2D Image Sequences Research Articles

Magnetic Resonance Microscopy at Cellular Resolution and Localised Spectroscopy of Medicago truncatula at 22.3 Tesla

Interactions between plants and the soil’s microbial & fungal flora are crucial for the health of soil ecosystems and food production. Microbe-plant interactions are difficult to investigate in situ due to their intertwined relationship involving morphology and metabolism. Here, we describe an approach to overcome this challenge by elucidating morphology and the metabolic profile of Medicago truncatula root nodules using Magnetic Resonance (MR) Microscopy, at the highest magnetic field strength (22.3 T) currently available for imaging. A home-built solenoid RF coil with an inner diameter of 1.5 mm was used to study individual root nodules. A 3D imaging sequence with an isotropic resolution of (7 μm)3 was able to resolve individual cells, and distinguish between cells infected with rhizobia and uninfected cells. Furthermore, we studied the metabolic profile of cells in different sections of the root nodule using localised MR spectroscopy and showed that several metabolites, including betaine, asparagine/aspartate and choline, have different concentrations across nodule zones. The metabolite spatial distribution was visualised using chemical shift imaging. Finally, we describe the technical challenges and outlook towards future in vivo MR microscopy of nodules and the plant root system.

Enhanced Algorithm for Reconstruction of Three-Dimensional Mesh from Medical Images using Tessellation of Recent Graphics Cards

The reconstruction of a 3D mesh using displacement vectors for medical images is a recent method that allows the exploitation of modern GPUs. This method demonstrated its efficiency by accelerating 3D visualization calculations and optimizing the storage process. In fact, it is divided into two main stages. The first step is the construction of a basic mesh by applying the Marching Cubes algorithm, and the second step is the extraction of the displacement vectors, which represent the details lost in the basic mesh. In fact, the Marching Cubes algorithm used to build the basic mesh suffers from some problems that we will try to overcome in this article. These problems are summarized in the ambiguity encountered during the construction of the basic mesh in some cases. Also, the resulting basic mesh must undergo modifications, in order not to have errors of form, which requires time and memory, and which gives the end a final mesh which is not optimal and even erroneous in certain situations. Our method is based on extracting the contours of the anatomy to be reconstructed from a sequence of 2D images. Each contour will be represented by a triangle. The shape of the basic mesh will then be the result of the connection of these triangles. This strategy avoids the use of the marching cubes algorithm in the reconstruction of the basic mesh in order to overcome the problems mentioned above.

Improved detection of focal cortical dysplasia using a novel 3D imaging sequence: Edge-Enhancing Gradient Echo (3D-EDGE) MRI

Epilepsy is a common neurological disorder with focal cortical dysplasia (FCD) being one of the most common lesional causes. Detection of FCD by MRI is a major determinant of surgical outcome. Evolution of MRI sequences and hardware has greatly increased the detection rate of FCD, but these gains have largely been related to the more visible Type IIb FCD, with Type I and IIa remaining elusive. While most sequence improvements have relied on increasing contrast between gray and white matter, we propose a novel imaging approach, 3D Edge-Enhancing Gradient Echo (3D-EDGE), to directly image the gray-white boundary. By acquiring images at an inversion time where gray and white matter have equal signal but opposite phases, voxels with a mixture of gray and white matter (e.g., at the gray-white boundary) will have cancellation of longitudinal magnetization producing a thin area of signal void at the normal boundary. By creating greater sensitivity for minor changes in T1 relaxation, microarchitectural abnormalities present in FCD produce greater contrast than on other common MRI sequences. 3D-EDGE had a significantly greater contrast ratio between lesion and white matter for FCD compared to MP2RAGE (98% vs 17%; p = 0.0006) and FLAIR (98% vs 19%; p = 0.0006), which highlights its potential to improve outcomes in epilepsy. We present a discussion of the framework for 3D-EDGE, optimization strategies, and analysis of a series of FCDs to highlight the benefit of 3D-EDGE in FCD detection compared to commonly used sequences in epilepsy.

The Underground Explosion Point Measurement Method Based on High-Precision Location of Energy Focus

Source positioning based on energy time-inverse focus is a hot subject in the sphere of shallow underground source positioning. Due to the grave wave group aliasing and the complex, irregular geological structure typical of the shallow underground explosion, the reconstruction accuracy of the energy focus is low and thus the recognition of the focus is a difficult task, ultimately leading to a low accuracy of source positioning. To address the above problems, this article proposes a method based on deep learning energy focus recognition, whereby the process of recognizing and positioning the energy focus in an energy field is made equivalent to the end-to-end feature extraction of the energy field-energy focus. The time-variant characteristics of explosive vibration signals are put to use in the construction of an adaptive time window. First, within the time window and by combining cross-correlation and autocorrelation operations, a 3D energy field image sequence in the time-space domain is produced by grouped energy synthesis; second, a densely connected 3DCNN network is built and, through multiple layer span layer splicing, a higher repetitive use is made of the focus point features in the energy field images; third, a spatial pyramid pooling network is used to extract multi-scale features from different focus areas, which helps achieve high-precision focus recognition. Finally, numerical simulations and field tests were conducted.The results demonstrated that compared with the quantum particle swarm optimization (QPSO)-based energy focus search method, the proposed one is more effectively in recognizing the coordinates of the focus in the energy field, thus allowing high-precision localization of shallow underground sources. This method is of some engineering application value in the field of underground source positioning.

Time-resolved phase-contrast microtomographic imaging of two-phase solid–liquid flow through porous media

Abstract Time-resolved microtomography with high frame rate, high lateral resolution and sufficiently high contrast allows for observing three-dimensional motion of particles in two-phase solid–liquid flows through porous structures. Knowledge of the interaction between the particles and an activated surface of the pore space is important for simulation of the flow as a part of the design of porous structures with improved properties. Investigations on such structures are, for example, a prerequisite for the development of new technologies for industrial cell chromatography. From the 3D image sequences obtained by time-resolved microtomography it is possible to estimate various flow characteristics such as the particle deposition rate at the surface of the pore space and the fraction of particles moving close to the surface.

Automatic Diagnosis Based on Spatial Information Fusion Feature for Intracranial Aneurysm.

Timely and accurate auxiliary diagnosis of intracranial aneurysm can help radiologist make treatment plans quickly, saving lives and cutting costs at the same time. At present, Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of intracranial aneurysm, but as radiologists interpret those imaging sequences frame by frame, misdiagnosis might occur. The utilization of computer-aided diagnosis (CAD) can ease the burdens of radiologists and improve the detection accuracy of aneurysms. In this article, a deep learning method is applied to detect the intracranial aneurysm in 3D Rotational Angiography (3D-RA) based on a spatial information fusion (SIF) method, and instead of a 3D vascular model, 2D image sequences are used. Given the intracranial aneurysm and vascular overlap having similar feature in the most time, rather than focusing on distinguishing them in one frame, the morphological differences between frames are considered as major feature. In the training data, consecutive frames of every imaging time series are extracted and concatenated in a specific way, so that the spatial contextual information could be embedded into a single two-dimensional image. This method enables the time series with obvious correlation between frames be directly trained on 2D convolutional neural network (CNN), instead of 3D-CNN with huge computational cost. Finally, we got an accuracy of 98.89%, with sensitivity and specificity of 99.38% and 98.19%, respectively, which proves the feasibility and availability of the SIF feature.

Evaluation of giant arachnoid granulations with high-resolution 3D-volumetric MR sequences at 3T

PurposeTo evaluate the contribution to the diagnosis of the giant arachnoid granulations (AGs) of three-dimensional (3D) high-resolution magnetic resonance (MR) imaging sequences such as T2-weighted sampling perfection with application optimized contrasts using different flip-angle evolution (SPACE) and post-contrast T1-weighted magnetization prepared rapid gradient echo (MPRAGE). Materials and MethodsPatients with 45 giant AGs were included in this retrospective study. All the patients were performed 3D T2-weighted SPACE and contrast enhanced MR venography sequences, as well as conventional cerebral MR imaging sequences. Post-contrast T1 weighted MPRAGE sequence were performed on 38/45 patients. All cerebral MR examinations were reviewed by the 2 neuroradiologists. Each GA was evaluated carefully to assess location and mean diameter. ResultsThe most common location for giant AGs was at both transverse sinuses. Fluid signal feature within the giant AGs was not isointense to CSF on SE T1 and FLAIR MR imaging in 32 of 45 giant AGs. There were cerebral herniation into AG in 10 (22.2 %) of 45 giant AGs. 33 (73.3 %) of 45 giant AGs had central vein finding into AG in contrast enhanced MR venography. Signal void phenomenon into AG in 3D T2-weighted SPACE MR sequence was identified in 28 (62.2 %) of 45 giant AGs. ConclusionsFluid within giant AGs had no completely CSF-like signal intensity on conventional and 3D high-resolution MR imaging sequences. Majority of CSF-incongruent fluid within giant AGs on conventional sequences is mostly due to intra-AG CSF flow.

Optimising lung imaging for cancer radiation therapy

Effective radiotherapy is dependent on being able to (i) visualise the tumour clearly, and (ii) deliver the correct dose to the cancerous tissue, whilst sparing the healthy tissue as much as possible. In the presence of tumour motion, both of these tasks become increasingly difficult to perform accurately. This increases the likelihood of an incorrect dose being delivered to cancerous tissue and exposure of healthy tissue to unnecessary radiation. For tumours in the lung and thoracic region subject to respiratory-induced motion, 4D Cone-Beam CT (4D-CBCT) is a novel approach for producing a sequence of 3D images of the patient’s anatomy throughout different phases of the respiratory cycle. However, current implementations involve sub-optimal heuristic approaches to acquire the imaging data required to account for tumour motion. This leads to undersampling of images for particular phases in the respiratory cycle (such as peak inhale and exhale), resulting in noisy or poorly reconstructed 3D images. In this paper we present a novel Mixed Integer Program (MIP) to optimise the timing and angles for the acquisition of imaging data. The result is greatly enhanced image quality for each image across the respiratory cycle, whilst minimising motion blur. Numerical experiments indicate that our approach universally improves over the conventional acquisition process by 93% and simultaneously reduces unnecessary dose to the patient and can be solved in under a minute.

Application of Deep Neural Networks in the Problem of Obtaining Depth Maps from Two-Dimensional Images

Purpose of research is to study approaches to the depth map generation for deep neural networks testing and learning. The problem of obtaining information about the distance from the camera to the scenery object using a 2D image by means of deep neural networks without applying a stereocamera is considered. Methods. Generation of 3D scenery for training and assessment of the neural network was carried out using the 3D-computer graphics application Blender. The standard deviation (RMS) was used to estimate the accuracy of learning. Machine learning was implemented using the Keras library and optimization was implemented using the AdaGrad approach. Results . The architecture of a deep neural network which receives three sequences of 2D images from the 3D scenery video stream in the input and outputs the predicted depth map for the considered 3D scenery, is provided. The method for creating training data sets containing information about the depth of the map using Blender software is described. The problem of overtraining involving the fact that the created models work using specially generated data sets but still can not predict the correct depth map for random images is studied. The results of the testing actual methods for depth maps creation using deep neural networks are presented. Conclusion . The main problem of the proposed method is overtraining which can be expressed in predicting a certain average value for different images or predicting the same output for different inputs. To solve this problem, we can use already trained networks or training and variation samples containing 2D images of different sceneries.

Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise

Recovering three-dimensional (3D) shape of an object from two-dimensional (2D) information is one of the major domains of computer vision applications. Shape from Focus (SFF) is a passive optical technique that reconstructs 3D shape of an object using 2D images with different focus settings. When a 2D image sequence is obtained with constant step size in SFF, mechanical vibrations, referred as jitter noise, occur in each step. Since the jitter noise changes the focus values of 2D images, it causes erroneous recovery of 3D shape. In this paper, a new filtering method for estimating optimal image positions is proposed. First, jitter noise is modeled as Gaussian or speckle function, secondly, the focus curves acquired by one of the focus measure operators are modeled as a quadratic function for application of the filter. Finally, Kalman filter as the proposed method is designed and applied for removing jitter noise. The proposed method is experimented by using image sequences of synthetic and real objects. The performance is evaluated through various metrics to show the effectiveness of the proposed method in terms of reconstruction accuracy and computational complexity. Root Mean Square Error (RMSE), correlation, Peak Signal-to-Noise Ratio (PSNR), and computational time of the proposed method are improved on average by about 48%, 11%, 15%, and 5691%, respectively, compared with conventional filtering methods.

Whole-heart T1 mapping using a 2D fat image navigator for respiratory motion compensation.

PurposeTo combine a 3D saturation‐recovery‐based myocardial T1 mapping (3D SASHA) sequence with a 2D image navigator with fat excitation (fat‐iNAV) to allow 3D T1 maps with 100% respiratory scan efficiency and predictable scan time.MethodsData from T1 phantom and 10 subjects were acquired at 1.5T. For respiratory motion compensation, a 2D fat‐iNAV was acquired before each 3D SASHA k‐space segment to correct for 2D translational motion in a beat‐to‐beat fashion. The effect of the fat‐iNAV on the 3D SASHA T1 estimation was evaluated on the T1 phantom. For 3 representative subjects, the proposed free‐breathing 3D SASHA with fat‐iNAV was compared to the original implementation with the diaphragmatic navigator. The 3D SASHA with fat‐iNAV was compared to the breath‐hold 2D SASHA sequence in terms of accuracy and precision.ResultsIn the phantom study, the Bland‐Altman plot shows that the 2D fat‐iNAVs does not affect the T1 quantification of the 3D SASHA acquisition (0 ± 12.5 ms). For the in vivo study, the 2D fat‐iNAV permits to estimate the respiratory motion of the heart, while allowing for 100% scan efficiency, improving the precision of the T1 measurement compared to non‐motion‐corrected 3D SASHA. However, the image quality achieved with the proposed 3D SASHA with fat‐iNAV is lower compared to the original implementation, with reduced delineation of the myocardial borders and papillary muscles.ConclusionsWe demonstrate the feasibility to combine the 3D SASHA T1 mapping imaging sequence with a 2D fat‐iNAV for respiratory motion compensation, allowing 100% respiratory scan efficiency and predictable scan time.

T1 and T2* mapping of the human quadriceps and patellar tendons using ultra-short echo-time (UTE) imaging and bivariate relaxation parameter-based volumetric visualization

Quantification of magnetic resonance (MR)-based relaxation parameters of tendons and ligaments is challenging due to their very short transverse relaxation times, requiring application of ultra-short echo-time (UTE) imaging sequences. We quantify both T1 and T2* in the quadriceps and patellar tendons of healthy volunteers at a field strength of 3 T and visualize the results based on 3D segmentation by using bivariate histogram analysis. We applied a 3D ultra-short echo-time imaging sequence with either variable repetition times (VTR) or variable flip angles (VFA) for T1 quantification in combination with multi-echo acquisition for extracting T2*. The values of both relaxation parameters were subsequently binned for bivariate histogram analysis and corresponding cluster identification, which were subsequently visualized. Based on manually-drawn regions of interest in the tendons on the relaxation parameter maps, T1 and T2* boundaries were selected in the bivariate histogram to segment the quadriceps and patellar tendons and visualize the relaxation times by 3D volumetric rendering. Segmentation of bone marrow, fat, muscle and tendons was successfully performed based on the bivariate histogram analysis. Based on the segmentation results mean T2* relaxation times, over the entire tendon volumes averaged over all subjects, were 1.8 ms ± 0.1 ms and 1.4 ms ± 0.2 ms for the patellar and quadriceps tendons, respectively. The mean T1 value of the patellar tendon, averaged over all subjects, was 527 ms ± 42 ms and 476 ms ± 40 ms for the VFA and VTR acquisitions, respectively. The quadriceps tendon had higher mean T1 values of 662 ms ± 97 ms (VFA method) and 637 ms ± 40 ms (VTR method) compared to the patellar tendon. 3D volumetric visualization of the relaxation times revealed that T1 values are not constant over the volume of both tendons, but vary locally. This work provided additional data to build upon the scarce literature available on relaxation times in the quadriceps and patellar tendons. We were able to segment both tendons and to visualize the relaxation parameter distributions over the entire tendon volumes.

Cerebral microbleeds are not associated with postoperative delirium and postoperative cognitive dysfunction in older individuals.

BackgroundCerebral microbleeds (CMB) occur in the context of cerebral small vessel disease. Other brain MRI markers of cerebral small vessel disease are associated with the occurrence of postoperative delirium (POD) and postoperative cognitive dysfunction (POCD), but for CMB this is unknown. We aimed to study the association between CMB and the occurrence of POD and POCD in older individuals.MethodsThe current study consists of 65 patients (72±5 years) from the BIOCOG study, which is a prospective, observational study of patients who underwent an elective surgery of at least 60 minutes. Patients in the current study received a preoperative cerebral MRI scan including a 3D susceptibility-weighted imaging sequence to detect CMB. The occurrence of POD was screened for twice a day until postoperative day 7 by using the DSM-5, NuDesc, CAM, and CAM-ICU. The occurrence of POCD was determined by the reliable change index model at 7 days after surgery or discharge, respectively, and 3 months after surgery. Statistical analyses consisted of logistic regression adjusted for age and gender.ResultsA total of 39 CMB were detected in 17 patients (26%) prior to surgery. POD occurred in 14 out of 65 patients (22%). POCD at 7 days after surgery occurred in 11 out of 54 patients (20%) and in 3 out of 40 patients at the 3 month follow-up (8%). Preoperative CMB were not associated with the occurrence of POD (OR (95%-CI): 0.28 (0.05, 1.57); p = 0.147) or POCD at 7 days after surgery (0.76 (0.16, 3.54); p = 0.727) or at 3 months follow-up (0.61 (0.03, 11.64); p = 0.740).ConclusionWe did not find an association between preoperative CMB and the occurrence of POD or POCD.Trial registrationclinicaltrials.gov (NCT02265263) on 23 September 2014.

Motion Analysis for People with Cerebral Palsy: A Vision Based Approach.

We propose a methodology to classify motion of subjects with cerebral palsy based on RGB image sequences and present a new dataset with 2D facial landmark trajectories from RGB images of people with and without disabilities while performing specific types of movements. Depending on these movements, parts of the face can be occluded and we are able to recover the 3D face's shape and its motion based on the Structure from Motion framework. Using the 3D structure and the motion, we propose two different motion descriptors, one is focused on describing the spatial distribution of the motion and the other on the temporal distribution. Finally, we discuss the physical meaning of these descriptors and show that they are very informative about the degree of the subjects' disabilities. Our descriptor can classify people with and without cerebral palsy from 2D image sequences.

Impacts of Equirectangular 360-degrees Videos in the Intra-Frame Prediction of HEVC

Recent technological advancements allowed videos to come from a simple sequence of 2D images to be displayed in a flat screen display into spherical representations of one’s surroundings, capable of creating a realistic immersive experience when allied to head-mounted displays. In order to explore the existing infrastructure for video coding, 360-degrees videos are pre-processed and then encoded by conventional video coding standards. However, the flattened version of 360-degrees videos present some peculiarities which are not present in conventional videos, and therefore, may not be properly exploited by conventional video coders. Aiming to find evidence that conventional video encoders can be adapted to perform better over 360-degrees videos, this work performs an evaluation on the intra-frame prediction performed by the High Efficiency Video Coding over 360-degrees videos in the equirectangular projection. Experimental results point that 360-degrees videos present spatial properties that make some regions of the frame likely to be encoded using a reduced set of prediction modes and block sizes. This behavior could be used in the development of fast decision and energy saving algorithms by evaluating a reduced set of prediction modes and block sizes depending on the regions of the frame being encoded.

Quantification of Blood Flow in Dialysis Access Using Custom-Acquisition Protocol and Imaging Methods: A Clinical Validation Study

PurposeTo determine access blood flow (ABF) rate using 2D image sequences acquired with digital subtraction angiography (DSA) and fluoroscopy. Materials and MethodsA total of 23 patients with known or suspected malfunctioning accesses were imaged using 2 filming modes: DSA at 3 or 6 frames/s (F/s), and fluoroscopy at 10 or 15 pulses/s (P/s). ABF rates were quantified using a bolus tracking method based on cross-correlation algorithm and compared with catheter-based thermal dilution (TD) flow measurements. The indicator-dilution curves were fitted with a gamma-variate (GV) curve fitting model to assess the effect on accuracy. Radiation doses were calculated to examine any increased susceptibility to tissue reactions and stochastic effects. ResultsFor DSA images, the absolute percent deviations (mean ± standard error of mean) in computed flow vs TD flow measurements at 3 F/s and 6 F/s were 34% ± 4.5% and 20% ± 4.7%, respectively, without curve fitting, and 31% ± 3.3% and 20% ± 4.1%, respectively, with curve fitting. For fluoroscopic images, the deviations at 10 P/s and 15 P/s were 44% ± 7.3% and 68% ± 10.7%, respectively, without curve fitting and 36% ± 6.4% and 48% ± 7.1%, respectively, with curve fitting. The mean peak skin dose and effective dose at 6 F/s were 3.28 mGy and 75 μSv, respectively. ConclusionsDigital subtraction angiography images obtained at 6 F/s offered the highest accuracy for dialysis access blood flow quantification.

Muscle fat quantification using magnetic resonance imaging: case-control study of Charcot-Marie-Tooth disease patients and volunteers.

BackgroundThis study aimed to evaluate the potential value of 3D multiple gradient echo Dixon‐based magnetic resonance imaging (MRI) sequence as a tool for thigh intramuscular fat quantification in Charcot–Marie–Tooth disease (CMT) patients.MethodsA prospective comparison study comprising 18 CMT patients and 18 age/sex‐matched volunteers was performed. MRI including 3D multiple gradient echo Dixon‐based imaging was performed for each subject. Region of interest analyses were performed at the upper and lower third of both thighs. The two‐sample t‐test or Wilcoxon rank sum test was used for intergroup comparison of the mean muscle fat fraction. Intraclass correlation coefficients were used to evaluate the interobserver agreement and test–retest reproducibility. Semiquantitive analysis using the Goutallier classification (Grades 0–4) was performed on T1‐weighted images in upper thigh muscles. For Goutallier Grade 0 muscles, comparison of the mean intramuscular fat fraction between volunteers and CMT patients was performed.ResultsThe interobserver agreements were excellent for all measurements (intraclass correlation coefficients > 0.8). Mean muscle fat fractions were significantly higher in all the measured muscles of CMT patients (P < 0.05) except in the adductor magnus in the upper thigh (P = 0.109). Goutallier Grade 0 muscles of the CMT patients showed a significantly higher mean fat fraction compared with that of the volunteers (P < 0.05).Conclusions3D multiple gradient echo Dixon‐based MRI is a reproducible and sensitive technique which can reveal a significant difference in the fat fraction of thigh muscle, including comparison between Goutallier Grade 0 muscles, between CMT patients and volunteers.

Design and Construction of a Device for Free Form Surfaces 3D Reconstruction Using Microsoft Kinect

There is an increasing need for geometric 3D reconstruction models in the Reverse engineering which is ones of computer Vision applications, reconstruction from a sequence of 2D images is much cheaper than 3D scanners and coordinate measurement machine and also been able to take advantage of the developments in digital cameras and the increasing resolution and quality of images. This work aims to develop a concept of 3D free form surface reconstruction rig and generate the dimension of the surface depending on proposed image processing technique. Through the results obtained is observed that. The overall of calibration accuracy the Root mean square (RMS) value of the point was produced the residuals value in the image space was (0.2830 mm) in the IR images.

A Global Method for Non-Rigid Registration of Cell Nuclei in Live Cell Time-Lapse Images.

Non-rigid registration of cell nuclei in time-lapse microscopy images can be achieved through estimating the deformation fields using optical flow methods. In contrast to local optical flow models employed in the existing non-rigid registration methods, we introduce approaches based on a global optical flow model. Our registration model consists of a data fidelity term and a regularization term. We compared different regularizers for the deformation fields and found that a convex quadratic function is more suitable than non-convex ones. To improve the robustness, we propose an adaptive weighting scheme based on the statistics of the noise in fluorescence microscopy images as well as a combined local-global scheme. Moreover, we extend the global method by exploiting high-order image features. The best suitable high-order features are determined through learning two generative image models, namely, fields of experts and convolutional Gaussian restricted Boltzmann machine, whose model formulations are both consistent with the assumption of high-order feature constancy in the registration model. Using multiple data sets of real 2D and 3D live cell microscopy image sequences as well as synthetic image data, we demonstrate that our proposed approach outperforms the previous methods in terms of both registration accuracy and computational efficiency.

The Use of Gigapixel Photogrammetry for the Understanding of Landslide Processes in Alpine Terrain

The work in this paper illustrates an experimental application for geosciences by coupling new and low cost photogrammetric techniques: Gigapixel and Structure-from-Motion (SfM). Gigapixel photography is a digital image composed of billions of pixels (≥1000 megapixels) obtained from a conventional Digital single-lens reflex camera (DSLR), whereas the SfM technique obtains three-dimensional (3D) information from two-dimensional (2D) image sequences. The field test was carried out at the Ingelsberg slope (Bad Hofgastein, Austria), which hosts one of the most dangerous landslides in the Salzburg Land. The stereographic analysis carried out on the preliminary 3D model, integrated with Ground Based Synthetic Aperture Radar Interferometry (GBInSAR) data, allowed us to obtain the main fractures and discontinuities of the unstable rock mass.