guinea underwater volcanic vent system. The seeps are characterized by seawater pH values ranging from 8.0 to about 7.7. The skeletal porosity of Galaxea fascicularis, Acropora millepora, massive Porites, and Pocillopora damicornis was higher (up to ~ 40%, depending on the species) at the seep sites compared to the control sites. Pocillopora damicornis also showed a decrease of micro-density (up to ~ 7%). Thus, further investigations conducted on this species showed an increase of the volume fraction of the larger pores (up to ~ 7%), a decrease of the intraskeletal organic matrix content (up to ~ 15%), and an increase of the intraskeletal water content (up to ~ 59%) at the seep sites. The organic matrix related strain and crystallite size did not vary between seep and control sites. This multi-species study showed a common phenotypic response among different zooxanthellate corals subjected to the same environmental pressures, leading to the development of a more porous skeletal phenotype under OA.

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data.

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging-Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations.

MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimensionality, which determines an explosion of resource requirements. In this work, we describe a deep learning approach for MRF parameter map reconstruction using a fully connected architecture. Employing simulations, we have investigated how the performance of the Neural Networks (NN) approach scales with the number of parameters to be retrieved, compared to the standard dictionary approach. We have also studied optimal training procedures by comparing different strategies for noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1, and IR-bSSFP-B1. A comparison between NN and the dictionary approaches in reconstructing parameter maps as a function of the number of parameters to be retrieved was performed using a numerical brain phantom. Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance. NN performance was at least as good as the dictionary-based approach in reconstructing parameter maps using Gaussian noise as a source of artifacts: the difference in performance increased with the number of estimated parameters because the dictionary method suffers from the coarse resolution of the parameter space sampling. The NN proved to be more efficient in memory usage and computational burden, and has great potential for solving large-scale MRF problems.

Characterization of Structural Bone Properties through Portable Single-Sided NMR Devices: State of the Art and Future Perspectives.

Nuclear Magnetic Resonance (NMR) is a well-suited methodology to study bone composition and structural properties. This is because the NMR parameters, such as the T2 relaxation time, are sensitive to the chemical and physical environment of the 1H nuclei. Although magnetic resonance imaging (MRI) allows bone structure assessment in vivo, its cost limits the suitability of conventional MRI for routine bone screening. With difficulty accessing clinically suitable exams, the diagnosis of bone diseases, such as osteoporosis, and the associated fracture risk estimation is based on the assessment of bone mineral density (BMD), obtained by the dual-energy X-ray absorptiometry (DXA). However, integrating the information about the structure of the bone with the bone mineral density has been shown to improve fracture risk estimation related to osteoporosis. Portable NMR, based on low-field single-sided NMR devices, is a promising and appealing approach to assess NMR properties of biological tissues with the aim of medical applications. Since these scanners detect the signal from a sensitive volume external to the magnet, they can be used to perform NMR measurement without the need to fit a sample inside a bore of a magnet, allowing, in principle, in vivo application. Techniques based on NMR single-sided devices have the potential to provide a high impact on the clinical routine because of low purchasing and running costs and low maintenance of such scanners. In this review, the development of new methodologies to investigate structural properties of trabecular bone exploiting single-sided NMR devices is reviewed, and current limitations and future perspectives are discussed.

Brain functional MRI responses to blue light stimulation in Leber's hereditary optic neuropathy.

Melanopsin retinal ganglion cells (mRGCs) are intrinsically photosensitive photoreceptors contributing both to image and non-image-forming (NIF) functions of the eye. They convey light signal to the brain to modulate circadian entrainment, sleep, alertness, cognition, brightness perception and coarse vision. Given that rods and cones also contribute to all these impacts of light, isolating mRGC visual and NIF roles in humans is challenging so that mRGC functions remains to be fully characterized. Here, we evaluated light-driven visual and cognitive brain responses in Leber's Hereditary Optic Neuropathy (LHON), an inherited optic neuropathy that is characterized by a selective relative sparing of the melanopsin-expressing retinal ganglion cells (mRGCs). Twelve patients and twelve matched healthy controls (HC) were enrolled in a functional brain magnetic resonance imaging (fMRI) protocol including visual and visual-cognitive paradigms under blue (480 nm) and red (620 nm) light exposures. Primary visual cortex activation was detected in LHON patients; in particular higher occipital activation was found in response to sustained blue vs. red stimulation in LHON vs. HC. Similarly, brain responses to the executive task were larger under blue vs. red light in LHON over lateral prefrontal cortex. These findings are in line with the relative mRGC sparing demonstrated in LHON and support the mRGC contribution to both non-visual and visual brain functions, with potential implication for visual rehabilitation in hereditary optic neuropathy patients.

Single-sided NMR to estimate morphological parameters of the trabecular bone structure.

PURPOSE: Single-sided 1 H-NMR is proposed for the estimation of morphological parameters of trabecular bone, and potentially the detection of pathophysiological alterations of bone structure. In this study, a new methodology was used to estimate such parameters without using an external reference signal, and to study intratrabecular and intertrabecular porosities, with a view to eventually scanning patients. METHODS: Animal trabecular bone samples were analyzed by a single-sided device. The Carr-Purcell-Meiboom-Gill sequence of 1 H nuclei of fluids, including marrow, confined inside the bone, was analyzed by quasi-continuous T2 distributions and separated into two 1 H pools: short and long T2 components. The NMR parameters were estimated using models of trabecular bone structure, and compared with the corresponding micro-CT. RESULTS: Without any further assumptions, the internal reference parameter (short T2 signal intensity fraction) enabled prediction of the micro-CT parameters BV/TV (volume of the trabeculae/total sample volume) and BS/TV (external surface of the trabeculae/total sample volume) with linear correlation coefficient >0.80. The assignment of the two pools to intratrabecular and intertrabecular components yielded an estimate of average intratrabecular porosity (33 ± 5)%. Using the proposed models, the NMR-estimated BV/TV and BS/TV were found to be linearly related to the corresponding micro-CT values with high correlation (>0.90 for BV/TV; >0.80 for BS/TV) and agreement coefficients. CONCLUSION: Low-field, low-cost portable devices that rely on intrinsic magnetic field gradients and do not use ionizing radiation are viable tools for in vitro preclinical studies of pathophysiological structural alterations of trabecular bone.

Identification of complex structures of paintings on canvas by NMR: Correlation between NMR profile and stratigraphy.

Paintings on canvas are complex structures created by superimposing layers of different composition. Investigations on the structure of these artworks can provide essential information on their state of conservation, pictorial technique, possible overpaintings, and in planning a proper conservation plan. Standard methods of investigation consist in sampling a limited number of fragments for stratigraphic analyses. Despite the recognized validity of these methods, they are affected by evident limitations. Nuclear magnetic resonance (NMR) profiling, often named NMR stratigraphy, is an NMR relaxometry technique applied by single-sided portable devices developed to overcome the disadvantages of microinvasive stratigraphic analyses. The potential of this approach on artworks, including wall paintings and a few examples of painted canvas, is described in the literature. In this study, NMR profiles of painting on canvas were examined by analyzing transverse relaxation time data by T2 quasi-continuous distributions and the results compared with standard stratigraphic cross-sections analysis. Combining signal intensity and T2 quasi-continuous distributions, the identification of textile, preparatory, and paint layers was enhanced. The diction "NMR stratigraphy" for these inhomogeneous layered artworks is also discussed. Indeed, unlike the stratigraphic cross-sections, NMR profiles provide information on a volume (flat slice), rather than on a surface, and the collected signal can derive from nonuniform and partially overlapping layers. This study paves the way for extensive investigations on relaxation time quasi-continuous distributions in various binder/pigment mixtures in order to improve the reliability of NMR profile as an innovative, non-invasive, and nondestructive method for analyzing paintings on canvas.

Bone volume-to-total volume ratio measured in trabecular bone by single-sided NMR devices.

PURPOSE: Reduced bone strength is associated with a loss of bone mass, usually evaluated by dual-energy X-ray absorptiometry, although it is known that the bone microstructure also affects the bone strength. Here, a method is proposed to measure (in laboratory) the bone volume-to-total volume ratio by single-sided NMR scanners, which is related to the microstructure of the trabecular bone. METHODS: Three single-sided scanners were used on animal bone samples. These low-field, mobile, low-cost devices are able to detect the NMR signal, regardless of the sample sizes, without the use of ionizing radiations, with the further advantage of signal localization offered by their intrinsic magnetic field gradients. RESULTS: The performance of the different single-sided scanners have been discussed. The results have been compared with bone volume-to-total volume ratio by micro CT and MRI, obtaining consistent values. CONCLUSIONS: Our results demonstrate the feasibility of the method for laboratory analyses, which are useful for measurements like porosity on bone specimens. This can be considered as the first step to develop an NMR method based on the use of a mobile single-sided device, for the diagnosis of osteoporosis, through the acquisition of the signal from the appendicular skeleton, allowing for low-cost, wide screening campaigns. Magn Reson Med 79:501-510, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

An Environmental Friendly Fluorinated Oligoamide for Producing Nonwetting Coatings with High Performance on Porous Surfaces.

The changes in the surface wettability of many materials are receiving increased attention in recent years. It is not too hard to fabricate resistant hydrophobic surfaces through products bearing both hydrophobic and reactive hydrophilic end groups. More challenging is obtaining resistant nonwetting surfaces through noncovalent reversible bonds. In this work, a fluorinated oligo(ethylenesuccinamide), soluble in solvent benign for operators and environment, has been synthesized. It contains two opposite functional groups (perfluoropolyether segments and amidic groups) (SC2-PFPE) that provide water repellency while hydrophilicity is retained. Its performance has been tested on porous calcarenite and investigated by magnetic resonance imaging, water capillary absorption, and vapor diffusivity tests. The results demonstrate that SC2-PFPE modifies the wettability of porous substrates in a drastic and durable way and reduces the vapor condensation inside the pore space due to the perfluoropolyether segments that act at the air/surface interface.

Shell properties of commercial clam Chamelea gallina are influenced by temperature and solar radiation along a wide latitudinal gradient.

Phenotype can express different morphologies in response to biotic or abiotic environmental influences. Mollusks are particularly sensitive to different environmental parameters, showing macroscale shell morphology variations in response to environmental parameters. Few studies concern shell variations at the different scale levels along environmental gradients. Here, we investigate shell features at the macro, micro and nanoscale, in populations of the commercially important clam Chamelea gallina along a latitudinal gradient (~400 km) of temperature and solar radiation in the Adriatic Sea (Italian cost). Six populations of clams with shells of the same length were analyzed. Shells from the warmest and the most irradiated population were thinner, with more oval shape, more porous and lighter, showing lower load fracture. However, no variation was observed in shell CaCO3 polymorphism (100% aragonite) or in compositional and textural shell parameters, indicating no effect of the environmental parameters on the basic processes of biomineralization. Because of the importance of this species as commercial resource in the Adriatic Sea, the experimentally quantified and significant variations of mass and fracture load in C. gallina shells along the latitudinal gradient may have economic implications for fisheries producing different economical yield for fishermen and consumers along the Adriatic coastline.

Gains and losses of coral skeletal porosity changes with ocean acidification acclimation.

Ocean acidification is predicted to impact ecosystems reliant on calcifying organisms, potentially reducing the socioeconomic benefits these habitats provide. Here we investigate the acclimation potential of stony corals living along a pH gradient caused by a Mediterranean CO2 vent that serves as a natural long-term experimental setting. We show that in response to reduced skeletal mineralization at lower pH, corals increase their skeletal macroporosity (features >10 µm) in order to maintain constant linear extension rate, an important criterion for reproductive output. At the nanoscale, the coral skeleton's structural features are not altered. However, higher skeletal porosity, and reduced bulk density and stiffness may contribute to reduce population density and increase damage susceptibility under low pH conditions. Based on these observations, the almost universally employed measure of coral biomineralization, the rate of linear extension, might not be a reliable metric for assessing coral health and resilience in a warming and acidifying ocean.

Nano and sub-nano multiscale porosity formation and other features revealed by 1H NMR relaxometry during cement hydration.

Cement hydration occurs when water is added to cement powder, leading to the formation of crystalline products like Portlandite and the quasi-amorphous, poorly crystalline, calcium silicate hydrate (C-S-H) gel. Despite its importance in determining the final properties of the cement, many models exist for the nano and sub-nano level organization of this "liquid stone." (1)H NMR relaxometry in White Portland Cement paste during hydration allowed us to monitor the formation and evolution of the multiscale porosity of the cement, with the formation of structures at nano and sub-nano levels of C-S-H gel (calcium silicate interlayer water, water in small and large gel pores) along with three low-mobility (1)H pools, identified as (1)H nuclei in C-S-H layers, likely belonging to OH groups, with (1)H nuclei in Portlandite, and in crystal water of Ettringite. By assuming these assignments, our data allowed us to compute the distances of pairs of (1)H nuclei in Portlandite and in crystal water ((1.9 ± 0.2) Å and (1.6 ± 0.1) Å, respectively), consistent with the known values of these distances. The picture of the porous structure at nano and sub-nano levels emerging from our results is consistent with the Jennings colloidal model for C-S-H gel. Moreover, the constant values observed during hydration of parameters extracted from our data analysis strongly support that model, being compatible with the picture of C-S-H gel developing in comparable-sized clumps of the same composition, but not easily interpretable by models proposing quasi continuous sheets of C-S-H layers.

A time-domain nuclear magnetic resonance study of Mediterranean scleractinian corals reveals skeletal-porosity sensitivity to environmental changes.

Mediterranean corals are a natural model for studying global warming, as the Mediterranean basin is expected to be one of the most affected regions and the increase in temperature is one of the greatest threats for coral survival. We have analyzed for the first time with time-domain nuclear magnetic resonance (TD-NMR) the porosity and pore-space structure, important aspects of coral skeletons, of two scleractinian corals, Balanophyllia europaea (zooxanthellate) and Leptopsammia pruvoti (nonzooxanthellate), taken from three different sites on the western Italian coast along a temperature gradient. Comparisons have been made with mercury intrusion porosimetry and scanning electron microscopy images. TD-NMR parameters are sensitive to changes in the pore structure of the two coral species. A parameter, related to the porosity, is larger for L. pruvoti than for B. europaea, confirming previous non-NMR results. Another parameter representing the fraction of the pore volume with pore sizes of less than 10-20 µm is inversely related, with a high degree of statistical significance, to the mass of the specimen and, for B. europaea, to the temperature of the growing site. This effect in the zooxanthellate species, which could reduce its resistance to mechanical stresses, may depend on an inhibition of the photosynthetic process at elevated temperatures and could have particular consequences in determining the effects of global warming on these species.