Accurate 3D Model Research Articles

Waveform Inversion of Shallow Seismic data with Randomly Selected Sources

Applying waveform inversion with randomly selected sources (RS) increases the convergence rate of the optimization process within full-waveform inversion (FWI) workflows and reduces overall computational time. FWI has been shown to be a valuable addition to the existing geophysical methods for near-surface characterization. Accurate 3D modeling of the (visco) elastic wavefield allows to diminish assumptions about wave propagation and include surface- and body-wave based arrivals within the inversion workflow. This approach could result in reliable high-resolution subsurface models, but generally it comes at a high computational cost for each individual source modeling. Commonly, multiple sources are involved in the inversion process, which proportionally increases the resulting computations for a time-domain FWI, but seismic data with dense source/receiver coverage usually contain redundant information. This is especially true for seismic near-surface applications, where the number of recorded sources per wavelength of interest are normally excessive. The inversion performance was increased by randomly selecting a subset of sources at each FWI iteration. The method's effectiveness is obvious on a FWI near-surface void detection application. Synthetic 2D experiments for fixed and rolling spreads showed comparable results with fewer calculations. The best performance was achieved when a single random source was used for each inversion iteration. The effectiveness of the method was also evident on a shallow 3D field dataset collected in the Sonora Desert in western Arizona, where data were acquired over a 10-m deep void with known location, orientation, and dimensions.

Multi-Sensor Data Fusion for 3D Reconstruction of Complex Structures: A Case Study on a Real High Formwork Project

As the most comprehensive document types for the recording and display of real-world information regarding construction projects, 3D realistic models are capable of recording and displaying simultaneously textures and geometric shapes in the same 3D scene. However, at present, the documentation for much of construction infrastructure faces significant challenges. Based on TLS, GNSS/IMU, mature photogrammetry, a UAV platform, computer vision technologies, and AI algorithms, this study proposes a workflow for 3D modeling of complex structures with multiple-source data. A deep learning LoFTR network was used first for image matching, which can improve matching accuracy. Then, a NeuralRecon network was employed to generate a 3D point cloud with global consistency. GNSS information was used to reduce search space in image matching and produce an accurate transformation matrix between the image scene and the global reference system. In addition, to enhance the effectiveness and efficiency of the co-registration of the two-source point clouds, an RPM-net was used. The proposed workflow processed the 3D laser point cloud and UAV low-altitude multi-view image data to generate a complete, accurate, high-resolution, and detailed 3D model. Experimental validation on a real high formwork project was carried out, and the result indicates that the generated 3D model has satisfactory accuracy with a registration error value of 5 cm. Model comparison between the TLS, image-based, data fusion 1 (using the common method), and data fusion 2 (using the proposed method) models were conducted in terms of completeness, geometrical accuracy, texture appearance, and appeal to professionals. The results denote that the generated 3D model has similar accuracy to the TLS model yet also provides a complete model with a photorealistic appearance that most professionals chose as their favorite.

Guidelines for Accurate Multi-Temporal Model Registration of 3D Scanned Objects.

Changes in object morphology can be quantified using 3D optical scanning to generate 3D models of an object at different time points. This process requires registration techniques that align target and reference 3D models using mapping functions based on common object features that are unaltered over time. The goal of this study was to determine guidelines when selecting these localized features to ensure robust and accurate 3D model registration. For this study, an object of interest (tibia bone replica) was 3D scanned at multiple time points, and the acquired 3D models were aligned using a simple cubic registration block attached to the object. The size of the registration block and the number of planar block surfaces selected to calculate the mapping functions used for 3D model registration were varied. Registration error was then calculated as the average linear surface variation between the target and reference tibial plateau surfaces. We obtained very low target registration errors when selecting block features with an area equivalent to at least 4% of the scanning field of view. Additionally, we found that at least two orthogonal surfaces should be selected to minimize registration error. Therefore, when registering 3D models to measure multi-temporal morphological change (e.g., mechanical wear), we recommend selecting multiplanar features that account for at least 4% of the scanning field of view. For the first time, this study has provided guidelines for selecting localized object features that can provide accurate 3D model registration for 3D scanned objects.

USING ELECTRONIC NAVIGATIONAL CHART FOR 3D BATHYMETRIC MODEL OF THE PORT OF NAPLES

Abstract. Nautical charts generally report fundamental knowledge for the safety of navigation. This information also includes sea depth data reported as depth points or contour lines, which can be used to build a 3D model of the seabed. However, there are different interpolation methods for creating digital depth models, and there is no way to know in advance which of them is the best performing. The aim of this work is to compare different spatial interpolation methods applied on a dataset concerning the seabed of the Port of Naples (Italy) and extracted from the Electronic Navigational Chart (ENC) produced by the Hydrographic Institute of the Italian Navy, in scale 1:10.000. Four deterministic interpolation methods, i.e. Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), Local Polynomial Interpolation (LPI), Radial Basis Function (RBF), and two stochastic interpolation methods, i.e. Ordinary Kriging (OK) and Universal Kriging (UK), are applied using Geographic Information System (GIS) software. Since each method requires to set specific parameters and different options are available, e.g. the order of the polynomial function for GPI and LPI, or semi-variograms for OK and UK, twenty-three models are generated. The result quality is evaluated by Leave-One-Out cross-validation and the statistics of the residuals produced by each interpolation method in the measured points are compared and analysed. The experiments confirm that the stochastic approach is more versatile compared to deterministic approach and can produce better results, as it is testified by the great performance of the Ordinary Kriging, which produces the most accurate 3D models.

UAV Photogrammetry Application for Determining the Influence of Shading on Solar Photovoltaic Array Energy Efficiency

The field of solar photovoltaic (PV) plants has seen significant growth in recent years, with an increasing number of installations being developed worldwide. However, despite advancements in technology and design, the impact of shading on the performance of PV plants remains an area of concern. Accurate 3D models produced using unmanned aerial vehicle (UAV) photogrammetry can provide aid to evaluate shading from nearby surroundings and to determine the potential of a site for electricity production via solar PV plants. The main objective of this paper is to address the problem of shadows significantly reducing energy yield in solar PV plants by proposing a methodology that aims at assessing the shading effects on PV systems and determining the optimal configuration for a PV module array using an accurate digital environment 3D model built using UAV photogrammetry. A high-level-of-detail 3D model allows us to evaluate possible obstacles for PV module array construction and accurately recreate the proximities that can cast shadows. The methodology was applied to grid-connected PV systems in Kaunas, Lithuania. The results of the case study show that electricity production in PV modules is highest at a 15° tilt angle when the distance between PV rows is 1.25 m. The proposed methodology gives an 11% difference in PV yield due to shading compared with other tools that do not include shading. This study also highlights that at least 30% financing support is necessary for solar PV plants to be economically attractive, resulting in a payback of 9 years and an internal rate of return of 8%. Additionally, this study can help optimize the design and layout of PV systems, making them more efficient and cost-effective.

Pediatric Genitourinary 3D Modeling and Printing Using Multiphase Postcontrast Imaging Segmentation

To describe the development and implementation of a process for creating accurate Pediatric genitourinary 3D modeling and printing with multiphase postcontrast imaging for surgical planning. Additive manufacturing and 3D model present opportunities to support clinical planning, this manuscript's specific process and considerations for creating pediatric genitourinary 3D modeling to support urology. The process for creating the 3D models and prints covers 3 key aspects from image acquisition, imaging review and selection, and segmentation and modification (as needed). Each step is outlined with the key roles and procedures. The described case had digital and printed models prepared with references to theoptimized imaging sequence for 3D modeling of Pediatric genitourinary. Case shared include complex genitourinary reconstruction and Kideny with Wilms tumors. The processes described have become a standard of practice for complex kidney tumors and exstrophy planning. The team continues to work on ever-changing improvements to make the best possible models to support clinical and surgical planning.

An Open-Source Photogrammetry Workflow for Reconstructing 3D Models.

Acquiring accurate 3D biological models efficiently and economically is important for morphological data collection and analysis in organismal biology. In recent years, structure-from-motion (SFM) photogrammetry has become increasingly popular in biological research due to its flexibility and being relatively low cost. SFM photogrammetry registers 2D images for reconstructing camera positions as the basis for 3D modeling and texturing. However, most studies of organismal biology still relied on commercial software to reconstruct the 3D model from photographs, which impeded the adoption of this workflow in our field due the blocking issues such as cost and affordability. Also, prior investigations in photogrammetry did not sufficiently assess the geometric accuracy of the models reconstructed. Consequently, this study has two goals. First, we presented an affordable and highly flexible SFM photogrammetry pipeline based on the open-source package OpenDroneMap (ODM) and its user interface WebODM. Second, we assessed the geometric accuracy of the photogrammetric models acquired from the ODM pipeline by comparing them to the models acquired via microCT scanning, the de facto method to image skeleton. Our sample comprised 15 Aplodontia rufa (mountain beaver) skulls. Using models derived from microCT scans of the samples as reference, our results showed that the geometry of the models derived from ODM was sufficiently accurate for gross metric and morphometric analysis as the measurement errors are usually around or below 2%, and morphometric analysis captured consistent patterns of shape variations in both modalities. However, subtle but distinct differences between the photogrammetric and microCT-derived 3D models could affect the landmark placement, which in return affected the downstream shape analysis, especially when the variance within a sample is relatively small. At the minimum, we strongly advise not combining 3D models derived from these two modalities for geometric morphometric analysis. Our findings can be indictive of similar issues in other SFM photogrammetry tools since the underlying pipelines are similar. We recommend that users run a pilot test of geometric accuracy before using photogrammetric models for morphometric analysis. For the research community, we provide detailed guidance on using our pipeline for building 3D models from photographs.

Improvement of protein tertiary and quaternary structure predictions using the ReFOLD refinement method and the AlphaFold2 recycling process.

The accuracy gap between predicted and experimental structures has been significantly reduced following the development of AlphaFold2 (AF2). However, for many targets, AF2 models still have room for improvement. In previous CASP experiments, highly computationally intensive MD simulation-based methods have been widely used to improve the accuracy of single 3D models. Here, our ReFOLD pipeline was adapted to refine AF2 predictions while maintaining high model accuracy at a modest computational cost. Furthermore, the AF2 recycling process was utilized to improve 3D models by using them as custom template inputs for tertiary and quaternary structure predictions. According to the Molprobity score, 94% of the generated 3D models by ReFOLD were improved. AF2 recycling showed an improvement rate of 87.5% (using MSAs) and 81.25% (using single sequences) for monomeric AF2 models and 100% (MSA) and 97.8% (single sequence) for monomeric non-AF2 models, as measured by the average change in lDDT. By the same measure, the recycling of multimeric models showed an improvement rate of as much as 80% for AF2-Multimer (AF2M) models and 94% for non-AF2M models. Refinement using AlphaFold2-Multimer recycling is available as part of the MultiFOLD docker package (https://hub.docker.com/r/mcguffin/multifold). The ReFOLD server is available at https://www.reading.ac.uk/bioinf/ReFOLD/ and the modified scripts can be downloaded from https://www.reading.ac.uk/bioinf/downloads/. Supplementary data are available at Bioinformatics Advances online.

Lumbar intervertebral disc segmentation for computer modeling and simulation

Background and objectiveThe present work had as its main objective the development of a method for localizing and automatically segmenting lumbar intervertebral discs (IVD) in 3D from magnetic resonance imaging (MRI), with the goal of supporting the generation of finite element (FE) models from actual lumbar spine anatomy, by providing accurate and personalized information on the shape of the patient's IVD. The extension of the method to allow performing separate segmentations of the IVD's two main structures – annulus fibrosus (AF) and nucleus pulposus (NP) – as well as automatically detecting degenerated IVD where this distinction is no longer possible was also an objective of the work. MethodsThe method presented here evolves from 2D segmentations in the sagittal profile using Gabor filters towards 3D segmentations. It works by detecting the spine curves and intensity regions corresponding to IVD. As so, the 2D method from Zhu et al. (2013) was partially implemented, modified and adapted to 3D use, and then tested with eight spines from two separated online datasets. The 3D adaptation was achieved by using vertebral body segmentation masks to approximate the shape of the vertebrae and to adjust the spine curves accordingly. ResultsThe method showed average values of 85%, 83% and 96% for the Dice coefficient, sensitivity and specificity, respectively. The method correctly identified 65 of 68 (96%) IVD as either healthy or degenerated. The method's Dice coefficient is within the range of existing 3D IVD segmentation methods in the literature (81–92%). The method took on average 6–7 s to perform a full 3D segmentation, which is well within the range of the existing methods (2 s – 19 min). ConclusionsThe developed method can be used to generate accurate 3D models of the IVD based on MRI, with AF/NP distinction and detection of marked degeneration by comparing each IVD with the remaining spine levels. Further work shall improve the method towards distinguishing between specific levels of degeneration for clinically oriented FE modeling.

Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

AbstractComputer vision applications usually rely on the features extracted from input images with good visibility. Image acquisition systems may produce degraded images with low contrast or distorted colours. For instance, bad weather (haze, fog) can cause images captured outdoor with low visibility. Image processing algorithms generally assume that the input image is the scene radiance. Haze removal, with the recovery of image radiance, ensures reliable features extracted from images and the image processing algorithm can achieve optimal performance. Inspired by the concept of image dehazing, the authors propose an image enhancement method that can be used to improve the visibility of the images. Each original image is first transformed into multiple exposure images by means of gamma‐correction operations and adaptive histogram equalization. The transformed images are analyzed by the computation of the local ternary pattern. The image is then enhanced, with each pixel generated from the set of transformed image pixels weighted by a function of the local pattern feature. The authors evaluate their proposed method on four benchmark image dehazing datasets. The quantitative results show that our method outperforms many deterministic algorithms and deep learning models. Moreover, the authors investigate the impact of image enhancement on a practical image‐based application—the reconstruction of three‐dimensional (3D) model of survey scene. Accurate 3D model reconstruction depends on high‐quality images. Degraded images will result in large errors in the reconstructed 3D model. Experimentations have been carried out on outdoor and indoor surveys. Our analysis finds that when fed into the photogrammetry software, the images enhanced by the authors’ method can reconstruct 3D scene models with sub‐millimetre mean errors, which are much better than those with the original images. As shown in the visual and quantitative results of 3D model reconstruction, the authors’ proposed method also outperforms other image enhancement methods.

Linear Feature-Based Image/LiDAR Integration for a Stockpile Monitoring and Reporting Technology

Stockpile monitoring has been recently conducted with the help of modern remote sensing techniques – e.g., terrestrial/aerial photogrammetry/LiDAR – that can efficiently produce accurate 3D models for the area of interest. However, monitoring of indoor stockpiles still requires more investigation due to unfavorable conditions in these environments such as lack of global navigation satellite system (GNSS) signals and/or homogenous texture. This study develops a fully-automated image/LiDAR integration framework that is capable of generating accurate 3D models with color information for stockpiles under challenging environmental conditions. The derived colorized 3D point cloud can be subsequently used for volume estimation and visual inspection of stockpiles. The main contribution of the developed strategy is using automatically derived conjugate image/LiDAR linear features for simultaneous registration and camera/LiDAR system calibration. Data for this study is acquired using a camera-assisted LiDAR mapping platform – denoted as stockpile monitoring and reporting technology (SMART) – which was recently designed as a time-efficient and cost-effective bulk material tracking. Experimental results on three datasets show that the developed framework outperforms a classical planar feature-based registration technique in terms of the alignment of acquired point cloud. Results also indicate that the proposed approach can lead to a high relative accuracy between image lines and their corresponding back-projected LiDAR features in the range of 4–7 pixels.

Study to evaluate the effect of terrain surface on performance of a wind farm in Ninh Thuan province, Vietnam

Topography is one of the important factors directly related to the distribution of wind resources, so it plays an important role in determining the layout and operation efficiency of wind power farms. In this research, we use a combination of Computational Fluid Dynamics (CFD) method and Geographic Information Systems (GIS) data to determine suitable locations for building wind turbines in complex terrain conditions. The selected region to build the analytical model is an area with many hills and mountains adjacent to the East Sea, in Thuan Nam and Ninh Phuoc districts, Ninh Thuan province. The first part, this article will provides a general method for determining the best locations for installation of wind turbines according to specific terrain conditions. Then, apply this method to build accurate 3D models for the area, the models are meshed by hexagonal elements combined with tetrahedron elements with side lengths of 200m. The results obtained from the models are the distributions of wind speed by altitude at specific locations such as mountain peaks, mountain slopes, valleys of the area pointed out. Based on these results, the locations with high and stable wind speed, suitable for wind turbine operation are suggested. In addition, the article also presents some locations where wind with high turbulence or eddy winds may appear, which may adversely affect turbine performance. Finally, the paper gives an optimal location map for a wind farm with a capacity of less than 100MW using a turbine with a 4MW capacity.

Effect mechanism of block convexity on the shear behaviors of soil-rock mixtures by the developed 3D spherical harmonics-based modeling approach

The effect of block convexity on the shear behaviors of soil-rock mixtures (S-RMs) can be investigated using accurate 3D models constructed with the discrete element method (DEM). Based on the improved spherical harmonics series, a new block geometry modeling approach was proposed for generating 3D blocks with different convexities but the same form and angularity. By introducing tracer particles, a DEM modeling approach was also proposed for generating S-RM models with the same block distribution characteristics and possible block breakage. Numerical direct shear tests were performed on the generated stiff and soft S-RM models with different block convexities. The meso- and the macro-shear behaviors of the S-RMs were analyzed in detail to reveal the effect mechanism of block convexity. The results showed that the breakage degree of soft blocks increased linearly with the block convexity degree. The interlocking degrees of blocks with higher convexities are higher, leading to higher contributions of the blocks to the shear strength and greater block rotation. As the effect of block breakage is more significant, soft blocks with higher convexity degrees contribute smaller to the shear resistance. For S-RMs with higher block convexity degrees, the shear strength of stiff S-RMs is higher owing to their higher block interlocking degrees, while the shear strength of soft S-RMs under high normal stress is smaller due to their higher block breakage degrees. The dilation magnitude is greater for stiff S-RMs while lower for soft S-RMs with higher convexity degrees, owing to the greater effect of block breakage than the rotation behavior. The friction angles of stiff S-RMs with higher block convexity degrees are larger due to their higher block interlocking degrees, while the friction angles of soft S-RMs with lower block convexity degrees are smaller under high normal stress due to their higher block breakage degrees.

Assessing the CO2 emission reduction potential of steam cracking furnace innovations via computational fluid dynamics: From high-emissivity coatings, over coil modifications to firing control

Accurate 3D modeling of steam cracking fireboxes is instrumental in the identification of the strengths and weaknesses of a specific firebox. A 3D computational fluid dynamics model for the firebox, combined with a 1D reactor model, is validated using industrial data of a naphtha steam cracker. The framework is used to assess the impact of a high-emissivity coating, geometrical changes and operational conditions on the CO2 emissions per ton ethene produced. A high-emissivity coating reduces the CO2 emissions by 4% and increases the radiation efficiency by 1.9%. A geometry with a higher number of inlets per reactor coil reduces the CO2 emissions by 3%. When the excess oxygen concentration increases at the bridgewall, the firebox efficiency decreases while CO2 emissions increase. The operational changes and technologies studied in this work can be combined to further minimize the CO2 emissions, but controlling and monitoring the excess bridgewall oxygen proved to be the most crucial parameter to realize a reduction in CO2 emissions.

New Approaches to 3D Vision.

New approaches to 3D vision are enabling new advances in artificial intelligence and autonomous vehicles, a better understanding of how animals navigate the 3D world, and new insights into human perception in virtual and augmented reality. Whilst traditional approaches to 3D vision in computer vision (SLAM: simultaneous localization and mapping), animal navigation (cognitive maps), and human vision (optimal cue integration) start from the assumption that the aim of 3D vision is to provide an accurate 3D model of the world, the new approaches to 3D vision explored in this issue challenge this assumption. Instead, they investigate the possibility that computer vision, animal navigation, and human vision can rely on partial or distorted models or no model at all. This issue also highlights the implications for artificial intelligence, autonomous vehicles, human perception in virtual and augmented reality, and the treatment of visual disorders, all of which are explored by individual articles. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

A Preliminary Field Work on Digital Heritage and the Use of Virtual Reality for Cultural Heritage Management

This paper presents the results of a multidisciplinary research project carried on during the Covid-19 Pandemic and supported by TÜBİTAK (The Scientific and Technological Research Council of Turkey). The results are gathered from six months of field and office work, as the project was limited with this period. The archaeological site of Letoon in Muğla/Turkey has been chosen as the test area, specifically the triple temples of Leto, Apollo, and Artemis. Photogrammetric reconstruction of the current situation, as well as archaeologically accurate 3D models, have been created and converted into interactive immersive VR content to measure consumer behaviour and experience. These two different types of 3D content are integrated into the VR environment both separately and as a single content with switching from one to the other. After the creation process, the content was experienced by the visitors with different demographic characteristics and a survey was conducted to measure this experience.

Cloner 3D photogrammetric facial scanner: Assessment of accuracy in a controlled clinical study.

To evaluate the accuracy of facial measurements on three-dimensional images obtained using a new photogrammetric scanner. A total of 11 participants were included in the study. Nine customized adhesive labels were used to identify the facial landmarks: Trichion (Tri), Glabella (G), Right (Exr) and Left (Exl), Pronasal (Pn), Subnasal (Sn), Chelion right (Chr) and left (Chl) and Mentonian (Me). Two trained and calibrated examiners were responsible for performing seven linear measurements for each participant (Tri-G, Sn-Me, Exr-Exl, Chr-Chl, Exr-Chr, Exl-Chl, Pn-Sn) first with a digital caliper and later with a three-dimensional model obtained after digitalization with photogrammetric technology. The intraclass correlation coefficient (ICC), mean difference, SD, and Bland-Altman correlation were used to compare the measurements performed. Intra and inter-examiner reliability were excellent (ICC >0.9). In general, the measurements presented a variation of a minor 2.0 mm. However, only three measures (Sn-Me, Exr-Exl, and Exr-Chr) were outside the clinical acceptability range. The 3D Cloner scanner showed clinically acceptable accuracy comparable to the digital caliper with a variation of -0.8 ± 1.2 mm. Inter- and intra-examiner agreement on digital measurements was also observed. Scanners with accurate 3D model reproductions associated with reliable digital measurements provide a more precise diagnosis and better planning in orofacial treatment.

Accuracy Analysis for 3D Model Measurement Based on Digital Close-range Photogrammetry Technique for the Deep Foundation Pit Deformation Monitoring

The deformation of deep foundation pits (DFP) may pose a huge potential threat to the safety of the bracing system and the surrounding environment. A large amount of deformation data facilitates the deformation prediction of DFP and the dynamic adjustment of the bracing scheme. This study proposes and validates a 3D modeling technique based on digital close-range photogrammetry, which can obtain sufficient deformation data and enhance the understanding of deformation patterns. Two typical sets of deformation experiments, namely the vertical deformation test and the horizontal deformation test, were conducted to examine the viability and accuracy of 3D model measurement. Furthermore, the factors affecting the accuracy of the measurement were discussed in detail. The results indicate that the measurement accuracy of 3D models can reach millimeter-level or even sub-millimeter level. The measurement accuracy is affected by the coupling of the pixel resolution of CCD, focal length, photographic distance, and photographic baseline. The increase in the pixel resolution of CCD and focal length improve the measurement accuracy of 3D model measurement, but increasing photographic distance shows the opposite trend. The ratio of photographic baseline to photographic distance (BDR) has a much greater effect on accuracy than other factors. The photographic distance with a resolution of less than 1.0mm and the BDR value of 0.6–1.8 are recommended. Based on the results, it is found that this technique can be adopted as an effective monitoring method in DFP deformation monitoring due to the high precision and labor-saving.

3D Modeling of Bosscha Observatory With TLS and UAV Integration Data

Terrestrial Laser Scanner is a tool capable of generating millions 3D points with mm accuracy, but upper structure is difficult to model. Unmanned Aerial Vehicle is an unmanned aircraft system technology that can provide structural data on buildings. Measurements with one technique can lead to unsatisfactory results, so an integration process is carried out to obtain a more accurate 3D model. The purpose of this research is to see the successful integration of TLS and UAV point cloud data for 3D modeling. The data used is secondary data from previous research. TLS and UAV data were processed with Agisoft Metashape and Cyclone in the same coordinate system. The integration process is carried out by aligning the same point cloud between the two data in CloudCompare with an RMSE of 25.60 mm. Validation is done by comparing the distance between the results of the 3D model with the actual conditions. The integrated 3D model can be implemented for the purposes of Bosscha Observatory 3D modeling.

NIMG-108. PHYSICAL STRESSES IN BRAIN TUMORS

Abstract Solid stress, distinct from fluid pressure, is a physical force contained in and transmitted by solid components of the brain tumor, including cells and the matrix they produce. Solid stress has been shown to promote tumor progression, and decrease anticancer therapy efficacy. This is especially relevant in brain tumors, as the rigid skull results in these trapped forces, increasing intracranial pressure, and potentially leading to other complications, including neuronal cell death. Here we present a novel method of quantifying these physical stresses in situ in both mice (glioblastoma [U87], brain metastasis [BT474], and ependymoma models) and patients. Briefly, following a craniotomy, mechanical forces that include solid stress are released, which causes the tissue to deform in peaks (areas under compression) and valleys (areas originally under tension). This tissue deformation is imaged via high-resolution ultrasound and analysed via custom MATLAB code to produce an accurate 3D model of the entire mouse brain, including the tumour region. For human samples, a pre-operative MRI is used to generate a detailed 3D model of the human brain. During surgery, the trapped physical stresses results in a bulge of the dura post craniotomy. We use BrainLab to measure the craniotomy induced brain deformation, which is then registered to the pre-operation MRI before being analysed in an identical fashion to the murine models using SolidWorks and Abaqus. We further show that in the brain metastases model, chemotherapy reduces compression stresses by 51%. Further, our technique results in fast processing time (~ 15 minutes), and has the potential to prevent the need for intraoperative MRI based on position simulations. As such, solid stress measurements provide a new class of mechanical biomarkers that can be correlated to clinical outcomes for predictive and prognostic value.