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We propose a handheld projective imaging device for orthotopic projection of near-infrared fluorescence images onto target biological tissue at visible wavelengths without any additional visual aid. The device integrates a laser diode light source module, a camera module, a projector, an ultrasonic distance sensor, a Raspberry Pi single-board computer, and a battery module in a rugged handheld unit. It is calibrated at the detected working distance for seamless coregistration between fluorescence emission and projective imaging at the target tissue site. The proposed device is able to achieve a projection resolution higher than 314  μm and a planar projection bias less than 1 mm at a projection field of view of 58  ×  108  mm2 and a working distance of 27 cm. Technical feasibility for projective imaging is verified in an ex vivo model of chicken breast tissue using indocyanine green as a fluorescence agent. Clinical utility for image-guided surgery is demonstrated in a clinical trial where sentinel lymph nodes in breast cancer patients are identified and resected under the guidance of projective imaging. Our ex vivo and in vivo experiments imply the clinical utility of deploying the proposed device for image-guided surgical interventions in resource-limited settings.

A coaxial projective imaging (CPI) module acquires surgical scene images from the local site of surgery, transfers them wirelessly to the remote site, and projects instructive annotations to the surgical field. At the remote site, the surgical scene images are displayed, and the instructive annotations from a surgical specialist are wirelessly transferred back to the local site in order to guide the surgical intervention by a less experienced surgeon. The CPI module achieves seamless imaging of the surgical field and accurate projection of the instructive annotations, by a coaxial optical path design that couples the imaging arm with the projection arm and by a color correction algorithm that recovers the true color of the surgical scene. Our benchtop study of tele-guided intervention verifies that the proposed system has a positional accuracy of better than 1 mm at a working distance ranging from 300 to 500 mm. Our in vivo study of cricothyrotomy in a rabbit model proves the concept of tele-mentored surgical navigation. This is the first report of tele-guided surgery based on CPI. The proposed technique can be potentially used for surgical training and for telementored surgery in resource-limited settings.

Sculpture has been a central aspect of almost every art culture, contemporary or historical. This volume comprises ten essays at the cutting edge of thinking about sculpture in philosophical terms, representing approaches to sculpture from the perspectives of both Anglo-American and European philosophy. Some of the essays are historically situated, while others are more straightforwardly conceptual. All of the essays, however, pay strict attention to actual sculptural examples in their discussions. This reflects the overall aim of the volume to not merely “apply” philosophy to sculpture, but rather to test the philosophical approaches taken in tandem with deep analyses of sculptural examples. There is an array of philosophical problems unique to sculpture, namely certain aspects of its three-dimensionality, physicality, temporality, and morality. The authors in this volume respond to a number of challenging philosophical questions related to these characteristics. Furthermore, while the focus of most of the essays is on Western sculptural traditions, there are contributions that feature discussion of sculptural examples from non-Western sources. Philosophy of Sculpture is the first full-length book treatment of the philosophical significance of sculpture in English. It is a valuable resource for advanced students and scholars across aesthetics, art history, history, performance studies, and visual studies.

This chapter focuses on the affine moment invariants (AMIs), which play a very important role in moment‐based theories and in invariant object recognition. It begins with an explanation of the projective imaging and the relationship between projective and affine transforms. The chapter presents four different methods that allow a systematic design of the AMIs of any order, the graph method, the normalization method, the method based on the Caley‐Aronhold equation, and the transvectant method. It discusses the differences between them and illustrates the numerical properties of the AMIs. The chapter introduces affine invariants of color images and vector fields, 3D AMIs, and the idea of moment matching. The AMIs play an important role in view‐independent object recognition and have been widely used not only in tasks where image deformation is intrinsically affine but also commonly substitute projective invariants.

This chapter contains sections titled: Introduction AMIs derived from the Fundamental theorem AMIs generated by graphs AMIs via image normalization Derivation of the AMIs from the Cayley–Aronhold equation Numerical experiments Affine invariants of color images Generalization to three dimensions Conclusion Appendix References

Current Deep Learning (DL)-based methods for vertebra localization in biplanar X-ray images mainly focus on two-dimensional (2D) information and neglect the projective geometry, limiting the accuracy of 3D navigation in X-ray-guided spine surgery. A 3D vertebra localization method from calibrated biplanar X-ray images is highly desired to address the problem. In this study, a projective-geometry-aware network for localizing 3D vertebrae in calibrated biplanar X-ray images, referred to as ProVLNet, is proposed. The network design of ProVLNet features three components: a Siamese 2D feature extractor to extract local appearance features from the biplanar X-ray images, a spatial alignment fusion module to incorporate the projective geometry in fusing the extracted 2D features in 3D space, and a 3D landmark regression module to regress the 3D coordinates of the vertebrae from the 3D fused features. Evaluated on two typical and challenging datasets acquired from the lumbar and the thoracic spine, ProVLNet achieved an identification rate of 99.53% and 98.98% and a point-to-point error of 0.64 mm and 1.38 mm, demonstrating superior performance of our proposed approach over the state-of-the-art (SOTA) methods.

Accurate prediction of individuals’ brain age is critical to establish a baseline for normal brain development. This study proposes to model brain development with a novel non-negative projective dictionary learning (NPDL) approach, which learns a discriminative representation of multi-modal neuroimaging data for predicting brain age. Our approach encodes the variability of subjects in different age groups using separate dictionaries, projecting features into a low-dimensional manifold such that information is preserved only for the corresponding age group. The proposed framework improves upon previous discriminative dictionary learning methods by incorporating orthogonality and non-negativity constraints, which remove representation redundancy and perform implicit feature selection. We study brain development on multi-modal brain imaging data from the PING dataset (N = 841, age = 3−21 years). The proposed analysis uses our NDPL framework to predict the age of subjects based on cortical measures from T1-weighted MRI and connectome from diffusion weighted imaging (DWI). We also investigate the association between age prediction and cognition, and study the influence of gender on prediction accuracy. Experimental results demonstrate the usefulness of NDPL for modeling brain development.

This paper presents a generalized version of the classic projective reconstruction theorem which helps to choose or assess depth constraints for projective depth estimation algorithms. The theorem shows that projective reconstruction is possible under a much weaker constraint than requiring all estimated projective depths to be nonzero. This result enables us to present classes of depth constraints under which any reconstruction of cameras and points projecting into given image points is projectively equivalent to the true camera-point configuration. It also completely specifies the possible wrong configurations allowed by other constraints. We demonstrate the application of the theorem by analysing several constraints used in the literature, as well as presenting new constraints with desirable properties. We mention some of the implications of our results on iterative depth estimation algorithms and projective reconstruction via rank minimization. Our theory is verified by running experiments on both synthetic and real data.

In about 30-50% of patients with posttraumatic stress disorder (PTSD), symptoms persist after treatment. Although neurobiological research has advanced our understanding of PTSD, little is known about the neurobiology underlying persistence of PTSD. Two functional MRI scans were collected from 72 war veterans with and without PTSD over a 6- to 8-month interval, during which PTSD patients received trauma-focused therapy. All participants performed a trauma-unrelated emotional processing task in the scanner. Based on post-treatment symptom severity, a distinction was made between remitted and persistent patients. Behavioral and imaging measures of trauma-unrelated emotional processing were compared between the three groups (remitted patients, N=21; persistent patients, N=22; and combat controls, N=25) with repeated-measures (pre- and post-treatment) analyses. Second, logistic regression was used to predict treatment outcome. Before and after treatment, persistent patients showed a higher dorsal anterior cingulate cortex (dACC) and insula response to negative pictures compared with remitted patients and combat controls. Before treatment, persistent patients showed increased amygdala activation in response to negative pictures compared with remitted patients. The remitted patients and combat controls did not differ on the behavioral or imaging measures. Finally, higher dACC, insula, and amygdala activation before treatment were significant predictors of symptom persistence. Our results highlight a pattern of brain activation that may predict poor response to PTSD treatment. These findings can contribute to the development of alternative or additional therapies. Further research is needed to elucidate the heterogeneity within PTSD and describe how differences in neural function are related to treatment outcome. Such approaches are critical for defining parameters to customize PTSD treatment and improve treatment response rates.

The vast majority of Shape-from-Polarization (SfP) methods work under the oversimplified assumption of using orthographic cameras. Indeed, it is still unclear how Stokes vector projection behaves when the incoming rays are not orthogonal to the image plane. In this paper, we try to answer this question with a new geometric model describing how a general projective camera captures the light polarization state. Based on the optical properties of a tilted polarizer, our model is implemented as a pre-processing operation acting on raw images, and a scene-independent rotation of the reconstructed normal field. Moreover, our model is consistent with state-of-the-art forward and inverse renderers (as Mitsuba3 and ART), intrinsically enforces physical constraints among the captured channels, and handles the demosaicing of DoFP sensors. Experiments on existing and new datasets demonstrate the accuracy of the model when applied to commercially available polarimetric cameras.