Spatial Noise Research Articles

A directional spatial active noise control system with a sound field separation algorithm

Spatial active noise control (ANC) systems aim to control noise fields within the entire region of interest. However, current spatial ANC systems commonly lack the spatial selectivity to target a specific noise field from the whole noise fields, which cannot satisfy user requirements in some cases. This paper designs a directional spatial ANC system to control noise fields selectively based on the direction of arrival (DOA). The key idea is to introduce a DOA-informed sound field separation (SFS) algorithm to design the directional spatial ANC system. In detail, the directional spatial ANC system is designed to control noise fields selectively, with the ability to select the target sound field introduced by the SFS algorithm. Simulation results demonstrate that the designed directional spatial ANC system can control noise fields selectively within the region of interest in a noisy environment.

Clinical Applications of Photon-counting CT: A Review of Pioneer Studies and a Glimpse into the Future.

CT systems equipped with photon-counting detectors (PCDs), referred to as photon-counting CT (PCCT), are beginning to change imaging in several subspecialties, such as cardiac, vascular, thoracic, and musculoskeletal radiology. Evidence has been building in the literature underpinning the many advantages of PCCT for different clinical applications. These benefits derive from the distinct features of PCDs, which are made of semiconductor materials capable of converting photons directly into electric signal. PCCT advancements include, among the most important, improved spatial resolution, noise reduction, and spectral properties. PCCT spatial resolution on the order of 0.25 mm allows for the improved visualization of small structures (eg, small vessels, arterial walls, distal bronchi, and bone trabeculations) and their pathologies, as well as the identification of previously undetectable anomalies. In addition, blooming artifacts from calcifications, stents, and other dense structures are reduced. The benefits of the spectral capabilities of PCCT are broad and include reducing radiation and contrast material dose for patients. In addition, multiple types of information can be extracted from a single data set (ie, multiparametric imaging), including quantitative data often regarded as surrogates of functional information (eg, lung perfusion). PCCT also allows for a novel type of CT imaging, K-edge imaging. This technique, combined with new contrast materials specifically designed for this modality, opens the door to new applications for imaging in the future.

A hat-shape error microphone array for user-friendly spatial active noise control

Spatial active noise control (ANC) has been developed in the past decades to reduce unwanted acoustic noise over a desired region by generating an anti-noise field with secondary loudspeakers. Unlike conventional multi-channel ANC systems, the integration of spherical harmonic-based sound field analysis allows the ANC system to reduce noise over a continuous region surrounding the user's head rather than discrete points. However, the use of a spherical error microphone array on the boundary of the region for spherical harmonic analysis poses a limitation as it obstructs user access to the controlled area. In this work, we introduce a hat-shaped error microphone array design which can be placed above the listener's head. The proposed error microphone array can achieve spherical harmonic-based residual noise field analysis and is more user-friendly, allowing unrestricted user head movement. We construct the hat-shaped error microphone array and present the results of ANC experiments conducted using the array.

Natural soundscape variation appears to influence the structure of stereotypic calls and repertoires in killer whales

Underwater soundscapes show dynamic variations in natural ambient sound levels at different locations and water depths and levels can vary over a course of a day. This soundscape dynamicity may pose a vocal challenge for highly mobile marine mammals whose habitat overlaps with many dynamic soundscapes. The most prominent communication tool of killer whales is a pulsated often multicomponent call which can be detected at more than 15 kilometers under quiet conditions. Social learning of call structure is a driver for stereotypic long lasting group- or population-specific call repertoires. Here we describe the potential vocal adaptations of killer whales to reduce the influence of sound propagation loss (PL) on their calls. Reliable propagation to identify groups and populations at considerable distances is important. In experimental field trials, the PL of killer whale calls and that of tones and sweeps was examined to determine if call component PL differs among soundscapes. PL is positively correlated with spatial and temporal ambient noise level variation. Furthermore, the use of burst pulses, a common feature of calls increased propagation distances at higher noise levels. We conclude that noise variation is one of the drivers of call structure and may also influence the call repertoire size.

Estimation of seabed properties from ocean ambient noise in shallow water: A review

Ocean ambient noise is the sound persisting in the ocean, which includes contributions from natural and anthropogenic sources, such as rain, wind, shipping, and marine mammals. Multiple interactions of the sound with the surface and bottom in shallow water affect the spatial characteristics of ambient noise. Thus, the spatial noise properties, coherence, and directionality of ambient noise depend on the characteristics of the shallow water environment, such as depth, sound speed profile, and seabed sediment properties. Acoustic properties of seafloor sediments can be extracted from ambient noise based on these noise properties using various types of noise data and geo-acoustic inversion methods. This paper presents a review of literature concerning investigations into available methods, the implementations of these methods, and their verification and validation. The efficacy and the applicability of these inversion methods for a wide range of ocean noise data are also examined in this review.

Boundary Pass Underwater Listening Station—Data management and processing

The Boundary Pass Underwater Listening Station, sponsored by Transport Canada, Port of Vancouver and JASCO Applied Sciences, has continuously operated two compact tetrahedral hydrophone arrays beneath the busy shipping lanes leading to Vancouver, Canada since June 2020. The arrays are deployed 300 m apart on the seabed at 190 m depth and are cabled 2.6km to shore. A high sample rate of 512 kHz on all eight channels, with 24-bit samples, produces almost 400 TB/yr of raw acoustic data. Automated real-time data analysis calculates ambient noise statistics, detects marine mammal sounds, measures underwater radiated noise of individual ships, and creates spatial noise maps of the hulls of some vessels passing closely overhead. The derived data products are stored in an easy-to-maintain online database of just a few TB/yr. Results in graphic, tabular and compressed audio formats are made available to scientists and project managers through an intuitive web interface. The project follows international standards for ship noise and ambient noise calculations, so the results can be compared with measurements made elsewhere.

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control.

This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter. Fuzzy-rule based filtering algorithms may be designed assuming the tailoring of specific functional sub-modules: (a) logical controlled variable selection, (b) the consideration of different methods for the generation of fuzzy rules and membership functions, (c) the integration of the logical rules for detecting and filtering impulse noise from digital images. More specifically, we discuss impulse noise models and window-based filtering using fuzzy inference based on vector directional filters as associated with the filtering of RGB color images and then explain how fuzzy vector fields can be generated using standard operations on fuzzy sets taking into consideration fixed or random valued impulse noise and fuzzy vector partitioning. We also discuss how fuzzy cellular automata may be used for noise removal by adopting a Moore neighbourhood architecture. We also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (CNN), a recurrent neural networks (RNN), a long short term memory neural network (LSTM) and a gated recurrent unit (GRU) approaches, all within a fuzzy min-max (FMM) ensemble. Fuzzy non-local mean filter approaches are also considered. A comparison of various performance metrics for conventional and fuzzy logic based filters as well as deep learning filters is provided. The algorhitms discussed have the following advantageous properties: high quality of edge preservation, high quality of spatial noise suppression capability especially for complex images, sound properties of noise removal (in cases when both mixed additive and impulse noise are present), and very fast computational implementation.

Influence of Age on Speech Recognition in Noise and Hearing Effort in Listeners with Age-Related Hearing Loss.

The aim of this study was to measure how age affects the speech recognition threshold (SRT50) of the Oldenburg Sentence Test (OLSA) and the listening effort at the corresponding signal-to-noise ratio (SNRcut). The study also investigated the effect of the spatial configuration of sound sources and noise signals on SRT50 and SNRcut. To achieve this goal, the study used olnoise and icra5 noise presented from one or more spatial locations from the front and back. Ninety-nine participants with age-related hearing loss in the 18-80 years age range, specifically in the 18-30, 31-40, 41-50, 51-60, 61-70, and 71-80 age groups, participated in this study. Speech recognition and listening effort in noise were measured and compared between the different age groups, different spatial sound configurations and noise signals. Speech recognition in noise decreased with age and became significant from the age group of 50-51. The decrease in SRT50 with age was greater for icra5 noise than for olnoise. For all age groups, SRT50 and SNRcut were better for icra5 noise than for olnoise. The measured age-related reference data for SRT50 and SNRcut can be used in further studies in listeners with age-related hearing loss and hearing aid or implant users.

Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study.

This study aimed to assess the image characteristics of deep-learning-based image processing software (DLIP; FCT PixelShine, FUJIFILM, Tokyo, Japan) and compare it with filtered back projection (FBP), model-based iterative reconstruction (MBIR), and deep-learning-based reconstruction (DLR). This phantom study assessed the object-specific spatial resolution (task-based transfer function [TTF]), noise characteristics (noise power spectrum [NPS]), and low-contrast detectability (low-contrast object-specific contrast-to-noise ratio [CNRLO]) at three different output doses (standard: 10mGy; low: 3.9mGy; ultralow: 2.0mGy). The processing strength of DLIPFBP with A1, A4, and A9 was compared with those of FBP, MBIR, and DLR. The standard dose with high-contrast TTFs of DLIPFBP exceeded that of FBP. Low-contrast TTFs were comparable to or lower than that of FBP. The NPS peak frequency (fP) of DLIPFBP shifts to low spatial frequencies of up to 8.6% at ultralow doses compared to the standard FBP dose. MBIR shifted the most fP compared to FBP-a marked shift of up to 49%. DLIPFBP showed a CNRLO equal to or greater than that of DLR in standard or low doses. In contrast, the CNRLO of the DLIPFBP was equal to or lower than that of the DLR in ultralow doses. DLIPFBP reduced image noise while maintaining a resolution similar to commercially available MBIR and DLR. The slight spatial frequency shift of fP in DLIPFBP contributed to the noise texture degradation suppression. The NPS suppression in the low spatial frequency range effectively improved the low-contrast detectability.

Spectral performance of two split-filter dual-energy CT systems: A phantom study.

Recently, a second generation of split filter dual-energy CT (SFCT) platform has been developed. The thicknesses of the gold and tin filters used to obtain both low- and high-energy spectra have been changed. These differences in filter thickness may affect the spectral separation between the two spectra and thus the quality of spectral images. To compare the spectral performance of two Split-Filter Dual-Energy CT systems (SFCT-1st and SFCT-2nd ) on virtual monoenergetic images (VMIs) and iodine map. A Multi-Energy CT phantom was scanned on two SFCT with a tube voltage of 120 kVp for both systems (SFCT-1st -120 and SFCT-2nd -120) and 140 kVp only for the second generation (SFCT-2nd -140). Acquisitions were performed on the phantom with a CTDIvol close to 11 mGy. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated on VMIs from 40 to 70keV. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions on VMIs. Hounsfield Unit (HU) accuracy was assessed on VMIs and the accuracy of iodine concentration was assessed on iodine maps. For all keV, noise magnitude values were lower with the SFCT-2nd -120 than with the SFCT-1st -120 (on average: -22.5±2.9%) and higher with the SFCT-2nd -140 than with the SFCT-2nd -120 (on average: 25.0±6.2%). Average NPS spatial frequencies (fav ) were lower with the SFCT-1st -120 than with the SFCT-2nd -120 (-6.0±0.5%) and the SFCT-2nd -140 (-3.6±1.6%). Similar TTF50% values were found for both systems and both kVp for blood and iodine inserts at 2mg/mL (0.29±0.01mm-1 ) and at 4mg/mL (0.31±0.01mm-1 ). d' values peaked at 40keV for the SFCT-2nd and at 70keV for the SFCT-1st . Highest d' values were found for the SFCT-2nd -120 for both simulated lesions. Accuracy of HU values and iodine concentration was higher with the SFCT-2nd than with the SFCT 1st . Compared to the SFCT-1st , with similar spatial resolution and noise texture values, the SFCT-2nd -120 exhibited the lowest values for noise magnitude, the highest detectability index values, and more accurate HU values and iodine concentrations.

Development, validation, and simplification of a scanner-specific CT simulator.

Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results. To develop a scanner-specific CT simulation using physics-based simulations for the position-dependent effects and shift-invariant image corruption methods for the detector effects. And to investigate the impact on image realism of introducing simplifications in the simulation process that lead to faster and less memory-demanding simulations. To make the simulator realistic and scanner-specific, the spatial resolution and noise characteristics, and the exposure-to-detector output relationship of a clinical CT system were determined. The simulator includes a finite focal spot size, raytracing of the digital phantom, gantry rotation during projection acquisition, and finite detector element size. Previously published spectral models were used to model the spectrum for the given tube voltage. The integrated energy at each element of the detector was calculated using the Beer-Lambert law. The resulting angular projections were subsequently corrupted by the detector modulation transfer function (MTF), and by addition of noise according to the noise power spectrum (NPS) and signal mean-variance relationship, which were measured for different scanner settings. The simulated sinograms were reconstructed on the clinical CT system and compared to real CT images in terms of CT numbers, noise magnitude using the standard deviation, noise frequency content using the NPS, and spatial resolution using the MTF throughout the field of view (FOV). The CT numbers were validated using a multi-energy CT phantom, the noise magnitude and frequency were validated with a water phantom, and the spatial resolution was validated with a tungsten wire. These metrics were compared at multiple scanner settings, and locations in the FOV. Once validated, the simulation was simplified by reducing the level of subsampling of the focal spot area, rotation and of detector pixel size, and the changes in MTFs were analyzed. The average relative errors for spatial resolution within and across image slices, noise magnitude, and noise frequency content within and across slices were 3.4%, 3.3%, 4.9%, 3.9%, and 6.2%, respectively. The average absolute difference in CT numbers was 10.2HU and the maximum was 22.5HU. The simulation simplification showed that all subsampling can be avoided, except for angular, while the error in frequency at 10% MTF would be maximum 16.3%. The simulation of a scanner-specific CT allows for the generation of realistic CT images by combining physics-based simulations for the position-dependent effects and image-corruption methods for the shift-invariant ones. Together with the available ground truth of the digital phantom, it results in a useful tool to perform quantitative analysis of reconstruction or post-processing algorithms. Some simulation simplifications allow for reduced time and computer power requirements with minimal loss of realism.

Connectivity by the Frontal Aslant Tract (FAT) Explains Local Functional Specialization of the Superior and Inferior Frontal Gyri in Humans When Choosing Predictive over Reactive Strategies: A Tractography-Guided TMS Study.

Predictive and reactive behaviors represent two mutually exclusive strategies in a sensorimotor task. Predictive behavior consists in internally estimating timing and features of a target stimulus and relies on a cortical medial frontal system [superior frontal gyrus (SFG)]. Reactive behavior consists in waiting for actual perception of the target stimulus and relies on the lateral frontal cortex [inferior frontal gyrus (IFG)]. We investigated whether SFG-IFG connections by the frontal aslant tract (FAT) can mediate predictive/reactive interactions. In 19 healthy human volunteers, we applied online transcranial magnetic stimulation (TMS) to six spots along the medial and lateral terminations of the FAT, during the set period of a delayed reaction task. Such scenario can be solved using either predictive or reactive strategies. TMS increased the propensity toward reactive behavior if applied to a specific portion of the IFG and increased predictive behavior when applied to a specific SFG spot. The two active spots in the SFG and IFG were directly connected by a sub-bundle of FAT fibers as indicated by diffusion-weighted imaging (DWI) tractography. Since FAT connectivity identifies two distant cortical nodes with opposite functions, we propose that the FAT mediates mutually inhibitory interactions between SFG and IFG to implement a "winner takes all" decisional process. We hypothesize such role of the FAT to be domain-general, whenever competition occurs between internal predictive and external reactive behaviors. Finally, we also show that anatomic connectivity is a powerful factor to explain and predict the spatial distribution of brain stimulation effects.SIGNIFICANCE STATEMENT We interact with sensory cues adopting two main mutually-exclusive strategies: (1) trying to anticipate the occurrence of the cue or (2) waiting for the GO-signal to be manifest and react to it. Here, we showed, by using noninvasive brain stimulation [transcranial magnetic stimulation (TMS)], that two specific cortical regions in the superior frontal gyrus (SFG) and the inferior frontal gyrus (IFG) have opposite roles in facilitating a predictive or a reactive strategy. Importantly these two very distant regions but with highly interconnected functions are specifically connected by a small white matter bundle, which mediates the direct competition and exclusiveness between predictive and reactive strategies. More generally, implementing anatomic connectivity in TMS studies strongly reduces spatial noise.

An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm

InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation.

Thermal imaging spatial noise removal via deep image prior and step-variable total variation regularization

The imaging quality of an uncooled long wave infrared camera suffers severely from time-drifted spatial noises if not regulated by a thermoelectric cooler (TEC). These noises can be categorized within the frequency domain as either fixed pattern noise (FPN) or low-frequency noise (LPN). The time-drifted FPN is primarily governed by the temperature fluctuations of the focal plane array (FPA), whereas the LPN predominantly originates from non-uniform background radiations. Most existing methods have been proposed to address the FPN or LPN independently rather than from a unified perspective. In this paper, a self-supervised convolution neural network (CNN) is proposed to remove these two types of noise simultaneously. Because of the combination of the deep image prior (DIP) architecture and the total variation (TV) regularizer, this method is named TV-DIP. With the assumption that the spatial noises are constant in a short period of time, the spatial noise pattern is extracted directly from multiple continuous raw frames without ground-truth noise-free images. In this way, the noise pattern can be updated periodically during running time, without requiring the use of a mechanical shutter. Besides, the original TV regularizer is extended with a variable step, which shows a significant enhancement on the LPN denoising task. Furthermore, both simulation and experimental results have demonstrated that this approach achieves better performance in terms of the removal of spatial noise, the preservation of detail, and the suppression of artifacts. The source code and dataset are available on the website: https://github.com/brunolinux/TV-DIP

Spatiotemporal evaluation of Rayleigh surface wave estimated from roadside dark fiber DAS array and traffic noise

Seismic imaging and monitoring of the near-surface structure are crucial for the sustainable development of urban areas. However, standard seismic surveys based on cabled or autonomous geophone arrays are expensive and hard to adapt to noisy metropolitan environments. Distributed acoustic sensing (DAS) with pre-existing telecom fiber optic cables, together with seismic ambient noise interferometry, have the potential to fulfill this gap. However, a detailed noise wavefield characterization is needed before retrievingcoherent waves from chaotic noise sources. We analyze local seismic ambient noise by tracking five-month changes in signal-to-noise ratio (SNR) of Rayleigh surface wave estimated from traffic noise recorded by DAS along the straight university campus busy road. We apply the seismic interferometry method to the 800 m long part of the Penn State Fiber-Optic For Environment Sensing (FORESEE) array. We evaluate the 160 virtual shot gathers (VSGs) by determining the SNR using the slant-stack technique. We observe strong SNR variations in time and space. We notice higher SNR for virtual source points close to road obstacles. The spatial noise distribution confirms that noise energy focuses mainly on bumps and utility holes. We also see the destructive impact of precipitation, pedestrian traffic, and traffic along main intersections on VSGs. A similar processing workflow can be applied to various straight roadside fiber optic arrays in metropolitan areas.

Surpassing spectator qubits with photonic modes and continuous measurement for Heisenberg-limited noise mitigation

Noise is an ever-present challenge to the creation and preservation of fragile quantum states. Recent work suggests that spatial noise correlations can be harnessed as a resource for noise mitigation via the use of spectator qubits to measure environmental noise. In this work we generalize this concept from spectator qubits to a spectator mode: a photonic mode which continuously measures spatially correlated classical dephasing noise and applies a continuous correction drive to frequency-tunable data qubits. Our analysis shows that by using many photon states, spectator modes can surpass many of the quantum measurement constraints that limit spectator qubit approaches. We also find that long-time data qubit dephasing can be arbitrarily suppressed, even for white noise dephasing. Further, using a squeezing (parametric) drive, the error in the spectator mode approach can exhibit Heisenberg-limited scaling in the number of photons used. We also show that spectator mode noise mitigation can be implemented completely autonomously using engineered dissipation. In this case no explicit measurement or processing of a classical measurement record is needed. Our work establishes spectator modes as a potentially powerful alternative to spectator qubits for noise mitigation.

Speech understanding in noise for cochlear implant recipients using a spatial noise reduction setting in an off the ear sound processor with directional microphones

Purpose Cochlear implant (CI) recipients struggle to hear in competing background noise. ForwardFocus is a spatial noise reduction setting from Cochlear Ltd. (Sydney) that can simultaneously attenuate noise from multiple sources behind the listener. This study assessed hearing performance with ForwardFocus in an off-the-ear (OTE) sound processor. Method Twenty-two experienced adult CI recipients participated. Speech reception data was collected in fixed noise acutely in the clinic. After three to five weeks take home experience, subjective impressions were recorded, and evaluations were conducted for speech reception in quiet and roving noise. Results Group mean speech reception thresholds (SRT) were below 0 dB in two spatially-separated noise test conditions when using ForwardFocus in the OTE sound processor. SRT were −8.5 dB (SD 2.9) in 4-talker babble roving in a rear hemi-field (S0Nrearhemi) and −3.9 dB (SD 3.3) in 12-talker babble presented laterally and behind (S0N3). Results in S0N3 were significantly better with ForwardFocus On (p = 0.0018). Subjective ratings with the OTE were comparable to, or better than, with their walk-in BTE or OTE sound processor. Conclusions ForwardFocus provides significant benefits for speech recognition in competing background noise in an OTE sound processor. These results support clinicians in counselling CI recipients on potential sound processor options to consider.

Impact of Spatial Noise Correlation on Bearing Accuracy in DIFAR Systems

Impact of Spatial Noise Correlation on Bearing Accuracy in DIFAR Systems

MU-Net: Embedding MixFormer into Unet to Extract Water Bodies from Remote Sensing Images

Water bodies extraction is important in water resource utilization and flood prevention and mitigation. Remote sensing images contain rich information, but due to the complex spatial background features and noise interference, problems such as inaccurate tributary extraction and inaccurate segmentation occur when extracting water bodies. Recently, using a convolutional neural network (CNN) to extract water bodies is gradually becoming popular. However, the local property of CNN limits the extraction of global information, while Transformer, using a self-attention mechanism, has great potential in modeling global information. This paper proposes the MU-Net, a hybrid MixFormer architecture, as a novel method for automatically extracting water bodies. First, the MixFormer block is embedded into Unet. The combination of CNN and MixFormer is used to model the local spatial detail information and global contextual information of the image to improve the ability of the network to capture semantic features of the water body. Then, the features generated by the encoder are refined by the attention mechanism module to suppress the interference of image background noise and non-water body features, which further improves the accuracy of water body extraction. The experiments show that our method has higher segmentation accuracy and robust performance compared with the mainstream CNN- and Transformer-based semantic segmentation networks. The proposed MU-Net achieves 90.25% and 76.52% IoU on the GID and LoveDA datasets, respectively. The experimental results also validate the potential of MixFormer in water extraction studies.

Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography.

To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA). Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms. For each image series, image noise, and contrast-to-noise ratio (CNR) at the left main trunk, right coronary artery, left anterior descending artery, and left circumflex artery were quantified. Blooming artifacts from calcified plaques were measured. Image sharpness, noise magnitude, noise texture, edge smoothness, overall quality, and delineation of the coronary wall, calcified and noncalcified plaques, cardiac muscle, and valves were subjectively ranked on a 4-point scale (1, worst; 4, best). The quantitative parameters and subjective scores were compared among the four reconstructions. Task-based image quality was assessed with a physical evaluation phantom. The detectability index for the objects simulating the coronary lumen, calcified plaques, and noncalcified plaques was calculated from the noise power spectrum (NPS) and task-based transfer function (TTF). SR-DLR yielded significantly lower image noise and blooming artifacts with higher CNR than HIR, MBIR, and NR-DLR (all p < 0.001). The best subjective scores for all the evaluation criteria were attained with SR-DLR, with significant differences from all other reconstructions (p < 0.001). In the phantom study, SR-DLR provided the highest NPS average frequency, TTF50%, and detectability for all task objects. SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms. The novel SR-DLR algorithm has the potential to facilitate accurate assessment of coronary artery disease on CCTA by providing excellent image quality in terms of spatial resolution, noise characteristics, and object detectability. • SR-DLR designed for CCTA improved image sharpness, noise property, and delineation of cardiac structures with reduced blooming artifacts from calcified plaques relative to HIR, MBIR, and NR-DLR. • In the task-based image-quality assessments, SR-DLR yielded better spatial resolution, noise property, and detectability for objects simulating the coronary lumen, coronary calcifications, and noncalcified plaques than other reconstruction techniques. • The image reconstruction times of SR-DLR were shorter than those of MBIR, potentially serving as a novel standard-of-care reconstruction technique for CCTA performed on a 320-row CT scanner.