Lower Image Noise Research Articles

Feasibility of thin-slice, low noise images created using multi-kernel synthesis to replace multiple image series in head CT.

SynthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON) is a multi-kernel synthesis method that creates a single series of thin-slice computed tomography (CT) images displaying low noise and high spatial resolution, increasing reader efficiency and minimizing partial volume averaging. To compare the diagnostic performance of a single set of ZIRCON images to two routine clinical image series using conventional CT head and bone reconstruction kernels for diagnosing intracranial findings and fractures in patients with trauma or suspected acute neurologic deficit. In total, 50 patients underwent clinically indicated head CT in the ER (15 normal, 35 abnormal cases). A non-reader neuroradiologist established the reference standard. Three neuroradiologists reviewed two routine clinical series (head and bone kernels) and a single ZIRCON series, detecting intracranial findings or fractures and rating confidence (0-100). Sensitivity, specificity, and jackknife free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were compared (limit of non-inferiority: -0.10). ZIRCON and conventional images demonstrated comparable performance for fractures (sensitivity: 51.5% vs. 54.5%; specificity: 40.2% vs. 34.2%) and intracranial findings (sensitivity: 88.2% vs. 91.4%; specificity: 77.2% vs. 73.7%).The estimated difference of JAFROC FOM demonstrated ZIRCON non-inferiority for acute pathologies overall (0.003 [95% CI=-0.051-0.057]) and fractures (0.048 [95% CI=-0.050-0.145]) but not for intracranial findings alone (-0.024 [95% CI=-0.100-0.052]). Thin-slice, low noise, and high spatial resolution images can be created to display intracranial findings and fractures replacing multiple images series in head CT with similar performance. Future studies in more patients and further algorithmic development are warranted.

Abdominal Applications of Photon-Counting CT.

Photon-counting computed tomography (PCCT) has shown promising advancements in abdominal imaging in clinical use. Though more peer-reviewed primary literature is needed, this commentary explores PCCT's potential applications, focusing on enhancing diagnostic accuracy, optimizing radiation dose management, and improving patient care. PCCT offers improved spatial and contrast resolution, lower image noise, and reduced radiation dose. Increased spatial resolution provides better detail in abdominal imaging, aiding in the detection of small lesions and subtle pathological changes. However, this generates more images per scan, raising concerns about "image overload" in PACS, potentially leading to longer reading times and increased stress for radiologists. PCCT's improved contrast resolution enhances tissue differentiation, reducing the need for intravenous contrast agents. The technology's advanced tissue characterization provides several advantages, such as non-invasive and opportunistic liver disease evaluation and improved differentiation of renal and adrenal masses. PCCT's optimized radiation dose management is crucial for patients requiring frequent scans. Enhanced diagnostic accuracy through spectral information aids in tissue differentiation, improving confidence in diagnoses. Streamlined workflows, particularly in emergency settings, and oncologic imaging, are potential benefits, reducing the need for additional imaging studies. Future integration of PCCT into clinical practice requires collaboration, education, and research to fully harness its potential, ensuring optimized abdominal imaging and improved patient care.

The impact of deep learning image reconstruction of spectral CTU virtual non contrast images for patients with renal stones

PurposeTo compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU). MethodsA retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility. ResultsDH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P > 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p < 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P > 0.05). ConclusionThe DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.

Assessing the influence of dose modulation on water equivalent diameter estimation in pediatric head CT imaging

Pediatric head computed tomography (CT) scans necessitate precise determination of radiation dose to optimize image quality while minimizing patient exposure. This study explores the influence of head size variation on radiation dose and image quality using phantom and patients set, aiming to investigate the efficacy of Automatic Exposure Control (AEC) in dose modulation. Image noise and radiation dose were assessed using with and without AEC, maintaining a constant tube current of 180 mA. The correlation between CT dose index volume (CTDIvol) and water-equivalent diameter (Dw) was examined across 134 image sets, revealing an exponential relationship indicative of AEC's contribution to elevated radiation exposure in larger heads. Furthermore, a slice-based analysis demonstrated a consistent variation in tube current along the slice location, effectively modulated by Dw. However, discrepancies in tube current were observed in the caudal region, suggesting the influence of factors beyond section size. Refinement of analysis improved curve fitting, highlighting the importance of section size in dose modulation. AEC proved essential in maintaining image quality consistency across slices, with inactive AEC resulting in lower image noise in cranial slices. These findings highlight the significance of AEC-based dose modulation in pediatric head CT to ensure optimal image quality while minimizing radiation exposure, emphasizing the need for comprehensive understanding and implementation of AEC systems from different manufacturers.

Robust polarimetric dehazing algorithm based on low-rank approximation and multiple virtual-exposure fusion

Polarimetric dehazing is an effective way to enhance the quality of images captured in foggy weather. However, images of essential polarization parameters are vulnerable to noise, and the brightness of dehazed images is usually unstable due to different environmental illuminations. These two weaknesses reveal that current polarimetric dehazing algorithms are not robust enough to deal with different scenarios. This paper proposes a novel, to our knowledge, and robust polarimetric dehazing algorithm to enhance the quality of hazy images, where a low-rank approximation method is used to obtain low-noise polarization parameter images. Besides, in order to improve the brightness stability of the dehazed image and thus keep the image have more details within the standard dynamic range, this study proposes a multiple virtual-exposure fusion (MVEF) scheme to process the dehazed image (usually having a high dynamic range) obtained through polarimetric dehazing. Comparative experiments show that the proposed dehazing algorithm is robust and effective, which can significantly improve overall quality of hazy images captured under different environments.

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR)showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight≥90 kg) yielded similar findings to the overall study population.

A nonparametric measure of contrast in x-ray images

Objective. We propose a nonparametric figure of merit, the contrast equivalent distance CED, to measure contrast directly from clinical images. Approach. A relative brightness distance δ is calculated by making use of the order statistic of the pixel values. By multiplying δ with the grey value range R, the mean brightness distance MBD is obtained. From the MBD, the CED and the distance-to-noise ratio DNR can be derived. The latter is the ratio of the MBD and a previously suggested nonparametric measure τ for the noise.Since the order statistic is independent of the spatial arrangement of the pixel values, the measures can be obtained directly from clinical images. We apply the new measures to mammography images of an anthropomorphic phantom and of a phantom with a step wedge as well as to CT images of a head phantom. Main results. For low-noise images of a step wedge, the MBD is equivalent to the conventional grey value distance. While this measure permits the evaluation of clinical images, it is sensitive to noise. Therefore, noise has to be quantified at the same time. When the ratio σ/τ of the noise standard deviation σ to τ is available, validity limits for the CED as a measure of contrast can be established. The new figures of merit can be calculated for entire images as well as on regions of interest (ROI) with an edge length not smaller than 32 px.Significance. The new figures of merit are suited to quantify the quality of clinical images without relying on the assumption of a linear, shift-invariant system. They can be used for any kind of greyscale image, provided the ratio σ/τ can be estimated. This will hopefully help to achieve the optimisation of image quality vs dose required by radioprotection laws.

Binary classification of dead detector elements in flat panel detectors using convolutional neural networks.

Objective.Medical physicists routinely perform quality assurance on digital detection systems, part of which involves the testing of flat panel detectors. Flat panels may degrade over time as an increasing number of individual detector elements begin to malfunction. The pixels that correspond to these elements are corrected for using information elsewhere in the detector system, however these corrected elements still constitute a loss in image quality for the system as a whole. These correction methods, as well as the location and number of dead detector elements, are often only available to the vendor of the digital detection system, but not to the medical physicist responsible for the quality assurance of the system.Approach.We greatly expand upon a previous work by providing a novel technique for classifying dead detector elements at single pixel resolution. We also demonstrate that this technique can be trained on one detector, and then tested and validated on another with moderate success, which demonstrates some ability to generalize to different detectors. The technique requires 3 flat field, or 'noise', images to be taken to predict the dead detector element maps for the system.Main results.Models using only for-processing pixel data were unable to successfully generalize from one detector to the other. Models preprocessed using the standard deviation across three for-processing images were able to classify dead detector element maps with an F1score ranging from 0.4527 to 0.8107 and recall ranging from 0.5420 to 0.9303 with better performance, on average, observed using the low exposure data set.Significance. Many physicists do not have access to the dead detector maps for their diagnostic digital radiography systems. CNNs are capable of predicting the dead detector maps of flat panel detectors with single pixel resolution. Physicists can implement this tool by acquiring three flat field images and then inputting them into the model. Model performance saw a marginal increase when trained on the low exposure set data, as opposed to the high exposure set data, indicating high exposure, low relative noise images may not be necessary for optimal performance. Model performance across detectors manufactured by different vendors requires further investigation.

A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.

To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement compared with other image reconstructions. This single-centre retrospective study included 50 patients (mean age, 59 years; range, 44-81 years; 13 men) who were treated with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement between January and July 2023. The images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR), and SR-DLR. The objective image analysis included image noise in the Hounsfield unit (HU), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and full width at half maximum (FWHM). Subjectively, two radiologists evaluated the overall image quality for the visualization of the flow-diverting stent, coil, and stent. The image noise in HU in SR-DLR was 6.99 ± 1.49, which was significantly lower than that in images reconstructed with FBP (12.32 ± 3.01) and hybrid IR (8.63 ± 2.12) (P < .001). Both the mean SNR and CNR were significantly higher in SR-DLR than in FBP and hybrid IR (P < .001 and P < .001). The FWHMs for the stent (P < .004), flow-diverting stent (P < .001), and coil (P < .001) were significantly lower in SR-DLR than in FBP and hybrid IR. The subjective visual scores were significantly higher in SR-DLR than in other image reconstructions (P < .001). SR-DLR with cardiac option is useful for follow-up imaging in stent-assisted coil embolization and flow-diverting stent placement in terms of lower image noise, higher SNR and CNR, superior subjective image analysis, and less blooming artifact than other image reconstructions. SR-DLR with cardiac option allows better visualization of the peripheral and smaller cerebral arteries. SR-DLR with cardiac option can be beneficial for CT imaging of stent-assisted coil embolization and flow-diverting stent.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT. The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step. In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.

Evaluation method for the intactness of tunnel face surrounding rock based on tunnel face images

The intactness of rock masses is a fundamental parameter in classifying surrounding rocks. Due to limitations imposed by the extent of tunnel face outcrops, the assessment of rock mass intactness necessitates the manual extraction of the positions, orientations and spacing of joints/fissures. To mitigate the labour-intensive nature of this process, in this paper deep learning is employed to develop an integrated method for the automated extraction of joints/fissures and the quantitative analysis of rock mass intactness. We introduce an image preprocessing method based on multiscale histogram equalization to obtain high-contrast, low-noise images. The DeepIntactness model, which incorporates the strategy of curriculum learning to utilize a large number of unlabelled tunnel rock images for model training, is introduced for the extraction of joints/fissures. Following the extraction of joints/fissures, a multiline centre statistic method based on the rock mass block index method is employed to evaluate the intactness of the most vulnerable part of the tunnel face. By applying this approach to an engineering structure, its capacity to automatically extract and quantitatively evaluate the engineering properties of the surrounding rock mass intactness is demonstrated. Hence, this method provides a novel approach to evaluating the tunnel surrounding rock intactness using two-dimensional images. Supplementary material: The I-RBI calculation process of the cases in this paper is available at https://doi.org/10.6084/m9.figshare.c.7154677

Deep-learning denoising minimizes radiation exposure in neck CT beyond the limits of conventional reconstruction

BackgroundNeck computed tomography (NCT) is essential for diagnosing suspected neck tumors and abscesses, but radiation exposure can be an issue. In conventional reconstruction techniques, limiting radiation dose comes at the cost of diminished diagnostic accuracy. Therefore, this study aimed to evaluate the effects of an AI-based denoising post-processing software solution in low-dose neck computer tomography. Materials and MethodsFrom 01 September 2023 to 01 December 2023, we retrospectively included patients with clinically suspected neck tumors from the same single-source scanner. The scans were reconstructed using Advanced Modeled Iterative Reconstruction (Original) at 100% and simulated 50% and 25% radiation doses. Each dataset was post-processed using a novel denoising software solution (Denoising). Three radiologists with varying experience levels subjectively rated image quality, diagnostic confidence, sharpness, and contrast for all pairwise combinations of radiation dose and reconstruction mode in a randomized, blinded forced-choice setup. Objective image quality was assessed using ROI measurements of mean CT numbers, noise, and a contrast-to-noise ratio (CNR). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. ResultsAt each radiation dose level, pairwise comparisons showed significantly lower image noise and higher CNR for Denoising than for Original (p < 0.001). In subjective analysis, image quality, diagnostic confidence, sharpness, and contrast were significantly higher for Denoising than for Original at 100 and 50 % (p < 0.001). However, there were no significant differences in the subjective ratings between Original 100 % and Denoising 25 % (p = 0.906). ConclusionsThe investigated denoising algorithm enables diagnostic-quality neck CT images with radiation doses reduced to 25% of conventional levels, significantly minimizing patient exposure.

Improved stent sharpness evaluation with super-resolution deep learning reconstruction in coronary CT angiography.

This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary CT angiography (CCTA) image quality and blooming artifacts from coronary artery stents in comparison to conventional methods, including hybrid iterative reconstruction (HIR) and deep learning-based reconstruction (DLR). A retrospective analysis included 66 CCTA patients from July to November 2022. Major coronary arteries were evaluated for image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Stent sharpness was quantified using 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD). Qualitative analysis employed a 5-point scoring system to assess overall image quality, image noise, vessel wall, and stent structure. SR-DLR demonstrated significantly lower image noise compared to HIR and DLR. SNR and CNR were notably higher in SR-DLR. Stent ERS was significantly improved in SR-DLR, with mean ERD values of 0.70 ± 0.20 mm for SR-DLR, 1.13 ± 0.28 mm for HIR, and 0.85 ± 0.26 mm for DLR. Qualitatively, SR-DLR scored higher in all categories. SR-DLR produces images with lower image noise, leading to improved overall image quality, compared with HIR and DLR. SR-DLR is a valuable image reconstruction algorithm for enhancing the spatial resolution and sharpness of coronary artery stents without being constrained by hardware limitations. The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.

Low illumination fog noise image denoising method based on ACE-GPM.

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image's foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality.

A self-adaptive correlated multiple sampling technique based on single-slope ADC for low-noise CMOS image sensors

This paper presents a self-adaptive correlated multiple sampling (SACMS) technique for low-noise CMOS image sensors. Compared to the traditional correlated multiple sampling (TCMS), SACMS technique adds a pre-determination stage to determine the brightness of the pixel, reducing the 4 large-range ramps to 3 small-range ramps and 1 large-range ramp when quantizing the signal. For dark pixels, the signal is quantized using small-range ramps with short sampling interval to reduce low-frequency noise. For bright pixels, the CMS operation is turned off and the signal is quantized only once in the large-range ramp to improve the frame rate. Simulation results demonstrate that, for dark pixels, SACMS achieves a minimum readout noise of 84.22μVrms at eight times analog gain, compared to 108.76μVrms with TCMS, representing an approximately 21.7 % improvement in noise suppression. Additionally, the sampling rate of the SSADC employing SACMS technique is 36.36 kHz, while the sampling rate of the SSADC using TCMS technique is 21 kHz.

Gamma camera imaging characteristics of 166Ho and 99mTc used in Selective Internal Radiation Therapy

BackgroundThe administration of a 166Ho scout dose is available as an alternative to 99mTc particles for pre-treatment imaging in Selective Internal Radiation Therapy (SIRT). It has been reported that the 166Ho scout dose may be more accurate for the prediction of microsphere distribution and the associated therapy planning. The aim of the current study is to compare the scintigraphic imaging characteristics of both isotopes, considering the objectives of the pre-treatment imaging using clinically geared phantoms.MethodsPlanar and SPECT/CT images were obtained using a NEMA image quality phantom in different phantom setups and another body-shaped phantom with several inserts. The influence of collimator type, count statistics, dead time effects, isotope properties and patient obesity on spatial resolution, contrast recovery and the detectability of small activity accumulations was investigated. Furthermore, the effects of the imaging characteristics on personalized dosimetry are discussed.ResultsThe images with 99mTc showed up to 3 mm better spatial resolution, up to two times higher contrast recovery and significantly lower image noise than those with 166Ho. The contrast-to-noise ratio was up to five times higher for 99mTc than for 166Ho. Only when using 99mTc all activity-filled spheres could be distinguished from the activity-filled background. The measurements mimicking an obese patient resulted in a degraded image quality for both isotopes.ConclusionsOur measurements demonstrate better scintigraphic imaging properties for 99mTc compared to 166Ho in terms of spatial resolution, contrast recovery, image noise, and lesion detectability. While the 166Ho scout dose promises better prediction of the microsphere distribution, it is important to consider the inferior imaging characteristics of 166Ho, which may affect individualized treatment planning in SIRT.

Snapshot dual-wavelength digital holography with LED and laser hybrid illumination

To address the problem of the time-sharing recording of dual-wavelength low-coherence holograms while avoiding the use of customized achromatic optical elements, a snapshot dual-wavelength digital holography with LED and laser hybrid illumination is proposed. In this method, the parallel phase-shifting method is firstly employed to suppress zero-order and twin-image noise, and to record a LED hologram with low speckle noise and full field of view. Secondly, another laser hologram with a different center wavelength affected by speckle noise is recorded simultaneously using the spatial multiplexing technique. Finally, dual-wavelength wrapped phase images are reconstructed from a spatial multiplexing hologram, and then are combined to achieve low-noise phase unwrapping utilizing the iterative algorithm. Simulation and optical experiments on a reflective step with a depth of 1.38µm demonstrate that the proposed method can achieve single-shot and large-range height measurements while maintaining low-noise and full-field imaging.

Tunable broadband terahertz graphene metasurface for complex-amplitude vortex beam generator and hologram

The independent controls of phase and amplitude, known as complex-amplitude modulation, play a crucial role for modulating the electromagnetic wavefronts. In this paper, a tunable broadband terahertz (THz) graphene metasurface with complex-amplitude modulation is proposed. A novel hybrid modulation mechanism that combines the absorption, polarization conversion, and Pancharatnam–Berry (PB) phase is employed to achieve a wide complex-amplitude modulation range over a broad frequency band. In addition, the metasurface can be dynamically tuned from the electrically tunable nature of the graphene in the THz range. To reveal the tunability characteristics of the metasurface as the graphene chemical potential varies, we study the Kubo formula of the graphene conductivity model and combine it with the equivalent circuit model. Two THz graphene meta-devices are designed for complex-amplitude vortex beam generator and hologram. By varying the graphene chemical potentials of 0 and 1 eV as the ON and OFF states, a THz quad-vortex-beam generator with switchable modes is designed and verified. We also demonstrate a THz hologram with complex-amplitude modulation by adjusting the chemical potential, exhibiting low imaging noise and high image quality. The proposed method is promised to be applied to the design of more complex-amplitude THz graphene meta-devices.

A priority-based self-guided serial–parallel genetic algorithm for low-dose computed tomography

Computed tomography (CT) is a non-destructive evaluation technique to know the internal structure of the objects under scan. It has numerous applications in engineering as well as in the medical field. The prime objective of this manuscript is to reduce the radiation dose to the object under scanning and to reconstruct the low-noise CT images, even in limited-view projections data scenarios. The present manuscript proposes a novel priority-based self-guided serial-parallel hybrid genetic algorithm for low-dose CT reconstruction. The current algorithm combines serial and parallel processing elements to reduce the loss of diversity in the population and increase the convergence rate. The proposed algorithm uses a novel priority-based self-guided competition operator. It consists of two approaches for maintaining exploration and exploitation tradeoffs. Here, the first approach uses serial processing, whereas the second approach uses parallel processing. The algorithm also uses priority-based hybrid crossover (PHCO) and adaptive priority-based mutation operator (PMO) to generate the most suitable offspring in every generation. Experimental results reveal that the presented algorithm produces satisfactory results with limited-view projections. The presented algorithm also outperforms other limited-view CT reconstruction algorithms.

Diagnostic performance and image quality of an image-based denoising algorithm applied to radiation dose-reduced CT in diagnosing acute appendicitis

PurposeTo evaluate diagnostic performance and image quality of ultralow-dose CT (ULDCT) in diagnosing acute appendicitis with an image-based deep-learning denoising algorithm (IDLDA).MethodsThis retrospective multicenter study included 180 patients (mean ± standard deviation, 29 ± 9 years; 91 female) who underwent contrast-enhanced 2-mSv CT for suspected appendicitis from February 2014 to August 2016. We simulated ULDCT from 2-mSv CT, reducing the dose by at least 50%. Then we applied an IDLDA on ULDCT to produce denoised ULDCT (D-ULDCT). Six radiologists with different experience levels (three board-certified radiologists and three residents) independently reviewed the ULDCT and D-ULDCT. They rated the likelihood of appendicitis and subjective image qualities (subjective image noise, diagnostic acceptability, and artificial sensation). One radiologist measured image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). We used the receiver operating characteristic (ROC) analyses, Wilcoxon’s signed-rank tests, and paired t-tests.ResultsThe area under the ROC curves (AUC) for diagnosing appendicitis ranged 0.90–0.97 for ULDCT and 0.94–0.97 for D-ULDCT. The AUCs of two residents were significantly higher on D-ULDCT (AUC difference = 0.06 [95% confidence interval, 0.01–0.11; p = .022] and 0.05 [0.00–0.10; p = .046], respectively). D-ULDCT provided better subjective image noise and diagnostic acceptability to all six readers. However, the response of board-certified radiologists and residents differed in artificial sensation (all p ≤ .003). D-ULDCT showed significantly lower image noise, higher SNR, and higher CNR (all p < .001).ConclusionAn IDLDA can provide better ULDCT image quality and enhance diagnostic performance for less-experienced radiologists.