Projection Images Research Articles

Crystal Analyzer Based Multispectral Microtomography Using CCD-Sensor.

To solve the problems of spectral tomography, an X-ray optical scheme was proposed, using a crystal analyzer in Laue geometry between the sample and the detector, which allowed for the selection of predetermined pairs of wavelengths from the incident polychromatic radiation to obtain projection images. On a laboratory X-ray microtomography setup, an experiment was carried out for the first time where a mixture of micro-granules of sodium chloride NaCl, silver behenate AgC22H43O2, and lithium niobate LiNbO3 was used as a test sample to identify their spatial arrangement. The elements were chosen based on the presence of absorption edges in two of the elements in the energy range of the polychromatic spectrum of the probing radiation. The method of projection distortion correction was used to preprocess the obtained projections. To interpret the obtained reconstruction results, the segmentation method based on the analysis of joint histograms was used. This allowed us to identify each of the three substances. To compare the results obtained, additional "reference" tomographic measurements were performed: one in polychromatic and two in monochromatic (MoKα-, MoKβ-lines) modes. It took three times less time for the tomographic experiment with the crystal analyzer, while the reconstruction accuracy was comparable to that of the "reference" tomography.

Automated F18-FDG PET/CT image quality assessment using deep neural networks on a latest 6-ring digital detector system

To evaluate whether a machine learning classifier can evaluate image quality of maximum intensity projection (MIP) images from F18-FDG-PET scans. A total of 400 MIP images from F18-FDG-PET with simulated decreasing acquisition time (120 s, 90 s, 60 s, 30 s and 15 s per bed-position) using block sequential regularized expectation maximization (BSREM) with a beta-value of 450 and 600 were created. A machine learning classifier was fed with 283 images rated “sufficient image quality” and 117 images rated “insufficient image quality”. The classification performance of the machine learning classifier was assessed by calculating sensitivity, specificity, and area under the receiver operating characteristics curve (AUC) using reader-based classification as the target. Classification performance of the machine learning classifier was AUC 0.978 for BSREM beta 450 and 0.967 for BSREM beta 600. The algorithm showed a sensitivity of 89% and 94% and a specificity of 94% and 94% for the reconstruction BSREM 450 and 600, respectively. Automated assessment of image quality from F18-FDG-PET images using a machine learning classifier provides equivalent performance to manual assessment by experienced radiologists.

Technical Note: Realization and uncertainty analysis for an adjustable 3D structured breast phantom in digital breast tomosynthesis.

Projection imaging phantoms are often optimized for 2-dimensional image characteristics in homogeneous backgrounds. Therefore, evaluation of image quality in tomosynthesis (DBT) lacks accepted and establishedphantoms. We describe a 3D breast phantom with a structured, variable background. The phantom is an adaptable and advanced version of the L1 phantom by Cockmartin etal. Phantom design and its use for quality assurance measurements for DBT devices are described. Four phantoms were compared to assess theobjectivity. The container size was increased to a diameter of 24cm and a total height of 53.5 mm. Spiculated masses were replaced by five additional non-spiculated masses for higher granularity in threshold diameter resolution. These patterns are adjustable to the imaging device. The masses were printed in one session with a base layer using two-component 3D printing. New materials compared to the L1 phantom improved the attenuation difference between the lesion models and the background. Four phantoms were built and intra-human observer, inter-human observer and inter-phantom variations were determined. The latter assess the reproducibility of the phantom production. Coefficients of variance (V) were calculated for all threevariations. The difference of the attenuation coefficients between the lesion models and the background was 0.20cm-1 (with W/Al at 32kV, equivalent to 19-20keV effective energy) compared to 0.21cm-1 for 50/50 glandular/adipose breast tissue and cancerous lesions. PMMA equivalent thickness of the phantom was 47.0mm for the Siemens Mammomat Revelation. For the masses, the for the intra-observer variation was 0.248, the averaged inter-observer variation, was 0.383. for phantom variance was 0.321. For the micro-calcifications, was 0.0429, 0.0731 and 0.0759. Position, orientation and shape of the masses are reproducible and attenuation differences appropriate. The phantom presented proved to be a candidate test object for qualitycontrol.

Motion detection in diffraction tomography by common circle methods

The method of common lines is a well-established reconstruction technique in cryogenic electron microscopy (cryo-EM), which can be used to extract the relative orientations of an object given tomographic projection images from different directions. In this paper, we deal with an analogous problem in optical diffraction tomography. Based on the Fourier diffraction theorem, we show that rigid motions of the object, i.e., rotations and translations, can be determined by detecting common circles in the Fourier-transformed data. We introduce two methods to identify common circles. The first one is motivated by the common line approach for projection images and detects the relative orientation by parameterizing the common circles in the two images. The second one assumes a smooth motion over time and calculates the angular velocity of the rotational motion via an infinitesimal version of the common circle method. Interestingly, using the stereographic projection, both methods can be reformulated as common line methods, but these lines are, in contrast to those used in cryo-EM, not confined to pass through the origin and allow for a full reconstruction of the relative orientations. Numerical proof-of-the-concept examples demonstrate the performance of our reconstruction methods.

Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study.

Intrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging. To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR). Patient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung. Both the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom. The sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT.

Skin pore imaging using spectral-domain optical coherence tomography: a case report.

Sebum is an important component of the skin that has attracted attention in many fields, including dermatology and cosmetics. Pore expansion due to sebum on the skin can lead to various problems. Therefore, it is necessary to analyze the morphological characteristics of sebum. In this study, we used optical coherence tomography (OCT) to evaluate facial sebum areas. We obtained the OCT maximum amplitude projection (MAP) image and a cross-sectional image of skin pores in the facial area. Subsequently, we detected the sebum in skin pores using the detection algorithm of the ImageJ software to quantitatively determine the size of randomly selected pores in the proposed MAP images. Additionally, the pore size was analyzed by acquiring images before and after facial sebum extraction. According to our research, facial sebum can be morphologically described using the OCT system. Since OCT imaging enables specific analysis of skin parameters, including pores and sebum, skin analysis employing OCT could be an effective method for further research.

Empirical optimization of energy bin weights for compressing measurements with realistic photon counting x-ray detectors.

Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast-to-noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store. This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors. We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi-empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramér-Rao lower bound (CRLB) due to the energy-weighted bin compression, for MD and VMI tasks over a range of material area density (0-40g/cm2 water, 0-2.16g/cm2 calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively. The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K-edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively. We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.

Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo-3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative accuracy due to scatter radiation. Recent advancements in deep learning (DL) have shown promise in using fast convolutional neural networks for scatter correction, achieving comparable results to Monte Carlo (MC) simulations. To predict the scatter radiation signal in DBT projections within clinically-acceptable times and using only clinically-available data, such as compressed breast thickness and acquisition angle. MC simulations to obtain scatter estimates were generated from two types of digital breast phantoms. One set consisted of 600 realistically-shaped homogeneous breast phantoms for initial DL training. The other set was composed of 80 anthropomorphic phantoms, containing realistic internal tissue texture, aimed at fine tuning the DL model for clinical applications. The MC simulations generated scatter and primary maps per projection angle for a wide-angle DBT system. Both datasets were used to train (using 7680 projections from homogeneous phantoms), validate (using 960 and 192 projections from the homogeneous and anthropomorphic phantoms, respectively), and test (using 960 and 48 projections from the homogeneous and anthropomorphic phantoms, respectively) the DL model. The DL output was compared to the corresponding MC ground truth using both quantitative and qualitative metrics, such as mean relative and mean absolute relative differences (MRD and MARD), and to previously-published scatter-to-primary (SPR) ratios for similar breast phantoms. The scatter corrected DBT reconstructions were evaluated by analyzing the obtained linear attenuation values and by visual assessment of corrected projections in a clinical dataset. The time required for training and prediction per projection, as well as the time it takes to produce scatter-corrected projection images, were also tracked. The quantitative comparison between DL scatter predictions and MC simulations showed a median MRD of 0.05% (interquartile range (IQR), -0.04% to 0.13%) and a median MARD of 1.32% (IQR, 0.98% to 1.85%) for homogeneous phantom projections and a median MRD of -0.21% (IQR, -0.35% to -0.07%) and a median MARD of 1.43% (IQR, 1.32% to 1.66%) for the anthropomorphic phantoms. The SPRs for different breast thicknesses and at different projection angles were within±15% of the previously-published ranges. The visual assessment showed good prediction capabilities of the DL model with a close match between MC and DL scatter estimates, as well as between DL-based scatter corrected and anti-scatter grid corrected cases. The scatter correction improved the accuracy of the reconstructed linear attenuation of adipose tissue, reducing the error from -16% and -11% to -2.3% and 4.4% for an anthropomorphic digital phantom and clinical case with similar breast thickness, respectively. The DL model training took 40 min and prediction of a single projection took less than 0.01s. Generating scatter corrected images took 0.03s per projection for clinical exams and 0.16s for one entire projection set. This DL-based method for estimating the scatter signal in DBT projections is fast and accurate, paving the way for future quantitative applications.

Research on Virtual Reality Head-Mounted Displays based on MEMS Technology

In order to render the gaze area and increase the pixels per degree of the area, this virtual reality head-mounted display device adds two MEMS 2D miniature scanning mirrors to the main display imaging, with a laser diode as the projection light source and the main display as the projection receiving screen. The light emitted by the laser lamp is projected onto the main display through multiple reflections. Through pre-processing of the image of the main display and the projected display, the image of the main display is combined with the projected image to achieve the rendering effect. It is integrated with an eye tracking system that allows the projected image to be consistently projected on the human eye area. The projection image is projected over the entire imaging area of the main display by resonant rotation of two MEMS 2D microscope mirrors.

Multi-beam X-ray optical system for high-speed tomography using a σ-polarization diffraction geometry

A multi-beam X-ray optical system using a σ-polarization diffraction geometry is proposed and its potential for high-speed tomography using synchrotron radiation is experimentally evaluated. Projection images of a sample are obtained simultaneously from different directions with X-ray beams generated by diffraction of a white synchrotron radiation beam at silicon single crystals. This makes it possible to record a tomographic dataset without rotation of the sample or X-ray source. Data sets of two samples obtained in a proof-of-principle experiment with an exposure time of 1 ms were successfully reconstructed using an advanced compressed-sensing algorithm.

Sub-millisecond 4D X-ray tomography achieved with a multibeam X-ray imaging system

A proof-of-concept experiment for sub-millisecond temporal and 10 μm order spatial resolution 4D X-ray tomography imaging using a multibeam X-ray imaging system is reported. The 3D structure of a tungsten wire during mechanical deformation was reconstructed using a super-compressed sensing-based algorithm from 28 projection images acquired simultaneously with a temporal resolution of 0.5 ms. The multibeam imaging system does not require rotation of the sample, X-ray source or detector. The experiment demonstrates the potential for improving the time resolution in observing non-repeatable dynamic phenomena, such as those occurring in fluids, living beings, or material fractures.

Stereo X-Ray Tomography

X-ray tomography is a powerful volumetric imaging technique, but detailed three dimensional (3D) imaging requires the acquisition of a large number of individual X-ray images, which is time consuming. For applications where spatial information needs to be collected quickly, for example, when studying dynamic processes, standard X-ray tomography is therefore not applicable. Inspired by stereo vision, in this paper, we develop X-ray imaging methods that work with two X-ray projection images. In this setting, without the use of additional strong prior information, we no longer have enough information to fully recover the 3D tomographic images. However, up to a point, we are nevertheless able to extract spatial locations of point and line features. From stereo vision, it is well known that, for a known imaging geometry, once the same point is identified in two images taken from different directions, then the point’s location in 3D space is exactly specified. The challenge is the matching of points between images. As X-ray transmission images are fundamentally different from the surface reflection images used in standard computer vision, we here develop a different feature identification and matching approach. In fact, once point like features are identified, if there are limited points in the image, then they can often be matched exactly. In fact, by utilising a third observation from an appropriate direction, matching becomes unique. Once matched, point locations in 3D space are easily computed using geometric considerations. Linear features, with clear end points, can be located using a similar approach.

Compressed sensing reconstruction shortens the acquisition time for myocardial perfusion imaging: a simulation study.

Compressed sensing (CS) has been used to improve image quality in single-photon emission tomography (SPECT) imaging. However, the effects of CS on image quality parameters in myocardial perfusion imaging (MPI) have not been investigated in detail. This preliminary study aimed to compare the performance of CS-iterative reconstruction (CS-IR) with filtered back-projection (FBP) and maximum likelihood expectation maximization (ML-EM) on their ability to reduce the acquisition time of MPI. A digital phantom that mimicked the left ventricular myocardium was created. Projection images with 120 and 30 directions (360°), and with 60 and 15 directions (180°) were generated. The SPECT images were reconstructed using FBP, ML-EM, and CS-IR. The coefficient of variation (CV) for the uniformity of myocardial accumulation, septal wall thickness, and contrast ratio (Contrast) of the defect/normal lateral wall were calculated for evaluation. The simulation was performed ten times. The CV of CS-IR was lower than that of FBP and ML-EM in both 360° and 180° acquisitions. The septal wall thickness of CS-IR at the 360° acquisition was inferior to that of ML-EM, with a difference of 2.5mm. Contrast did not differ between ML-EM and CS-IR for the 360° and 180° acquisitions. The CV for the quarter-acquisition time in CS-IR was lower than that for the full-acquisition time in the other reconstruction methods. CS-IR has the potential to reduce the acquisition time of MPI.

Noninvasive evaluation of donor and native pancreases following simultaneous pancreas-kidney transplantation using positron emission tomography/computed tomography.

It is crucial to develop practical and noninvasive methods to assess the functional beta-cell mass in a donor pancreas, in which monitoring and precise evaluation is challenging. A patient with type 1 diabetes underwent noninvasive imaging following simultaneous kidney-pancreas transplantation with positron emission tomography/computed tomography (PET/CT) using an exendin-based probe, [18 F]FB(ePEG12)12-exendin-4. Following transplantation, PET imaging with [18 F]FB(ePEG12)12-exendin-4 revealed simultaneous and distinct accumulations in the donor and native pancreases. The pancreases were outlined at a reasonable distance from the surrounding organs using [18 F]FB(ePEG12)12-exendin-4 whole-body maximum intensity projection and axial PET images. At 1 and 2 h after [18 F]FB(ePEG12)12-exendin-4 administration, the mean standardized uptake values were 2.96 and 3.08, respectively, in the donor pancreas and 1.97 and 2.25, respectively, in the native pancreas. [18 F]FB(ePEG12)12-exendin-4 positron emission tomography imaging allowed repeatable and quantitative assessment of beta-cell mass following simultaneous kidney-pancreas transplantation.

Three-dimensional reconstruction of the pelvic bones on MRI scans

BACKGROUND: Pelvimetry is an important part of the obstetric examination for predicting a mismatch between the size of the fetus and the mothers pelvis, which leads to difficulty or impossibility of vaginal delivery. Contracted pelvis is one of the main causes of maternal birth trauma and perinatal morbidity and mortality.
 AIM: To create a computer vision model for automatic segmentation and three-dimensional (3D) reconstruction of the pelvic bones.
 METHODS: A 3D U-Net-based neural network was used and trained on T2 weighted images in frontal projection (repetition time, 7500; echo time, 130; slice thickness, 4mm; field-of-view, 4039; matrix, 256256). The sample size covered 49 patients. The training and test samples included 42 and 7 examinations, respectively. The segmentation of areas of interest was done manually and verified by a specialist. The sample size was justified by achieving representativeness of the data for obtaining a qualitative model (according to the SorensenDice coefficient).
 RESULTS: 3D reconstructions of the pelvic bones were obtained. The average Sorensen-Dice coefficient on the accuracy of pelvic bone segmentation in the test sample was 0.86. The result justified the use of a 3D U-Net-based neural network as a tool capable of perceiving a 3D structure of images and conducting qualitative segmentation. The results allow further work on automating the determination of key points at reconstructions.
 CONCLUSIONS: A computer vision model for automatic segmentation of the pelvic bones to obtain 3D reconstruction of images was created. This enabled the next stage of the study, i.e. the development of a model for determining the key points in the images and the distances between the points.

Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images

BackgroundEarly diagnosis of the potentially fatal but curable chronic pulmonary embolism (CPE) is challenging. We have developed and investigated a novel convolutional neural network (CNN) model to recognise CPE from CT pulmonary angiograms (CTPA) based on the general vascular morphology in two-dimensional (2D) maximum intensity projection images.MethodsA CNN model was trained on a curated subset of a public pulmonary embolism CT dataset (RSPECT) with 755 CTPA studies, including patient-level labels of CPE, acute pulmonary embolism (APE), or no pulmonary embolism. CPE patients with right-to-left-ventricular ratio (RV/LV) < 1 and APE patients with RV/LV ≥ 1 were excluded from the training. Additional CNN model selection and testing were done on local data with 78 patients without the RV/LV-based exclusion. We calculated area under the receiver operating characteristic curves (AUC) and balanced accuracies to evaluate the CNN performance.ResultsWe achieved a very high CPE versus no-CPE classification AUC 0.94 and balanced accuracy 0.89 on the local dataset using an ensemble model and considering CPE to be present in either one or both lungs.ConclusionsWe propose a novel CNN model with excellent predictive accuracy to differentiate chronic pulmonary embolism with RV/LV ≥ 1 from acute pulmonary embolism and non-embolic cases from 2D maximum intensity projection reconstructions of CTPA.Relevance statementA DL CNN model identifies chronic pulmonary embolism from CTA with an excellent predictive accuracy.Key points• Automatic recognition of CPE from computed tomography pulmonary angiography was developed.• Deep learning was applied on two-dimensional maximum intensity projection images.• A large public dataset was used for training the deep learning model.• The proposed model showed an excellent predictive accuracy.Graphical

The Role of 68 Ga-FAPI PET/CT in Breast Cancer Response Assessment and Follow-Up.

68 Ga-fibroblast activation protein inhibitor (FAPI), a new PET/CT radiotracer targeting cancer-associated fibroblasts in tumor microenvironment, can detect many types of cancer. We aimed to assess whether it can also be used for response assessment and follow-up. We followed up patients with FAPI-avid invasive lobular breast cancer (ILC) before and after treatment changes and correlated qualitative maximal intensity projection images and quantitative tumor volume with CT results and blood tumor biomarkers. Six consenting ILC breast cancer patients (53 ± 8 years old) underwent a total of 24 scans (baseline for each patient and 2-4 follow-up scans). We found a strong correlation between 68 Ga-FAPI tumor volume and blood biomarkers ( r = 0.7, P < 0.01), but weak correlation between CT and 68 Ga-FAPI maximal intensity projection-based qualitative response assessment. We found a strong correlation between ILC progression and regression (as assessed by blood biomarkers) and 68 Ga-FAPI tumor volume. 68 Ga-FAPI PET/CT could possibly be used for disease response assessment and follow-up.

360° Map Establishment and Real-Time Simultaneous Localization and Mapping Based on Equirectangular Projection for Autonomous Driving Vehicles.

This paper proposes the design of a 360° map establishment and real-time simultaneous localization and mapping (SLAM) algorithm based on equirectangular projection. All equirectangular projection images with an aspect ratio of 2:1 are supported for input image types of the proposed system, allowing an unlimited number and arrangement of cameras. Firstly, the proposed system uses dual back-to-back fisheye cameras to capture 360° images, followed by the adoption of the perspective transformation with any yaw degree given to shrink the feature extraction area in order to reduce the computational time, as well as retain the 360° field of view. Secondly, the oriented fast and rotated brief (ORB) feature points extracted from perspective images with a GPU acceleration are used for tracking, mapping, and camera pose estimation in the system. The 360° binary map supports the functions of saving, loading, and online updating to enhance the flexibility, convenience, and stability of the 360° system. The proposed system is also implemented on an nVidia Jetson TX2 embedded platform with 1% accumulated RMS error of 250 m. The average performance of the proposed system achieves 20 frames per second (FPS) in the case with a single-fisheye camera of resolution 1024 × 768, and the system performs panoramic stitching and blending under 1416 × 708 resolution from a dual-fisheye camera at the same time.

Contextual Teaching and Learning Method Media Props for Building Projection Images

The time of teaching the construction of utility building educators had difficulty. Most of the students have difficulty in translating three-dimensional (3D) images into two-dimensional (2D) images. Learning media is needed to deal with these difficulties. Learning media that can encourage and guide students' thinking skills. The purpose of this study is to determine students' understanding in translating three-dimensional images into two-dimensional images. The method used is classroom action research. ) with two stages or cycles. The first cycle knows the initial concept of students' difficulties, while the second cycle of learning uses three-dimensional cube media and tests to determine students' understanding. This study was applied to class XI DPIB3 SMK N 1 Mojokerto with 32 students. Data were analyzed using the Rasch Model. The results of the study using the contextual method of 3D cube props obtained quite good results, improving a good teaching process can improve understanding of drawing two-dimensional projections. The principle of this projection image is used for bestek drawings, working drawings and other drawings on building works. Drawings at the planning and implementation stages in building projects.

Feasibility of internal-source tracking with C-arm CT/SPECT imaging with limited-angle projection data for online in vivo dose verification in brachytherapy: A Monte Carlo simulation study

PURPOSEThe current protocol for use of the image-guided adaptive brachytherapy (IGABT) procedure entails transport of a patient between the treatment room and the 3-D tomographic imaging room after implantation of the applicators in the body, which movement can cause position displacement of the applicator. Moreover, it is not possible to track 3-D radioactive source movement inside the body, even though there can be significant inter- and intra-fractional patient-setup changes. In this paper, therefore, we propose an online single-photon emission computed tomography (SPECT) imaging technique with a combined C-arm fluoroscopy X-ray system and attachable parallel-hole collimator for internal radioactive source tracking of every source position in the applicator. METHODS AND MATERIALSIn the present study, using Geant4 Monte Carlo (MC) simulation, the feasibility of high-energy gamma detection with a flat-panel detector for X-ray imaging was assessed. Further, a parallel-hole collimator geometry was designed based on an evaluation of projection image quality for a 192Ir point source, and 3-D limited-angle SPECT-image-based source-tracking performances were evaluated for various source intensities and positions. RESULTSThe detector module attached to the collimator could discriminate the 192Ir point source with about 3.4% detection efficiency when including the total counts in the entire deposited energy region. As the result of collimator optimization, hole size, thickness, and length were determined to be 0.5, 0.2, and 45 mm, respectively. Accordingly, the source intensities and positions also were successfully tracked with the 3-D SPECT imaging system when the C-arm was rotated within 110° in 2 seconds. CONCLUSIONSWe expect that this system can be effectively implemented for online IGABT and in vivo patient dose verification.