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Automated ACR Image Analysis for MR Imaging for All Seven Tests

C Burton*, Boston Children's Hospital, Boston, MA

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: For a medical physicist, an American College of Radiology (ACR) image analysis for magnetic resonance imaging (MRI) must be performed annually. For the physicist to make measurements on these images, they must use image analysis software to make subjective measurements that follow the ACR phantom guidelines. Depending on the institution, the physicist may be required to analyze 10-40 of ACR image sets. While there is already software available, it is expensive. I have developed an in-house MR image analysis tool, Boston Children’s Hospital (BCH) algorithm, that can be distributed for no cost and it is MATLAB-based.


Methods: MATLAB code (created with MATLAB R2017b, including the Image Processing Toolbox) was used to program automatic evaluation of all tests in the Phantom Test Guide for ACR. The program also does a signal-to-noise (SNR) as a function of image gap analysis. The phantom is positioned along the z-direction according to the manual for the ACR MRI accreditation phantom. The algorithm sorts the images by slice number. The algorithm will correct for any rotation in transverse plane. The total execution time for the three tests was on the order of seconds. The images were acquired on a Siemens scanner, which stores each slice as an individual DICOM file.


Results: For all tests for BCH algorithm are within 2% of the manual measurements, better than the existing software.


Conclusion: This study has shown that the MRI ACR test can be automated with the entire program running in 5 seconds. It can assist with the manual method of taking measurements. And this MATLAB program can be distributed for free instead of the institution purchasing a more costly program. I have shown that our results are comparable to those of an existing MRI ACR image analyzer and is more precise than existing software.

Download ePoster [PDF]

Keywords

Quality Control, Quality Assurance, MRI

Taxonomy

IM- MRI : Quantitative Imaging

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