3D Localization Research Articles

Automatic detection and 2D localization using a network of unsynchronized passive acoustic sensors in a dispersive waveguide

The time-frequency positions of modal dispersion curves in shallow-water low-frequency impulsive signals are strongly dependent on source-receiver range, making them suitable for range-based localization. Here, we apply a temporal convolutional network (TCN) to spectrograms estimated from individual sensors in an array of unsynchronized hydrophones to simultaneously detect dispersive signals and produce source-range estimates. The TCN is trained on simulated signals generated over a spatial grid and various environmental parameters using the adiabatic approximation for normal modes. Assuming that the number of unique sources is unknown, range measurements from the same source across different sensors are simultaneously associated and used in localization. To accomplish this automatically, the proposed method considers all unique combinations of range measurements from every collection of k sensors. For every range measurement combination, if the location estimate generated using each subcombination of k-1 measurements is within a certain threshold of the remaining measurement, the whole collection is labeled as group-k consistent. All such groups of measurements are represented as neighboring nodes in a graph, and strongly connected components are used to calculate the final source location estimates. The whole detection/localization method is validated using both simulated and experimental marine data. [Work supported by NDSEG and ONR].

3-D LiDAR Localization Based on Novel Nonlinear Optimization Method for Autonomous Ground Robot

3D Light Detection and Ranging (LiDAR)-based localization for autonomous ground robot in unknown environment is a critical ability, which has been extensively studied. In this paper, we propose a novel nonlinear optimization method to promote the 3D LiDAR localization performance. Firstly, due to that the objective function is the summation of point correspondence matching error items, a novel item quality evaluation criterion is proposed. The quality of each item is directly proportional to the degree of its matching error distance descent during the optimization process. Then, an attention mechanism based on the criterion is proposed and the objective function is iteratively refined by putting different attention weights on the matching error items. Thirdly, in the Gauss-Newton optimization framework for LiDAR localization, we point out that the Hessian matrix is essentially the sum of the Hessian matrices of high-quality and low-quality point correspondence subsets. To increase the dominance of the Hessian matrix derived from the high-quality point correspondences, the Hessian matrix of the estimated high-quality point correspondence subset in the last updating step is added to the current Hessian matrix. The proposed LiDAR localization is extensively evaluated on public and custom datasets. The experimental results demonstrate that the proposed mechanisms can effectively promote the LiDAR localization performance. We will make our source code open to serve as a new baseline for the robotic community.

Robot Indoor Navigation: Comparative Analysis of LiDAR 2D and Visual SLAM

<span lang="EN-US">Robot indoor navigation has become a significant area of research and development for applications such as autonomous robots, smart homes, and industrial automation. This article presents an in-depth comparative analysis of LiDAR 2D and visual sensor simultaneous localization and mapping (SLAM) approaches for robot indoor navigation. The increasing demand for autonomous robots in indoor environments has led to the development of various SLAM techniques for mapping and localization. LiDAR 2D and visual sensor-based SLAM methods are widely used due to their low cost and ease of implementation. The article provides an overview of LiDAR 2D and visual sensor-based SLAM techniques, including their working principles, advantages, and limitations. A comprehensive comparative analysis is conducted, assesing their capabilities in terms of robustness, accuracy, and computational requirements. The article also discusses the impact of environmental factors, such as lighting conditions and obstacles, on the performance of both approaches. The analysis’s findings highlight each approach’s strengths and weaknesses, providing valuable insights for researchers and practitioners in selecting the appropriate SLAM method for robot indoor navigation based on specific requirements and constraints</span>

Robust 3D localization of anomalies in reverberation time measurements using the sound field scanning method

Reverberation time measurements form the basis for room acoustic optimizations of existing building structures. During the verification of the achieved room acoustic improvements, anomalies may appear in the reverberation time signal which may be hard to spatially localize, especially in spaces with demanding acoustic requirements such as large, open workspaces, or concert halls. This contribution focuses on the application of the Sound Field Scanning technology to the fast spatial localization of room reflections. In this process, an omnidirectional sound source is positioned at an observation point in the room and periodically excited with band-limited pulses. At the same observation point, an acoustic camera system consisting of a rotating linear microphone array is oriented towards the preferred spatial direction. The emitted pulses and associated room reflections are captured on the measurement surface of the rotating microphone array. Acoustic images with high depth resolution are generated in parallel planes to the measurement surface. In complex situations, the task of spatially localizing anomalies in the reverberation time signal can be reduced to a few measurements from different viewing angles, thus, significantly accelerating the problem-solving process with high confidence. The method is exemplarily described through the room acoustic analysis of a University Lecture Hall.

Aperiodicity is more effective than disorder in localizing electromagnetic waves [Invited

Strong localization of electromagnetic waves in 3D has never been experimentally achieved in uncorrelated disordered systems. By going beyond the paradigm of disordered systems, this goal has been recently reached in deterministic aperiodic planar Vogel spiral arrays in the microwave regime. Here we present a comprehensive description of the experimental details that have been used to observe electromagnetic localization in Vogel spirals beyond 2D. By providing an experimental roadmap to describe microwave transport in Vogel spirals we introduce a novel system platform to demonstrate electromagnetic localization in 3D that outperforms traditional disordered systems for that purpose.

Digital Apparel Try-Ons: A Technological Odyssey into the World of Virtual Dressing Rooms and Consumer Engagement

This individual can now enjoy internet shopping whenever they want and from anywhere in the world thanks to the invention of smartphones and tablets. Online shopping has unquestionably taken the place of conventional methods for purchasing apparel and daily necessities. People benefit from a reputation as they pick internet buying. Almost all online retailers offer payment on delivery, free shipment, and special rates these days. These internet merchants have eliminated the hassles of parking, stuck in traffic, and standing in line to make purchases. They have also helped folks who consistently lament their lack of time. This is the primary justification for why most consumers now shop online. They may easily access a wide choice of affordable prices here, and they also get quick customer service and free home delivery. There is no denying that these are a few of the alluring aspects that draw customers in. Even though there is a minor problem that can make people lose interest in online buying, they might not be able to try on garments in such situations. By establishing a virtual changing room setting, the aim is to improve accessibility and time efficiency for trying on clothing. Our proposed methodology consists of three main components: skin color recognition, model alignment, and user image extraction from the video stream. Through user extraction, we may create an augmented reality environment by taking the user area out of the video stream and superimposing it on top of a simulated world within the user interface. To match the 2D cloth models with the use of scaling, rotation, and positioning using the 3D locations of the joints.

A Deep-Learning Approach to a Volumetric Radio Environment Map Construction for UAV-Assisted Networks

Providing global coverage for ubiquitous users is a key requirement of the fifth generation (5G) and beyond wireless technologies. This can be achieved by integrating airborne networks, such as unmanned aerial vehicles (UAVs) and satellite networks, with terrestrial networks. However, the deployment of airborne networks in a three-dimensional (3D) or volumetric space requires a new understanding of the propagation channel and its losses in both the areal and altitude dimensions. Despite significant research on radio environment map (REM) construction, much of it has been limited to two-dimensional (2D) contexts. This neglects the altitude-related characteristics of electromagnetic wave propagation and confines REMs to 2D formats, which limits the comprehensive and continuous visualization of propagation environment variation in spatial dimensions. This paper proposes a volumetric REM (VREM) construction approach to compute 3D propagation losses. The proposed approach addresses the limitations of existing approaches by learning the spatial correlation of wireless propagation channel characteristics and visualizing REM in areal and height/altitude dimensions using deep learning models. Specifically, the approach uses two deep learning-based models: volume-to-volume (Vol2Vol) VREM with 3D-generative adversarial networks and sliced VREM with altitude-aware spider-UNets. In both cases, knowledge of the propagation environment and transmitter locations in 3D space is used to capture the spatial and altitude dependency of the propagation channel’s characteristics. We developed the Addis dataset, a large REM dataset comprising 54,000 samples collected from the urban part of Addis Ababa, Ethiopia, to train the proposed models. Each sample of data comprises a 512-meter by 512-meter areal resolution with different 3D obstacles (buildings and terrain), 15 simulated propagation loss maps at every 3-meter altitude resolution, and 80 different 3D transmitter locations. The results of the training and testing of the proposed models reveal that the constructed VREMs are statistically comparable. In particular, the Vol2Vol approach has a minimum L1 loss of 0.01, which further decreases to 0.0084 as the line-of-sight (LoS) probability increases to 0.95.

Line-based iterative geometric calibration method for a tomosynthesis system.

A next generation tomosynthesis (NGT) system, capable of two-dimensional source motion, detector motion in the perpendicular direction, and magnification tomosynthesis, was constructed to investigate different acquisition geometries. Existing position-based geometric calibration methods proved ineffective when applied to the NGTgeometries. A line-based iterative calibration method is developed to perform accurate geometric calibration for the NGTsystem. The proposed method calculates the system geometry through virtual line segments created by pairs of fiducials within a calibration phantom, by minimizing the error between the line equationscomputed from the true and estimated fiducial projection pairs. It further attempts to correct the 3D fiducial locations based on the initial geometric calibration. The method's performance was assessed via simulation and experimental setups with four distinct NGT geometries: X, T, XZ, and TZ. The X geometry resembles a conventional DBT acquisition along the chest wall. The T geometry forms a "T"-shaped source path in mediolateral (ML) and posteroanterior (PA) directions. A descending detector motion is added to both X and T geometries to form the XZ and TZ geometries, respectively. Simulation studies were conducted to assess the robustness of the method to geometric perturbations and inaccuracies in fiducial locations. Experimental studies were performed to assess the impact of phantom magnification and the performance of the proposed method for various geometries, compared to the traditional position-based method. Star patterns were evaluated for both qualitative and quantitative analyses; the Fourier spectral distortions (FSDs) graphs and the contrast transfer function (CTF) were extracted. The limit of spatial resolution (LSR) was measured at 5% modulation of theCTF. The proposed method presented is highly robust to geometric perturbation and fiducial inaccuracies. After the line-based iterative method, the mean distance between the true and estimated fiducial projections was [X, T, XZ, TZ]: [0.01, 0.01, 0.02, 0.01] mm. The impact of phantom magnification was observed; a contact-mode acquisition of a calibration phantom successfully provided an accurate geometry for 1.85× magnification images of a star pattern, with the X geometry. The FSD graphs for the contact-mode T geometry acquisition presented evidence of super-resolution, with the LSR of [0°-quadrant: 8.57, 90°-quadrant: 8.47] lp/mm. Finally, a contact-mode XZ geometry acquisition and a 1.50× magnification TZ geometry acquisition were reconstructed with three calibration methods-position-based, line-based, and iterative line-based. As more advanced methods are applied, the CTF becomes more isotropic, the FSD graphs demonstrate less spectral leakage as super-resolution is achieved, and the degree of blurring artifacts reducessignificantly. This study introduces a robust calibration method tailored to the unique requirements of advanced tomosynthesis systems. By employing virtual line segments and iterative techniques, we ensure accurate geometric calibration while mitigating the limitations posed by the complex acquisition geometries of the NGT system. Our method's ability to handle various NGT configurations and its tolerance to fiducial misalignment make it a superior choice compared to traditional calibrationtechniques.

3D Modeling of Façade Elements Using Multi-View Images from Mobile Scanning Systems

There is a growing demand for detailed building façade models (Level-of-Detail 3: LoD 3) in a variety of applications. Despite the increasing number of papers addressing this issue in the literature, occlusions are still a significant problem when processing building façade elements. Conversely, depending on the view angle of the images, the detected elements might not be projected to their accurate locations causing uncertainties in their 3D locations. In this paper, we address the aforementioned issues utilizing multi-view images. Using a building footprint layer, we first locate the points belonging to buildings. We then detect the 2D windows and doors in images by combining Faster R-CNN and Segment Anything (SAM) deep learning models. The 2D borders are projected into the 3D object space using a pinhole camera model and collinearity equations. Utilizing the multi-view capabilities of mobile scanning systems, this method effectively mitigates uncertainties associated with occlusion and exterior orientation parameters (EOP). This study provides a comprehensive evaluation of 3D spatial accuracy, achieving an average of 84% Intersection over Union (IoU) accuracy for 12 different single-sided façades over 750 multi-view images for 312 windows and doors of various sizes with rectangular and curved shapes.

Deep convolutional-neural-network-based metal artifact reduction for CT-guided interventional oncology procedures (MARIO).

Computed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT-guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal-induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable lesions. In this work, we propose an image-based neural network (CNN) model for metal artifact reduction for CT-guided interventional procedures. An image domain metal artifact simulation framework was developed and validated for deep-learning-based metal artifact reduction for interventional oncology (MARIO). CT scans were acquired for 19 different cryoablation probe configurations. The probe configurations varied in the number of probes and the relative orientations. A combination of intensity thresholding and masking based on maximum intensity projections (MIPs) was used to segment both the probes only and probes + artifact in each phantom image. Each of the probe and probe + artifact images were then inserted into 19 unique patient exams, in the image domain, to simulate metal artifact appearance for CT-guided interventional oncology procedures. The resulting 361 pairs of simulated image volumes were partitioned into disjoint training and test datasets of 304 and 57 volumes, respectively. From the training partition, 116600 image patches with a shape of 128×128×5 pixels were randomly extracted to be used for training data. The input images consisted of a superposition of the patient and probe + artifact images. The target images consisted of a superposition of the patient and probe only images. This dataset was used to optimize a U-Net type model. The trained model was then applied to 50 independent, previously unseen CT images obtained during renal cryoablations. Three board-certified radiologists with experience in CT-guided ablations performed a blinded review of the MARIO images. A total of 100 images (50 original, 50 MARIO processed) were assessed across different aspects of image quality on a 4-point likert-type item. Statistical analyses were performed using Wilcoxon signed-rank test for paired samples. Reader scores were significantly higher for MARIO processed images compared to the original images across all metrics (all p<0.001). The average scores of the overall image quality, iceball conspicuity, overall metal artifact, needle tip visualization, target region confidence, and worst metal artifact, needle tip visualization, iceball conspicuity, and target region confidence improved by 34.91%, 36.29%, 39.94%, 34.17%, 35.13%, and 45.70%, respectively. The proposed method of image-based metal artifact simulation can be used to train a MARIO algorithm to effectively reduce probe-related metal artifacts in CT-guided cryoablation procedures.

Quantifying nanoscopic alterations associated with mitochondrial dysfunction using three-dimensional single-molecule localization microscopy.

Mitochondrial morphology provides unique insights into their integrity and function. Among fluorescence microscopy techniques, 3D super-resolution microscopy uniquely enables the analysis of mitochondrial morphological features individually. However, there is a lack of tools to extract morphological parameters from super-resolution images of mitochondria. We report a quantitative method to extract mitochondrial morphological metrics, including volume, aspect ratio, and local protein density, from 3D single-molecule localization microscopy images, with single-mitochondrion sensitivity. We validated our approach using simulated ground-truth SMLM images of mitochondria. We further tested our morphological analysis on mitochondria that have been altered functionally and morphologically in controlled manners. This work sets the stage to quantitatively analyze mitochondrial morphological alterations associated with disease progression on an individual basis.

Searching the SN 1987A SETI Ellipsoid with TESS

The SETI Ellipsoid is a strategy for technosignature candidate selection that assumes that extraterrestrial civilizations who have observed a galactic-scale event—such as supernova 1987A—may use it as a Schelling point to broadcast synchronized signals indicating their presence. Continuous wide-field surveys of the sky offer a powerful new opportunity to look for these signals, compensating for the uncertainty in their estimated time of arrival. We explore sources in the TESS continuous viewing zone, which corresponds to 5% of all TESS data, observed during the first 3 yr of the mission. Using improved 3D locations for stars from Gaia Early Data Release 3, we identified 32 SN 1987A SETI Ellipsoid targets in the TESS continuous viewing zone with uncertainties better than 0.5 lt-yr. We examined the TESS light curves of these stars during the Ellipsoid crossing event and found no anomalous signatures. We discuss ways to expand this methodology to other surveys, more targets, and different potential signal types.

Quantitative pulsatility measurements using 3D dynamic ultrasound localization microscopy

A rise in blood flow velocity variations (i.e. pulsatility) in the brain, caused by the stiffening of upstream arteries, is associated with cognitive impairment and neurodegenerative diseases. The study of this phenomenon requires brain-wide pulsatility measurements, with large penetration depth and high spatiotemporal resolution. The development of dynamic ultrasound localization microscopy (DULM), based on ULM, has enabled pulsatility measurements in the rodent brain in 2D. However, 2D imaging accesses only one slice of the brain and measures only 2D-projected and hence biased velocities . Herein, we present 3D DULM: using a single ultrasound scanner at high frame rate (1000–2000 Hz), this method can produce dynamic maps of microbubbles flowing in the bloodstream and extract quantitative pulsatility measurements in the cat brain with craniotomy and in the mouse brain through the skull, showing a wide range of flow hemodynamics in both large and small vessels. We highlighted a decrease in pulsatility along the vascular tree in the cat brain, which could be mapped with ultrasound down to a few tens of micrometers for the first time. We also performed an intra-animal validation of the method by showing consistent measurements between the two sides of the Willis circle in the mouse brain. Our study provides the first step towards a new biomarker that would allow the detection of dynamic abnormalities in microvessels in the brain, which could be linked to early signs of neurodegenerative diseases.

Three-dimensional topography of rat trigeminal ganglion neurons using a combination of retrograde labeling and tissue-clearing techniques.

The trigeminal nerve is the sensory afferent of the orofacial regions and divided into three major branches. Cell bodies of the trigeminal nerve lie in the trigeminal ganglion and are surrounded by satellite cells. There is a close interaction between ganglion cells via satellite cells, but the function is not fully understood. In the present study, we clarified the ganglion cells' three-dimensional (3D) localization, which is essential to understand the functions of cell-cell interactions in the trigeminal ganglion. Fast blue was injected into 12 sites of the rat orofacial regions, and ganglion cells were retrogradely labeled. The labeled trigeminal ganglia were cleared by modified 3DISCO, imaged with confocal laser-scanning microscopy, and reconstructed in 3D. Histograms of the major axes of the fast blue-positive somata revealed that the peak major axes of the cells innervating the skin/mucosa were smaller than those of cells innervating the deep structures. Ganglion cells innervating the ophthalmic, maxillary, and mandibular divisions were distributed in the anterodorsal, central, and posterolateral portions of the trigeminal ganglion, respectively, with considerable overlap in the border region. The intermingling in the distribution of ganglion cells within each division was also high, in particular, within the mandibular division. Specifically, intermingling was observed in combinations of tongue and masseter/temporal muscles, maxillary/mandibular molars and masseter/temporal muscles, and tongue and mandibular molars. Double retrograde labeling confirmed that some ganglion cells innervating these combinations were closely apposed. Our data provide essential information for understanding the function of ganglion cell-cell interactions via satellite cells.

An Indoor 2-D LiDAR SLAM and Localization Method Based on Artificial Landmark Assistance

An Indoor 2-D LiDAR SLAM and Localization Method Based on Artificial Landmark Assistance

Evaluation of tumor microvasculature with 3D ultrasound localization microscopy based on 2D matrix array.

Evaluation of tumor microvascular morphology is of great significance in tumor diagnosis, therapeutic effect prediction, and surgical planning. Recently, two-dimensional ultrasound localization microscopy (2DULM) has demonstrated its superiority in the field of microvascular imaging. However, it suffers from planar dependence and is unintuitive. We propose a novel three-dimensional ultrasound localization microscopy (3DULM) to avoid these limitations. We investigated 3DULM based on a 2D array for tumor microvascular imaging. After intravenous injection of contrast agents, all elements of the 2D array transmit and receive signals to ensure a high and stable frame rate. Microbubble signal extraction, filtering, positioning, tracking, and other processing were used to obtain a 3D vascular map, flow velocity, and flow direction. To verify the effectiveness of 3DULM, it was validated on double helix tubes and rabbit VX2 tumors. Cisplatin was used to verify the ability of 3DULM to detect microvascular changes during tumor treatment. In vitro, the sizes measured by 3DULM at 3mm and 13mm were 178 and 182 , respectively. In the rabbit tumors, we acquired 9000 volumes to reveal vessels about 30 in diameter, which surpasses the diffraction limit of ultrasound in traditional ultrasound imaging, and the results matched with micro-angiography. In addition, there were significant changes in vascular density and curvature between the treatment and control groups. The effectiveness of 3DULM was verified in vitro and in vivo. Hence, 3DULM may have potential applications in tumor diagnosis, tumor treatment evaluation, surgical protocol guidance, and cardiovascular disease. 3D ultrasound localization microscopy is highly sensitive to microvascular changes; thus, it has clinical potential for tumor diagnosis and treatment evaluation. • 3D ultrasound localization microscopy is demonstrated on double helix tubes and rabbit VX2 tumors. • 3D ultrasound localization microscopy can reveal vessels about 30 in diameter-far smaller than traditional ultrasound. • This form of imaging has potential applications in tumor diagnosis, tumor treatment evaluation, surgical protocol guidance, and cardiovascular disease.

The Development of a Stereo Vision System to Study the Nutation Movement of Climbing Plants.

Climbing plants, such as common beans (Phaseolus vulgaris L.), exhibit complex motion patterns that have long captivated researchers. In this study, we introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision. Our approach involves two synchronized cameras, one lateral to the plant and the other overhead, enabling the simultaneous 2D position tracking of the plant tip. These data are then leveraged to reconstruct the 3D position of the tip. Furthermore, we investigate the impact of external factors, particularly the presence of support structures, on plant movement dynamics. The proposed method is able to extract the position of the tip in 86-98% of cases, achieving an average reprojection error below 4 px, which means an approximate error in the 3D localization of about 0.5 cm. Our method makes it possible to analyze how the plant nutation responds to its environment, offering insights into the interplay between climbing plants and their surroundings.

Terminal Phase Navigation for AUV Docking: An Innovative Electromagnetic Approach

This study introduces a groundbreaking approach for real-time 3D localization, specifically focusing on achieving seamless and precise localization during the terminal guidance phase of an autonomous underwater vehicle (AUV) as it approaches an omnidirectional docking component in an automated deployable launch and recovery system (LARS). Using the AUV’s magnetometer, an economical electromagnetic beacon embedded in the docking component, and an advanced signal processing algorithm, this novel approach ensures the accurate localization of the docking component in three dimensions without the need for direct line-of-sight contact. The method’s real-time capabilities were rigorously evaluated via simulations, prototype experiments in a controlled lab setting, and extensive full-scale pool experiments. These assessments consistently demonstrated an exceptional average positioning accuracy of under 3 cm, marking a significant advancement in AUV guidance systems.

In vivo real-time positron emission particle tracking (PEPT) and single particle PET

Positron emission particle tracking (PEPT) enables 3D localization and tracking of single positron-emitting radiolabelled particles with high spatiotemporal resolution. The translation of PEPT to the biomedical imaging field has been limited due to the lack of methods to radiolabel biocompatible particles with sufficient specific activity and protocols to isolate a single particle in the sub-micrometre size range, below the threshold for capillary embolization. Here we report two key developments: the synthesis and 68Ga-radiolabelling of homogeneous silica particles of 950 nm diameter with unprecedented specific activities (2.1 ± 1.4 kBq per particle), and the isolation and manipulation of a single particle. We have combined these developments to perform in vivo PEPT and dynamic positron emission tomography (PET) imaging of a single radiolabelled sub-micrometre size particle using a pre-clinical positron emission tomography/computed tomography scanner. This work opens possibilities for quantitative assessment of haemodynamics in vivo in real time, at the whole-body level using minimal amounts of injected radioactive dose and material.

Integration of 3D Fluorescence Imaging and Luminescent Thermometry with Core-Shell Engineered NaYF4:Nd3+/Yb3+/Ho3+ Nanoparticles.

The design of rare-earth-doped upconversion/downshifting nanoparticles (NPs) for theoretical use in nanomedicine has garnered considerable interest. Previous research has emphasized luminescent nanothermometry and photothermal therapy, while three-dimensional (3D) near-infrared (NIR) luminescent tracers have received less attention. Our study introduces Nd3+-, Yb3+-, and Ho3+-doped NaYF4 core-shell luminescent NPs as potential multiparametric nanothermometers and NIR imaging tracers. Nd3+ sensitizes at 804 nm, while Yb3+ bridges to activators Ho3+. We evaluated the photoluminescence properties of Nd3+-, Yb3+-, and Ho3+-doped core and core-shell NPs synthesized via polyol-mediated and thermal decomposition methods. The NaYF4:NdYbHo(7/15/3%)@NaYF4:Nd(15%) core-shell NPs demonstrate competitive nanothermometry capabilities. Specifically, the polyol-synthesized sample exhibits a sensitivity of 0.27% K-1 at 313 K (40 °C), whereas the thermally decomposed synthesized sample shows a significantly higher sensitivity of 0.55% K-1 at 313 K (40 °C) in the near-infrared range. Control samples indicate back energy transfer processes from both Yb and Ho to Nd, while Yb to Ho energy transfer enhances Ho3+-driven upconversion transitions in green and red wavelengths, suggesting promise for photodynamic therapy. Fluorescence molecular tomography confirms 3D NIR fluorescence nanoparticle localization in a biological media after injection, highlighting the potential of core-shell NPs as NIR luminescent tracers. The strategy's clinical impact lies in photothermal treatment planning, leveraging core-shell NPs for (pre)clinical applications, and enabling the easy addition of new functionalities through distinct ion doping.