Projection Images Research Articles

Evaluation of the Contrast Enhancement Performance of Gadopiclenol for Magnetic Resonance Angiography in Healthy Rabbits and Pigs.

Unexpected accumulations of gadolinium in various organs were reported after the administration of gadolinium-based contrast agents, making desirable to reduce the dose while maintaining equivalent diagnostic performance. The aim of this study was to evaluate the contrast enhancement performance of high relaxivity gadopiclenol compared with gadoterate meglumine in abdominal contrast-enhanced magnetic resonance angiography (CE-MRA). In a first study in healthy rabbits, axial 3D gradient echo sequences were applied at 4.7 T to study arterial enhancement as a function of gadopiclenol dose (0.025, 0.05, 0.075, and 0.1 mmol Gd/kg) or gadoterate meglumine at 0.1 mmol Gd/kg (n = 5-6/group). The increase in signal-to-noise ratio (ΔSNR) in the aorta at the first pass was measured and compared. In a second, crossover study in 6 healthy pigs, abdominal CE-MRA sequences were acquired at 3 T with gadopiclenol at 0.05 mmol Gd/kg or gadoterate meglumine at 0.1 mmol Gd/kg at a 1-week interval. Quantitatively on the maximum intensity projection (MIP) images, the mean MIP SNR within the aorta of both groups was compared. Qualitatively, a blinded comparison of the angiograms was performed by an experienced radiologist to determine the preferred contrast agent. In the rabbit, ∆SNR is linearly correlated with the gadopiclenol dose (P = 0.0010). Compared with gadoterate meglumine 0.1 mmol Gd/kg, an increase in the ∆SNR is observed after 0.05, 0.075, and 0.1 mmol Gd/kg of gadopiclenol (+63% P = 0.0731, +78% P = 0.0081, and +72% P = 0.0773, respectively), whereas at 0.025 mmol Gd/kg, ∆SNR is in the same range as with gadoterate meglumine 0.1 mmol Gd/kg (+15% P > 0.9999). In pigs, contrast enhancement after gadopiclenol at 0.05 mmol/kg is +22% superior to MIP SNR after gadoterate meglumine at 0.1 mmol Gd/kg (P = 0.3095). Qualitatively, a preference was shown for gadopiclenol images (3/6) over the gadoterate meglumine examinations (1/6), with no preference being shown for the remainder (2/6). First-pass CE-MRA is feasible with gadopiclenol at 0.05 mmol Gd/kg with at least the same arterial signal enhancement and image quality as gadoterate meglumine at 0.1 mmol Gd/kg.

Mapping-based design method for high-quality integral projection system

A general method for designing an integral projection system is proposed, including optical design and digital preprocessing based on the mapping within the projection system. The per-pixel mapping between the sub-images and the integral projection image is generated by incorporating an integral projection imaging model as well as the ray data of all sub-channels. By tracing rays for sparsely sampled field points of the central sub-channel and constructing the mapping between the central sub-channel and other sub-channels, the efficient acquisition of ray data for all sub-channels is achieved. The sub-image preprocessing pipeline is presented to effectively address issues such as overlapping misalignment, optical aberrations, inhomogeneous illumination, and their collective contribution. An integral projection optical system with a field of view (FOV) of 80°, an F-number of 2, and uniform image performance is given as a design example. The ray tracing simulation results and quantitative analysis demonstrate that the proposed system yields distortion-free, uniformly illuminated, and high-quality integral projection images.

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.

Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels. A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy -means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings. Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, ). Scores from all breast regions performed significantly better than guessing ( from the -test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points. Results demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

Lensless shadow microscopy-based shortcut analysis strategy for fast quantification of microplastic fibers released to water

Fast quantification is the primary challenge in monitoring microplastic fiber (MPF) pollution in water. The process of quantifying the number of MPFs in water typically involves filtration, imaging on a filter membrane, and manual counting. However, this routine workflow has limitations in terms of speed and accuracy. Here, we present an alternative analysis strategy based on our high-resolution lensless shadow microscope (LSM) for rapid imaging of MPFs on a chip and modified deep learning algorithms for automatic counting. Our LSM system was equipped with wide field-of-view submicron-pixel imaging sensors (>1 cm2; ∼500 nm/pixel) and could simultaneously capture the projection image of >3-μm microplastic spheres within 90 s. The algorithms enabled accurate classification and detection of the numbers and sizes of >7-μm linear and branched MPFs derived from melamine cleaning sponges in each image (∼0.4 gigapixels) within 60 s. Importantly, neither MPF morphology (dispersed or aggregated) nor environmental matrix had a notable impact on the automatic recognition of the MPFs by the algorithms. This new strategy had a detection limit of 10 particles/mL and significantly reduced the time of MPF imaging and counting from several hours with membrane-based methods to just a few minutes per sample. The strategy could be employed to monitor water pollution caused by microplastics if an efficient sample separation and a comprehensive sample image database were available.

Treatment planning and delivery practice of lung SBRT: Results of the 2022 ESTRO physics survey

Background and purposeThe use of Stereotactic Body Radiation Therapy (SBRT) in lung cancer is increasing. However, there is no consensus on the most appropriate treatment planning and delivery practice for lung SBRT. To gauge the range of practice, quantify its variability and identify where consensus might be achieved, ESTRO surveyed the medical physics community. Materials and methodsAn online survey was distributed to ESTRO’s physicist membership in 2022, covering experience, dose and fractionation, target delineation, dose calculation and planning practice, imaging protocols, and quality assurance. ResultsTwo-hundred and forty-four unique answers were collected after data cleaning. Most respondents were from Europe the majority of which had more than 5 years’ experience in SBRT. The large majority of respondents deliver lung SBRT with the VMAT technique on C-arm Linear Accelerators (Linacs) employing daily pre-treatment CBCT imaging. A broad spectrum of fractionation schemes were reported, alongside an equally wide range of dose prescription protocols. A clear preference was noted for prescribing to 95% or greater of the PTV. Several issues emerged regarding the dose calculation algorithm: 22% did not state it while 24% neglected to specify the conditions under which the dose was calculated. Contouring was usually performed on Maximum or Average Intensity Projection images while dose was mainly computed on the latter. No clear indications emerged for plan homogeneity, complexity, and conformity assessment. Approximately 40% of the responders participated in inter-centre credentialing of SBRT in the last five years. Substantial differences emerged between high and low experience centres, with the latter employing less accurate algorithms and older equipment. ConclusionThe survey revealed an evident heterogeneity in numerous aspects of the clinical implementation of lung SBRT treatments. International guidelines and codes of practice might promote harmonisation.

Low-cost dual-energy CBCT by spectral filtration of a dual focal spot X-ray source

Dual-energy cone beam computed tomography (DE-CBCT) has been shown to provide more information and improve performance compared to a conventional single energy spectrum CBCT. Here we report a low-cost DE-CBCT by spectral filtration of a carbon nanotube x-ray source array. The x-ray photons from two focal spots were filtered respectively by a low and a high energy filter. Projection images were collected by alternatively activating the two beams while the source array and detector rotated around the object, and were processed by a one-step materials decomposition and reconstruction method. The performance of the DE-CBCT scanner was evaluated by imaging a water-equivalent plastic phantom with inserts containing known densities of calcium or iodine and an anthropomorphic head phantom with dental implants. A mean energy separation of 15.5 keV was achieved at acceptable dose rates and imaging time. Accurate materials quantification was obtained by materials decomposition. Metal artifacts were reduced in the virtual monoenergetic images synthesized at high energies. The results demonstrated the feasibility of high quality DE-CBCT imaging by spectral filtration without using either an energy sensitive detector or rapid high voltage switching.

Trajectory Deviation Estimation Method for UAV-Borne Through-Wall Radar

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly.

A Digital Image Method for Calculating the Working Chamber Volume of a Combined Profile Scroll Compressor

Background:: The efficient and accurate calculation of working chamber volume can greatly contribute to the optimized design efficiency of the combined profile scroll compressor, but current papers and patents lack research on the method of calculating the working chamber volume of a combined profile scroll compressor. Objective:: A method of using digital image processing is proposed to efficiently calculate the working chamber volume of a combined scroll compressor. Methods:: This method discretizes and reconstructs the scroll profile that forms the working chamber into a coordinate sequence with equal involute angle intervals. By calculating the relationship between the coordinate sequence and the rotation angle, a general coordinate sequence that forms the working chamber profile is obtained. The real-time changes in the projection image of the working chamber during all the suction, compression and discharge processes can be accurately depicted. The digital image is processed to obtain the actual projection area of the working chamber, and thus, the volume of the working chamber is accurately calculated. Results:: The digital image method can accurately calculate the volume change of the working chamber of the combined profile scroll compressor by selecting the appropriate involute angle interval and digital image size, with a mean relative error of less than 1%. At the beginning of suction and the end of discharge, the calculated volume has been found to have poor accuracy, with a maximum relative error higher than 10%. Conclusion:: The digital image method has been found to have high accuracy, greatly reduce the difficulty of the analysis of the working chamber volume, and promote the design optimization of the combined profile scroll compressor, thus broadening the idea for the calculation method of the working chamber volume of the scroll compressor.

An Omnidirectional Image Super-Resolution Method Based on Enhanced SwinIR

For the significant distortion problem caused by the special projection method of equi-rectangular projection (ERP) images, this paper proposes an omnidirectional image super-resolution algorithm model based on position information transformation, taking SwinIR as the base. By introducing a space position transformation module that supports deformable convolution, the image preprocessing process is optimized to reduce the distortion effects in the polar regions of the ERP image. Meanwhile, by introducing deformable convolution in the deep feature extraction process, the model’s adaptability to local deformations of images is enhanced. Experimental results on publicly available datasets have shown that our method outperforms SwinIR, with an average improvement of over 0.2 dB in WS-PSNR and over 0.030 in WS-SSIM for ×4 pixel upscaling.

A Novel Method for Enhancing the Image Quality of Neutron Projection Image

A Novel Method for Enhancing the Image Quality of Neutron Projection Image

[Automatic detection method of intracranial aneurysms on maximum intensity projection images based on SE-CaraNet].

Conventional maximum intensity projection (MIP) images tend to ignore some morphological features in the detection of intracranial aneurysms, resulting in missed detection and misdetection. To solve this problem, a new method for intracranial aneurysm detection based on omni-directional MIP image is proposed in this paper. Firstly, the three-dimensional magnetic resonance angiography (MRA) images were projected with the maximum density in all directions to obtain the MIP images. Then, the region of intracranial aneurysm was prepositioned by matching filter. Finally, the Squeeze and Excitation (SE) module was used to improve the CaraNet model. Excitation and the improved model were used to detect the predetermined location in the omni-directional MIP image to determine whether there was intracranial aneurysm. In this paper, 245 cases of images were collected to test the proposed method. The results showed that the accuracy and specificity of the proposed method could reach 93.75% and 93.86%, respectively, significantly improved the detection performance of intracranial aneurysms in MIP images.

Measurement technology based on a Stokes parametric polarization system

Structured light measurement systems often use polarization filters to reduce image interference from highly reflective areas. This method can be effective, but it may also reduce the brightness of specific areas, particularly overly dark portions, which can affect the accuracy of the measurement results. This paper proposes a measurement method for a polarization system based on Stokes parameters to solve the problem. After adjusting the polarization filter to angles of 0°, 45°, and 90°, the camera captures an image of the object and calculates the corresponding Stokes parameters to generate the expected polarization angle histogram. Then, based on the detailed information on the angle distribution, the accurate mathematical model is used to screen the interval, and the optimal polarization angle is determined by orthogonal processing while ensuring the signal-to-noise ratio and image quality. Finally, an image fusion technology synthesizes a set of fringe projection images with the preferred polarization angles. Experiments have shown that this new method effectively addresses the issue of interference in the highlighted region when using conventional polarization filters. Additionally, it significantly improves the quality of the fringe pattern. The polarization angle selection in the experimental process is made more rapid and accurate through the quantitative mathematical model calculation of the polarization angle, significantly improving the system’s measurement efficiency.

Design, implementation, and analysis of a compressed sensing photoacoustic projection imaging system.

Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs). Our previous research involved rather general CS measurements, where each ILD can contribute to any measurement. In the real world, however, the design of CS measurements is subject to practical constraints. In this research, we aim at a CS-PAPI system where each measurement involves only a subset of ILDs, and which can be implemented in a cost-effective manner. We extend the existing PAPI with a self-developed CS unit. The system provides structured CS matrices for which the existing recovery theory cannot be applied directly. A random search strategy is applied to select the CS measurement matrix within this class for which we obtain exact sparse recovery. We implement a CS PAPI system for a compression factor of 4:3, where specific measurements are made on separate groups of 16 ILDs. We algorithmically design optimal CS measurements that have proven sparse CS capabilities. Numerical experiments are used to support our results. CS with proven sparse recovery capabilities can be integrated into PAPI, and numerical results support this setup. Future work will focus on applying it to experimental data and utilizing data-driven approaches to enhance the compression factor and generalize the signal class.

LIDAR-based SLAM system for autonomous vehicles in degraded point cloud scenarios: dynamic obstacle removal

Purpose This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component. Design/methodology/approach This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated. Findings Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target. Originality/value Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

A ternary complementary Gray code phase unwrapping method in fringe projection profilometry

This paper proposes a phase unwrapping method based on ternary Gray code in fringe projection profilometry. This method does not require additional projection images or any prior knowledge. It accurately determines the segmentation thresholds of the ternary Gray code using only the average intensity and intensity modulation calculated from phase-shifting fringes, effectively reducing projection costs and achieving lower codeword errors. Additionally, a segment selection strategy suitable for the ternary Gray code is proposed. By employing the complementary method, three sets of candidate orders with certain codeword shifts can be generated. The wrapped phase is then divided into three segments: upper, middle, and lower segments, and correspondingly matched with the central regions of the three sets of candidate orders, thus enabling the solution of the absolute phase. Through the segment selection strategy, error-prone areas at the edges of orders are effectively avoided, further enhancing the accuracy of measurement results. Compared to the binary Gray code, the ternary complementary Gray code can encode up to 3m−1 orders when the number of Gray code patterns is m; when the required number of encoding orders is K, only ⌈log3K⌉+1 Gray code patterns are needed. Its additional value capacity precisely enables it to encode more orders, thereby increasing the frequency of phase-shifting fringes and further enhancing measurement efficiency. A series of experiments validates the feasibility, robustness, and superiority of this method.

Motion-Inclusive Treatment Planning to Assess Normal Tissue Dose for Central Lung Stereotactic Body Radiation Therapy

PurposeFor lung SBRT, 4DCT is often used to delineate target volumes, whereas organs at risk (OARs) are typically outlined on either average intensity projection (AIP) or mid-ventilation (MidV = 30% phase) images. AIP has been widely adopted as it represents a true average, but image blurring often precludes accurate contouring of critical structures such as central airways. Here we compare AIP vs MidV planning for centrally located tumors via respiratory motion-inclusive (RMI) plans to better evaluate dose delivered throughout the breathing cycle. MethodsIndependently contoured and optimized AIP and MidV plans were created for 16 treatments and rigidly copied to each of the 10 breathing phase-specific CT image sets. Resulting dose distributions were deformably registered back to the MidV image set (used as reference due to clearer depiction of anatomy compared to motion-blurred AIP) and averaged to create RMI plans. Doses to central OARs were compared between plans. ResultsMean absolute dose differences were low for all comparisons (range 0.01 – 2.87 Gy), however, individual plans exhibited differences > 20 Gy. Dose differences >5 Gy were observed most often for plan comparisons involving AIP-based plans (MidV vs AIP 23, AIP RMI vs AIP 12, MidV RMI vs AIP RMI 7, and MidV RMI vs MidV 8 times). Inclusion of respiratory motion reduced large dose differences. Standard OAR thresholds were exceeded up to five times for each plan comparison scenario and always involved PBT D4cc tolerance dose. AIP-based contours were larger by on average 3-15%. ConclusionLarge dose differences were observed when plans with AIP-based contours were compared with MidV-based contours indicating that observed dose differences were likely due to contoured volume differences rather than the effect of motion. Because of blurring with AIP images, MidV RMI-based planning may offer a more accurate method to determine dose to critical OARs in the presence of respiratory motion.

Cryo-EM images of phase-separated lipid bilayer vesicles analyzed with a machine-learning approach

Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-melting lipids plus cholesterol offer a simplified experimental model for raft domains in which a liquid-disordered (Ld) phase coexists with a liquid-ordered (Lo) phase. Using such models, we recently showed that cryogenic electron microscopy (cryo-EM) can detect phase separation in lipid vesicles based on differences in bilayer thickness. However, the considerable noise within cryo-EM data poses a significant challenge for accurately determining the membrane phase state at high spatial resolution. To this end, we have developed an image-processing pipeline that utilizes machine learning (ML) to predict the bilayer phase in projection images of lipid vesicles. Importantly, the ML method exploits differences in both the thickness and molecular density of Lo compared to Ld, which leads to improved phase identification. To assess accuracy, we used artificial images of phase-separated lipid vesicles generated from all-atom molecular dynamics simulations of Lo and Ld phases. Synthetic ground-truth data sets mimicking a series of compositions along a tieline of Ld + Lo coexistence were created and then analyzed with various ML models. For all tieline compositions, we find that the ML approach can correctly identify the bilayer phase with >90% accuracy, thus providing a means to isolate the intensity profiles of coexisting Ld and Lo phases, as well as accurately determine domain-size distributions, number of domains, and phase-area fractions. The method described here provides a framework for characterizing nanoscopic lateral heterogeneities in membranes and paves the way for a more detailed understanding of raft properties in biological contexts.

Projective light-sheet microscopy with flexible parameter selection

Projection imaging accelerates volumetric interrogation in fluorescence microscopy, but for multi-cellular samples, the resulting images may lack contrast, as many structures and haze are summed up. Here, we demonstrate rapid projective light-sheet imaging with parameter selection (props) of imaging depth, position and viewing angle. This allows us to selectively image different sub-volumes of a sample, rapidly switch between them and exclude background fluorescence. Here we demonstrate the power of props by functional imaging within distinct regions of the zebrafish brain, monitoring calcium firing inside muscle cells of moving Drosophila larvae, super-resolution imaging of selected cell layers, and by optically unwrapping the curved surface of a Drosophila embryo. We anticipate that props will accelerate volumetric interrogation, ranging from subcellular to mesoscopic scales.

Posterior cerebral artery (PCA)-accessory PCA (hyperplastic anterior choroidal artery) anastomosis detected on magnetic resonance angiography.

To describe a case of posterior cerebral artery (PCA)-accessory PCA (hyperplastic anterior choroidal artery) anastomosis detected on magnetic resonance angiography. A 76-year-old man with a history of cerebral infarction underwent cranial magnetic resonance (MR) imaging and MR angiography of the intracranial region for the evaluation of brain and vascular lesions. The MR machine was a 3-Tesla scanner. MR angiography was performed using a standard three-dimensional time-of-flight technique. There were two right PCAs. The parieto-occipital and calcarine arteries of the right PCA arose from the right ICA, indicative of accessory PCA, and there were three stenotic lesions at the proximal segment of this artery. The temporal artery of the right PCA originated from the basilar artery. A small anastomotic channel between these two arteries was identified on partial maximum intensity projection (MIP) images. Computed tomography angiography was additionally performed and the findings were confirmed. We speculated that the pressure gradient between the PCA and the accessory PCA enlarged the anastomotic channel. Partial MIP images are useful for diagnosing small arterial variations using MR angiography.

Simulations of dislocation contrast in dark-field X-ray microscopy.

Dark-field X-ray microscopy (DFXM) is a full-field imaging technique that non-destructively maps the structure and local strain inside deeply embedded crystalline elements in three dimensions. In DFXM, an objective lens is placed along the diffracted beam to generate a magnified projection image of the local diffracted volume. This work explores contrast methods and optimizes the DFXM setup specifically for the case of mapping dislocations. Forward projections of detector images are generated using two complementary simulation tools based on geometrical optics and wavefront propagation, respectively. Weak and strong beam contrast and the mapping of strain components are studied. The feasibility of observing dislocations in a wall is elucidated as a function of the distance between neighbouring dislocations and the spatial resolution. Dislocation studies should be feasible with energy band widths of 10-2, of relevance for fourth-generation synchrotron and X-ray free-electron laser sources.