Primer on Commonly Occurring MRI Artifacts and How to Overcome Them

Towards clinical applicability of fMRI via systematic filtering

It is a common practice to evaluate the reproducibility of fMRI at the group level. However, for clinical applications of fMRI, where the focus is on reproducibility of single individuals, the high test-retest reliability that is sometimes reported for group-based measures can be misleading. On the level of single subjects, reproducibility of fMRI is still far too low for clinical applications, not even meeting the standards to use fMRI for scientific purposes. The goal of this work is to enhance the poor single-subject time course reproducibility of fMRI. For this purpose, we have developed a framework for post-processing fMRI signals using Savitzky-Golay (SG) filters in conjunction with general linear model (GLM) based data cleaning. The parameters of these filters were trained to be the optimal ones based on a dataset of working memory relevant signals. By employing our data-driven filtering framework, we successfully improve the average reproducibility correlation of a single fMRI time course from r = 0.26 (as obtained with a conventional statistical parametric mapping (SPM) data cleaning pipeline) to a fair level of r = 0.41. Additionally, we are able to enhance the average connectivity correlation from r = 0.44 to r = 0.54. Our conclusion is that signal post-processing with a data-driven SG filter framework may substantially improve time course reproducibility compared to conventional denoising pipelines. As a conservative estimate, we conjecture that roughly 10-30% of the population may benefit from optimized fMRI pipelines in a clinical setting depending on the measure of interest while this number was nihil for conventional fMRI pipelines.

An evaluation of the current status of quality assurance program in MRI: A multicenter study

INTRODUCTION

Quality Assurance Programs (QAPs) in Magnetic Resonance Imaging (MRI) are essential for acquiring images that provide information for medical diagnosis and treatment. As part of a quality assurance program, quality control tests (QC) are conducted to ensure that high-quality images are acquired during the acquisition process. Implementing QAPs in MRI is crucial to ensuring diagnostic image acquisition and personnel safety.

METHODS

In this study, 21 indicators of compliance/non-compliance were used to evaluate the QAP in ten Mexican MRI facilities, through a documentary review. Also, nine QC tests were developed under ACR protocol in the MRIs scanners including 1.5 T and 3.0 T systems. The association of the QAP and QC tests results for MRI scanners was studied.

RESULTS

It was found that all facilities evaluated failed at least one of the QAP indicators. Only 20 % of the facilities achieved more than 90 % on all QAP indicators, while 30 % achieved 100 % acceptability in QC tests. The statistical analysis revealed a significant positive correlation between the QAP indicators and QC tests, particularly the room technical factors and MRI scanners.

CONCLUSION

The results indicate that each MRI facility needs to improve QAP implementation and surveillance to establish QC reference values to enhance operational conditions and ensure consistent image quality throughout the facilities. It is imperative that the Mexican government update the legal framework regarding MRI safety in accordance with international recommendations and guidelines in order to ensure the implementation of QAPs in MRI facilities.

IMPLICATIONS FOR PRACTICE

As a result of standardizing protocols and monitoring reference values for image quality, the implementation of QA and QC indicators in MRI facilities may improve clinical practice and patient safety.

Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results

BACKGROUND

Several factors can impair image quality and reliability of liver magnetic resonance elastography (MRE), such as inadequate driver positioning, insufficient wave propagation and patient-related factors.

PURPOSE

To report initial results on automatic classification of liver MRE image quality using various deep learning (DL) architectures.

STUDY TYPE

Retrospective, single center, IRB-approved human study.

POPULATION

Ninety patients (male = 51, mean age 52.8 ± 14.1 years).

FIELD STRENGTHS/SEQUENCES

1.5 T and 3 T MRI, 2D GRE, and 2D SE-EPI.

ASSESSMENT

The curated dataset was comprised of 914 slices obtained from 149 MRE exams in 90 patients. Two independent observers examined the confidence map overlaid elastograms (CMOEs) for liver stiffness measurement and assigned a quality score (non-diagnostic vs. diagnostic) for each slice. Several DL architectures (ResNet18, ResNet34, ResNet50, SqueezeNet, and MobileNetV2) for binary quality classification of individual CMOE slice inputs were evaluated, using an 8-fold stratified cross-validation (800 slices) and a test dataset (114 slices). A majority vote ensemble combining the models' predictions of the highest-performing architecture was evaluated.

STATISTICAL TEST

The inter-observer agreement and the agreement between DL models and one observer were assessed using Cohen's unweighted Kappa coefficient. Accuracy, precision, and recall of the cross-validation and the ensemble were calculated for the test dataset.

RESULTS

The average accuracy across the eight models trained using each architecture ranged from 0.692 to 0.851 for the test dataset. The ensemble of the best performing architecture (SqueezeNet) yielded an accuracy of 0.921. The inter-observer agreement was excellent (Kappa 0.896 [95% CI 0.845-0.947]). The agreement between observer 1 and the predictions of each SqueezeNet model was slight to almost perfect (Kappa range: 0.197-0.831) and almost perfect for the ensemble (Kappa: 0.833).

CONCLUSION

Our initial study demonstrates an automated DL-based approach for classifying liver 2D MRE diagnostic quality with an average accuracy of 0.851 (range 0.675-0.921) across the SqueezeNet models.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 1.

Comprehensive review of artifacts in cardiac MRI and their mitigation

Cardiac magnetic resonance imaging (CMR) is an important clinical tool that obtains high-quality images for assessment of cardiac morphology, function, and tissue characteristics. However, the technique may be prone to artifacts that may limit the diagnostic interpretation of images. This article reviews common artifacts which may appear in CMR exams by describing their appearance, the challenges they mitigate true pathology, and offering possible solutions to reduce their impact. Additionally, this article acts as an update to previous CMR artifacts reports by including discussion about new CMR innovations.

Radiofrequency interference in magnetic resonance imaging: Identification and rectification

Radiofrequency (RF) interference artifact is a common type of magnetic resonance imaging (MRI) artifacts caused by the presence of unwanted RF field inside the scanner room. The artifact has the appearance of parallel bright lines or bands that resemble zippers, which can mimic pathology, obstruct the viewing of underlining tissues, and lower image signal-to-noise ratio, affecting the diagnostic evaluation of the image and sometimes even rendering it non-diagnostic. Due to the presence of multiple possible sources of RF interference in MRI and potential nonrelated MRI artifacts that resemble RF interference artifact, it may be difficult to effectively and timely resolve the artifact problem. The objective of this paper is to provide a review of RF interference in MRI and to offer guidance in the prompt and correct identification of the associated image artifacts as well as efficient approaches to resolve and prevent RF interference problems. This article should serve as a useful educational reference to magnetic resonance (MR) technologists and radiologists in dealing with MR image artifacts that may be caused by RF interference.

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision

The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum-coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.

Postsurgical Evaluation of the Pediatric Foot and Ankle

This article reviews situations in which surgical treatment is required in the context of different types of pathology in the foot and ankle of children, focusing on the role of imaging in surgical planning and postsurgical assessment. The types of pathology analyzed from this perspective are congenital pathology, neuromuscular disorders, osteochondral lesions, fractures and infection, ligament injuries, and tumors.We address the most common pitfalls of postsurgical imaging of the ankle and foot in children. With some exceptions, postsurgical follow-up focuses on clinical assessment, with imaging follow-up only needed in patients where complications are suspected or symptoms recur. Postoperative assessment of the foot and ankle in children and adolescents requires knowing the indications for surgery and the most common types of interventions, along with their imaging characteristics. The radiologist needs to be aware of the normal sequence of development of structures and how to select adequate imaging techniques to assess anatomy at various stages or in different postsurgical scenarios.