Transformation Parameters Research Articles

Результаты исследования матриц деформаций координатной основы СК-42

For transition from the coordinate system CS-42 to the Earth-centered, Earth-fixed SCS-2011, correct transformation models are required. Such a model is a non-linear transformation of coordinate systems via the grid shift method. The Data Grid of transformation parameters from CS-42, CS-95 to SCS-2011 were developed in 2016 at the Federal Scientific and Technical Center of Geodesy, Cartography and Spatial Data Infrastructure and are called deformation matrices of CS-95 (CS-42). The accuracy of coordinate transformation is declared as 10 cm for areas with a high density of state geodetic network points. The author presents the results of the deformation matrix analysis for the Republic of Bashkortostan. Evaluation of errors in it due to violation of the mesh irregularity is presented. Recommendations for improving the model are given

Mueller matrix polarization parameters correlate with local recurrence in patients with stage III colorectal cancer

The peri-tumoural stroma has been explored as a useful source of prognostic information in colorectal cancer. Using Mueller matrix (MM) polarized light microscopy for quantification of unstained histology slides, the current study assesses the prognostic potential of polarimetric characteristics of peri-tumoural collagenous stroma architecture in 38 human stage III colorectal cancer (CRC) patient samples. Specifically, Mueller matrix transformation and polar decomposition parameters were tested for association with 5-year patient local recurrence outcomes. The results show that some of these polarimetric parameters were significantly different (p value < 0.05) for the recurrence versus the no-recurrence patient cohorts (Mann–Whitney U test). MM parameters may thus be prognostically valuable towards improving clinical management/treatment stratification in CRC patients.

Survey of Point Cloud Registration Methods and New Statistical Approach

The use of a 3D range scanning device for autonomous object description or unknown environment mapping leads to the necessity of improving computer methods based on identical point pairs from different point clouds (so-called registration problem). The registration problem and three-dimensional transformation of coordinates still require further research. The paper attempts to guide the reader through the vast field of existing registration methods so that he can choose the appropriate approach for his particular problem. Furthermore, the article contains a regression method that enables the estimation of the covariance matrix of the transformation parameters and the calculation of the uncertainty of the estimated points. This makes it possible to extend existing registration methods with uncertainty estimates and to improve knowledge about the performed registration. The paper’s primary purpose is to present a survey of known methods and basic estimation theory concepts for the point cloud registration problem. The focus will be on the guiding principles of the estimation theory: ICP algorithm; Normal Distribution Transform; Feature-based registration; Iterative dual correspondences; Probabilistic iterative correspondence method; Point-based registration; Quadratic patches; Likelihood-field matching; Conditional random fields; Branch-and-bound registration; PointReg. The secondary purpose of this article is to show an innovative statistical model for this transformation problem. The new theory needs known covariance matrices of identical point coordinates. An unknown rotation matrix and shift vector have been estimated using a nonlinear regression model with nonlinear constraints. The paper ends with a relevant numerical example.

Deep rigid registration for slice-to-volume in real time

Slice-to-volume registration that achieves high accuracy in real time is one of the enabling technologies for clinical imaging scenarios. Recently, deep learning methods have been explored to significantly improve the accuracy and efficiency of the registration. Nevertheless, such a 2D/3D registration problem is very challenging due to several considerable barriers including highly computational cost brought by dense sampling in six dimensional parameter space and significant modal appearance differences. We proposed a Differentiable resampling based Slice-to-Volume Registration network, which can achieve real-time and accurate registration in both mono- and multi-modal scenarios. The proposed network learns the out-of-plane transformation parameters in the spherical coordinates that decide the content and is invariant to the remaining in-plane parameters by feeding 2D rigid transformed data on-the-fly, thus reducing the training sample size remarkably. To further improve the performance, we introduce an auxiliary image similarity connected by differentiable resampling. For multi-modal scenarios, we optionally include the enhanced modality independent neighborhood descriptor to unify different modalities into common space. Training size is considerably reduced to 30k per dataset with half of all possible orientations and 80% of the volume space covered while the average registration error drops to 1.84 mm and 6.49 mm respectively for mono- and multi-modal experiments. Extensive experiments show the proposed methods’ superiority over existing deep learning methods for real-time 2D/3D rigid registration in both mono- and multi-modal settings.

Improving Estimation of Tree Parameters by Fusing ALS and TLS Point Cloud Data Based on Canopy Gap Shape Feature Points

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree attributes (e.g., location, diameter at breast height, and tree height), but the registration is affected by individual tree segmentation and is inefficient. In this study, we proposed a method to automatically fuse ALS and TLS point clouds by using feature points of canopy gap shapes. First, the ALS and TLS canopy gap boundary vectors were extracted by the canopy point cloud density model, and the turning or feature points were obtained from the canopy gap vectors using the weighted effective area (WEA) algorithm. The feature points were then aligned, the transformation parameters were solved using the coherent point drift (CPD) algorithm, and the TLS point clouds were further aligned using the recovery transformation matrix and refined by utilizing the iterative closest point (ICP) algorithm. Finally, individual tree segmentations were performed to estimate tree parameters using the TLS and fusion point clouds, respectively. The results show that the proposed method achieved more accurate registration of ALS and TLS point clouds in four plots, with the average distance residuals of coarse and fine registration of 194.83 cm and 2.14 cm being much smaller compared with those from the widely used crown feature point-based method. Using the fused point cloud data led to more accurate estimates of tree height than using the TLS point cloud data alone. Thus, the proposed method has the potential to improve the registration of ALS and TLS point cloud data and the accuracy of tree height estimation.

CR-Net: A robust craniofacial registration network by introducing Wasserstein distance constraint and geometric attention mechanism

Accurate registration of three-dimensional (3D) craniofacial data is fundamental work for craniofacial reconstruction and analysis. The complex topology and low-quality 3D models make craniofacial registration challenging in the iterative optimization process. In this paper, we proposed a craniofacial registration network (CR-Net) that can automatically learn the registration parameters of the non-rigid thin plate spline (TPS) transformation from the training data sets and perform the required geometric transformations to align craniofacial point clouds. The proposed CR-Net employs an improved point cloud encoder architecture, a specially designed attention mechanism that can perceive the geometric structure of the point cloud. In order to align the source and target data, Wasserstein distance loss is introduced to combined with Chamfer loss and Gaussian Mixture Models (GMM) loss as an unsupervised loss function dedicated to improves registration accuracy. After efficient training, the network can automatically generate the transformation parameters for registration, transforming the reference craniofacial data to the target craniofacial data without manual calibration of feature points or performing an iterative optimization process. Experimental results show that our method has high registration accuracy and is robust to low-quality models.

GCMTN: Low-Overlap Point Cloud Registration Network Combining Dense Graph Convolution and Multilevel Interactive Transformer

A single receptive field limits the expression of multilevel receptive field features in point cloud registration, leading to the pseudo-matching of objects with similar geometric structures in low-overlap scenes, which causes a significant degradation in registration performance. To handle this problem, a point cloud registration network that incorporates dense graph convolution and a mutilevel interaction Transformer (GCMTN) in pursuit of better registration performance in low-overlap scenes is proposed in this paper. In GCMTN, a dense graph feature aggregation module is designed for expanding the receptive field of points and fusing graph features at multiple scales. To make pointwise features more discriminative, a multilevel interaction Transformer module combining Multihead Offset Attention and Multihead Cross Attention is proposed to refine the internal features of the point cloud and perform feature interaction. To filter out the undesirable effects of outliers, an overlap prediction module containing overlap factor and matching factor is also proposed for determining the match ability of points and predicting the overlap region. The final rigid transformation parameters are generated based on the distribution of the overlap region. The proposed GCMTN was extensively verified on publicly available ModelNet and ModelLoNet, 3DMatch and 3DLoMatch, and odometryKITTI datasets and compared with recent methods. The experimental results demonstrate that GCMTN significantly improves the capability of feature extraction and achieves competitive registration performance in low-overlap scenes. Meanwhile, GCMTN has value and potential for application in practical remote sensing tasks.

Parametrization of the Hungarian Stereographic Map Sheets

Abstract. A topographic map series of Northern Transylvania was drawn in the 1940s. Georeferencing them needs information about their coordinate systems. These maps used two reference systems known as the Budapest and the Marosvásárhely Stereographic projections. The datum transformation parameters of the former had already been determined in previous studies, but they had to be adjusted slightly to use a common Ferro–Greenwich difference for both systems. Parameters of the Marosvásárhely system were only available as a Molodenskiy transformation determined with insufficient accuracy. In this study, we recalculate the Molodenskiy parameters with higher accuracy and determine six parameters from the usual 7-parameter Helmert (aka. Burša–Wolf) transformation. The ideas given in this paper can be useful in other similar cases when the information about base point coordinates on the old system is very limited: We had access only to the ellipsoidal coordinates of the origin and the projected coordinates of one single point. The accuracy of the resulting transformation was evaluated by aligning the old map sheets with recent base maps.

Impacts of non-linear ITRF2020 on reference frame alignment

The geometric similarity between two sets of coordinates should be assumed for frame alignment. When aligning Global Navigation Satellite System (GNSS) positions with seasonal variations to the linear Terrestrial Reference Frame (TRF), non-negligible aliasing errors of transformation parameters can appear and bias the station positions. The recent release of the seasonally augmented International Terrestrial Reference Frame 2020 (ITRF2020) necessitates an investigation into the impacts of seasonal terms on the alignment of regional networks. Using four-year of GPS data for Crustal Movement Observation Network of China (CMONOC) stations, we realize five sets of regional reference frames (RFs) aligned to the linear ITRF2020 (REF-LIN), the seasonal ITRF2020 (REF-FRQ), the linear ITRF2020 augmented either with seasonal signals or with full signals from loading models (REF-FIT and REF-NTL), and the combination of seasonal displacements from ITRF2020 and sub-seasonal signals from loading model (REF-CMB). Our analysis revealed that the augmented ITRF2020 had significant impact on the global stations used for frame alignment. Aligning daily solutions to the augmented ITRF2020 instead of the linear ITRF2020 resulted in a noticeable root mean square (RMS) decrease for over 70% of global stations. The REF-CMB method was the most preferred for frame alignment of global networks, with the largest decreased RMS of 0.3 and 1.0 mm in horizontal and vertical components. This indicates that most true signals are preserved in the aligned daily solutions. For regional network, we found only marginal impacts on regional stations in terms of linear velocity, residuals of time series stacking between the augmented solutions and REF-LIN. However, the larger discrepancies are noticed between annual estimates and loading model for the vertical component of REF-CMB and REF-FRQ, compared to RER-LIN solutions.

An effective method for establishing a regeneration and genetic transformation system for Actinidia arguta.

The all-red A. arguta (Actinidia arguta) is an anthocyanin-rich and excellent hardy fruit. Many studies have focused on the green-fleshed A. arguta, and fewer studies have been conducted on the all-red A. arguta. Here we reported a regeneration and Agrobacterium-mediated transformation protocol by using leaves of all-red A. arguta as explants. Aseptic seedling leaves of A. arguta were used as callus-inducing materials. MS medium supplemented with 0.3 mg·L-1 2,4-D and 1.0 mg·L-1 BA was the optimal medium for callus induction of leaves, and medium supplemented with 3 mg·L-1 tZ and 0.5 mg·L-1 IAA was optimal for adventitious shoot regeneration. The best proliferation medium for adventitious buds was MS + 1.0 mg·L-1 BA + 0.3 mg·L-1 NAA. The best rooting medium was 1/2MS + 0.7 mg·L-1 IBA with a 100% rooting rate. For the red flesh hardy kiwi variety 'Purpurna Saduwa' (A. arguta var. purpurea), leaves are receptors for Agrobacterium (EHA105)-mediated transformation. The orthogonal experiment was used for the optimization of each genetic transformation parameter and the genetic transformation of the leaves was 21% under optimal conditions. Our study provides technical parameters for applying genetic resources and molecular breeding of kiwifruit with red flesh.

CAREER PHENOMENON IN THE CONTEXT OF MODERN SOCIO-CULTURAL SITUATION: FACTORS OF DETERMINATION AND TRENDS OF MANIFESTATION

The fundamental changes that took place at the beginning of the 21st century in all spheres of public life actualized the personalized trend of the modern personality, namely its social and professional development, which is associated with the concept of “career”. The purpose of the article is to characterize the pool of factors that determine a career as a socio-cultural phenomenon of modern society. Based on macro- and microsociological approaches, the parameters of career transformation are determined. Economic ones consist in the transition from working professions to expert-type professions that require a high level of education and specialization. Technological - in the development of information technologies, which led to changes in the technical and technological component of social production and the growth of a separate social group of intellectual workers. The transformation of interests and value orientations of a person has led to a shift in the emphasis of personal performance from external signs to individualistic factors. The patterns of new realities in the world of work, which led to the transformation of the concept of career and new trends, are characterized. The first model includes the presence of higher education, intellectual work in the specialty and a fairly low level of income. The second model is based on ideas about the American dream and the unlimited possibilities of the individual, where the main component is a high material status and own business as a symbol of success. The third model is a highly paid professional with a high level of prestigious education and a good profession. A new type of career is defined - “polyvariant”. These days, success in career strategies requires constant professional development and gaining experience. Through the individualization and pluralization of lifestyles and consumption, people are more able to choose their own path to success, including various forms of life and career development. The tendencies of the spread of self-employment and the development of professional qualities in the career strategies of a modern person are determined. Through the individualization and pluralization of lifestyles and consumption, people are more able to choose their own path to success, including various forms of life and career development.

Agrobacterium-mediated transformation of Nang Thom Cho Dao, an indica rice variety

Agrobacterium-mediated transformation is the most efficient method to transfer gene of interest into the plant genome. However, the transformation efficiency of this method with indica rice varieties is still low and needs to be improved. In this study, the Agrobacterium-mediated method was used to transfer reporter genes into the Nang Thom Cho Dao, an indica rice variety. Different transformation parameters, such as selection to used hygromycin at concentrations of 0, 25, 30, 40, and 50 mg/L, bacterial density, and infected duration, were tested and optimized. The results showed that 30 mg/L of hygromycin was the most appropriate concentration for selecting transgenic callus in Nang Thom Cho Dao. In addition, the highest transformation efficiency of this Nang Thom Cho Dao rice cultivar was observed at the bacterial suspension density OD600nm of 0.1, the infected duration of 20 minutes, and the 3-day co-culture period. The presence and expression of transgenes were confirmed by gus staining and PCR with specific primers for hptII and gus genes. This transformation procedure should be used for further studies in genetic engineering of Nang Thom Cho Dao and other indica rice varieties.

Дифференциация этапов трансформации в сельском хозяйстве

Abstract. The aim is the study of the parameters of the influence of the structure of investments in fixed assets of agriculture of the Russian Federation by sources of financing as an external factor in the development of the industry. Methods. Based on Rosstat data, time series of indicators of agriculture in Russia for 1995–2021 have been compiled and chain growth rates have been calculated. The periodization of long dynamic series of investments in fixed assets of agriculture in Russia based on the predominance of own and attracted funds of organizations (1995–2005 – the predominance of own funds, 2006–2013 – predominance of attracted funds, 2014–2021 – predominance of own funds). Within each stage, average growth rates are calculated and analyzed; transformation parameters are determined. Results. Based on the chain dynamics of indicators of agriculture in Russia, on the basis of scientifically based approaches and expert assessments, the criteria and parameters of transformation in agriculture have been identified. The criterion of transformation in agriculture is the change of the predominant share of own or borrowed funds in the financial structure of capital investments. Three stages of transformation are highlighted. At the stage of the predominance of attracted funds, the average rate increased for the sown areas of grain and leguminous crops, the gross harvest of sugar beet, sunflower seeds, potatoes, as well as the number of pigs, sheep and goats, the production of livestock and poultry for slaughter (in slaughter weight), eggs and wool. The efficiency of transformation is estimated by the average rate of yield of grain and leguminous crops, sugar beet, sunflower seeds and potatoes. The elasticity coefficient for a number of indicators can also be used as a transformation parameter. The scientific novelty lies in a new methodological approach for the periodization of the transformation of investments in agriculture based on the proposed criterion for identifying parameters.

A new approach to phonocardiogram severity analysis

Phonocardiogram signal (PCG) has been the subject of several signal processing studies, where researchers applied various analysis techniques and extracted numerous features for different purposes, like cardiac pathologies identification, healthy/pathologic case discrimination, and severity assessment. When talking about cardiac severity, many think directly about the intensity or energy of the signal as the most reliable parameter. However, cardiac severity is not always reflected by the intensity or energy of the signal but includes other variables as well. In this paper, we will discuss the probability of having a Discrete Wavelet Transform (DWT) parameter that discriminates, identifies, and assesses the pathological cardiac severity levels, a parameter that takes into consideration other variables and elements for the severity study. For this purpose, we studied six PCGs signals that contain reduced murmurs (clicks) and eight murmur signals with four different cardiac severity levels. We extracted the Entropy of Approximation Coefficients (EAC) from the Discrete Wavelet Transform (DWT) sub-bands as the feature to study in this novel approach. The Energetic Ratio (ER) served as a reference parameter to evaluate the EAC evolution, due to its proven efficiency in cardiac severity tracking. While the DWT-EAC algorithm results revealed that the EAC provides better results for the paper purposes, the One versus All Support Vector Machine (OVA-SVM) classifier affirmed the efficiency of the Entropy of Approximation Coefficients (EAC) for cardiac severity assessment and proved the accuracy of this novel approach.

Harmonic Modeling of the Series-Connected Multipulse Rectifiers Under Unbalanced Conditions

With the promotion of high voltage direct current (HVDC) system construction around the world, the series-connected multi-pulse rectifiers (SCMPRs) have been widely used in high-power rectifying occasions in the power system. Since the certain order of harmonics cannot be effectively eliminated under the non-ideal conditions, many different phenomena, such as the unbalance of supply voltage and line/transformer parameters, the influence of background harmonics, and the non-equidistance of thyristor triggering pulses, may cause the non-characteristic harmonics (NCHs) of SCMPR. However, the existing frequency-domain harmonic model cannot be used to analyze the harmonic characteristics of the SCMPRs under non-ideal conditions. In this paper, a general frequency-domain harmonic coupling matrix model (HCMM) is proposed. The proposed HCMM can consider most of the above imbalance factors and the model can explicitly illustrate the harmonic coupling relationship between the SCMPRs supplied voltage and the generated harmonic currents. The generality of the SCMPR model under non-ideal conditions is improved accordingly. The HCMM is simplified by comparing and analyzing the effects of the transformer structure and parameters. Moreover, by analyzing the harmonic generation characteristic of the negative-sequence component, the HCMM can be further simplified. Finally, time-domain simulation and lab experimentation are performed to verify the accuracy and efficiency of the frequency-domain HCMM of SCMPRs. The results prove the generality of the proposed model and also illustrate the harmonic generating influence of unbalanced parameters from the transformer and supply voltage.

Вплив швидкості охолодження при кристалізації на структуру сплавів В95 та Д16

It is known that the alloys of the Al-Zn-Mg-Cu and Al-Mg-Cu systems have a very wide crystallization interval (over 100°C) and therefore the technology of roll pouring-rolling of sheet blanks from such alloys has not yet been developed. In this regard, the development of scientific and technological foundations of roll casting for such a range of aluminum alloys is an important scientific and practical task. The purpose of this work is to determine the possibility of aluminum alloys B95 and D16 (crystallization intervals are 160°C and 132°C, respectively) for casting-rolling based on the analysis of the effect of cooling intensity on their structure and mechanical properties. The research was carried out on full-scale samples of B95 and D16 alloys by comparing the obtained experimental values. Determination of the temperature parameters of phase transformations in the studied alloys was carried out using the Derivatograph Q-1500-D device. As a result of the tests, the curves of the differential thermal analysis and the fraction of the liquid phase depending on the temperature were obtained. Based on the results of the research, the suitability of B95 and D16 aluminum alloys for casting and rolling has been substantiated. It was found that the size of α-phase grains for B95 and D16 alloys as the cooling rate increases decreases to 120...200 μm (for cooling rates of 2.5...8.8°С/s), and to 40...70 μm (for cooling rates cooling ~1000°С/s). It has been proven that the hardness of B95 and D16 aluminum alloys mainly depends on the α-phase, therefore, when developing roll casting modes, it is advisable to take into account the hardening features of this structural component (α-phase). At the same time, it should be noted that at the intensity of heat removal, which is typical for roll crystallizers, the obtained sheet blanks from the studied alloys will have dispersed crystal structures with a uniform distribution of phase components along their cross sections. A natural consequence of such primary crystalline structures of cast blanks will be a guaranteed high level of strength characteristics of sheet metal rolled from them. Keywords: cooling rate, alloy, crystallization, structure, phase composition, metal rolling

Iris Recognition System Based on Zak-Gabor Wavelet Packets

The paper proposes a new iris coding method based on Zak-Gabor wavelet packet transform. The essential component of the iris recognition methodology design is an effective adaptation of the transformation parameters that makes the coding sensitive to the frequencies characterizing ones eye. We thus propose to calculate the between-to-within class ratio of weakly correlated Zak-Gabor transformation coefficients allowing for selection the frequencies the most suitable for iris recognition. The Zak-Gabor-based coding is non-reversible, i.e., it is impossible to reconstruct the original iris image given the iris template. Additionally, the inference about the iris image properties from the Zak-Gabor-based code is limited, providing a possibility to embed the biometric replay attack prevention methodology into the coding. We present the final prototype system design, including the hardware, and evaluate its performance using the database of 720 iris images.

A METHOD FOR ESTIMATING THE ACCURACY OF THE TIE POINTS CLOUD: THE CASE STUDY OF PALAZZO DE FALCO

Abstract. In the context of cultural heritage documentation and conservation, an accurate 3D model is essential to properly describe an artifact of historical relevance and provide the expert user with verified metric data. In this paper we propose a workflow aimed at estimating errors in the image orientation steps, which is useful for improving the accuracy of the sparse cloud (composed of Tie Points). Some parameters provided as input to the processing software, often overlooked by the user in favour of the default ones, are analysed. Specifically, tests are carried out using the commercial software package Agisoft Metashape and involve the analysis of three parameters: (i) the accuracy of measuring the Ground Control Points (GCPs) provided by the user; (ii) the accuracy of the Tie Points (TPs) computed by the software; and (iii) the accuracy of collimating the GCPs on the image. The accuracy of the GCPs is estimated by implementing the formulas of error propagation in MATLAB, considering the three concurring sources of error: the coordinate measurement with the Total Station (TS), the coordinate transformation from the local system to the UTM/ETRF00 System, and the GNSS measurement for estimating the transformation parameters. The transformation parameters are calculated using the Bursa Wolf method. The collimation accuracy of GCPs on the image is estimated by the reprojection error of each single GCPs on the image plane. The accuracy of the GCPs will be estimated by the standard error per unit weight (SEUW), accurate GCPs being expected to have a SEUW value close to unity. SEUW is also the overall indicator for estimating the accuracy of the TPS cloud because it is influenced, in different ways, by all three parameters analysed. Test Area is a historic building located in the town of Fisciano, a few kilometres from the city of Salerno, known as Palazzo De Falco. The results highlight how these parameters, if not properly considered, can significantly affect the final 3D model.

Robust solution of coordinate transformation parameters with a high breakdown point

Because the M estimation method may lead to poor robustness or even failure owing to excessive outliers, a robust algorithm with a high breakdown point was proposed and applied to the estimation of coordinate transformation parameters. Firstly, the sampling method was used to calculate multiple sets of model parameters, and some sampling results were sifted according to posterior information. Then, the samples were sorted according to their number in the sampling results, and the F-test was adopted to screen and reserve valid information. Finally, the initial values of the reliable parameters were computed using the valid information, and the final parameters were obtained by the Institute of Geodesy and Geophysics III scheme. Monte Carlo method was adopted for the simulation test, and a case analysis was chosen for verification. The results show that the proposed method can identify and process outliers more accurately than those of Rousseeuw and Hubert (2011 Wires Data Min. Knowl. 1 73–79) and Tao et al (2016 Acta Geod. Cartogr. Sin. 45 297–301). When the proportion of outliers exceeded 50%, the proposed algorithm maintained a strong robustness and had a high breakdown point.

Investigating the Effect of Brain Size on Deformation Magnitude Using Subject-Specific Finite Element Models.

Abstract In the last decade, computational models of the brain have become the gold standard tool for investigating traumatic brain injury (TBI) mechanisms and developing novel protective equipment and other safety countermeasures. However, most studies utilizing finite element (FE) models of the brain have been conducted using models developed to represent the average neuroanatomy of a target demographic, such as the 50th percentile male. Although this is an efficient strategy, it neglects normal anatomical variations present within the population and their contributions on the brain's deformation response. As a result, the contributions of structural characteristics of the brain, such as brain volume, on brain deformation are not well understood. The objective of this study was to develop a set of statistical regression models relating measures of the size and shape of the brain to the resulting brain deformation. This was performed using a database of 125 subject-specific models, simulated under six independent head kinematic boundary conditions, spanning a range of impact modes (frontal, oblique, side), severity (non-injurious and injurious), and environments (volunteer, automotive, and American football). Two statistical regression techniques were utilized. First, simple linear regression (SLR) models were trained to relate intracranial volume (ICV) and the 95th percentile of maximum principal strain (MPS-95) for each of the impact cases. Second, a partial least squares regression model was constructed to predict MPS-95 based on the affine transformation parameters from each subject, representing the size and shape of their brain, considering the six impact conditions collectively. Both techniques indicated a strong linear relationship between ICV and MPS-95, with MPS-95 varying by approximately 5% between the smallest and largest brains. This difference represented up to 40% of the mean strain across all subjects. This study represents a comprehensive assessment of the relationships between brain anatomy and deformation, which is crucial for the development of personalized protective equipment, identifying individuals at higher risk of injury, and using computational models to aid clinical diagnostics of TBI.