A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images

Feasibility study of the application of magnetic resonance elastography (MRE) to measure oral posture related changes in stiffness of zygomaticus major muscle

PURPOSE

Development of a technique for measuring the mechanical properties of zygomaticus major (ZM) may aid advances in clinical treatments for correcting abnormal oral posture. The objective of this work was to demonstrate the feasibility of measuring the stiffness of ZM using an MR elastography technique that incorporates a custom local driver and a phase-gradient (PG) inversion.

METHODS

2D MRE investigations were performed for 3 healthy subjects using a vibration frequency of 90 Hz to test the prediction that the stiffness of ZM would be greater in the mouth-open compared to the mouth-closed position. MRE wave images were acquired along the long axis of ZM and processed using a 2D spatial-temporal directional filter applied in the direction of wave propagation along the long axis of the muscle. Stiffness measurements were obtained by applying the PG technique to a 1D-profile drawn in the phase image of the first harmonic of the wave images and a one-tailed paired t-test was used to compare the ZM stiffness between the two mouth postures (p < 0.05).

RESULTS

The mean stiffness and standard deviation (SD) of ZM across the three participants in the mouth-closed and mouth-open postures was 6.75 kPa (SD 3.36 kPa) and 15.5 kPa (SD 5.15 kPa), respectively. Changes of ZM stiffness were significantly greater in the mouth-open than the mouth-closed posture (p = 0.038).

CONCLUSION

The feasibility of using the PG MRE technique to measure stiffness changes in a small muscle such as ZM for different mouth postures has been demonstrated. Further investigations are required in a larger cohort of participants to investigate the sensitivity and reproducibility of the technique for potential clinical application as well as in health and beauty related studies.

Characteristics of the Structural Connectivity in Patients with Brain Injury and Chronic Health Symptoms: A Pilot Study

Diffusion properties from diffusion tensor imaging (DTI) are exquisitely sensitive to white matter abnormalities incurred during traumatic brain injury (TBI), especially for those patients with chronic post-TBI symptoms such as headaches, dizziness, fatigue, etc. The evaluation of structural and functional connectivity using DTI has become a promising method for identifying subtle alterations in brain connectivity associated with TBI that are otherwise not visible with conventional imaging. This study assessed whether TBI patients with (n = 17) or without (n = 16) chronic symptoms (TBIcs/TBIncs) exhibit any changes in structural connectivity (SC) and mean fractional anisotropy (mFA) of intra- and inter-hemispheric connections when compared to a control group (CG) (n = 13). Reductions in SC and mFA were observed for TBIcs compared to CG, but not for TBIncs. More connections were found to have mFA reductions than SC reductions. On the whole, SC is dominated by ipsilateral connections for all the groups after the comparison of contralateral and ipsilateral connections. More contra-ipsi reductions of mFA were found for TBIcs than TBIncs compared to CG. These findings suggest that TBI patients with chronic symptoms not only demonstrate decreased global and regional mFA but also reduced structural network connectivity.

Elective one-minute full brain multi-contrast MRI versus brain CT in pediatric patients: a prospective feasibility study

BACKGROUND

Brain CT can be used to evaluate pediatric patients with suspicion of cerebral pathology when anesthetic and MRI resources are scarce. This study aimed to assess if pediatric patients referred for an elective brain CT could endure a diagnostic fast brain MRI without general anesthesia using a one-minute multi-contrast EPI-based sequence (EPIMix) with comparable diagnostic performance.

METHODS

Pediatric patients referred for an elective brain CT between March 2019 and March 2020 were prospectively included and underwent EPIMix without general anesthesia in addition to CT. Three readers (R1-3) independently evaluated EPIMix and CT images on two separate occasions. The two main study outcomes were the tolerance to undergo an EPIMix scan without general anesthesia and its performance to classify a scan as normal or abnormal. Secondary outcomes were assessment of disease category, incidental findings, diagnostic image quality, diagnostic confidence, and image artifacts. Further, a side-by-side evaluation of EPIMix and CT was performed. The signal-to-noise ratio (SNR) was calculated for EPIMix on T1-weighted, T2-weighted, and ADC images. Descriptive statistics, Fisher's exact test, and Chi-squared test were used to compare the two imaging modalities.

RESULTS

EPIMix was well tolerated by all included patients (n = 15) aged 5-16 (mean 11, SD 3) years old. Thirteen cases on EPIMix and twelve cases on CT were classified as normal by all readers (R1-3), while two cases on EPIMix and three cases on CT were classified as abnormal by one reader (R1), (R1-3, p = 1.00). There was no evidence of a difference in diagnostic confidence, image quality, or the presence of motion artifacts between EPIMix and CT (R1-3, p ≥ 0.10). Side-by-side evaluation (R2 + R4 + R5) reviewed all scans as lacking significant pathological findings on EPIMix and CT images.

CONCLUSIONS

Full brain MRI-based EPIMix sequence was well tolerated without general anesthesia with a diagnostic performance comparable to CT in elective pediatric patients.

TRIAL REGISTRATION

This study was approved by the Swedish Ethical Review Authority (ethical approval number/ID Ethical approval 2017/2424-31/1). This study was a clinical trial study, with study protocol published at ClinicalTrials.gov with Trial registration number NCT03847051, date of registration 18/02/2019.

Effects of radiofrequency channel numbers on B1+ mapping using the Bloch-Siegert shift method

PURPOSE

This paper aims to investigate the impact of the channel numbers on the performance of B1+ mapping, by using the Bloch-Siegert shift (BSS) method. B1+ mapping plays a crucial role in various brain imaging protocols.

THEORY AND METHODS

We simulated the radiofrequency field of the human head model in six groups of multi-channel receive coil with a range of different channel numbers. MR signals were synthesized according to the standard BSS sequence, with quantified Gaussian added. Next, we combined the signals of each channel to reconstruct the B1+ map by weighted averaging and maximum likelihood estimation strategies and evaluate the bias by relative standard deviation of each coil.

RESULTS

The simulation results revealed that the accuracy of B1+ maps improved with the increasing of channel numbers, meanwhile the per channel efficiency of B1+maps accuracy gradually decrease. Both trends slowed down when the channel numbers reached 12 or above.

CONCLUSION

Our finding suggests that increasing the channel numbers can improve the accuracy of B1+map. However, a diminishing efficiency of per channel accuracy improvement was overserved, indicating that the relationship between quality of B1+ map and the channel numbers is nonlinear. Based on these findings, our study provides a reference for determining channel numbers to achieve a balance of coil selection and manufacturing cost. It also provides a theoretical basis for evaluating other B1+ mapping techniques.

A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets

In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic methodology for the unpaired visual conversion of the two most prevalent scanning presets is proposed. Using contrastive unpaired translation generative adversarial architectures, low quality images acquired with the faster Macular Cube preset can be converted to the visual style of high visibility Seven Lines scans and vice-versa. This modifies the visual appearance of the OCT images generated by each preset while preserving natural tissue structure. The quality of original and synthetic generated images was compared using BRISQUE. The synthetic generated images achieved very similar scores to original images of their target preset. The generative models were validated in automatic and expert separability tests. These models demonstrated they were able to replicate the genuine look of the original images. This methodology has the potential to create multi-preset datasets with which to train robust computer-aided diagnosis systems by exposing them to the visual features of different presets they may encounter in real clinical scenarios without having to obtain additional data. Graphical Abstract Unpaired mutual conversion between scanning presets. Two generative adversarial models are trained for the conversion of OCT images into images of another scanning preset, replicating the visual features that characterise said preset.

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object’s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

INTRODUCTION

With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [¹⁸F]-FDOPA PET imaging as a biomarker for the dopamine system.

METHODS

Two different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [¹⁸F]-FDOPA PET images that had been manually quality controlled from three different PET/CT scanners. The scans were combined with an additional 400 scans, in which misalignment (200 scans) and low SNR (200 scans) were simulated. A cross-validation was performed, where 80% of the data were used for training and 20% for validation. Two additional datasets of [¹⁸F]-FDOPA PET images (50 and 100 scans respectively with at least 80% of good quality images) were used for out-of-sample validation.

RESULTS

The CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets.

CONCLUSIONS

This feasibility study shows that it is possible to perform automatic QC of [¹⁸F]-FDOPA PET imaging with CNNs. The approach has the potential to be extended to other PET tracers in both brain and non-brain applications, but it is dependent on the availability of large datasets necessary for the algorithm training.

Evaluation of a no-reference image quality metric for projection X-ray imaging using a 3D printed patient-specific phantom

PURPOSE

Feasability of a no-reference image quality metric was assessed on patient-like images using a patient-specific phantom simulating a frame of a coronary angiogram.

METHODS

One background and one contrast-filled frame of a coronary angiogram, acquired using a clinical imaging protocol, were selected from a Philips Integris Allura FD (Philips Healthcare, Best, The Netherlands). The background frame's pixels were extruded to a thickness proportional to their grey value. One phantom was 3D printed using composite 80% bronze filament (max. thickness of 5.1 mm), the other was a custom PMMA cast (max thickness of 8.5 cm). A vessel mold was created from the contrast-filled frame and injected with a solution of 320 mg I/ml contrast fluid (75%), water and gelatin. Still X-ray frames of the vessel mold + background phantom + 16 cm PMMA were acquired at manually selected different exposure settings using a Philips Azurion (Philips Healthcare, Best, The Netherlands) in User Quality Control Mode and were exported as RAW images. The signal-difference-to-noise-ratio-squared (SDNR²) and a spatial-domain-equivalent of the noise equivalent quanta (NEQ_SDE) were calculated. The Spearman's correlation of the latter parameters with a no-reference perceptual image quality metric (NIQE) was investigated.

RESULTS

The bronze phantom showed better resemblance to the original patient frame selected from a coronary angiogram of an actual patient, with better contrast and less blur than the PMMA phantom. Both phantoms were imaged using a comparable imaging protocol to the one used to acquire the original frame. The bronze phantom was hence used together with the vessel mold for image quality measurements on the 165 still phantom frames. A strong correlation was noted between NEQ_SDE and NIQE (SROCC = -0.99, p < 0.0005) and between SDNR² and NIQE (SROCC = -0.97, p < 0.0005).

CONCLUSION

Using a cost-effective and easy to realize patient-specific phantom we were able to generate patient-like X-ray frames. NIQE as a no-reference image quality model has the potential to predict physical image quality from patient images.

Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists.

Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis

An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores.

Clinical Strategies and Technical Challenges in Psychoradiology

Psychoradiology is an emerging discipline at the intersection between radiology and psychiatry. It holds promise for playing a role in clinical diagnosis, evaluation of treatment response and prognosis, and illness risk prediction for patients with psychiatric disorders. Addressing complex issues, such as the biological heterogeneity of psychiatric syndromes and unclear neurobiological mechanisms underpinning radiological abnormalities, is a challenge that needs to be resolved. With the advance of multimodal imaging and more efforts in standardization of image acquisition and analysis, psychoradiology is becoming a promising tool for the future of clinical care for patients with psychiatric disorders.