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

The performance of a new type accelerator uRT-linac 506c evaluated by a quality assurance automation system

PURPOSE

The purpose of this study was to evaluate the performance of our quality assurance (QA) automation system and to evaluate the machine performance of a new type linear accelerator uRT-linac 506c within 6 months using this system.

METHODS

This QA automation system consists of a hollow cylindrical phantom with 18 steel balls in the phantom surface and an analysis software to process electronic portal imaging device (EPID) measurement image data and report the results. The performance of the QA automation system was evaluated by the tests of repeatability, archivable precision, detectability of introduced errors, and the impact of set-up errors on QA results. The performance of this linac was evaluated by 31 items using this QA system over 6 months.

RESULTS

This QA system was able to automatically deliver QA plan, EPID image acquisition, and automatic analysis. All images acquiring and analysis took approximately 4.6 min per energy. The preset error of 0.1 mm in multi-leaf collimator (MLC) leaf were detected as 0.12 ± 0.01 mm for Bank A and 0.10 ± 0.01 mm in Bank B. The 2 mm setup error was detected as -1.95 ± 0.01 mm, -2.02 ± 0.01 mm, 2.01 ± 0.01 mm for X, Y, Z directions, respectively. And data from the tests of repeatability and detectability of introduced errors showed the standard deviation were all within 0.1 mm and 0.1°. and data of the machine performance were all within the tolerance specified by AAPM TG-142.

CONCLUSIONS

The QA automation system has high precision and good performance, and it can improve the QA efficiency. The performance of the new accelerator has also performed very well during the testing period.

Magnetic Resonance Imaging Image Segmentation under Edge Detection Intelligent Algorithm in Diagnosis of Surgical Wrist Joint Injuries

Background

Wrist joint injury refers to the injury of the wrist joint caused by excessive stretching of the ligaments and joint capsules around the joint caused by indirect violence. The tissue structure of the wrist joint is complex, and the clinical diagnosis effect is poor.

Methods

The purpose of this study was to improve the diagnostic accuracy of wrist joint injuries and provide evidence for imaging analysis and automatic diagnosis of lesions in patients with wrist joint injuries. The Canny algorithm was adopted to extract the edge features of the patient's magnetic resonance imaging (MRI) image, and the particle swarm optimization-support vector machine (PSO-SVM) algorithm was applied to segment the lesion. The image processing effect of the algorithm was evaluated by taking peak signal to noise ratio (PSNR), mean square error (MSE), figure of merit (FOM), and structural similarity (SSIM) as indicators. The accuracy, sensitivity, specificity, and Dice similarity coefficient of the algorithm were analyzed to evaluate the diagnostic accuracy in WJI.

Results

Compared with the Gradient Vector Flo (GVF) algorithm and the Elastic Automatic Region Growing (ERG) algorithm, the edge stability of the PSO-SVM algorithm was stable above 0.9. After the quality of images processed using different algorithms was analyzed, it was found that the PSNR of the PSO-SVM algorithm was 26.891 ± 5.331 dB, the MSE was 0.0014 ± 0.0003, the FOM was 0.8832 ± 0.0957, and the SSIM was 0.9032 ± 0.0807. The four indicators were all much better than those of the GVF algorithm and the EARG algorithm, showing statistically obvious differences (P < 0.05). Analysis on diagnostic accuracy of different algorithms for WJI suggested that the diagnostic accuracy of the PSO-SVM algorithm was 0.9413, the sensitivity was 0.9129, the specificity was 0.9088, and the Dice similarity coefficient was 0.8715. The four indicators all showed statistically great difference compared with those of the GVF algorithm and the EARG algorithm (P < 0.05).

Conclusions

The PSO-SVM algorithm showed excellent edge detection performance and higher accuracy in the diagnosis of WJI, which can assist clinicians in the clinical auxiliary diagnosis of WJI.

Factors influencing daily quality assurance measurements of magnetic resonance imaging scanners

Magnetic resonance imaging is commonly used in hospitals and clinics to aid medical diagnoses. Scanner performance should be assessed regularly, including daily, weekly, and yearly evaluations to ensure high-quality and artifact-free images. Of these assessments, the daily quality assurance monitors the image quality of the scanner using a manufacturer-provided protocol. In this study, we sought to determine the factors that introduced variability in daily quality assurance data. A phantom was scanned using a head coil in two schemes: with varied phantom placement daily, and with a single phantom placement, and evaluated over approximately 1 month. Minor placement and localization changes accounted for approximately 50% of the variability in the signal-to-noise ratios observed in these measures, driven by changes in the measured signal, while the noise remained constant. The changes in the signal-to-noise ratios were small over the 2-month study period.

Improvement in MR quality control workflow and outcomes with a web-based database

PURPOSE

To describe a custom-built, web-based MR Quality Control (QC) database, and to assess its impact on the QC workflow and outcomes in a large U.S. academic medical center.

METHODS

The MR QC database was built with Microsoft Access 2010 and published on a Microsoft Sharepoint website owned and maintained by the authors' institution. Authorized users can access the database remotely with mainstream web browsers on any institutional computers. QC technologists were granted access to add, review, and print daily and weekly QC records. Qualified medical physicists (QMPs) were granted additional access to edit, review, and approve existing QC records and to change tolerance limits. A macro was utilized to conduct an automatic weekly review of QC status and to email the results to a QMP. This web-based QC database was implemented on 17 clinical MRIs at the authors' institution. Weekly ACR QC findings within one year before and after implementation were compared.

RESULTS

We analyzed 158 QC issues detected by the web-based database and 127 QC issues identified in conventional paper records before we implemented the database. The web-based database significantly reduced the number of QC issues due to technologist error (before/after: 59/24 cases, P < 0.0001) but did not affect the number of QC issues related to scanner performance (before/after: 49/46 cases, P = 1). Further analysis revealed that the web-based database significantly reduced the average time for the QMPs to identify a QC issue (before/after: 177 ± 110/2 ± 2 days, P < 0.0001) and time to correction (before/after: 81 ± 102/7 ± 8 days, P < 0.0001). The correction rate also significantly increased (before/after: 22%/99%, P < 0.0001).

CONCLUSION

The web-based QC database provides a positive impact on our MR QC workflow and outcomes. It simplifies QC workflow, enables early detection of quality issues, and facilitates quick resolution of problems that may affect the quality of clinical MRI studies.

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.

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

Background

Quality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers.

Methods

In total, 192, 88, 76 and 55 brain images are acquired using T₂^*, T₁, T₂ and contrast-enhanced T₁ (T₁C) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed.

Results

To the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, p_w ≥ 0.11; paired-sample t test, p_p ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, p_icc ≥ 0.74). Furthermore, Pearson correlation coefficient (r_p) suggests that SNR_wm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T₂^* (r_p ≥ 0.78), BRISQUE and NIQE in T₁ (r_p ≥ 0.77), BLIINDS-II in T₂ (r_p ≥ 0.68) and BRISQUE and NIQE in T₁C (r_p ≥ 0.62) weighted MR images, while SNR_csf correlates strongly with BLIINDS-II in T₂^* (r_p ≥ 0.63) and in T₂ (r_p ≥ 0.64) weighted MR images.

Conclusions

The consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images.