Effect of radiation induced current on the quality of MR images in an integrated linac‐MR system
Bibliographic record
Abstract
PURPOSE: In integrated linac-MRI systems, the RF coils are exposed to the linac's pulsed radiation, leading to a measurable radiation induced current (RIC). This work (1) visualizes the RIC in MRI raw data and determines its effect on the MR image signal-to-noise ratio (SNR) (b) examines the effect of linac dose rate on SNR degradations, (c) examines the RIC effect on different MRI sequences, (d) examines the effect of altering the MRI sequence timing on the RIC, and (e) uses a postprocessing method to reduce the RIC signal from the MR raw data. METHODS: MR images were acquired on the linac-MR prototype system using various imaging sequences (gradient echo, spin echo, and bSSFP), dose rates (0, 50, 100, 150, 200, and 250 MU∕min) and repetition times (TR) with the gradient echo sequence. The images were acquired with the radiation beam either directly incident or blocked from the RF coils. The SNR was calculated for each of these scenarios, showing a loss in SNR due to RIC. Finally, a postprocessing method was applied to the image k-space data in order to remove partially the RIC signal and recover some of the lost SNR. RESULTS: The RIC produces visible spikes in the k-space data acquired with the linac's radiation incident on the RF coils. This RIC leads to a loss in imaging SNR that increases with increasing linac dose rate (15%-18% loss at 250 MU∕min). The SNR loss seen with increasing linac dose rate appears to be largely independent of the MR sequence used. Changing the imaging TR had interesting visual effects on the appearance of RIC in k-space due to the timing between the linac's pulsing and the MR sequence, but did not change the SNR loss for a given linac dose rate. The use of a postprocessing algorithm was able to remove much of the RIC noise spikes from the MR image k-space data, resulting in the recovery of a significant portion, up to 81% (Table II), of the lost image SNR. CONCLUSIONS: The presence of RIC in MR RF coils leads to a loss of SNR which is directly related to the linac dose rate. The RIC related loss in SNR is likely to increase for systems that are able to provide larger than 250 MU∕min dose. Some of this SNR loss can be recovered through the use of a postprocessing algorithm, which removes the RIC artefact from the image k-space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".