Improved Imaging Performance Research Articles

High contrast computational imaging with vortex phase diversity

Abstract Optical imaging systems employing spatially incoherent illumination are widely used in routine imaging applications like photography and bright-field microscopy. We describe an incoherent computational imaging system that uses an open aperture as well as a vortex phase aperture for recording the same scene. The two raw recorded images provide a diversity of information that can be effectively combined using the generalized Wiener filter. For the specific choice of aperture functions used here, the two corresponding generalized Wiener filters have nearly opposing polarity. This property leads to an effective computational PSF whose central lobe is $0.6$ times smaller compared to the diffraction-limited PSF and has a super-oscillatory character with side-lobes. The resultant computational imaging system provides images with significantly improved contrast. While our methodology requires two image records, the enhanced point spread function (PSF) with super-oscillatory character is obtained by employing bulk off-the-shelf optical elements instead of sub-wavelength structured masks. The vortex phase diversity concept along with computational image reconstructions are illustrated with both simulation and experimental data. The proposed imaging methodology may be used to improve imaging performance for wide ranging imaging systems without changing their form factor.

Implementing a two-stage, shim field-calibrated superconducting shimming method on a 7 T cryogen-free small animal MRI magnet

Ultrahigh field systems (≥ 7 T) can increase the signal-to-noise ratio of magnetic resonance imaging (MRI), improving imaging performance compared to systems with lower fields. However, these enhancements heavily rely on a high B0 magnetic field homogeneity level, which can be achieved through superconducting shimming. This paper presents a novel two-stage superconducting shimming method designed to achieve precise shimming for a 7 T MRI superconducting magnet. In the initial stage, detailed measurements and fittings were conducted to determine the current polarity and the axial or circumferential positions of the shim fields. Subsequently, an optimization strategy was implemented to determine the optimal shim currents with a flexible target field. The second stage involves an iterative process to fine-tune the current of a specific shim coil, identified as having the most significant impact on field homogeneity. The overall fitness of 99.5% underscores the precision in determining the current polarity and position of the shim fields. Significantly, the calibrated shim system substantially improves the peak-to-peak and Root Mean Square Error (RMSE) field homogeneities from 107.42 ppm and 37.00 ppm to 11.12 ppm and 3.26 ppm, respectively, representing improvements of 89.65% and 91.19%. Furthermore, the simulation results of the fine-tuning stage demonstrate additional enhancements in peak-to-peak field homogeneity, to 9.9 ppm by reducing the current of the Z2 shim coil by 51.3 mA. Additionally, the shimmed magnetic field exhibited high time stability, with a maximum variation of only 27 µT observed within 48 h. Thus, the proposed two-stage superconducting shimming framework effectively addresses the challenge of imperfect B0 magnetic fields, enhancing peak-to-peak and RMSE field homogeneity. The stepwise optimized approach also mitigates deviations caused by shim-to-shim coupling, demonstrating its efficacy in achieving precise shimming in ultrahigh-field MRI systems.

Custom integration of a magnetic-field monitoring system into a 32-channel MRI head coil.

Customizing a Siemens 32-channel coil for use in a Philips 3T MRI system with incorporated magnetic field probes for collecting high-quality MRI and magnetic-field monitoring data concurrently. The development process of the custom coil involved several (iterative) phases. Standard temporal SNR and B1 + data were collected with the 32-channel Siemens and for reference the 32-channel/8-channel Philips head coils before and afterthe custom coil was made compatible with the 3T Philips Achieva system, and magnetic field probes were installed into it along with ancillary electronics around it. Quality assurance tests were conducted in each of the build phases to ensure that the modifications did not affect MRI or field-monitoring data negatively. To test the finished custom coil, we collected high angular resolution diffusion imaging (HARDI) datasets on a spherical silicon oil phantom both with and without concurrent field monitoring and a 32-channel Philips coil without concurrent field monitoring, where the latter two served as reference scans to assess the improved performance of the custom coil with field monitoring. Similar HARDI-MRI data were also collected in vivo with the finished custom coil together with field monitoring data. The custom coil provided excellent temporal SNR especially at the edges where cortical gray matter is expected. When using concurrent field monitoring in HARDI acquisitions, the custom coil alleviated ghosting artifacts in phantom data and provided in vivo images with 1.4-mm isotropic resolution. The custom MRI coil with integrated magnetic-field monitoring probes provided improved imaging performance.

The Correction Method for Wavefront Aberration Caused by Spectrum-Splitting Filters in Multi-Modal Optical Imaging System

In current biomedical and environmental detection, multi-modal optical imaging technology is playing an increasingly important role. By utilizing information from dimensions such as spectra and polarization, it reflects the detailed characteristics and material properties of the targets. However, as detection system performance becomes more complex, issues such as aberrations introduced by multilayered lenses, signal attenuation, decreased polarization sensitivity, and latency can no longer be ignored. These factors directly affect the assessment of image details, influencing subsequent analyses. In this paper, we propose a method for designing and optimizing spectrum-splitting filters that considers the wavefront aberration and transmittance of the multi-modal optical imaging system. The method of optimizing coating phases to minimize scalar phase aberrations while maximizing system transmission leads to substantially improved imaging performance. Simulation and experimental results demonstrate that the method can improve the imaging performance. The proposed approach has potential applications in fields such as biomedical field, multi-spectral, remote sensing and microscopy.

PET detectors with depth-of-interaction and time-of-flight capabilities.

In positron emission tomography (PET), measurements of depth-of-interaction (DOI) information and time-of-flight (TOF) information are important. DOI information reduces the parallax error, and TOF information reduces noise by measuring the arrival time difference of the annihilation photons. Historically, these have been studied independently, and there has been less implementation of both DOI and TOF capabilities because previous DOI detectors did not have good TOF resolution. However, recent improvements in PET detector performance have resulted in commercial PET scanners achieving a coincidence resolving time of around 200ps, which result in an effect even for small objects. This means that TOF information can now be utilized even for a brain PET scanner, which also requires DOI information. Therefore, various methods have been proposed to obtain better DOI and TOF information. In addition, the cost of PET detectors is also an important factor to consider, since several hundred detectors are used per PET scanner. In this paper, we review the latest DOI-TOF detectors including the history of detector development. When put into practical use, these DOI-TOF detectors are expected to contribute to the improvement of imaging performance in brain PET scanners.

Improving the performance of speckle correlation imaging by using a speckle refinement method with self-calibrated homomorphic filtering

Scattering imaging based on speckle correlation is an attractive topic as it enables the rapid reconstruction of the object image through computational methods in a compact setup. However, this method typically relies on high-contrast speckle images obtained under narrowband light illumination in a darkroom environment, limiting its application scenarios. Here, we demonstrate a self-calibrated homomorphic filtering (SCHF) method that significantly enhances speckle contrast by modifying the distribution of components within the speckle image, thereby improving imaging performance. The experimental results reveal that our method leverages the physical mechanism of speckle correlation to obtain the optimal filtering solution under the guidance of a self-calibrated strategy. The integration of this method with current mainstream speckle correlation techniques showcases its scalability. Even in scenarios under ambient light interference or broadband light illumination, the SCHF is effective in improving imaging performance, demonstrating its practical application potential.

Design and development of the DE-SPECT system: a clinical SPECT system for broadband multi-isotope imaging of peripheral vascular disease

Objective. Peripheral Vascular Disease (PVD) affects more than 230 million people worldwide and is one of the leading causes of disability among people over age 60. Nowadays, PVD remains largely underdiagnosed and undertreated, and requires the development of tailored diagnostic approaches. We present the full design of the Dynamic Extremity SPECT (DE-SPECT) system, the first organ-dedicated SPECT system for lower extremity imaging, based on 1 cm thick Cadmium Zinc Telluride (CZT) spectrometers and a dynamic dual field-of-view (FOV) synthetic compound-eye (SCE) collimator. Approach. The proposed DE-SPECT detection system consists of 48 1 cm thick 3D-position-sensitive CZT spectrometers arranged in a partial ring of 59 cm in diameter in a checkerboard pattern. The detection system is coupled with a compact dynamic SCE collimator that allows the user to select between two different FOVs at any time during an imaging study: a wide-FOV (28 cm diameter) configuration for dual-leg or scout imaging or a high-resolution and high-sensitivity (HR-HS) FOV (16 cm diameter) for single-leg or focused imaging. Main results. The preliminary experimental data show that the CZT spectrometer achieves a 3D intrinsic spatial resolution of <0.75 mm FWHM and an excellent energy resolution over a broad energy range (2.6 keV FWHM at 218, 3.3 keV at 440 keV). From simulations, the wide-FOV configuration offers a 0.034% averaged sensitivity at 140 keV and <8 mm spatial resolution, whereas the HR-HS configuration presents a peak central sensitivity of 0.07% at 140 keV and a ∼5 mm spatial resolution. The dynamic SCE collimator enables the capability to perform joint reconstructions that would ensure an overall improvement in imaging performance. Significance. The DE-SPECT system is a stationary and high-performance SPECT system that offers an excellent spectroscopic performance with a unique computer-controlled dual-FOV imaging capability, and a relatively high sensitivity for multi-tracer and multi-functional SPECT imaging of the extremities.

Exploring advanced 2D acquisitions in breast tomosynthesis: T-shaped and Pentagon geometries.

In this study, we investigate the performance of advanced 2D acquisition geometries - Pentagon and T-shaped - in digital breast tomosynthesis (DBT) and compare them against the conventional 1D geometry. Unlike the conventional approach, our proposed 2D geometries also incorporate anterior projections away from the chest wall. Implemented on the Next-Generation Tomosynthesis (NGT) prototype developed by X-ray Physics Lab (XPL), UPenn, we utilized various phantoms to compare three geometries: a Defrise slab phantom with alternating plastic slabs to study low-frequency modulation; a Checkerboard breast phantom (a 2D adaptation of the Defrise phantom design) to study the ability to reconstruct the fine features of the checkerboard squares; and the 360° Star-pattern phantom to assess aliasing and compute the Fourier-spectral distortion (FSD) metric that assesses spectral leakage and the contrast transfer function. We find that both Pentagon and T-shaped scans provide greater modulation amplitude of the Defrise phantom slabs and better resolve the squares of the Checkerboard phantom against the conventional scan. Notably, the Pentagon geometry exhibited a significant reduction in aliasing of spatial frequencies oriented in the right-left (RL) medio-lateral direction, which was corroborated by a near complete elimination of spectral leakage in the FSD plot. Conversely T-shaped scan redistributes the aliasing between both posteroanterior (PA) and RL directions thus maintaining non-inferiority against the conventional scan which is predominantly affected by PA aliasing. The results of this study underscore the potential of incorporating advanced 2D geometries in DBT systems, offering marked improvements in imaging performance over the conventional 1D approach.

Deep learning network with new weighting strategy for ISAR image enhancement

ABSTRACT In inverse synthetic aperture radar (ISAR) imaging, the conventional range-Doppler (RD) algorithm cannot obtain satisfactory imaging results for sparse aperture. Compressed sensing (CS) imaging methods, such as the typical iterative shrinkage and thresholding algorithm (ISTA), are often used in sparse aperture radar imaging. However, CS-based methods have high computational complexity and difficulty in setting parameters. To overcome the shortcomings, a new deep unfolding network named complex-valued weighted learning ISTA (CV-WLISTA) is proposed in this paper. The new network is very efficient and can learn the parameters adaptively. Based on ISTA and a new weighting strategy, it takes advantage of both the sparsity and the structural property of ISAR images to improve imaging performance. The experimental results show that CV-WLISTA has better reconstruction performance and higher efficiency compared with the traditional algorithms. Therefore, CV-WLISTA is an efficient ISAR imaging method with excellent performance.

A Fast Total-Variation Driven Network for Sparse Aperture ISAR Imaging

Conventional Fourier transform-based Inverse Synthetic Aperture Radar (ISAR) imaging cannot deal with the cases of incomplete radar echoes, in which the data needs to be specially processed by Sparse Aperture (SA) imaging methods. Most current SA-ISAR imaging employs sparsity-driven optimization methods. However, the assumption that the target reflectivity is sparse in the spatial domain is insufficient to capture the surface and edge features of the target if no sparse representations are found for the target and well incorporated in the imaging process. In view of the edge-preserving capability of Total Variation (TV) and the strong learning ability of the Deep Neural Network (DNN), we propose a TV-driven network to improve the SA ISAR imaging performance. We first develop a fast TV regularization method to perform the imaging where the Gradient Descent (GD) along with the Momentum Acceleration (MA) are incorporated to increase the computational efficiency. Then, we unwrap the iterative fast TV regularization into a cascaded neural network to make the key imaging parameters learnable, leading to improved imaging performance, which we refer to as FGDTV-Net. Experiments under a variety of scenarios show that the proposed FGDTV-Net for SA-ISAR imaging is superior to existing SA imaging algorithms in preserving surface and edge features and is more robust in low signal-to-noise scenarios.

Bayesian hierarchical hyper-Laplacian priors for high-resolution defect imaging in pipe structures

The sparse defect imaging using guided waves for pipe structures has attracted a lot of attention due to its intuitive way of defect localization. However, there exist few attempts to employ the lp norm (p < 1) in sparse imaging to improve imaging performance. In this study, a new sparse imaging method based on Bayesian hierarchical hyper-Laplacian prior is proposed for pipe defect detection. A hyper-Laplacian prior is considered to obtain a sparser resolution, which aims at improving the imaging performance including defect imaging resolution and defect detection accuracy. A solid theoretical framework is constructed and derived to represent the hyper-Laplacian prior and to adaptively estimate all model parameters. Meanwhile, a two-step discretizing strategy is designed to reduce the computational cost. The experimental results demonstrate the superiority of the proposed method.

SCAPSM: attenuated phase-shift mask structure for EUV lithography.

The attenuated phase-shift mask (Att. PSM) is proven to be a promising resolution enhancement technology (RET) to improve the imaging performance in extreme ultraviolet (EUV) lithography. However, due to the reflective nature of the mask structure, the serious shadowing effect can affect the diffraction near field of the mask intensely and further impact the lithography imaging. With the purpose of improving the contrast of lithography imaging, a novel structure of the Att. PSM, to the best of our knowledge, is proposed in this paper. By introducing an absorbent sidewall along the edge of the mask absorber, the diffraction and shadowing effects can be mitigated. By applying the Kirchhoff approximation of mask diffraction, the ability of the novel structure to improve imaging performance is theoretically analyzed. Additionally, these analyses are confirmed by rigorous lithography simulations. The simulation results demonstrate that the proposed mask structure can improve the imaging contrast of EUV lithography, which has potential usage in advanced integrated circuit (IC) manufacturing.

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.

Feasibility of improving image performance in photon-counting computed tomography using X-ray spectrum filtration

Photon-counting computed tomography (PCCT) is an emerging technology based on new energy-resolving X-ray detectors (photon-counting detectors (PCDs)) that provide promising image performance compared to the conventional energy-integrating CT (EICT). It has the potential to provide higher resolution and contrast, lower radiation dose, and fewer artifacts, which has led to significant research interest. This study proposes a method to further improve the image performance of the PCCT using an X-ray spectrum filtration technique. We conducted a feasibility study via simulations using aluminum (Al), beryllium (Be), sodium (Na), nickel (Ni), tin (Sn), neodymium (Nd), and tantalum (Ta) incorporating varying thicknesses using a PCD simulation toolkit (PcTK). The PCCT system used in this simulation was modeled to have a cadmium telluride-based PCD with four multi-energy thresholds of E = 40, 60, 80, 100 keV, assuming that the object received the same number of photons. Using the PCCT images obtained with the highest threshold (i.e., E4 = 100 keV), the image quality was evaluated quantitatively in terms of the signal difference-to-noise ratio (SDNR) and structural similarity (SSIM). Among the filtrations selected in this simulation, a filtration of 2.0 mmAl (inherent) and 1.5 mmSn (added) showed the best image quality. The SDNR and SSIM values measured in the PCCT image obtained with an added filtration of 1.5 mmSn were 3.73 and 0.81, approximately 1.9 and 1.8 times higher than those with no added filtration, respectively. Consequently, the X-ray spectrum filtration technique in PCCT is useful to further improve image performance.

High-resolution single-pixel imaging based on a probe of single-mode fiber and hybrid multimode fiber

In single-pixel imaging, the combination of random speckles generated by the multimode fiber (MMF) as projection patterns and the compressed sensing theory is beneficial for improving imaging performance and expanding application scenarios. However, the image reconstruction quality using the MMF is usually unsatisfactory, which is helpless for retrieving objects with detailed information or multi-resolution objects. The reason lies in the limited number and compromised spatial resolution of low-correlated speckle patterns provided by the MMF in practical circumstances like long operating time or bending disturbances. Here, we propose a novel concept of hybrid multimode fibers (HMMF) for single-pixel imaging resolution enhancement. Based on the bending insensitivity property of single-mode fiber (SMF) and the compatibility property of small bending radius and high mode density of HMMF, the wavelength-modulated SMF-HMMF probe is designed to significantly improve the number and spatial resolution of speckle patterns while ensuring the robustness of the imaging system. The experimental results demonstrate that the spatial resolution of the speckle patterns has been improved by approximately 3 times (from 20.35 pixels to 7.24 pixels) compared to the non-HMMF probe. The correlation of the speckle patterns holds as low as about 0.1, even when the tuning wavelength interval is reduced to 5 pm. The maximum deviation of the repeatability of the speckle pattern was 0.044 for the 120-min interval tested, and the consistent output of the speckle pattern under various bending configurations demonstrates the strong robustness of the imaging system. High-quality imaging of multi-resolution target objects has been achieved even at a remarkably low sampling rate (SR) of 5 %. This study has the potential to expand the application of single-pixel imaging systems in flexible and long-distance detection imaging scenarios.

CoIR: Compressive Implicit Radar.

Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.

OP-FCNN: an optronic fully convolutional neural network for imaging through scattering media.

Imaging through scattering media is a classical inverse issue in computational imaging. In recent years, deep learning(DL) methods have excelled in speckle reconstruction by extracting the correlation of speckle patterns. However, high-performance DL-based speckle reconstruction also costs huge hardware computation and energy consumption. Here, we develop an opto-electronic DL method with low computation complexity for imaging through scattering media. We design the "end-to-end" optronic structure for speckle reconstruction, namely optronic fully convolutional neural network (OP-FCNN). In OP-FCNN, we utilize lens groups and spatial light modulators to implement the convolution, down/up-sampling, and skip connection in optics, which significantly reduces the computational complexity by two orders of magnitude, compared with the digital CNN. Moreover, the reconfigurable and scalable structure supports the OP-FCNN to further improve imaging performance and accommodate object datasets of varying complexity. We utilize MNIST handwritten digits, EMNIST handwritten letters, fashion MNIST, and MIT-CBCL-face datasets to validate the OP-FCNN imaging performance through random diffusers. Our OP-FCNN reveals a good balance between computational complexity and imaging performance. The average imaging performance on four datasets achieves 0.84, 0.91, 0.79, and 16.3dB for JI, PCC, SSIM, and PSNR, respectively. The OP-FCNN paves the way for all-optical systems in imaging through scattering media.

Research Trends and Hotspots of Medical Electrical Impedance Tomography Algorithms: A Bibliometric Analysis From 1987 to 2021.

Electrical impedance tomography (EIT) is a gradually maturing medical imaging technique that relies on computational algorithms for reconstructing and visualizing internal conductivity distributions within the human body.To provide a comprehensive and objective understanding of the current state and trendsin theEIT algorithm research, we conducted bibliometric analysis on a 25-year EIT algorithm research dataset sourced from Web of Science Core Collections. We visualized publication characteristics, collaboration patterns, keywords, and co-cited references. The results indicate a steady increase in annual publications over recent decades. The United States, United Kingdom, China, and South Korea contributed60% of the articles collaboratively. Keyword analysis unveiled three distinct stages in the evolution of EIT algorithm research: the establishment of fundamental algorithm frameworks, optimization for improved imaging performance, and the development of algorithms for clinical applications. Additionally, there has been a shift in research focus from traditional theories to the incorporation of new methods, such as artificial intelligence. Co-cited references suggest that integrating EIT with other established imaging techniques may emerge as a new trend in EIT algorithm research. In summary, EIT algorithms have been a consistent research focus, with current efforts centered on optimizing algorithms to enhance imaging performance. The emerging research trend involves utilizing more diverse and intersecting algorithms.

Optimized Virtual Sources Distributions for 3-D Ultrafast Diverging Wave Compounding Imaging: A Simulation Study.

Ultrafast ultrasound imaging allows observing rapid phenomena; combined with 3-D imaging it has the potential to provide more accurate analysis on organs which leads, at the end, to better diagnosis. Coherent compounding using diverging waves is commonly used to reconstruct high quality images on large volumes while keeping frame rate high enough to allow dynamic analysis. In practice, the virtual sources that drive the diverging waves are often distributed by a deterministic way: following a regular grid, concentric rings and spirals. Even though those deterministic distributions can offer various trade-off in terms of imaging performance, other distributions can be considered to improve imaging performance. It is herein suggested to look at alternative virtual sources distributions for optimizing the lateral resolution and the secondary lobes level on several PSFs by means of a multiobjective genetic algorithm. The optimization framework leads to seven pseudo-irregular distributions of virtual source distributions that have not yet been found in the literature. An analysis on the imaging performance with a simulated phantom shows that these new distributions offer different trade-offs between lateral resolution and contrast, respectively measured on point-like reflectors and anechoic cysts. As an example, one of these optimized distributions improves the lateral resolution by 16% and gives equivalent contrast values on cysts, when compared to a concentric-rings-based distribution.

Seven general radiography x-ray detectors with pixel sizes ranging from 175 to 76 μm: technical evaluation with the focus on orthopaedic imaging

Aim. Flat panel detectors with small pixel sizes general can potentially improve imaging performance in radiography applications requiring fine detail resolution. This study evaluated the imaging performance of seven detectors, covering a wide range of pixel sizes, in the frame of orthopaedic applications. Material and methods. Pixel sizes ranged from 175 (detector A175) to 76 μm (detector G76). Modulation transfer function (MTF) and detective quantum efficiency (DQE) were measured using International Electrotechnical Commission (IEC) RQA3 beam quality. Threshold contrast (C T) and a detectability index (d′) were measured at three air kerma/image levels. Rabbit shoulder images acquired at 60 kV, over five air kerma levels, were evaluated in a visual grading study for anatomical sharpness, image noise and overall diagnostic image quality by four radiologists. The detectors were compared to detector E124. Results. The 10% point of the MTF ranged from 3.21 to 4.80 mm−1, in going from detector A175 to detector G76. DQE(0.5 mm−1) measured at 2.38 μGy/image was 0.50 ± 0.05 for six detectors, but was higher for F100 at 0.62. High frequency DQE was superior for the smaller pixel detectors, however C T for 0.25 mm discs correlated best with DQE(0.5 mm−1). Correlation between C T and the detectability model was good (R 2 = 0.964). C T for 0.25 mm diameter discs was significantly higher for D150 and F100 compared to E124. The visual grading data revealed higher image quality ratings for detectors D125 and F100 compared to E124. An increase in air kerma was associated with improved perceived sharpness and overall quality score, independent of detector. Detectors B150, D125, F100 and G76, performed well in specific tests, however only F100 consistently outperformed the reference detector. Conclusion. Pixel size alone was not a reliable predictor of small detail detectability or even perceived sharpness in a visual grading analysis study.