Image Quality Metrics Research Articles

Evaluating geometrically-approximated principal component analysis vs. classical eigenfaces: a quantitative study using image quality metrics

Principal component analysis (PCA) is essential for diminishing the number of dimensions across various fields, preserving data integrity while simplifying complexity. Eigenfaces, a notable application of PCA, illustrates the method's effectiveness in facial recognition. This paper introduces a novel PCA approximation technique based on maximizing distance and compares it with the traditional eigenfaces approach. We employ several image quality metrics including Euclidean distance, mean absolute error (MAE), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and structural similarity index measure (SSIM) for a quantitative assessment. Experiments conducted on the Brazilian FEI database reveal significant differences between the approximated and classical eigenfaces. Despite these differences, our approximation method demonstrates superior performance in retrieval and search tasks, offering faster and parallelizable implementation. The results underscore the practical advantages of our approach, particularly in scenarios requiring rapid processing and expansion capabilities.

GPLM: Enhancing underwater images with Global Pyramid Linear Modulation

Underwater imagery often suffers from challenges such as color distortion, low contrast, blurring, and noise due to the absorption and scattering of light in water. These degradations complicate visual interpretation and hinder subsequent image processing. Existing methods struggle to effectively address the complex, spatially varying degradations without prior environmental knowledge or may produce unnatural enhancements.To overcome these limitations, we propose a novel method called Global Pyramid Linear Modulation that integrates physical degradation modeling with deep learning for underwater image enhancement. Our approach extends Feature-wise Linear Modulation to a four-dimensional structure, enabling fine-grained, spatially adaptive modulation of feature maps. Our method captures multi-scale contextual information by incorporating a feature pyramid architecture with self-attention and feature fusion mechanisms, effectively modeling complex degradations. We validate our method by integrating it into the MixDehazeNet model and conducting experiments on benchmark datasets. Our approach significantly improves the Peak Signal-to-Noise Ratio, increasing from 28.6 dB to 30.6 dB on the EUVP-515-test dataset. Compared to recent state-of-the-art methods, our method consistently outperforms them by over 3 dB in PSNR on datasets with ground truth. It improves the Underwater Image Quality Measure by more than one on datasets without ground truth. Furthermore, we demonstrate the practical applicability of our method on a real-world underwater dataset, achieving substantial improvements in image quality metrics and visually compelling results. These experiments confirm that our method effectively addresses the limitations of existing techniques by adaptively modeling complex underwater degradations, highlighting its potential for underwater image enhancement tasks.

Assessment of a New CT Detector and Filtration Technology: Part 2-Image Quality in Phantoms, Cadavers, and Patients.

The purpose of this work was to evaluate the image quality of a commercial CT scanner equipped with a novel detector and filtration technology called PureVision Optics (PVO). CT number, noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF), and noise power spectrum (NPS) were assessed using the ACR CT Accreditation phantom scanned with various acquisitions at 80 kV, 100 kV, 120 kV, and 135 kV, each with multiple CTDIvol values of 20 mGy, 40 mGy, and 65 mGy. Artifacts were evaluated in an anthropomorphic head phantom, a cadaver head, and in patient studies. Two neuroradiologists assessed image quality features in various patients who were examined with unenhanced brain CT on both scanners. Compared with the conventional scanner, for the same CTDIvol, the PVO scanner produced 20.3% less image noise (P < 0.001), 18.9% higher CNR (P < 0.01), and 24.6% higher spatial resolution (P < 0.001). Streak artifacts were less severe with the PVO scanner for the phantom, cadaver, and patient scans (P < 0.05). Radiologists scored the PVO scanner as significantly better for visualization of the cerebrospinal fluid space over the cerebral sulci in high convexity, image noise in gray and white matter, and artifacts in the posterior fossa. They also significantly preferred the PVO scanner for visualization of the border between brain gray and white matter, cerebrospinal fluid space around the mesencephalon, and overall diagnostic acceptability. For matched CTDIvol values, the scanner equipped with PVO technology produced better objective and subjective image quality metrics in brain CT imaging compared with a conventional CT scanner without PVO. In clinical settings, PVO may allow for lower doses while enhancing imaging through dense areas, improving visualization of subtle details, and offering more effective options for examining obese patients.This research received financial support from Canon Medical Systems USA. The study design and data were fully controlled by the coauthors, of which none are employees or consultants of Canon Medical Systems USA.

Longitudinal measures of peripheral optical quality in young children.

To assess longitudinal changes in optical quality across the periphery (horizontal meridian, 60°) in young children who are at high (HR) or low risk (LR) of developing myopia, as well asa small subgroup of children who developed myopia over a 3-year time frame. Aberrations were measured every 6 months in 92 children with functional emmetropia at baseline. Children were classified into HR or LR based on baseline refractive error and parental myopia. Zernike polynomials were calculated for 4 mm pupils, accounting for the elliptical shape of the pupil in the periphery. Various metrics were computed, including Strehl Ratioswith only high-order aberrations (HO-SR). Primary spherical aberration (SA), horizontal coma and defocus were also analysed given their relevance in emmetropisation. The areas under the image quality metrics for various regions of interest were computed. HO-SR were higher in children at HR and children with myopia, even when SA was removed from the Strehl Ratio (SR) calculation. SA was less positive in children at HR and children with myopia. Defocus was more negative in children at HR and children with myopia at all eccentricities and was even more negative when computed relative to the fovea, an effect that increased in the mid periphery. Relative peripheral defocus also became more negative over time in children at HR and children with myopia at the mid temporal retina. The other aberrations showed no significant changes in time overall. This longitudinal study showed differences in HO-SR, SA and defocus in the central and near-peripheral retina (±20°) of young children at HR before they develop myopia compared with children at LR for myopia. The results may indicate these eccentricities are significant in providing signals for emmetropisation. The small changes noted over time may indicate that the differences are a cause of myopia development.

Inexpensive SAR testbed for 3D mmWave imaging applications

AbstractIn this paper, the authors investigate and develop a low‐cost synthetic aperture radar (SAR) testbed and utilise it to capture and reconstruct high‐resolution 3D mmWave SAR images. Despite much attention from both industrial and academic scholars, cost restraints and complexity have hindered the deployment of SAR systems, particularly in the mmWave domain. This paper outlines the major components and systems of the 3D SAR testbed. The authors build a testbed with low‐cost, commercially available hardware components and include open‐source software that is highly reconfigurable and simple to reproduce. The multiple‐input multiple‐output (MIMO) radar sensor uses a frequency‐modulated continuous wave (FMCW) chirp at the V‐band (40–75 GHz) and synthesises a rectilinear two‐dimensional planar aperture. The authors presented several reconstructed 3D images imitating real‐world scenarios using the testbed. A non‐linear sampling technique that has significantly decreased the amount of time required for data acquisition was implemented. In addition, the authors employed multiple image quality metrics to quantify and compare the images produced by the testbed. The testbed can be used to test and validate new techniques and algorithms.

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers. Brain MRI examinations including MPRAGE taken at a single institution for investigation of mild cognitive impairment, dementia and epilepsy between January 2020 and December 2022 were included retrospectively. Images taken in 2020 or 2021 were assigned to training and validation datasets, and images from 2022 were used for the test dataset. Using the training and validation set, we determined one model using visual evaluation by radiologists with reference to image quality metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The test dataset was evaluated by visual assessment and quality metrics. Voxel-based morphometric analysis was also performed, and we evaluated Dice score and volume differences between generated and original images of major structures were calculated as absolute symmetrized percent change. Training, validation, and test datasets comprised 340 patients (mean age, 56.1 ± 24.4years; 195 women), 36 patients (67.3 ± 18.3years, 20 women), and 193 patients (59.5 ± 24.4years; 111 women), respectively. The test dataset showed: PSNR, 35.4 ± 4.91; SSIM, 0.871 ± 0.058; and LPIPS 0.045 ± 0.017. No overfitting was observed. Dice scores for the segmentation of main structures ranged from 0.788 (left amygdala) to 0.926 (left ventricle). Quadratic weighted Cohen kappa values of visual score for medial temporal lobe between original and generated images were 0.80-0.88. Images generated using our DL-based model can be used for post-processing and visual evaluation of medial temporal lobe atrophy.

Image quality assessment and automation in late gadolinium-enhanced MRI of the left atrium in atrial fibrillation patients.

Late gadolinium-enhanced (LGE) MRI has become a widely used technique to non-invasively image the left atrium prior to catheter ablation. However, LGE-MRI images are prone to variable image quality, with quality metrics that do not necessarily correlate to the image's diagnostic quality. In this study, we aimed to define consistent clinically relevant metrics for image and diagnostic quality in 3D LGE-MRI images of the left atrium, have multiple observers assess LGE-MRI image quality to identify key features that measure quality and intra/inter-observer variabilities, and train and test a CNN to assess image quality automatically. We identified four image quality categories that impact fibrosis assessment in LGE-MRI images and trained individuals to score 50 consecutive pre-ablation atrial fibrillation LGE-MRI scans from the University of Utah hospital image database. The trained individuals then scored 146 additional scans, which were used to train a convolutional neural network (CNN) to assess diagnostic quality. There was excellent agreement among trained observers when scoring LGE-MRI scans, with inter-rater reliability scores ranging from 0.65 to 0.76 for each category. When the quality scores were converted to a binary diagnostic/non-diagnostic, the CNN achieved a sensitivity of and a specificity of . The use of a training document with reference examples helped raters achieve excellent agreement in their quality scores. The CNN gave a reasonably accurate classification of diagnostic or non-diagnostic 3D LGE-MRI images of the left atrium, despite the use of a relatively small training set.

Photon counting CT versus energy-integrating CT: A comparative evaluation of advances in image resolution, noise, and dose efficiency.

Photon counting computed tomography (PCCT) employs direct and spectrally resolved counting of individual x-ray quanta, enhancing image quality compared to the standard energy-integrating CT (EICT). To evaluate the quantitative improvements in CT image quality metrics by comparing the first medical PCCT with a state-of-the-art EICT. The PCCT versus EICT noise improvement ratio R was derived from the quantum statistics of the measurement process and measured across the clinical x-ray flux range for both systems. Detector and system modulation transfer functions (MTFs) were obtained using tilted-slit and wire phantom measurements. Image root mean square (RMS) noise, noise power spectrum (NPS), and x-ray patient dose were compared using a CatPhan phantom at two identical clinical target resolutions. The measurement of the PCCT noise improvement ratio R showed an elimination of electronic noise and a 10% noise transfer advantage. The PCCT detector MTF exhibited 3x higher angular resolution limits in comparison to EICT and close to ideal sinc behavior due to the electromagnetic formation of pixels in the PCCT semiconductor detector. This translated to 3.5x enhancements in CT system MTF ratios at 10 LP/cm, reflecting a significant improvement in millimeter range CT imaging. Both the improved quantum detection and the system MTF ratio improvement contribute to the measured 3x enhancements in image NPS at 10 LP/cm for identical image target resolution. An improvement of up to 1.7x in RMS image noise was observed accordingly. For low and ultra-low dose imaging with image filtering, dose efficiency increased between 2x and 10x, demonstrating the PCCT's capability to advance CT ultra-low dose imaging. The direct counting detection in PCCT has been shown to significantly improve sinogram noise and detector MTF ratios compared to energy integrating EICT. The observed translations into CT system MTF, image NPS, image noise, and dose ratios reflect a paradigm shift for CT image quality and dose efficiency.

A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease.

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and image quality metrics during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment. Cross-validation experiments were performed to benchmark performance against other available strategies using data from 20 864 participants with and without cognitive impairment, spanning 11 prospective and retrospective cohorts and 43 scanners. Evaluation metrics assessed the ability to remove scanner-related variations in brain volumes (marker concordance between scanner pairs) while retaining the ability to delineate different diagnostic groups (preserving disease-related signal). Results For each strategy, marker concordances between scanners were significantly better (P < .001) compared with preharmonized data. The proposed multiclass model achieved significantly higher concordance (mean, 0.75 ± 0.09 [SD]) than the Neuroharmony model trained on individuals without cognitive impairment (mean, 0.70 ± 0.11) and preserved disease-related signal (∆AUC [area under the receiver operating characteristic curve] = -0.006 ± 0.027) better than the Neuroharmony model trained on individuals with and without cognitive impairment that did not use the proposed extension (∆AUC = -0.091 ± 0.036). The marker concordance was better in scanners seen during training (concordance > 0.97) than unseen (concordance < 0.79), independent of cognitive status. Conclusion In a large-scale multicenter dataset, the proposed multiclass Neuroharmony model outperformed other available strategies for harmonizing brain volumetric data from unseen scanners in a clinical setting. Keywords: Image Postprocessing, MR Imaging, Dementia, Random Forest Supplemental material is available for this article. Published under a CC BY 4.0 license See also commentary by Haller in this issue.

Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.

Deep learning (DL) often requires an image quality metric; however, widely used metrics are not designed for medical images. To develop an image quality metric that is specific to MRI using radiologists image rankings and DL models. Retrospective. A total of 19,344 rankings on 2916 unique image pairs from the NYU fastMRI Initiative neuro database was used for the neural network-based image quality metrics training with an 80%/20% training/validation split and fivefold cross-validation. 1.5 T and 3 T T1, T1 postcontrast, T2, and FLuid Attenuated Inversion Recovery (FLAIR). Synthetically corrupted image pairs were ranked by radiologists (N = 7), with a subset also scoring images using a Likert scale (N = 2). DL models were trained to match rankings using two architectures (EfficientNet and IQ-Net) with and without reference image subtraction and compared to ranking based on mean squared error (MSE) and structural similarity (SSIM). Image quality assessing DL models were evaluated as alternatives to MSE and SSIM as optimization targets for DL denoising and reconstruction. Radiologists' agreement was assessed by a percentage metric and quadratic weighted Cohen's kappa. Ranking accuracies were compared using repeated measurements analysis of variance. Reconstruction models trained with IQ-Net score, MSE and SSIM were compared by paired t test. P < 0.05 was considered significant. Compared to direct Likert scoring, ranking produced a higher level of agreement between radiologists (70.4% vs. 25%). Image ranking was subjective with a high level of intraobserver agreement ( ) and lower interobserver agreement ( ). IQ-Net and EfficientNet accurately predicted rankings with a reference image ( and ). However, EfficientNet resulted in images with artifacts and high MSE when used in denoising tasks while IQ-Net optimized networks performed well for both denoising and reconstruction tasks. Image quality networks can be trained from image ranking and used to optimize DL tasks. 3 TECHNICAL EFFICACY: Stage 1.

Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images.

This study investigated the effectiveness of augmenting datasets for super-resolution processing of brain Magnetic Resonance Images (MRI) T1-weighted images (T1WIs) using deep learning. By incorporating images with different contrasts from the same subject, this study sought to improve network performance and assess its impact on image quality metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). This retrospective study included 240 patients who underwent brain MRI. Two types of datasets were created: the Pure-Dataset group comprising T1WIs and the Mixed-Dataset group comprising T1WIs, T2-weighted images, and fluid-attenuated inversion recovery images. A U-Net-based network and an Enhanced Deep Super-Resolution network (EDSR) were trained on these datasets. Objective image quality analysis was performed using PSNR and SSIM. Statistical analyses, including paired t test and Pearson's correlation coefficient, were conducted to evaluate the results. Augmenting datasets with images of different contrasts significantly improved training accuracy as the dataset size increased. PSNR values ranged 29.84-30.26dB for U-Net trained on mixed datasets, and SSIM values ranged 0.9858-0.9868. Similarly, PSNR values ranged 32.34-32.64dB for EDSR trained on mixed datasets, and SSIM values ranged 0.9941-0.9945. Significant differences in PSNR and SSIM were observed between models trained on pure and mixed datasets. Pearson's correlation coefficient indicated a strong positive correlation between dataset size and image quality metrics. Using diverse image data obtained from the same subject can improve the performance of deep-learning models in medical image super-resolution tasks.

A retinex inspired deep image prior model for despeckling and deblurring of aerial and satellite images using proximal gradient method

ABSTRACT Unsupervised learning models, particularly in the remote sensing domain, have gained significant attention in recent years. Various degradations in the satellite images, primarily occurring during acquisition, pose a substantial hurdle in obtaining reliable ground truth and extensive training data. The Deep Image Prior model (DIP) addresses these issues by performing restoration tasks using a single image, relying on the implicit regularization inherent in the network architecture. In this paper, we propose a novel approach, integrating the DIP model within the retinex framework to restore aerial and satellite images from the Gamma distributed speckles and linear shift-invariant Gaussian blur along with contrast enhancement using the alternating proximal gradient descent ascent (PGDA) method. Our proposed methodology combines implicit regularization with explicit total variational (TV) regularization, incorporating automated estimation of local regularization parameters. The data-fidelity component in the optimization function is formulated using the Bayesian Maximum A posteriori (MAP) estimate, assuming the speckles follow the Gamma distribution. Demonstration of despeckling and deblurring alone and in addition as a combined task is carried out on aerial and Synthetic Aperture Radar (SAR) images with different resolutions and polarization from various sources. Results obtained are compared with various state-of-the-art despeckling and deblurring models using distinct image quality metrics such as Equivalent Number of Looks (ENL), Contrast to Noise Ratio (CNR), Edge Preserving Index (EPI), Entropy, Global Contrast Factor (GCF), Natural Image Quality Evaluator (NIQE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Bradley-Terry (B-T) score based on the various factors. The quality of restored images depicted superior performance of the proposed method over the existing models under study.

Clinical validation of MR-generated synthetic CT by MRCAT for brain tumor radiotherapy.

MRI is an emerging modality in radiotherapy (RT). Accuracy synthetic CT is the prerequisite for implementing MR-only RT planning. This study validated the commercial algorithm of MR for calculating attenuation (MRCAT) in terms of image quality and dosimetric agreement. Brain tumor cases with 18 treated using intensity-modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT), and 15 treated using stereotactic radiosurgery (SRS) were analyzed. Synthetic CTs were resampled referencing planning CT. Treatment plan calculated on planning CT was recalculated on resampled MRCAT. Image quality of selected metrics and dosimetric agreements were assessed by dose-volume-histogram and 3D gamma analysis. For IMRT/VMAT and SRS cases, mean error were 23.42±1.05 and 28.39±3.17 HU; mean absolute error were 38.03±1.42 and 52.36±2.63 HU; root mean squared error were 89.09±6.65 and 108.38±12.23 HU; peak signal-to-noise ratio were 29.11±0.60 and 27.65±0.59dB; and structural similarity index measures were 0.88±0.00 and 0.70±0.01 respectively. No significant differences were identified for DVH metrics accounting the target coverage. Most OARs did not have significant dose deviation, except left lens with 0.70% higher in D-mean after recalculation (p<0.001). For criteria of 3mm/3%, 2mm/2%, and 1mm/1%, gamma passing rates for IMRT/VMAT were 99.92%, 99.42%, and 96.47%, while SRS were 99.86%, 99.52%, and 97.57% respectively. Correlation between passing rate and image quality metrics was established in IMRT/VMAT cases, with higher similarity yield better dosimetric agreement between planning and synthetic CT. This study has validated the MRCAT for clinical use in terms of comparable image quality and dosimetric agreement with planning CT. Further case selection and MR-compatible immobilization device would be required.

Free-Breathing Ungated Radial Simultaneous Multi-Slice Cardiac T1 Mapping.

Modified Look-Locker imaging (MOLLI) T1 mapping sequences are acquired during breath-holding and require ECG gating with consistent R-R intervals, which is problematic for patients with atrial fibrillation (AF). Consequently, there is a need for a free-breathing and ungated framework for cardiac T1 mapping. To develop and evaluate a free-breathing ungated radial simultaneous multi-slice (SMS) cardiac T1 mapping (FURST) framework. Retrospective, nonconsecutive cohort study. Twenty-four datasets from 17 canine and 7 human subjects (4 males, years; 3 females, years). Canines were from studies involving AF induction and ablation treatment. The human population included separate subjects with suspected microvascular disease, acute coronary syndrome with persistent AF, and transthyretin amyloidosis with persistent AF. The remaining human subjects were healthy volunteers. Pre- and post-contrast T1 mapping with the free-breathing and ungated SMS inversion recovery sequence with gradient echo readout and with conventional MOLLI sequences at 1.5 T and 3.0 T. MOLLI and FURST were acquired in all subjects, and American Heart Association (AHA) segmentation was used for segment-wise analysis. Pre-contrast T1, post-contrast T1, and ECV were analyzed using correlation and Bland-Altman plots in 13 canines and 7 human subjects. T1 difference box plots for repeated acquisitions in four canine subjects were used to assess reproducibility. The PIQUE image quality metric was used to evaluate the perceptual quality of T1 maps. Paired t-tests were used for all comparisons between FURST and MOLLI, with indicating statistical significance. There were no significant differences between FURST and MOLLI pre-contrast T1 reproducibility ( and , ), FURST and MOLLI ECV ( and , ), or FURST and MOLLI PIQUE scores ( and , ). The ECV mean difference was with . FURST had similar quality pre-contrast T1, post-contrast T1, and ECV maps and similar reproducibility compared to MOLLI. 3 TECHNICAL EFFICACY: 1.

3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.

Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality. This retrospective study was performed on 456 participants respectively scanned by three different PET scanners with two different tracers. A DL method called spatially aware noise reduction network (SANR) was proposed to recover 3D full-dose (FD) PET volumes from LD data. The performance of SANR was compared with a 2D DL method taking regular FD PET volumes as the reference. Wilcoxon signed-rank test was conducted to compare the image quality metrics across different DL denoising methods. For clinical evaluation, two nuclear medicine physicians examined the recovered FD PET volumes using a 5-point grading scheme (5 = excellent) and gave a binary decision (negative or positive) for diagnostic quality assessment. Statistically significant differences (p < 0.05) were found in terms of image quality metrics when SANR was compared with the 2D DL method. For clinical evaluation, SANR achieved a lesion detection accuracy of 95.3% (95% CI: 90.1%, 100%), while the reference full-dose PET volumes obtained a lesion detection accuracy of 98.4% (95% CI: 95.4%, 100%). In Alzheimer's disease diagnosis, both the reference FD PET volumes and the FD PET volumes recovered by SANR exhibited the same accuracy. Compared with reference FD PET, LD PET denoised by the proposed approach significantly reduced radiotracer dosage and showed noninferior diagnostic performance in brain lesion detection and Alzheimer's disease diagnosis. Question The current trend in PET imaging is to reduce injected dosage, which leads to low-quality PET images and reduces diagnostic efficacy. Findings The proposed deep learning method could recover diagnostic quality PET images from data acquired with a markedly reduced radiotracer dosage. Clinical relevance The proposed method would enhance the utility of PET scanning at lower radiotracer dosage and inform future workflows for brain lesion detection and Alzheimer's disease diagnosis, especially for those patients who need multiple examinations.

Visually relevant on-bench through-focus analysis of intraocular lenses.

Cataract surgery involves the implantation of an intraocular lens (IOL) to replace the opacified crystalline lens. Monofocal IOLs, the most common type, are intended to have the eye in focus at a given distance, usually at infinity. Simultaneous vision IOLs (SVIOLs) and extended depth of focus (EDOF) aim to minimize postoperative dependence on spectacles by providing either multiple foci or an extended depth of focus. These lenses utilize a variety of diffractive and refractive designs to achieve varied focal depths. While common optical testing methods based on the IOL's modulation transfer function (MTF) or resolving power at best focus are essential for quality control, they do not fully address the lenses' performance requirements in daily visual tasks such as reading in a variety of distances. The purpose of this work was to introduce a visually relevant on-bench test method, which includes an image analysis technique and a visual acuity-related image quality metric, to evaluate the through-focus performance of different commercially available IOLs. This method consists of recording a series of optotype images in a realistic eye model with the IOL, adjusting the stimulus vergence through a focus-tunable lens. We compare the results obtained with mono-focal, enhanced mono-focal, EDOF, and (diffractive) trifocal IOLs.

A novel comprehensive metric balancing imaging dose and setup accuracy in image-guided radiotherapy: concept proposal and clinical validation.

To propose and validate a comprehensive novel metric balancing the registration accuracy and imaging dose for image-guided-radiotherapy based on real patient data. With written informed consent and ethical approval, 56 patients were scanned using 6MV CBCT, 140 kV CBCT, and 100 kV CBCT on Halcyon system for three consecutive treatment fractions. Online registration was performed by various on-duty therapists under routine clinical pressure and time limitation. Offline registration was carried out by an experienced physicist without pressure. The consistency between the online and offline results was used as a surrogate of the missing ground-truth of registration accuracy, which was usually developed by introducing 'known' setup errors and rescan the phantoms, yet is ethnically not applicable to real patients. The registration differences (ΔD) between various imaging methods and observers were analyzed. The weighted CT dose index (CTDIw) for kV and MV CBCT was acquired using the PTW CTDI head phantom. The weighted-Dose-Accuracy-Product (DAPw) index was defined as DAPw =ΔD(mm) w1* CTDIw(mGy) w2, where w1 and w2 are the weighting factors of accuracy and dose respectively (w1+w2=1). The mean and interquartile range (IQR) of ΔD decreased monotonically for MV CBCT, 100 kV CBCT, and 140 kV CBCT, supporting the registration consistency as a surrogate metric of image quality. Significant differences of ΔD were observed between the online and offline registration across three imaging methods (P<0.05). The 140 kV CBCT provides superior positioning accuracy, less dependency on observer subjectivity and time pressure of clinical workflow. Using w1=w2=0.5 as an example, the smallest mean, standard deviation, and IQR of DAPw were observed on the 100 kV CBCT, indicating optimal balance between dose and accuracy than the other two methods. Analysis of variance (ANOVA) showed statistically significant differences in DAPw among the different imaging methods (P<0.01, F=50.57). Using registration consistency as a surrogate indicator of image quality, this study proposed and validated a novel "DAPw" parameter based on real patient data, providing a purpose-specific tool for balancing setup accuracy and radiation dose in clinic.

A phantom study on the applicability of a detectability index in ultrasound imaging

The assessment of clinical image quality on ultrasound is currently often subjective. While image quality factors such as contrast response or depth of penetration can be evaluated semi-automatically, the evaluation of high contrast resolution requires test objects with specific inserts. The aim of this study was to evaluate the applicability of image quality metrics which were derived from Linear System Theory in the field of medical ultrasound imaging. Modular Transfer Function (MTF) and noise power spectrum (NPS) were determined on four phantoms. Image quality was assessed using a detectability index for different diameters. One phantom contained a cylinder filled with water, which appears as a circle in the US images. The other three phantoms were homogeneous and consisted of three different materials all based on PVA (polyvinyl alcohol). The basic phantom material was a 10% PVA hydrogel. The two other materials included microplastic spheres and starch to increase echogeneity. NPS and the MTF were determined using MATLAB routines. Two linear US transducers with bandwidths of 2.4–10 and 4–15 MHz were used to show the dependence of the index on the principal frequency of the US wave. The results show that for all phantom materials and object sizes (1–10 mm diameter), the detectability indices decreased with increasing penetration depth (from 6 to 10 cm). In addition, all indices of the higher frequency transducer were higher than those of the lower frequency transducer. When comparing the different phantom materials (PVA, PVA with starch and PVA with microspheres), different mean pixel value (MPV) were found, while the standard deviations for the materials were similar. This enabled us to evaluate the detectability index at different signal-to-noise ratios (SNR). Measures of image homogeneity (coefficient of the variance and variation) showed no significant difference to a commercial phantom (p-values ranging from 0.16 to 1, average p-value 0.5). These results suggest that the concept of a detectability index can also be applied to US imaging.

Analyzing Pulse Compression Performance and Image Quality Metrics of Different Excitations in MAET With Magnetic Field Measurements.

This study investigates the pulse compression technique to improve the performance of magneto-acousto-electrical tomography (MAET) with magnetic field measurements through numerical studies. Emphasizing the effects of specific coil configuration on MAET measurements, the study conducts evaluations using a linear phased array (LPA) transducer and numerical breast models with tumor inclusion. It provides feasibility and a detailed comparative analysis of various excitations, including linear frequency modulated (LFM), Barker code, and Golay code excitations in MAET. To simulate experimental conditions, additive White Gaussian noise is added to the MAET signal detected by the receiver coils. The results obtained from the LPA steering angle at 0° and the reconstructed B-mode MAET images using the pulse compression technique lead to improvements compared with conventional single-cycle excitation. The computed mean signal-to-noise ratio (SNR) improvements for LFM, Barker code, and Golay code excitations in B-mode MAET images for 10,000 iterations are 7.42, 8.36, and 8.44 dB, respectively, compared with single-cycle excitation. Similarly, the mean contrast-to-noise ratio (CNR) improvements for these excitations in B-mode MAET images are 1.43, 1.63, and 1.9 dB, respectively. The results demonstrate that Golay code is superior in CNR and image quality metrics, while Golay and Barker codes have comparable SNR and outperform LFM. The research shows that the coil configuration significantly impacts tumor detection. With Golay code excitation, detecting a tumor as small as 5 mm × 2 mm at a depth of 33 mm with an SNR of 6.38 dB is possible, achieving an axial resolution of 2 mm.

Evaluating the Impact of Retinal Vessel Segmentation Metrics on Retest Reliability in a Clinical Setting: A Comparative Analysis Using AutoMorph.

Current research on artificial intelligence-based fundus photography biomarkers has demonstrated inconsistent results. Consequently, we aimed to evaluate and predict the test-retest reliability of retinal parameters extracted from fundus photography. Two groups of patients were recruited for the study: an intervisit group (n = 28) to assess retest reliability over a period of 1 to 14days and an intravisit group (n = 44) to evaluate retest reliability within a single session. Using AutoMorph, we generated test and retest vessel segmentation maps; measured segmentation map agreement via accuracy, sensitivity, F1 score and Jaccard index; and calculated 76 metrics from each fundus image. The retest reliability of each metric was analyzed in terms of the Spearman correlation coefficient, intraclass correlation coefficient (ICC), and relative percentage change. A linear model with the input variables contrast-to-noise-ratio and fractal dimension, chosen by a P-value-based backward selection process, was developed to predict the median percentage difference on retest per image based on image-quality metrics. This model was trained on the intravisit dataset and validated using the intervisit dataset. In the intervisit group, retest reliability varied between Spearman correlation coefficients of 0.34 and 0.99, ICC values of 0.31 to 0.99, and mean absolute percentage differences of 0.96% to 223.67%. Similarly, in the intravisit group, the retest reliability ranged from Spearman correlation coefficients of 0.55 and 0.96, ICC values of 0.40 to 0.97, and mean percentage differences of 0.49% to 371.23%. Segmentation map accuracy between test and retest never dropped below 97%; the mean F1 scores were 0.85 for the intravisit dataset and 0.82 for the intervisit dataset. The best retest was achieved with disc-width regarding the Spearman correlation coefficient in both datasets. In terms of the Spearman correlation coefficient, the worst retests of the intervisit and intravisit groups were tortuosity density and artery tortuosity density, respectively. The intravisit group exhibited better retest reliability than the intervisit group (P < 0.001). Our linear model, with the two independent variables contrast-to-noise ratio and fractal dimension predicted the median retest reliability per image on its validation dataset, the intervisit group, with an R2 of 0.53 (P < 0.001). Our findings highlight a considerable volatility in the reliability of some retinal biomarkers. Improving retest could allow disease progression modeling in smaller datasets or an individualized treatment approach. Image quality is moderately predictive of retest reliability, and further work is warranted to understand the reasons behind our observations better and thus ensure consistent retest results.