Back Projection Research Articles

Enhancement of Stopping Power Ratio (SPR) Estimation Accuracy through Image-Domain Dual-Energy Computer Tomography for Pencil Beam Scanning System: A Simulation Study.

Our study aims to quantify the impact of spectral separation on achieved theoretical prediction accuracy of proton-stopping power when the volume discrepancy between calibration phantom and scanned object is observed. Such discrepancy can be commonly seen in our CSI pediatric patients. One of the representative image-domain DECT models is employed on a virtual phantom to derive electron density and effective atomic number for a total of 34 ICRU standard human tissues. The spectral pairs used in this study are 90 kVp/140 kVp, without and with 0.1 mm to 0.5 mm additional tin filter. The two DECT images are reconstructed via a conventional filtered back projection algorithm (FBP) on simulated noiseless projection data. The best-predicted accuracy occurs at a spectral pair of 90 kVp/140 kVp with a 0.3 mm tin filter, and the root-mean-squared average error is 0.12% for tissue substitutes. The results reveal that the selected image-domain model is sensitive to spectral pair deviation when there is a discrepancy between calibration and scanning conditions. This study suggests that an optimization process may be needed for clinically available DECT scanners to yield the best proton-stopping power estimation.

A Super-Resolution Reconstruction Method for Infrared Polarization Images with Sparse Representation of Over-Complete Basis Sets

The spatial resolution of an infrared focal plane polarization detection system is limited by the structure of the detector, resulting in lower resolution than the actual array size. To overcome this limitation and improve imaging resolution, we propose an infrared polarization super-resolution reconstruction model based on sparse representation, optimized using Stokes vector images. This model forms the basis for our method aimed at achieving super-resolution reconstruction of infrared polarization images. In this method, we utilize the proposed model to initially reconstruct low-resolution images in blocks. Subsequently, we perform a division by weight, followed by iterative back projection to enhance details and achieve high-resolution reconstruction results. As a supplement, we establish a near-real-time short-wave infrared time-sharing polarization system for data collection. The dataset was acquired to gather prior knowledge of the over-complete basis set and to generate a series of simulated focal plane images. Simulation experimental results demonstrate the superiority of our method over several advanced methods in objective evaluation indexes, exhibiting strong noise robustness in quantitative experiments. Finally, to validate the practical application of our method, we establish a split-focal plane polarization short-wave infrared system for scene testing. Experimental results confirm the effective processing of actual captured data by our method.

An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction.

Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically. The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V). We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated. High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant. The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

A Focusing Method of Buildings for Airborne Circular SAR

Airborne circular synthetic aperture radar (CSAR) can realize high-resolution imaging of the scene over 360 degrees azimuth angle variation. Aiming at the problem of focusing of buildings for the airborne CSAR, this paper first analyzes the phase errors of CSAR buildings focusing in detail, and the analytic relationship between the scatterer height and azimuth focusing quality is deduced. Then, a focusing method of CSAR buildings based on the back projection algorithm is proposed. This method adopts the processing strategy of multi-layers imaging, and it is able to improve azimuth focusing quality of the buildings which have large height dimension. The proposed method is especially suitable for the high-resolution imaging and monitoring of the urban site with high-rise buildings in the airborne CSAR scenario. The correctness of the theoretical analysis and the validity of the proposed method are verified by using both simulation results and Ku-band airborne CSAR real data processing results.

PARALLELPROJ-an open-source framework for fast calculation of projections in tomography.

In this article, we introduce parallelproj, a novel open-source framework designed for efficient parallel computation of projections in tomography leveraging either multiple CPU cores or GPUs. This framework efficiently implements forward and back projection functions for both sinogram and listmode data, utilizing Joseph's method, which is further extended to encompass time-of-flight (TOF) PET projections. Our evaluation involves a series of tests focusing on PET image reconstruction using data sourced from a state-of-the-art clinical PET/CT system. We thoroughly benchmark the performance of the projectors in non-TOF and TOF, sinogram, and listmode employing multi CPU-cores, hybrid CPU/GPU, and exclusive GPU mode. Moreover, we also investigate the timing of non-TOF sinogram projections calculated in STIR (Software for Tomographic Image Reconstruction) which recently integrated parallelproj as one of its projection backends. Our results indicate that the exclusive GPU mode provides acceleration factors between 25 and 68 relative to the multi-CPU-core mode. Furthermore, we demonstrate that OSEM listmode reconstruction of state-of-the-art real-world PET data sets is achievable within a few seconds using a single consumer GPU.

The first 30 min hidden aftershocks of the 2022 September 17, ML 6.4, Guanshan, Taiwan earthquake and its seismological implications

The first 30 min of the aftershock sequence following the 2022 September 17, ML 6.4, Guanshan, Taiwan earthquake is investigated by the back-projection (BP) method. Based on the Automatic Gain Control (AGC) to process individual earthquake record and sort waveforms by epicentral distance, 80 events are identified near the Guanshan earthquake epicenter to verify its temporal variation. However, most of these events cannot clearly image by the BP method to determine its spatial locations for its small amplitude and short time separation. In total, 29 aftershocks are imaged by this study to identify its spatial locations during this time period. The relocated events are compared with the Central Weather Bureau (CWB) rapid report catalogue. Only 5 events have been reported by the CWB rapid report catalogue. These unreported hidden aftershocks are identified to distribute surrounding the epicenter and extended to the east. This extracted information of hidden aftershocks is crucial for assessing the potential for future large aftershocks and estimating the associated seismic hazards in a given region, and provide extra message to evaluate the source physics in general.

Comparison of Image Quality and Radiation Dose between Filtered Back Projection and Sinogram Affirmed Iterative Reconstruction in CT Head: A Cross-sectional Study

Introduction: Computed Tomography (CT) scans account for only 15% of total diagnostic procedures but for over half of the collective dose of radiation. The increasing awareness about the harmful effects of radiation has also created a need for developing techniques that decrease radiation exposure while at the same time providing a reasonably good image quality. The majority of the CT units today use Filtered Back Projection (FBP) as the means of image reconstruction. However, FBP leads to coupling between image noise and radiation exposure and limits the dose reduction possible while providing diagnostic quality images. Second-generation Iterative Reconstruction (IR) techniques have caught the attention of medical researchers because they provide a superior image quality than FBP at the same radiation dose showing potential for dose reduction. Aim: To compare the image quality and radiation dose in standard dose CT head reconstructed with FBP and low dose CT head reconstructed with Sinogram Affirmed Iterative Reconstruction (SAFIRE) and FBP both. Materials and Methods: A cross-sectional analytical study was conducted on 100 patients in the emergency department of Vardhman Mahavir Medical College and Safdarjung Hospital in India from November 2018 to April 2020. Patients referred for emergency Non-contrast Computed Tomography (NCCT) head for any indication excluding post-op cases and those with metallic artifacts were included. Fifty patients chosen using systematic random sampling underwent low-dose CT head using the Care Dose 4D Automatic Exposure Control (AEC) system with reconstruction using SAFIRE (Group-A) and FBP (Group-B). Another 50 patients who were within five years of the corresponding Group-A and B patients underwent standard dose head CT reconstruction using FBP (Group-C). CTDI vol, DLP, and effective dose were recorded in all patients. Image quality was assessed objectively using the unpaired t-test for Group-A and C and paired t-test for Group-A and B. Subjective image analysis between the groups was done using a 4-point Likert scale. Results: Image quality parameters were found to be better in Group-A compared to Group-C (p<0.05). The mean values in low dose SAFIRE group, low dose FBP group, and standard dose FBP group were as follows: Grey Matter (GM) Signal-to-Noise Ratio (SNR) (14±3.29, 8.96±2 and 9.24±1.96), White Matter (WM) SNR (14.6 ±3.73, 6.9±3.79 and 7.11±1.68), Cerebrospinal Fluid (CSF) SNR (1.65±1.12, 0.86±0.80 and 1.08±0.61) and Contrastto-Noise Ratio (CNR) (3.06±0.94, 1.81±0.69 and 1.74±0.69), respectively. Group-A had significantly improved image quality parameters than Group-B and Group-C. Radiation dose reduction of 42%, 39.34%, and 42.5% was achieved in CTDIvol, DLP, and effective dose respectively in low dose group. Conclusion: Low dose CT head reconstructed with SAFIRE were significantly better in image quality compared to standarddose CT head images reconstructed using FBP, while allowing for up to 42% reduction in dose.

Defect imaging and identification in asphalt materials using coplanar capacitance sensors with single-pair electrodes

Coplanar capacitance imaging technology (CCIT) is a non-destructive method with intuitive and accurate identification. This paper aims to realize defect identification in asphalt materials based on the CCIT using coplanar single-pair electrode capacitance sensor (CSCS). Firstly, the Linear Back Projection (LBP) algorithm, the Landweber algorithm, and the Kalman-Filter (KF) algorithm, are compared and evaluated. Then, the principal component analysis (PCA) method is utilized to fuse the reconstructed images. In addition, the OTSU method, the iterative threshold (IT) method, and the genetic algorithm (OA) method, is compared and utilized to quantify the defective region. Finally, this investigation analyzes the reconstructed and segmented images of defects in asphalt materials. It is concluded that the KL algorithm is the most suitable algorithm to reconstruct the defective images. The PCA method improve the quality of the reconstructed defective images. The defective region is determined by the OTSU method, which is the most approximate imaging segment method. It is found that the CCIT can detect the invisible defect depth in asphalt materials. The different defect mediums in various asphalt materials can be identified by the CCIT. The segmented defective region error in asphalt materials is less than 13%, demonstrating that the CCIT is effective for the identification of defect shape details in asphalt materials. The segmented imaging precision of square defects in asphalt materials is the highest; circular defects are the next lowest; and triangular defects are the lowest. The outcomes of this research can assist engineers in realizing intuitive and high-accuracy identification of various invisible defects in shallow asphalt layers in bridge deck asphalt pavement utilizing the CCIT.

Exploring the Influence of Initial Estimates on Iterative Maximum Likelihood Expectation-maximization in Tomographic Reconstruction.

To explore the influence of initial guess or estimate (uniform as "ones" and "zeros" vs. filtered back projection [FBP] image) as an input image for maximum likelihood expectation-maximization (MLEM) tomographic reconstruction algorithm and provide the curves of error or convergence for each of these three initial estimates. Two phantoms, created as digital images, were utilized: one was a simple noiseless object and the other was a more complicated, noise-degraded object of the section of lower thorax in a matrix of 256 × 256 pixels. Both underwent radon transform or forward projection process and the corresponding sinograms were generated. For filtering during tomographic image reconstruction, ramp and Butterworth filters, as high-pass and low-pass ones, were applied to images. The second phantom (lower thorax) was radon-transformed and the resulting sinogram was degraded by noise. As initial guess or estimate images, in addition to FBP tomographic image, two uniform images, one with all pixels having a value of 1 ("ones") and the other with all having zero ("zeros"), were created. The three initial estimates (FBP, ones, and zeros) were reconstructed with iterative MLEM tomographic reconstruction (with 1, 2, 4, 8, 16, 32, and 64 iterations). The difference between the object and the updated slice was calculated at the end of each iteration (as error matrix), and the mean squared error (MSE) was computed and plotted separately or in conjunction with the MSE curves of other initial estimates. All computations were implemented in MATLAB software. The results of ones and zeros seemed strikingly similar. The curves of uniform ones and uniform zeros were so close to each other that overlap near-perfectly. However, in the FBP slice as an initial estimate, the resulting tomographic slice was similar with a much higher extent to the object even after 1 or 2 iterations. The pattern of convergence for all three curves was roughly similar. The normalized MSE decreased sharply up to 5 iterations and then, after 10 iterations, the curves reached a plateau until 32 iterations. For the phantom of the lower thorax section with its noise-degraded sinogram, similar to the pattern observed for simple disk-shaped phantom, the curves (normalized MSE) fell sharply up to 10 iterations and then rapidly converged thereafter until 64 iterations. Similar results are observed when choosing different initial guesses or estimates (uniform vs. FBP) as the starting point, based on the error calculation using MSE. The algorithm converges almost similarly for all initial estimates. Therefore, selecting a uniform initial guess image can be an appropriate choice and may be preferred over an FBP image. Reducing the processing time can be a valid reason for this choice.

Multichannel Back Projection (MC-BP) Algorithm and Its Accelerated Form for HRWS SAR

Multichannel Back Projection (MC-BP) Algorithm and Its Accelerated Form for HRWS SAR

Improvement of the longitudinal phase space tomography at the J-PARC synchrotrons

The longitudinal phase space tomography, which reconstructs the phase space distribution from the one-dimensional bunch profiles, is used in various accelerators to measure longitudinal beam parameters. At the J-PARC, an implementation of the phase space tomography based on the Convolution Back Projection method (CBP) has been used to measure the momentum spread of the injected beam. The method assumes that the beam distribution rotates without significant deformation during the synchrotron oscillation. Because of the nonlinearity of synchrotron motion with sinusoidal RF voltage, the method can be used only in limited situations such as small amplitude synchrotron oscillation. Algebraic Reconstruction Techniques (ART) in conjunction with particle tracking, which is implemented in the CERN’s tomography code, allows accurate reconstructions even for nonlinear large amplitude synchrotron oscillations. We present the overview of the application of the CERN’s tomography code to the J-PARC synchrotrons. The results of benchmarking are also reported.

Comparative Study of Image Quality and Radiation Dose between 120kVp Filtered Back Projection and 80kVp Iterative Reconstructed Computed Tomography Images

Background: Iterative Reconstruction techniques have been shown to produce diagnostically acceptable images at low doses to the patient. This study aimed to compare the image quality and radiation dose between 120kVp Filtered Back Projection and 80kVp Iterative Reconstructed (SAFIRE) CT images.Methods: This cross-sectional study was performed on patients referred for CT Urography examinations for various clinical indications to the Department of Radiology and Imaging, Tribhuvan University Teaching Hospital. Data were collected for a period of four months (From August to November 2019) after approval from the Institutional Review Committee of the Institute of Medicine. Convenience sampling was employed and a total of 96 examinations were included. Among them 48 were male and 48 were female. Data were obtained from the 128-slice MDCT Siemens Somaton Definition AS+ CT scanner. Venous phase scans were obtained with Protocol A (120kVp and Filtered Back Projection) and non-contrast scans were obtained with Protocol B (80kVp and SAFIRE). The mAs (tube current-time product) was fixed at 200 for both protocols.Results: There was a 72.5% reduction in Size Specific Dose Estimate (SSDE) in Protocol B compared to Protocol A. However, there was a 13.17% increase in noise in Protocol B compared to Protocol A. Image quality evaluation showed a 98.95% acceptability for the low dose i.e. Protocol B images.Conclusion: CT using low kVp (80kVp) and low current (200mAs) along with an iterative reconstruction algorithm (SAFIRE) can provide diagnostically acceptable images at very low doses for examinations of the Urinary tract

RAU-Net-Based Imaging Method for Spatial-Variant Correction and Denoising in Multiple-Input Multiple-Output Radar

The conventional back projection (BP) algorithm is an accurate time-domain algorithm widely used for multiple-input multiple-output (MIMO) radar imaging, owing to its independence of antenna array configuration. The time-delay curve correction back projection (TCC-BP) algorithm greatly reduces the computational complexity of BP but suffers from spatial-variant correction, sidelobe interference and background noise due to the use of coherent superposition of echo time-delay curves. In this article, a residual attention U-Net-based (RAU-Net) MIMO radar imaging method that adapts complex noisy scenarios with spatial variation and sidelobe interference is proposed. On the basis of the U-Net underlying structure, we develop the RAU-Net with two modules: a residual unit with identity mapping and a dual attention module to obtain resolution spatial-variant correction and denoising on real-world MIMO radar images. The network realizes MIMO radar imaging based on the TCC-BP algorithm and substantially reduces the total computational time of the BP algorithm on the basis of improving the imaging resolution and denoising capability. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms both the traditional methods and learning-imaging methods in terms of spatial-variant correction, denoising and computational complexity.

PRINCIPAL COMPONENTS VERSUS AUTOENCODERS FOR DIMENSIONALITY REDUCTION: A CASE OF SUPER-RESOLVED OUTPUTS FROM PRISMA HYPERSPECTRAL MISSION DATA

Abstract. This study attempts to solve these issues associated with hyperspectral (HS) data, i.e., coarse spatial resolution and high volume, by understanding the effect of deep learning and traditional dimensionality reduction on super-resolved products generated from the recently launched PRecursore IperSpettrale della Missione Applicativa (PRISMA) HS mission. Four single-frame super-resolution (SR) algorithms have been used to super-resolve a 30 m PRISMA scene of Ahmedabad, India and generate 15 m spatial resolution images with both spatial and spectral fidelity. Iterative back projection (IBP) and sparse representation (SIS) are the best and worst-performing SR algorithms following a comparative assessment and validation protocol. Next, denoising autoencoders and PCT computed using singular and eigenvalue decompositions have been executed on the original PRISMA, IBP and SIS-based super-resolved datasets. The resulting low-dimensional representations have been assessed to preserve the original dataset's topology using label-independent Lee and Verleysen's co-ranking matrix and loss of quality measure. Findings suggest that autoencoders are computationally expensive and require a higher neighbourhood size than PCT and its variants to produce a high-quality encoding. These insights remain significant for urban information extraction as there are few direct comparative assessments between machine learning-based linear and non-linear data compression methods in earlier studies.

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness.

Prior information-guided reconstruction network for positron emission tomography images.

Deep learning has recently shown great potential in medical image reconstruction tasks. For positron emission tomography (PET) images, the direct reconstruction from raw data to radioactivity images using deep learning without any constraint may lead to the production of nonexistent structures. The aim of this study was to specifically develop and test a flexibly deep learning-based reconstruction network guided by any form of prior knowledge to achieve high quality and high reliability reconstruction. We developed a novel prior information-guided reconstruction network (PIGRN) with a dual-channel generator and a 2-scale discriminator based on a conditional generative adversarial network (cGAN). Besides the raw data channel, an additional channel is provided in the generator for prior information (PI) to guide the training phase. The PI can be reconstructed images obtained via conventional methods, nuclear medical images from other modalities, attenuation correction maps from time-of-flight-PET (TOF-PET) data, or any other physical parameters. For this study, the reconstructed images generated by filtered back projection (FBP) were chosen as the input of the additional channel. To improve the image quality, a 2-scale discriminator was adopted which can focus on both the coarse and fine field of the reconstruction images. Experiments were carried out on both a simulation dataset and a real Sprague Dawley (SD) rat dataset. Two classic deep learning-based reconstruction networks, including U-Net and Deep-PET, were compared in our study. Compared with these two methods, our method could provide much higher quality PET image reconstruction in the study of the simulation dataset. The peak signal-to-noise ratio (PSNR) value reached 31.8498, and the structure similarity index measure (SSIM) value reached 0.9754. The real study on SD rats indicated that the proposed network also has strong generalization ability. The flexible PIGRN based on cGAN for PET images combines both raw data and PI. The results of comparison experiments and a generalization experiment based on simulation and SD rat datasets demonstrated that the proposed PIGRN has the ability to improve image quality and has strong generalization ability.

The value of a deep learning image reconstruction algorithm in whole-brain computed tomography perfusion in patients with acute ischemic stroke.

Computed tomography perfusion (CTP) and computed tomography angiography (CTA) are valuable tools for diagnosing acute ischemic stroke (AIS). It is essential to obtain high-quality CTP and CTA images in short time. This study aimed to evaluate the image quality and diagnostic performance of brain CTP and CTA images generated from CTP reconstructed by a deep learning image reconstruction (DLIR) algorithm on patients with AIS. The study prospectively enrolled 54 patients with suspected AIS undergoing non-contrast CT and CTP within 24 hours. CTP datasets were reconstructed with three levels of adaptive statistical iterative reconstruction-Veo algorithm [ASIR-V 0% with filtered back projection (FBP), ASIR-V 40%, and ASIR-V 80%] and three levels of DLIR, including low (DLIR-L), medium (DLIR-M), and high (DLIR-H). CTA images were generated using the CTP datasets at the peak arterial phase. Objective parameters including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and noise reduction rate. Subjective evaluation was assessed according to Abels scoring system. Perfusion parameters and detection accuracy for infarction core lesions were evaluated. The objective and subjective image quality of CTA images were also evaluated. All reconstructions produced similar CT values (P>0.05). With the increase of ASIR-V and DLIR reconstruction strength, image noise decreased, while SNR and CNR increased for CTP images, especially in white matter. DLIR-H, DLIR-M, and ASIR-V80% yielded higher subjective scores than did ASIR-V40% and FBP. DLIR-H provided the highest noise reduction rate and detection accuracy. No significant difference was found in conventional parameters, the volume of infarct core, or ischemic penumbra among the 6 groups (P>0.05). The objective evaluation of reconstructed CTA images was comparable in DLIR-H, DLIR-M, and ASIR-V80% (P>0.05). The subjective scores of the DLIR-H and DLIR-M images were higher than those of the other groups, especially ASIR-V40% and FBP (P<0.05). Compared with FBP and ASIR-V40%, DLIR-H, DLIR-M, and ASIR-V80% improved the overall image quality of CTP and CTA images to varying degrees. Furthermore, DLIR-H and DLIR-M showed the best performance. DLIR-H is the best choice in diagnosing AIS with improved detection accuracy for cerebral infarction. Reconstructing CTA images using CTP datasets could reduce contrast agent and radiation dose.

Deep learning reconstruction improves the image quality of low-dose temporal bone CT with otitis media and mastoiditis patients

ObjectiveTo evaluate the image quality of low-dose temporal bone computed tomography (CT) in otitis media and mastoiditis patients by using deep learning reconstruction (DLR). Materials and methodsA total of ninety-seven temporal bones from 53 consecutive adult patients who had suspected otitis media and mastoiditis and underwent temporal bone CT were prospectively enrolled. All patients underwent high resolution CT protocol (group A) and an additional low-dose protocol (group B). In group A, high resolution data were reconstructed by filter back projection (FBP). In group B, low-dose data were reconstructed by DLR mild (B1), DLR standard (B2) and DLR strong (B3). The objective image quality was analyzed by measuring the CT value and image noise on the transverse image and calculating the signal-to-noise ratio (SNR) on incudomallear joint, retroauricular muscle, vestibule and subcutaneous fat. Subjective image quality was analyzed by using a five-point scale to evaluate nine anatomical structures of middle and inner ear. The number of temporal bone lesions which involved in five structures of middle ear were assessed in group A, B1, B2 and B3 images. ResultsThere were no significant differences in the CT values of the four reconstruction methods at four structures (all p > 0.05). The DLR group B1, B2 and B3 had significantly less image noise and a significantly higher SNR than group A at four structures (all p < 0.001). The group B1 had comparable subjective image quality as group A in nine structures (all p > 0.05), however, the group B3 had lower subjective image quality than group A in modiolus, spiral osseous lamina and stapes (all p < 0.001), the group B2 had lower subjective image quality than group A in modiolus and spiral osseous lamina (both p < 0.05). The number of temporal bone lesions which involved in five structures for group A, B1 and B2 images were no significant difference (all p > 0.05), however, the number of temporal bone lesions which involved in mastoid for group B3 images were significantly more than group A (p < 0.05). The radiation dose of high resolution CT protocol and low-dose protocol were 0.55 mSv and 0.11 mSv, respectively. ConclusionCompared with high resolution CT protocol, in the low-dose protocol of temporal bone CT, DLR mild and standard could improve the objective image quality, maintain good subjective image quality and satisfy clinical diagnosis of otitis media and mastoiditis patients.

Artificial Intelligence Iterative Reconstruction in Computed Tomography Angiography: An Evaluation on Pulmonary Arteries and Aorta With Routine Dose Settings.

The objective of this study is to investigate whether a newly introduced deep learning-based iterative reconstruction algorithm, namely, the artificial intelligence iterative reconstruction (AIIR), has a clinical value in computed tomography angiography (CTA), especially for visualizing vascular structures and related lesions, with routine dose settings. A total of 63 patients were retrospectively collected from the triple rule-out CTA examinations, where both pulmonary and aortic data were available for each patient and were taken as the example for investigation. The images were reconstructed using the filtered back projection (FBP), hybrid iterative reconstruction (HIR), and the AIIR. The visibility of vasculature and pulmonary emboli and the general image quality were assessed. Artificial intelligence iterative reconstruction resulted in significantly ( P < 0.001) lower noise as well as higher signal-to-noise ratio and contrast-to-noise ratio compared with FBP and HIR. Besides, AIIR achieved the highest subjective scores on general image quality ( P < 0.05). For the vasculature visibility, AIIR offered the best vessel conspicuity, especially for the small vessels ( P < 0.05). Also, >90% of emboli on the AIIR images were graded as sharp (score 5), whereas <15% of emboli on FBP and HIR images were scored 5. As demonstrated for pulmonary and aortic CTAs, AIIR improves the image quality and offers a better depiction for vascular structures compared with FBP and HIR. The visibility of the pulmonary emboli was also increased by AIIR.

Establishing national clinical diagnostic reference levels and achievable doses for CT examinations in Brazil: A prospective study

PurposeDiagnostic reference levels (DRL) and achievable doses (AD) are important tools for radiation dose optimization. Therefore, a prospective study was performed which aimed to establish a multi-parametric, clinical indication based – DRL(DRLCI) and clinical indication – AD (ADCI) for adult CT in Brazil. MethodsThe prospective study included 4787 patients (50 ± 18 years old; male:female 2041:2746) at 13 Brazilian sites that have been submitted to head, paranasal sinus, cervical spine, chest, or abdomen-pelvis CT between January and October 2021 for 13 clinical indications. The sites provided the following information: patient age, gender, weight, height, body mass index[BMI], clinical indications, scanner information(vendor, model, detector configuration), scan parameters (number of scan phases, kV, mA, pitch) and dose-related quantities (CT dose index volume- CTDIvol, dose length product- DLP). Median(AD) and 75th(DRL) percentile CTDIvol and DLP values were estimated for each body region and clinical indications. Non-normal data were analyzed with the Kruskal-Wallis test. ResultsIn majority of Brazilian sites, body region and clinical indications based DRLs were at or lower than the corresponding DRLs in the US and higher than Europe. Although radiation doses varied significantly for patients in different body mass index groups (p < 0.001), within each body region, there were no differences in radiation doses for different clinical indications (p > 0.1). Radiation doses for 7/13 clinical indications were higher using iterative reconstruction technique than for the filtered back projection. ConclusionsThere was substantial variation in Brazil DRLCI across different institutions with higher doses compared to the European standards. There was also a lack of clinical indication-based protocol and dose optimization based on different clinical indications for the same body region.