Effects of induced arterial hypertension for vasospasm on unruptured and unsecured cerebral aneurysms (growth and rupture). A retrospective case-control study.

OBJECTIVES: Unruptured cerebral aneurysms (UCAs) often coexist with the ruptured one but are typically left unsecured during the weeks following aneurysmal subarachnoid hemorrhage (aSAH). We compared the rate of UCAs rupture or volume growth (≥5 mm3) between patients exposed to induced arterial hypertension (iHTN) for vasospasm and those not exposed (control group). MATERIALS AND METHODS: From 2013 to 2021, we retrospectively included consecutive adult patients with aSAH who had ≥1 UCA. Custom software for digital subtraction angiography (DSA) image analysis characterized UCAs volume, going beyond merely considering UCAs long axis. RESULTS: We analyzed 118 patients (180 UCAs): 45 in the iHTN group (64 UCAs) and 73 in the control group (116 UCAs). Systolic blood pressure in the iHTN group was significantly higher than in the control group for several days after aSAH. During the 107 day-monitoring period [interquartile range(IQR):92;128], no UCA rupture occurred in either group. UCA volume analysis was performed in 44 patients (60 UCAs): none of the UCAs in the iHTN group and 3 out of 42 (7%) in the control group had a >5 mm3 volume growth (p=0.55). Other morphologic parameters did not exhibit any variations that might indicate an increased risk of rupture in the iHTN group compared to the control group. CONCLUSION: iHTN did not increase the risk of rupture or volume growth of UCAs within several weeks following aSAH. These reassuring results encourage not to refrain, because of the existence of UCAs, from iHTN as an option to prevent cerebral infarction during cerebral vasospasm.

Exploring V1 by modeling the perceptual quality of images.

We propose an image quality model based on phase and amplitude differences between a reference and a distorted image. The proposed model is motivated by the fact that polar representations can separate visual information in a more independent and efficient manner than Cartesian representations in the primary visual cortex (V1). We subsequently estimate the model parameters from a large subjective data set using maximum likelihood methods. By comparing the various model hypotheses on the functional form about the phase and amplitude, we find that: (a) discrimination of visual orientation is important for quality assessment and yet a coarse level of such discrimination seems sufficient; and (b) a product-based amplitude-phase combination before pooling is effective, suggesting an interesting viewpoint about the functional structure of the simple cells and complex cells in V1.

Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis.

Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate.

Bifurcation geometry remodelling of vessels in de novo and growing intracranial aneurysms: a multicenter study.

BACKGROUND: Geometrical parameters, including arterial bifurcation angle, tortuosity, and arterial diameters, have been associated with the pathophysiology of intracranial aneurysm (IA) formation. The aim of this study was to investigate whether these parameters were present before or if they resulted from IA formation and growth. METHODS: Patients from nine academic centers were retrospectively identified if they presented with a de novo IA or a significant IA growth on subsequent imaging. For each patient, geometrical parameters were extracted using a semi-automated algorithm and compared between bifurcations with IA formation or growth (aneurysmal group), and their contralateral side without IA (control group). These parameters were compared at two different times using univariable models, multivariable models, and a sensitivity analysis with paired comparison. RESULTS: 46 patients were included with 21 de novo IAs (46%) and 25 significant IA growths (54%). The initial angle was not different between the aneurysmal and control groups (129.7±42.1 vs 119.8±34.3; p=0.264) but was significantly wider at the final stage (140.4±40.9 vs 121.5±34.1; p=0.032), with a more important widening of the aneurysmal angle (10.8±15.8 vs 1.78±7.38; p=0.001). Variations in other parameters were not significant. These results were confirmed by paired comparisons. CONCLUSION: Our study suggests that wider bifurcation angles that have long been deemed causal factors for IA formation or growth may be secondary to IA formation at pathologic bifurcation sites. This finding has implications for our understanding of IA formation pathophysiology.

From CIE 2006 physiological model to improved age-dependent and average colorimetric observers.

In the context of color perception on modern wide-gamut displays with narrowband spectral primaries, we performed a theoretical analysis on various aspects of physiological observers proposed by CIE TC 1-36 (CIEPO06). We allowed certain physiological factors to vary, which was not considered in the CIEPO06 framework. For example, we analyzed that the long-wave-sensitive (LWS) or medium-wave-sensitive (MWS) peak wavelength shift in the photopigment absorption spectra, a factor not modeled in CIEPO06, contributed more toward observer variability than some of the factors considered in the model. Further, we compared the color-matching functions derived from the CIEPO06 model and the CIE 10° standard colorimetric observer to the average observer data from three distinct subgroups of Stiles-Burch observers, formed on the basis of observer ages (22-23 years, 27-29 years, and 49-50 years). The errors in predicting the x(λ) and y(λ) color-matching functions of the intragroup average observers in the long-wave range and in the medium-wave range, respectively, were generally more in the case of the CIEPO06 model compared to the 10° standard colorimetric observer and manifested in both spectral and chromaticity space. In contrast, the short-wave-sensitive z10(λ) function of the 10° standard colorimetric observer performed poorly compared to the CIEPO06 model for all three subgroups. Finally, a constrained nonlinear optimization on the CIEPO06 model outputs showed that a peak wavelength shift of photopigment density alone could not improve the model prediction errors at higher wavelengths. As an alternative, two optimized weighting functions for each of the LWS and MWS cone photopigment densities led to significant improvement in the prediction of intra-age-group average data for both the 22-23 year and 49-50 year age groups. We hypothesize that the assumption in the CIEPO06 model that the peak optical density of visual pigments does not vary with age is false and is the source of these prediction errors at higher wavelengths. Correcting these errors in the model can lead to an improved age-dependent observer and can also help update the current CIE 10° standard colorimetric observer. Accordingly, it would reduce the discrepancies between color matches with broadband spectral primaries and color matches with narrowband spectral primaries.

Learning From Mouse CT-Scan Brain Images To Detect MRA-TOF Human Vasculatures.

The earlier studies on brain vasculature semantic segmentation used classical image analysis methods to extract the vascular tree from images. Nowadays, deep learning methods are widely exploited for various image analysis tasks. One of the strong restrictions when dealing with neural networks in the framework of semantic segmentation is the need to dispose of a ground truth segmentation dataset, on which the task will be learned. It may be cumbersome to manually segment the arteries in a 3D volumes (MRA-TOF typically). In this work, we aim to tackle the vascular tree segmentation from a new perspective. Our objective is to build an image dataset from mouse vasculatures acquired using CT-Scans, and enhance these vasculatures in such a way to precisely mimic the statistical properties of the human brain. The segmentation of mouse images is easily automatized thanks to their specific acquisition modality. Thus, such a framework allows to generate the data necessary for the training of a Convolutional Neural Network - i.e. the enhanced mouse images and there corresponding ground truth segmentation - without requiring any manual segmentation procedure. However, in order to generate an image dataset having consistent properties (strong resemblance with MRA images), we have to ensure that the statistical properties of the enhanced mouse images do match correctly the human MRA acquisitions. In this work, we evaluate at length the similarities between the human arteries as acquired on MRA-TOF and the "humanized" mouse arteries produced by our model. Finally, once the model duly validated, we experiment its applicability with a Convolutional Neural Network.

Injectable silanized hyaluronic acid hydrogel/biphasic calcium phosphate granule composites with improved handling and biodegradability promote bone regeneration in rabbits.

Biphasic calcium phosphate (BCP) granules are osteoconductive biomaterials used in clinics to favor bone reconstruction. Yet, poor cohesivity, injectability and mechanical properties restrain their use as bone fillers. In this study, we incorporated BCP granules into in situ forming silanized hyaluronic acid (Si-HA) and hydroxypropylmethylcellulose (Si-HPMC) hydrogels. Hydrogel composites were shown to be easily injectable (F < 30 N), with fast hardening properties (<5 min), and similar mechanical properties (E∼ 60 kPa). In vivo, both hydrogels were well tolerated by the host, but showed different biodegradability with Si-HA gels being partially degraded after 21d, while Si-HPMC gels remained stable. Both composites were easily injected into critical size rabbit defects and remained cohesive. After 4 weeks, Si-HPMC/BCP led to poor bone healing due to a lack of degradation. Conversely, Si-HA/BCP composites were fully degraded and beneficially influenced bone regeneration by increasing the space available for bone ingrowth, and by accelerating BCP granules turnover. Our study demonstrates that the degradation rate is key to control bone regeneration and that Si-HA/BCP composites are promising biomaterials to regenerate bone defects.

In Situ Forming, Silanized Hyaluronic Acid Hydrogels with Fine Control Over Mechanical Properties and In Vivo Degradation for Tissue Engineering Applications.

In situ forming hydrogels that can be injected into tissues in a minimally-invasive fashion are appealing as delivery vehicles for tissue engineering applications. Ideally, these hydrogels should have mechanical properties matching those of the host tissue, and a rate of degradation adapted for neo-tissue formation. Here, the development of in situ forming hyaluronic acid hydrogels based on the pH-triggered condensation of silicon alkoxide precursors into siloxanes is reported. Upon solubilization and pH adjustment, the low-viscosity precursor solutions are easily injectable through fine-gauge needles prior to in situ gelation. Tunable mechanical properties (stiffness from 1 to 40 kPa) and associated tunable degradability (from 4 days to more than 3 weeks in vivo) are obtained by varying the degree of silanization (from 4.3% to 57.7%) and molecular weight (120 and 267 kDa) of the hyaluronic acid component. Following cell encapsulation, high cell viability (> 80%) is obtained for at least 7 days. Finally, the in vivo biocompatibility of silanized hyaluronic acid gels is verified in a subcutaneous mouse model and a relationship between the inflammatory response and the crosslink density is observed. Silanized hyaluronic acid hydrogels constitute a tunable hydrogel platform for material-assisted cell therapies and tissue engineering applications.

Prediction of Unruptured Intracranial Aneurysm Evolution: The UCAN Project.

BACKGROUND: Management of small (<7 mm) unruptured intracranial aneurysms (UIA) remains controversial. Retrospective studies have suggested that post gadolinium arterial wall enhancement (AWE) of UIA on magnetic resonance imaging (MRI) may reflect aneurysm wall instability, and hence may highlight a higher risk of UIA growth. This trial aims at exploring wall imaging findings of UIAs with consecutive follow-up to substantiate these assumptions. OBJECTIVE: To develop diagnostic and predictive tools for the risk of IA evolution. Our aim is to demonstrate in clinical practice the predictive value of AWE for UIA growth. The growth will be determined by any modification of the UIA measurement. UIA growth and the UIA wall enhancement will be assessed in consensus by 2 expert neuroradiologists. METHODS: The French prospective UCAN project is a noninterventional international wide and multicentric cohort. UIA of bifurcation between 3 and 7 mm for whom a clinical and imaging follow-up without occlusion treatment was scheduled by local multidisciplinary staff will be included. Extensive clinical, biological, and imaging data will be recorded during a 3-yr follow-up. EXPECTED OUTCOMES: Discovering to improve the efficiency of UIA follow-up by identifying additional clinical, imaging, biological, and anatomic risk factors of UIA growth. DISCUSSION: A prospective nationwide recruitment allows for the inclusion of a large cohort of patients with UIA. It will combine clinical phenotyping and specific imaging with AWE screening. It will enable to exploit metadata and to explore some pathophysiological pathways by crossing clinical, genetic, biological, and imaging information.

Chromatic and wavefront aberrations: L-, M- and S-cone stimulation with typical and extreme retinal image quality.

The first physiological process influencing visual perception is the optics of the eye. The retinal image is affected by diffraction at the pupil and several kinds of optical imperfections. A model of the eye (Thibos & Bradley, 1999), which takes account of pupil aperture, chromatic aberration and wavefront aberrations, was used to determine wavelength-dependent point-spread functions, which can be convolved with any stimulus specified by its spectral distribution of light at each point. The resulting retinal spectral distribution of light was used to determine the spatial distribution of stimulation for each cone type (S, M and L). In addition, individual differences in retinal-image quality were assessed using a statistical model (Thibos, Bradley, & Hong, 2002) for population values of Zernike coefficients, which characterize imperfections of the eye's optics. The median and relatively extreme (5th and 95th percentile) modulation transfer functions (MTFs) for the S, M and L cones were determined for equal-energy-spectrum (EES) 'white' light. The typical MTF for S cones was more similar to the MTF for L and M cones after taking wavefront aberrations into account but even with aberrations the S-cone MTF typically was below the M- or L-cone MTF by a factor of at least 10 (one log unit). More generally, the model presented here provides a technique for estimating retinal image quality for the S, M and L cones for any stimulus presented to the eye. The model is applied to some informative examples.

Temporal nulling of induction from spatial patterns modulated in time.

Temporally varying chromatic-inducing light was used to infer receptive-field organization. Time-varying shifts in color appearance within a test field were induced by a surrounding chromatic pattern; the shifts were then nulled by adding a time-varying stimulus to the test area so the observer perceived a steady test. This method measured chromatic induction without requiring an observer to judge the color appearance of the test. The induced color shifts were consistent with a +s/-s spatially antagonistic neural receptive field, which also accounts for color shifts induced by static chromatic patterns (Monnier & Shevell, 2003, Monnier & Shevell, 2004). The response of this type of receptive-field, which is found only in the visual cortex, increases with S-cone stimulation at its center and decreases with S-cone stimulation within its surround. The measurements also showed a negligible influence of temporal inducing frequency in the range 0.5-4 Hz.