An evaluation of quality assurance guidelines comparing the American College of Radiology and American Association of Physicists in Medicine task group 284 for magnetic resonance simulation

PurposeACR and AAPM task group's guidelines addressing commissioning for dedicated MR simulators were recently published. The goal of the current paper is to present the authors' 2‐year experience regarding the commissioning and introduction of a QA program based on these guidelines and an associated automated workflow.MethodsAll mandatory commissioning tests suggested by AAPM report 284 were performed and results are reported for two MRI scanners (MAGNETOM Sola and Aera). Visual inspection, vendor clinical or service platform, third‐party software, or in‐house python‐based code were used. Automated QA and data analysis was performed via vendor, in‐house or third‐party software. QATrack+ was used for QA data logging and storage. 3D geometric distortion, B0 inhomogeneity, EPI, and parallel imaging performance were evaluated.ResultsContrasting with AAPM report 284 recommendations, homogeneity and RF tests were performed monthly. The QA program allowed us to detect major failures over time (shimming, gradient calibration and RF interference). Automated QA, data analysis, and logging allowed fast ACR analysis daily and monthly QA to be performed in 3 h. On the Sola, the average distortion is 1 mm for imaging radii of 250 mm or less. For radii of up to 200 mm, the maximum, average (standard deviation) distortion is 1.2 and 0.4 mm (0.3 mm). Aera values are roughly double the Sola for radii up to 200 mm. EPI geometric distortion, ghosting ratio, and long‐term stability were found to be under the maximum recommended values. Parallel imaging SNR ratio was stable and close to the theoretical value (ideal g‐factor). No major failures were detected during commissioning.ConclusionAn automated workflow and enhanced QA program allowed to automatically track machine and environmental changes over time and to detect periodic failures and errors that might otherwise have gone unnoticed. The Sola is more geometrically accurate, with a more homogenous B0 field than the Aera.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Commissioning and implementing a Quality Assurance program for dedicated radiation oncology MRI scanners

Poulin,

Lacroix,

Archambault

et al. 2024

“…1 Precision radiation therapy methods require MRI image quality that is markedly higher than diagnostic radiology, especially because it requires high geometrical accuracy. 2 For instance, the GK-SRS treatments require sub-millimeter MRI geometry distortions to deliver the prescribed steep dose to the target and spare the surrounding healthy tissues. 3 MRI geometrical distortions, including machinespecific and patient-specific, will make the difference between a successful treatment and an excessive dose to healthy structures.…”

Section: Introductionmentioning

confidence: 99%

“…Magnetic resonance imaging (MRI) plays a vital role in treatment of intracranial tumors using precision radiation therapy like Gamma Knife stereotactic radiosurgery (GK‐SRS) 1 . Precision radiation therapy methods require MRI image quality that is markedly higher than diagnostic radiology, especially because it requires high geometrical accuracy 2 . For instance, the GK‐SRS treatments require sub‐millimeter MRI geometry distortions to deliver the prescribed steep dose to the target and spare the surrounding healthy tissues 3 …”

Section: Introductionmentioning

confidence: 99%

Patient‐specific geometrical distortion corrections of MRI images improve dosimetric planning accuracy of vestibular schwannoma treated with gamma knife stereotactic radiosurgery

Safari,

Fatemi,

Afkham

et al. 2023

PurposeTo investigate the impact of MRI patient‐specific geometrical distortion (PSD) on the quality of Gamma Knife stereotactic radiosurgery (GK‐SRS) plans of the vestibular schwannoma (VS) tumors.Methods and materialsThree open access datasets including the MPI‐Leipzig Mind‐Brain‐Body (318 patients), the slow event‐related fMRI designs dataset (62 patients), and the VS dataset (242 patients) were used. We used first two datasets to train a 3D convolution network to predict the distortion map of third dataset that were then used to calculate and correct the PSD. GK‐SRS plans of VS dataset were used to evaluate dose distribution of PSD‐corrected MRI images. GK‐SRS prescription dose of VS cases was 12 Gy. Geometric and dosimetric discrepancies were assessed between the dose distributions and contours before and after the PSD corrections. Geometry indices were center of the contours, Dice coefficient (DC), Hausdorff distance (HD), and dosimetric indices were , , , and doses, target coverage (TC), Paddick's conformity index (PCI), Paddick's gradient index (GI), and homogeneity index (HI).ResultsGeometric distortions of about 1.2 mm were observed at the air‐tissue interfaces at the air canal and nasal cavity borders. Average center of the targets was significantly distorted along the frequency encoding direction after the PSD‐correction. Average DC and HD metrics were 0.90 and 2.13 mm. Average , and in Gy significantly increased after PSD correction from 16.85 to 17.25, 12.30 to 12.77, and from 8.98 to 9.92. did not significantly change after the correction. Average TC and PCI significantly increased from 0.97 to 0.98, and 0.94 to 0.96. Average GI decreased significantly from 2.24 to 2.15 after PSD correction. However, HI did not significantly change after the correction.ConclusionThe proposed method could predict and correct the PSD that indicates the importance of PSD correction before GK‐SRS plans of the VS patients.

An evaluation of quality assurance guidelines comparing the American College of Radiology and American Association of Physicists in Medicine task group 284 for magnetic resonance simulation

Buatti

Gallagher

Bailey

et al. 2022

Self Cite

Purpose The purpose of this study was to evaluate similarities and differences in quality assurance (QA) guidelines for a conventional diagnostic magnetic resonance (MR) system and a MR simulator (MR‐SIM) system used for radiotherapy. Methods In this study, we compared QA testing guidelines from the American College of Radiology (ACR) MR Quality Control (MR QC) Manual to the QA section of the American Association of Physicists in Medicine (AAPM) Task Group 284 report (TG‐284). Differences and similarities were identified in testing scope, frequency, and tolerances. QA testing results from an ACR accredited clinical diagnostic MR system following ACR MR QC instructions were then evaluated using TG‐284 tolerances. Results Five tests from the ACR MR QC Manual were not included in TG‐284. Five new tests were identified for MR‐SIM systems in TG‐284 and pertained exclusively to the external laser positioning system of MR‐SIM systems. “Low‐contrast object detectability” (LCD), “table motion smoothness and accuracy,” “transmitter gain,” and “geometric accuracy” tests differed between the two QA guides. Tighter tolerances were required in TG‐284 for “table motion smoothness and accuracy” and “low contrast object detectability.” “Transmitter gain” tolerance was dependent on initial baseline measurements, and TG‐284 required that geometric accuracy be tested over a larger field of view than the ACR testing method. All tests from the ACR MR QC Manual for a conventional MR system passed ACR tolerances. The T2‐weighted image acquired with ACR sequences failed the 40‐spoke requirement from TG‐284, transmitter gain was at the 5% tolerance of TG‐284, and geometric accuracy could not be evaluated because of required equipment differences. Table motion passed both TG‐284 and ACR required tolerances. Conclusion Our study evaluated QA guidelines for an MR‐SIM and demonstrated the additional QA requirements of a clinical diagnostic MR system to be used as an MR‐SIM in radiotherapy as recommended by TG‐284.