Post-correctional improvement of T2-weighted fast spin echo magnetic resonance imaging pulse sequence for detecting high intensity focused ultrasound thermal lesions
Bibliographic record
Abstract
High intensity focused ultrasound (HIFU) is a non-invasive therapy that induces heat in a small, localized volume of cancerous tissue without damaging neighbouring vital structures and cells. Precise targeting and treatment monitoring is typically achieved by pairing HIFU with an imaging modality such as magnetic resonance imaging (MRI). The most commonly used MRI pulse sequence for detecting HIFU thermal lesions is the T2-weighted fast spin echo (T2W-FSE) pulse sequence as it provides good contrast between normal and coagulated tissue. The drawbacks of the T2W-FSE pulse sequence are the manifestation of ringing artifacts and the loss of spatial resolution due to the signal modulation in k-space caused by the T2 decay. The inverse Fourier transform (IFT) multiplication scheme aims to remove the signal modulation by incorporating an inverse filter, which is an inverse of the signal modulation trend present in the k-space, to reduce the effects of T2 decay and improve image quality. In this study, four inverse filters (named as regular, narrow, wide, and compound) were developed and implemented on T2W-FSE MR images of ex vivo porcine muscle tissue with HIFU induced thermal lesion using a 0.55 T benchtop MRI research system (Pure Devices, Rimpar, Germany). Offline processing and enhancement of MR images of ex vivo porcine muscle tissue with HIFU induced thermal lesion using the narrow filter yielded the largest improvements of 13.8 ± 2.5 %, 17.0 ± 2.3 %, and 14.4 ± 1.1 % in lateral and axial spatial resolutions, and lesion signal-to-noise ratio (SNR), respectively, compared to the original images. Our results indicate an amplification of the signals in k-space and a reduction in the exponential signal modulation caused by T2 decay. These results also indicate the potential of the IFT multiplication scheme as an image processing method to improve thermal lesion detectability in MR-guided HIFU procedures.
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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 | 0.000 | 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".