Myocardial T1 mapping by cardiac magnetic resonance imaging shows early myocardial changes in treatment-naive patients with active rheumatoid arthritis and positive autoantibodies.

OBJECTIVES: We aimed to study whether myocardial changes are already detectable by cardiac magnetic resonance (CMR) imaging at the time of rheumatoid arthritis (RA) diagnosis. METHODS: This single-centre prospective study included 39 treatment-naive patients with early rheumatoid arthritis (ERA, symptom duration <1 year) without any history of heart disease, and 38 age- and sex-matched healthy volunteers. The disease severity was assessed with clinical evaluation (Disease Activity Score-28 for Rheumatoid Arthritis with CRP (DAS28-CRP) score) and serological testing (rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA)). The ERA patients were classified into group A (DAS28-CRP score ≥3.2, positive RF and ACPA; n=17) and group B (not fulfilling the group A criteria). The ERA patients and healthy controls underwent 1.5T CMR. RESULTS: Group A patients had significantly higher myocardial global T1 relaxation times than the healthy controls, 987 [965, 1003] ms vs. 979 [960, 991] ms (median [IQR]; p=0.041). A significant difference in T1 was found in the basal, mid inferior and mid anterolateral segments. In a multivariate analysis, prolonged global T1 relaxation time was independently associated with female sex (95% CI [5.62, 51.31] ms, p=0.016), and group A status (95% CI [4.65, 39.01] ms p=0.014). CONCLUSIONS: At the time of diagnosis, ERA patients with a higher disease activity (DAS28-CRP score ≥3.2) and both positive RF and ACPA showed prolonged T1 relaxation times in basal myocardial segments. These segments could be most susceptible to the development of myocardial fibrosis, and a segmental reporting style could be useful when estimating the first signs of myocardial fibrosis.

Automatic chest computed tomography image noise quantification using deep learning.

PURPOSE: This study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions. METHODS: A comprehensive phantom CT dataset (three dose levels, six reconstruction methods, amounting to 9240 slices) was acquired and used to train a convolutional neural network (CNN) to output an estimate of local image noise standard deviations (SD) from a single CT scan input. The CNN model consisting of seven convolutional layers was trained on the phantom image dataset representing a range of scan parameters and was tested with phantom images acquired in a variety of different scan conditions, as well as publicly available chest CT images to produce clinical noise SD maps. RESULTS: Noise SD maps predicted by the CNN agreed well with the ground truth both visually and numerically in the phantom dataset (errors of < 5 HU for most scan parameter combinations). In addition, the noise SD estimates obtained from clinical chest CT images were similar to running-average based reference estimates in areas without prominent tissue interfaces. CONCLUSIONS: Predicting local noise magnitudes without the need for repeated scans is feasible using DL. Our implementation trained with phantom data was successfully applied to open-source clinical data with heterogeneous tissue borders and textures. We suggest that automatic DL noise mapping from clinical patient images could be used as a tool for objective CT image quality estimation and protocol optimization.

Quality assurance framework for rapid automatic analysis deployment in medical imaging.

PURPOSE: Automatic image analysis algorithms have an increasing role in clinical quality assurance (QA) in medical imaging. Although the implementation of QA calculation algorithms may be straightforward at the development level, actual deployment of a new method to clinical routine may require substantial additional effort from supporting services. We sought to develop a multimodal system that enables rapid implementation of new QA analysis methods in clinical practice. METHODS: The QA system was built using freely available open-source software libraries. The included features were results database, database interface, interactive user interface, e-mail error dispatcher, data processing backend, and DICOM server. An in-house database interface was built, providing the developers of analyses with simple access to the results database. An open-source DICOM server was used for image traffic and automatic initiation of modality-specific QA image analyses. RESULTS: The QA framework enabled rapid adaptation of new analysis methods to automatic image processing workflows. The system provided online data review via an easily accessible user interface. In case of deviations, the system supported simultaneous review of the results for the user and QA expert to trigger corrective actions. In particular, embedded error thresholds, trend analyses, and error-feedback channels were provided to facilitate continuous monitoring and to enable pre-emptive corrective actions. CONCLUSION: An effective and novel QA framework incorporating easy adaptation and scalability to automated image analysis methods was developed. The framework provides an efficient and responsive web-based tool to manage the normal operation, trends, errors, and abnormalities in medical image quality.

Characterizing geometric distortions of 3D sequences in clinical head MRI.

OBJECTIVE: Phantoms are often used to estimate the geometric accuracy in magnetic resonance imaging (MRI). However, the distortions may differ between anatomical and phantom images. This study aimed to investigate the applicability of a phantom-based and a test-subject-based method in evaluating geometric distortion present in clinical head-imaging sequences. MATERIALS AND METHODS: We imaged a 3D-printed phantom and test subjects with two MRI scanners using two clinical head-imaging 3D sequences with varying patient-table positions and receiver bandwidths. The geometric distortions were evaluated through nonrigid registrations: the displaced acquisitions were compared against the ideal isocenter positioning, and the varied bandwidth volumes against the volume with the highest bandwidth. The phantom acquisitions were also registered to a computed tomography scan. RESULTS: Geometric distortion magnitudes increased with larger table displacements and were in good agreement between the phantom and test-subject acquisitions. The effect of increased distortions with decreasing receiver bandwidth was more prominent for test-subject acquisitions. CONCLUSION: Presented results emphasize the sensitivity of the geometric accuracy to positioning and imaging parameters. Phantom limitations may become an issue with some sequence types, encouraging the use of anatomical images for evaluating the geometric accuracy.

Magnetic resonance imaging safety in patients with abandoned or functioning epicardial pacing leads.

OBJECTIVES: The European Society of Cardiology Guidelines on cardiac pacing from 2021 allow magnetic resonance imaging (MRI) in patients with cardiac implantable electronic devices (CIEDs) but do not recommend MRI in patients with epicardial pacing leads. The clinical dilemma remains whether performing an MRI in patients with CIED and epicardial leads is safe. We aimed to evaluate the safety of performing an MRI in patients with CIED and abandoned or functioning epicardial pacing leads. METHODS: We included all adult patients who underwent clinically indicated MRIs with CIED and functioning or abandoned epicardial leads in a single tertiary hospital between November 2011 and October 2019. The data were retrospectively collected. RESULTS: Twenty-six MRIs were performed on 17 patients with functioning or abandoned epicardial pacing leads. Sixty-nine percent of the MRI scans (18/26) were conducted on patients with functioning epicardial pacing leads. A definite adverse event occurred in one MRI scan. This was a transient elevation of the pacing threshold in a patient with a functioning epicardial ventricular pacing lead implanted 29 years previously. An irreversible atrial pacing lead impedance elevation was detected 6 months after the MRI in another patient; the association with the previous MRI remained unclear. No adverse events were detected in MRIs performed on patients with modern (implanted in 2000 or later) functioning epicardial leads. CONCLUSIONS: MRIs in patients with CIED and modern functioning epicardial pacing leads were performed without detectable adverse events. Further large-scale studies are necessary to confirm MRI safety in patients with epicardial pacing leads. KEY POINTS: • Currently, MRI in patients with cardiac implantable electronic devices (CIEDs) and functioning or abandoned epicardial pacing leads is not recommended. • MRIs in patients with CIED and modern functioning epicardial leads (implanted in 2000 or later) were performed without detectable adverse events in our patient cohort. • Allowing MRI in patients with epicardial pacing leads may significantly improve the diagnostic work-up, especially in specific patient groups, such as patients with congenital heart disease.

Peak flow measurements in patients with severe aortic stenosis: a prospective comparative study between cardiovascular magnetic resonance 2D and 4D flow and transthoracic echocardiography.

BACKGROUND: Aortic valve stenosis (AS) is the most prevalent valvular disease in the developed countries. Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) is an emerging imaging technique, which has been suggested to improve the evaluation of AS severity compared to two-dimensional (2D) flow and transthoracic echocardiography (TTE). We investigated the reliability of CMR 2D flow and 4D flow techniques in measuring aortic transvalvular peak systolic flow in patients with severe AS. METHODS: We prospectively recruited 90 patients referred for aortic valve replacement due to severe AS (73.3 ± 11.3 years, aortic valve area 0.7 ± 0.1 cm2, and 54/36 tricuspid/bicuspid), and 10 non-valvular disease controls. All the patients underwent echocardiography and 2D flow and 4D flow CMR. Peak flow velocity measurements were compared using Wilcoxon signed rank sum test and Bland-Altman analysis. RESULTS: 4D flow underestimated peak flow velocity in the AS group when compared with TTE (bias - 1.1 m/s, limits of agreement ± 1.4 m/s) and 2D flow (bias - 1.2 m/s, limits of agreement ± 1.6 m/s). The differences between values obtained by TTE (median 4.3 m/s, range 2.7-6.1 m/s) and 2D flow (median 4.5 m/s, range 2.9-6.5 m/s) compared to 4D flow (median 3.1 m/s, range 1.7-5.1 m/s) were significant (p < 0.001). The difference between 2D flow and TTE were insignificant (bias 0.07 m/s, limits of agreement ± 1.5 m/s). In non-valvular disease controls, peak flow velocity was measured higher by 4D flow than 2D flow (1.4 m/s, 1.1-1.7 m/s and 1.3 m/s, 1.1-1.5 m/s, respectively; bias 0.2 m/s, limits of agreement ± 0.16 m/s). CONCLUSIONS: CMR 4D flow significantly underestimates systolic peak flow velocity in patients with severe AS. 2D flow, in turn, estimated the AS velocity accurately, with measured peak flow velocities comparable to TTE.

Automating chest radiograph imaging quality control.

PURPOSE: To automate diagnostic chest radiograph imaging quality control (lung inclusion at all four edges, patient rotation, and correct inspiration) using convolutional neural network models. METHODS: The data comprised of 2589 postero-anterior chest radiographs imaged in a standing position, which were divided into train, validation, and test sets. We increased the number of images for the inclusion by cropping appropriate images, and for the inclusion and the rotation by flipping the images horizontally. The image histograms were equalized, and the images were resized to a 512 × 512 resolution. We trained six convolutional neural networks models to detect the image quality features using manual image annotations as training targets. Additionally, we studied the inter-observer variability of the image annotation. RESULTS: The convolutional neural networks' areas under the receiver operating characteristic curve were >0.88 for the inclusions, and >0.70 and >0.79 for the rotation and the inspiration, respectively. The inter-observer agreement between two human annotators for the assessed image-quality features were: 92%, 90%, 82%, and 88% for the inclusion at patient's left, patient's right, cranial, and caudal edges, and 78% and 89% for the rotation and inspiration, respectively. Higher inter-observer agreement was related to a smaller variance in the network confidence. CONCLUSIONS: The developed models provide automated tools for the quality control in a radiological department. Additionally, the convolutional neural networks could be used to obtain immediate feedback of the chest radiograph image quality, which could serve as an educational instrument.

Metal artifacts in intraoperative O-arm CBCT scans.

BACKGROUND: Cone-beam computed tomography (CBCT) has become an increasingly important medical imaging modality in orthopedic operating rooms. Metal implants and related image artifacts create challenges for image quality optimization in CBCT. The purpose of this study was to develop a robust and quantitative method for the comprehensive determination of metal artifacts in novel CBCT applications. METHODS: The image quality of an O-arm CBCT device was assessed with an anthropomorphic pelvis phantom in the presence of metal implants. Three different kilovoltage and two different exposure settings were used to scan the phantom both with and without the presence of metal rods. RESULTS: The amount of metal artifact was related to the applied CBCT imaging protocol parameters. The size of the artifact was moderate with all imaging settings. The highest applied kilovoltage and exposure level distinctly increased artifact severity. CONCLUSIONS: The developed method offers a practical and robust way to quantify metal artifacts in CBCT. Changes in imaging parameters may have nonlinear effects on image quality which are not anticipated based on physics.

Inter- and intra-scanner variations in four magnetic resonance imaging image quality parameters.

Purpose: In addition to less frequent and more comprehensive tests, quality assurance (QA) protocol for a magnetic resonance imaging (MRI) scanner may include cursory daily or weekly phantom checks to verify equipment constancy. With an automatic image analysis workflow, the daily QA images can be further used to study scanner baseline performance and both long- and short-term variations in image quality. With known baselines and variation profiles, automatic error detection can be employed. Approach: Four image quality parameters were followed for 17 MRI scanners over six months: signal-to-noise ratio (SNR), image intensity uniformity, ghosting artifact, and geometrical distortions. Baselines and normal variations were determined. An automatic detection of abnormal QA images was compared with image deviations visually detected by human observers. Results: There were significant inter-scanner differences in the QA parameters. In some cases, the results exceeded commonly accepted tolerances. Scanner field strengths, or a unit being stationary versus mobile, did not have a clear relationship with the QA results. Conclusions: The variations and baseline levels of image QA parameters can differ significantly between MRI scanners. Scanner specific error thresholds based on parameter means and standard deviations are a viable option for detecting abnormal QA images.

MRI quality assurance based on 3D FLAIR brain images.

OBJECTIVE: Quality assurance (QA) of magnetic resonance imaging (MRI) often relies on imaging phantoms with suitable structures and uniform regions. However, the connection between phantom measurements and actual clinical image quality is ambiguous. Thus, it is desirable to measure objective image quality directly from clinical images. MATERIALS AND METHODS: In this work, four measurements suitable for clinical image QA were presented: image resolution, contrast-to-noise ratio, quality index and bias index. The methods were applied to a large cohort of clinical 3D FLAIR volumes over a test period of 9.5 months. The results were compared with phantom QA. Additionally, the effect of patient movement on the presented measures was studied. RESULTS: A connection between the presented clinical QA methods and scanner performance was observed: the values reacted to MRI equipment breakdowns that occurred during the study period. No apparent correlation with phantom QA results was found. The patient movement was found to have a significant effect on the resolution and contrast-to-noise ratio values. DISCUSSION: QA based on clinical images provides a direct method for following MRI scanner performance. The methods could be used to detect problems, and potentially reduce scanner downtime. Furthermore, with the presented methodologies comparisons could be made between different sequences and imaging settings. In the future, an online QA system could recognize insufficient image quality and suggest an immediate re-scan.

Routine Ultrasound Quality Assurance in a Multi-Unit Radiology Department: A Retrospective Evaluation of Transducer Failures.

The importance of quality assurance (QA) in medical ultrasound (US) has been widely recognized and recommendations concerning technical QA have been published over the years. However, the demonstrated impact of a properly working QA protocol on clinical routine has been scarce. We investigated the transducer write-off causes for a 5-y period in a multi-unit radiology department with an annual average of 230 transducers in demanding diagnostic use. The transducer faults and the initial observers of the faults leading to transducer write-offs were traced and categorized. The most common cause of transducer write-off was an image uniformity problem or element failure. Mechanical faults or excessive leakage current and defects in the lens constituted smaller yet substantial shares. Our results suggest that a properly working routine QA program can detect majority of the faults before they are reported by users.

An Automatic Image Processing Workflow for Daily Magnetic Resonance Imaging Quality Assurance.

The performance of magnetic resonance imaging (MRI) equipment is typically monitored with a quality assurance (QA) program. The QA program includes various tests performed at regular intervals. Users may execute specific tests, e.g., daily, weekly, or monthly. The exact interval of these measurements varies according to the department policies, machine setup and usage, manufacturer's recommendations, and available resources. In our experience, a single image acquired before the first patient of the day offers a low effort and effective system check. When this daily QA check is repeated with identical imaging parameters and phantom setup, the data can be used to derive various time series of the scanner performance. However, daily QA with manual processing can quickly become laborious in a multi-scanner environment. Fully automated image analysis and results output can positively impact the QA process by decreasing reaction time, improving repeatability, and by offering novel performance evaluation methods. In this study, we have developed a daily MRI QA workflow that can measure multiple scanner performance parameters with minimal manual labor required. The daily QA system is built around a phantom image taken by the radiographers at the beginning of day. The image is acquired with a consistent phantom setup and standardized imaging parameters. Recorded parameters are processed into graphs available to everyone involved in the MRI QA process via a web-based interface. The presented automatic MRI QA system provides an efficient tool for following the short- and long-term stability of MRI scanners.

MRI quality assurance using the ACR phantom in a multi-unit imaging center.

BACKGROUND: Magnetic resonance imaging (MRI) instrumentation is vulnerable to technical and image quality problems, and quality assurance is essential. In the studied regional imaging center the long-term quality assurance has been based on MagNET phantom measurements. American College of Radiology (ACR) has an accreditation program including a standardized image quality measurement protocol and phantom. The ACR protocol includes recommended acceptance criteria for clinical sequences and thus provides possibility to assess the clinical relevance of quality assurance. The purpose of this study was to test the ACR MRI phantom in quality assurance of a multi-unit imaging center. MATERIAL AND METHODS: The imaging center operates 11 MRI systems of three major manufacturers with field strengths of 3.0 T, 1.5 T and 1.0 T. Images of the ACR phantom were acquired using a head coil following the ACR scanning instructions. Both ACR T1- and T2-weighted sequences as well as T1- and T2-weighted brain sequences in clinical use at each site were acquired. Measurements were performed twice. The images were analyzed and the results were compared with the ACR acceptance levels. RESULTS: The acquisition procedure with the ACR phantom was faster than with the MagNET phantoms. On the first and second measurement rounds 91% and 73% of the systems passed the ACR test. Measured slice thickness accuracies were not within the acceptance limits in site T2 sequences. Differences in the high contrast spatial resolution between the ACR and the site sequences were observed. In 3.0 T systems the image intensity uniformity was slightly lower than the ACR acceptance limit. CONCLUSION: The ACR method was feasible in quality assurance of a multi-unit imaging center and the ACR protocol could replace the MagNET phantom tests. An automatic analysis of the images will further improve cost-effectiveness and objectiveness of the ACR protocol.