Tomographic Reconstruction Research Articles

Exoscopic Clipping of a Ruptured Posterior Communicating Artery Aneurysm Through a Minipterional Approach: 2-Dimensional Operative Video

Posterior communicating artery (PCoA) aneurysms represent approximately 15% to 25% of intracranial aneurysms and carry an estimated risk of rupture of 44%. 1-4 Surgical clipping of PCoA aneurysms bears notable challenges associated with operating in a confined space with limited visualization of deep structures. 1,5 We illustrate the case of a 54-year-old woman who presented with a seven-hour course of intense headaches, vomiting and loss of consciousness. Her neurological examination revealed she had a 4/5 left-sided hemiparesis, a Glasgow coma scale score of 12, and a Hunt and Hess score of 3. A head computed tomography scan without contrast showed subarachnoid hemorrhage in the basal cisterns and Sylvian fissures (Fisher grade 3). Digital subtraction angiography and three-dimensional reconstruction imaging revealed a right bilobed saccular PCoA aneurysm of 4.8 × 5.9 mm projecting anterior-inferiorly. After consideration of her clinical course and aneurysmal features, she underwent a right minipterional craniotomy for exoscopic clipping of the aneurysm neck. The patient tolerated the procedure well, her hemiparesis improved, and she was discharged with a modified Rankin scale score of 1 on postoperative day 3. Exoscopy offered high image quality and expanded 3-dimensional view with digital zoom during the aneurysm repair. 6,7 Although evidence suggests exoscopy is non-inferior to microscopy regarding surgery duration, safety, and outcomes, the capability of providing operator ergonomy during surgery is a worth-noting advantage. 8 IRB and patient consent declaration: This study was exempt from the Institution Review Board because patient consent was granted for the use of clinical information, images, and operative videos. The patient consented to the procedure, and the participants and any identifiable individuals consented to publication of his/her image.

Functional organization of 3D plant thylakoid membranes as seen by high resolution microscopy

In the field of photosynthesis, only a limited number of approaches of super-resolution fluorescence microscopy can be used, as the functional architecture of the thylakoid membrane in chloroplasts is probed through the natural fluorescence of chlorophyll molecules. In this work, we have used a custom-built fluorescence microscopy method called Single Pixel Reconstruction Imaging (SPiRI) that yields a 1.4 gain in lateral and axial resolution relative to confocal fluorescence microscopy, to obtain 2D images and 3D-reconstucted volumes of isolated chloroplasts, obtained from pea (Pisum sativum), spinach (Spinacia oleracea) and Arabidopsis thaliana. In agreement with previous studies, SPiRI images exhibit larger thylakoid grana diameters when extracted from plants under low-light regimes. The three-dimensional thylakoid architecture, revealing the complete network of the thylakoid membrane in intact, non-chemically-fixed chloroplasts can be visualized from the volume reconstructions obtained at high resolution. From such reconstructions, the stromal connections between each granum can be determined and the fluorescence intensity in the stromal lamellae compared to those of neighboring grana.

Pore analysis of dendritic mesoporous silica nanoparticles by STEM tomography and nitrogen sorption

Dendritic mesoporous silica nanoparticles (DMSN) provide a high diversity and complexity regarding their pore structure. Herein, we present results related to electron tomographic reconstruction and nitrogen sorption analysis of DMSNs with different pore structures obtained through variation of the surfactant concentration in the synthesis. The tomograms revealed that at all DMSNs exhibit a highly porous shell structure and a denser porous core with smaller pores. The shell generally consists of dendritic silica walls forming a disordered pore system of center radially aligned mesopores. At the highest surfactant concentration these mesopores had a roughly conical geometry, providing a highly accessible pore network. Nitrogen sorption revealed a broad pore size distribution, in agreement with this conical mesopore shape. The mesopore dimensions obtained through reconstruction were in good agreement with the pore size distribution obtained by nitrogen sorption. For intermediate and low surfactant concentrations, different pore geometries were observed in the reconstruction with increasing restriction of the pore volume towards the outer surface of the particles by branching of large pores into smaller ones. Nitrogen sorption analyses support these observations by featuring an increasing hysteresis with decreasing surfactant concentration in the synthesis, which indicates that the accessible pore volume is restricted by smaller mesopores towards the rim of the particles.

Application of virtual endoscopic imaging to parapelvic cyst incision by flexible ureteroscopy: a multicenter retrospective cohort study.

A three-dimensional (3D) reconstruction of the kidney, parapelvic cyst and the collecting system was conducted using the 3D Slicer software. The reconstructed image was used to form a virtual endoscope to assist flexible ureteroscopic incision and drainage was performed with a holmium laser for treating parapelvic cysts. The effectiveness of this assistive technique was assessed. This was a retrospective cohort study. The clinical information of 59 patients undergoing flexible ureteroscopic incision and drainage for parapelvic cysts in two medical centers was collected. 3D Slicer software reconstruction and virtual endoscopic imaging were performed for 28 cases. Before the operation, the best point for incision on the collecting system's mucosa was assessed by virtual endoscope imaging. Propensity score matching was adopted for the reconstructive and non-reconstructive groups. After matching, the reconstructive group and non-reconstructive group both had 21 cases each. The operation time in the reconstructive and non-reconstructive groups was 38.81±5.01 and 51.00±18 minutes, respectively. Statistically significant differences existed between the two groups (t=7.024, P<0.001). No statistical significance was found in postoperative fever, immediate postoperative C reactive protein (CRP), length of postoperative hospital stay and cyst diameter three months after the operation. The operator was provided with a more direct and real vision when 3D Slicer software reconstruction was adopted via virtual endoscopic imaging to assist flexible ureteroscopic parapelvic cyst incision. This helped reduce the operation time. Further follow-ups and observations are required to assess the long-term efficacy of flexible ureteroscopic parapelvic cyst incision.

X-ray tomography system concept for stellarator TJ-II

A method based on the principle of x-ray tomography is a good way of assessing the parameters of the radiation emission distribution in the plasma. In this method, a set of radiation detectors is usually placed around a selected section of the plasma or in two groups of detectors arranged perpendicularly to each other in the top and side positions. Such an arrangement is intended to provide a sufficient amount of data to reconstruct the distribution of radiation emission intensities across the plasma cross section. For the plasma produced in the TJ-II stellarator, a two-dimensional (2D) imaging detector is proposed with the ability to discriminate photon energy in only one specifically selected position in the toroidal direction. The proposed diagnostics could come with advantages of a 2D toroidal camera, providing access to 3D information, and would complement standard poloidal tomography. It could be used either for better constraining of the 2D tomography in simple cases with axisymmetric SXR emissivity, for simulation validation of 3D effects in a synthetic diagnostic approach, or to attempt direct 3D tomography. The subject of this contribution is to present the idea of a 3D x-ray tomography system based on the use of a square matrix of GEM detector and the obtained preliminary results of the conducted simulations. The developed diagnostics is to contain 33 462 detection pixels associated with lines of sight. The analysis was carried out on the basis of the numerical simulations based on the GEM_MP.exe and Tomography.exe computer codes (both created at IPPLM) enabling the consideration of free-free, free-bound, and bound-bound x-ray radiation reaching each of the pixels of the GEM diagnostics (GEM_MP.exe code) and testing algorithms proposed for tomographic reconstruction of the plasma emissivity (Tomography.exe code). During calculations, real profiles of electron density and electron temperature, as well as real geometry of the measuring system were taken into account. Additionally, the influence of factors such as filters used in the detector and the efficiency of the used active medium on the quality of tomographic reconstruction was examined. The summary evaluates the proposed tomographic reconstruction method.

Automated fiducial-based alignment of cryo-electron tomography tilt series in Dynamo

With the advent of modern technologies for cryo-electron tomography (cryo-ET), high-quality tilt series are more rapidly acquired than processed and analyzed. Thus, a robust and fast-automated alignment for batch processing in cryo-ET is needed. While different software packages have made available several approaches for automated marker-based alignment of tilt series, manual user intervention remains necessary for many datasets, thus preventing high-throughput tomography. We have developed a MATLAB-based framework integrated into the Dynamo software package for automatic detection of fiducial markers that generates a robust alignment model with minimal input parameters. This approach allows high-throughput, unsupervised volume reconstruction. This new module extends Dynamo with a large repertory of tools for tomographic alignment and reconstruction, as well as specific visualization browsers to rapidly assess the biological relevance of the dataset. Our approach has been successfully tested on a broad range of datasets that include diverse biological samples and cryo-ET modalities.

Machine learning based tomographic image reconstruction technique to detect hollows in wood

A new technique based on machine learning algorithms was introduced to detect internal wood defects. This technique relies on analyzing segmented propagation rays of stress waves and successfully generates the tomographic images of the defects by using the stress wave velocity. Utilizing a dual-stage methodology, the initial phase involves ray segmentation for the precise delineation of stress wave propagation, while the subsequent stage integrates advanced classification and clustering algorithms to facilitate the generation of tomographic images. This approach effectively tackles the inherent challenges associated with accurate segmentation and classification of stress wave velocity rays. The effectiveness of the proposed method was evaluated using both synthetic and experimental data. The results showed that the proposed method, when compared with some state-of-the-art methods, has a superior ability to accurately detect defective regions in the wood. The success of the proposed method is evaluated with four different evaluation metrics. It determined that over 90% success is achieved for all metrics. In comparison with related studies, it determined that the results are improved by 7–22% compared to the literature.

Dose robustness of deep learning models for anatomic segmentation of computed tomography images.

The trend towards lower radiation doses and advances in computed tomography (CT) reconstruction may impair the operation of pretrained segmentation models, giving rise to the problem of estimating the dose robustness of existing segmentation models. Previous studies addressing the issue suffer either from a lack of registered low- and full-dose CT images or from simplified simulations. We employed raw data from full-dose acquisitions to simulate low-dose CT scans, avoiding the need to rescan a patient. The accuracy of the simulation is validated using a real CT scan of a phantom. We consider down to 20% reduction of radiation dose, for which we measure deviations of several pretrained segmentation models from the full-dose prediction. In addition, compatibility with existing denoising methods is considered. The results reveal the surprising robustness of the TotalSegmentator approach, showing minimal differences at the pixel level even without denoising. Less robust models show good compatibility with the denoising methods, which help to improve robustness in almost all cases. With denoising based on a convolutional neural network (CNN), the median Dice between low- and full-dose data does not fall below 0.9 (12 for the Hausdorff distance) for all but one model. We observe volatile results for labels with effective radii less than 19mm and improved results for contrasted CT acquisitions. The proposed approach facilitates clinically relevant analysis of dose robustness for human organ segmentation models. The results outline the robustness properties of a diverse set of models. Further studies are needed to identify the robustness of approaches for lesion segmentation and to rank the factors contributing to dose robustness.

3D reconstruction of an inertial-confinement fusion implosion with neural networks using multiple heterogeneous data sources.

3D asymmetries are major degradation mechanisms in inertial-confinement fusion implosions at the National Ignition Facility (NIF). These asymmetries can be diagnosed and reconstructed with the neutron imaging system (NIS) on three lines of sight around the NIF target chamber. Conventional tomographic reconstructions are used to reconstruct the 3D morphology of the implosion using NIS [Volegov et al., J. Appl. Phys. 127, 083301 (2020)], but the problem is ill-posed with only three imaging lines of sight. Asymmetries can also be diagnosed with the real-time neutron activation diagnostics (RTNAD) and the neutron time-of-flight (nToF) suite. Since the NIS, RTNAD, and nToF each sample a different part of the implosion using different physical principles, we propose that it is possible to overcome the limitations of too few imaging lines of sight by performing 3D reconstructions that combine information from all three heterogeneous data sources. This work presents a new machine learning-based reconstruction technique to do just this. By using a simple physics model and group of neural networks to map 3D morphologies to data, this technique can easily account for data of multiple different types. A simple proof-of-principle is presented, demonstrating that this technique can accurately reconstruct a hot-spot shape using synthetic primary neutron images and a hot-spot velocity vector. In particular, the hot-spot's asymmetry, quantified as spherical harmonic coefficients, is reconstructed to within ±4% of the radius in 90% of test cases. In the future, this technique will be applied to actual NIS, RTNAD, and nToF data to better understand 3D asymmetries at the NIF.

Reconstruction of Tomographic Size Using a Filtered Back Projection Algorithm

Due to their widespread use, image processors are among the most essential computer processors. FBP technique is one of the most widely used tomographic reconstruction methods. Images from a computed tomography (CT) scanner with 512x512 pixels of raw data for parallel beam projections are the metadata to which this approach is applied. This work is divided into three parts. Several projections are created from an input test picture termed Shepp-Logan phantom in the first section, utilizing radon transformation. The picture is rebuilt from the projection of a parallel beam in the second section to minimize reconstruction time. Finally, the proposed system filters the projections using the Ram Lak filter and the Hann window in the third section to refine the picture and then recombine the projections to generate the reconstructed image. The reconstruction procedure is performed on CT images using a filtered back-projection algorithm in this article for image optimization and then projection recombination to generate the rebuilt picture. Since CT can accurately depict anatomical elements like bones and organs and may pick slides in any orientation, it is frequently used in biological applications and diagnostics. It also offers a great spatial resolution and soft tissue contrast. We develop, create, and run computer programs when we work with MATLAB. After projecting the 2D image onto a CT scanner, a very good result in reconstructing the image is received.

Reconstruction of Tomographic Size Using a Filtered Back Projection Algorithm

Due to their widespread use, image processors are among the most essential computer processors. FBP technique is one of the most widely used tomographic reconstruction methods. Images from a computed tomography (CT) scanner with 512x512 pixels of raw data for parallel beam projections are the metadata to which this approach is applied. This work is divided into three parts. Several projections are created from an input test picture termed Shepp-Logan phantom in the first section, utilizing radon transformation. The picture is rebuilt from the projection of a parallel beam in the second section to minimize reconstruction time. Finally, the proposed system filters the projections using the Ram Lak filter and the Hann window in the third section to refine the picture and then recombine the projections to generate the reconstructed image. The reconstruction procedure is performed on CT images using a filtered back-projection algorithm in this article for image optimization and then projection recombination to generate the rebuilt picture. Since CT can accurately depict anatomical elements like bones and organs and may pick slides in any orientation, it is frequently used in biological applications and diagnostics. It also offers a great spatial resolution and soft tissue contrast. We develop, create, and run computer programs when we work with MATLAB. After projecting the 2D image onto a CT scanner, a very good result in reconstructing the image is received.

CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.

Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model. We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies. The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols. This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training.

MDUNet: deep-prior unrolling network with multi-parameter data integration for low-dose computed tomography reconstruction

MDUNet: deep-prior unrolling network with multi-parameter data integration for low-dose computed tomography reconstruction

Three-dimensional computed tomography reconstruction of an elongated styloid process.

Three-dimensional computed tomography reconstruction of an elongated styloid process.

Effect of transbronchial or intravenous administration of indocyanine green on resection margins during near-infrared-guided segmentectomy: a review.

For early-stage non-small cell lung cancer, surgical resection remains the best treatment option. Currently, sublobar resection, including segmentectomy, is recommended in these cases, as it provides a better quality of life with the same oncological outcomes; however, is requires adequate resection margins. Accurate preoperative planning and proper identification of the intersegmental planes during thoracic surgery are crucial for ensuring precise surgical management and adequate resection margins. Three dimensional computed tomography reconstruction and near-infrared-guided intersegmental plane identification can greatly facilitate the surgical procedures. Three-dimensional computed tomography reconstruction can simulate both the resection and resection margins. Indocyanine green is one of the most frequently used and affordable fluorophores. There are two ways to identify the intersegmental planes using indocyanine green: intravenous and transbronchial administration. Intravenous application is simple; however, its effectiveness may be affected by underlying lung disease, and it requires the isolation of segmental structures before administration. Transbronchial use requires appropriate bronchoscopic skills and preoperative planning; however, it also allows for delineation deep in the parenchyma and can be used for complex segmentectomies. Both methods can be used to ensure adequate resection margins and, therefore, achieve the correct oncological radicality of the surgical procedure. Here, we summarise these applications and provide an overview of their different possibilities.

Three-dimensional computed tomography reconstruction of a calcified stylohyoid ligament.

Three-dimensional computed tomography reconstruction of a calcified stylohyoid ligament.

Masked cross-domain self-supervised deep learning framework for photoacoustic computed tomography reconstruction

Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct PA images with a supervised scheme, which requires high-quality images as ground truth labels. However, practical implementations encounter inevitable trade-offs between cost and performance due to the expensive nature of employing additional channels for accessing more measurements. Here, we propose a masked cross-domain self-supervised (CDSS) reconstruction strategy to overcome the lack of ground truth labels from limited PA measurements. We implement the self-supervised reconstruction in a model-based form. Simultaneously, we take advantage of self-supervision to enforce the consistency of measurements and images across three partitions of the measured PA data, achieved by randomly masking different channels. Our findings indicate that dynamically masking a substantial proportion of channels, such as 80%, yields meaningful self-supervisors in both the image and signal domains. Consequently, this approach reduces the multiplicity of pseudo solutions and enables efficient image reconstruction using fewer PA measurements, ultimately minimizing reconstruction error. Experimental results on in-vivo PACT dataset of mice demonstrate the potential of our self-supervised framework. Moreover, our method exhibits impressive performance, achieving a structural similarity index (SSIM) of 0.87 in an extreme sparse case utilizing only 13 channels, which outperforms the performance of the supervised scheme with 16 channels (0.77 SSIM). Adding to its advantages, our method can be deployed on different trainable models in an end-to-end manner, further enhancing its versatility and applicability.

TomoPyUI: a user-friendly tool for rapid tomography alignment and reconstruction.

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data (i.e. organizing data/metadata, applying image filters etc.) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI, a user interface for the well known tomography data processing package TomoPy. This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI, along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI, and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.

Computed tomography reconstructions of burrow networks for the Opheliid polychaete, Armandia cirrhosa

The morphology and architecture of structures formed by sediment-dwelling invertebrates, such as excavations or burrows, are often assumed to be characteristic of a given species, consistent across a range of environmental conditions, and used to categorise species contributions to ecosystem functioning. However, very few investigations use non-invasive high-resolution techniques capable of determining fine scale variations in burrow form and complexity, or consider whether or not the form of the burrow is context dependent. Here, we provide replicate high-resolution micro-focus computed tomography data for the complete burrow systems of the Opheliid polychaete, Armandia cirrhosa, across a range of salinity and habitat conditions. These data provide reference models which can be used by ecologists investigating intraspecific variation in species traits and organism-sediment interactions and, more generally, by those tasked with pattern and shape recognition of objects that are morphologically highly variable and which adjust their architecture with changing circumstance or context.

X-ray scattering based scanning tomography for imaging and structural characterization of cellulose in plants.

X-ray and neutron scattering have long been used for structural characterization of cellulose in plants. Due to averaging over the illuminated sample volume, these measurements traditionally overlooked the compositional and morphological heterogeneity within the sample. Here, a scanning tomographic imaging method is described, using contrast derived from the X-ray scattering intensity, for virtually sectioning the sample to reveal its internal structure at a resolution of a few micrometres. This method provides a means for retrieving the local scattering signal that corresponds to any voxel within the virtual section, enabling characterization of the local structure using traditional data-analysis methods. This is accomplished through tomographic reconstruction of the spatial distribution of a handful of mathematical components identified by non-negative matrix factorization from the large dataset of X-ray scattering intensity. Joint analysis of multiple datasets, to find similarity between voxels by clustering of the decomposed data, could help elucidate systematic differences between samples, such as those expected from genetic modifications, chemical treatments or fungal decay. The spatial distribution of the microfibril angle can also be analyzed, based on the tomographically reconstructed scattering intensity as a function of the azimuthal angle.