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Record W1964449190 · doi:10.1118/1.3355856

Implications of tissue magnetic susceptibility-related distortion on the rotating magnet in an MR-linac design

2010· article· en· W1964449190 on OpenAlexafffund
Keith Wachowicz, T. Stanescu, Steven Thomas, B. G. Fallone

Bibliographic record

VenueMedical Physics · 2010
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersAlberta Cancer Foundation
KeywordsMagnetLinear particle acceleratorDistortion (music)Nuclear magnetic resonancePhysicsNuclear medicineMedicineOpticsBeam (structure)Amplifier

Abstract

fetched live from OpenAlex

PURPOSE: One of the recently published concepts that combine the soft-tissue imaging capabilities of MRI with external beam radiotherapy involves the rigid coupling of a linac with a rotating biplanar low-field MR imaging system. While such a system would prevent possible image distortion resulting from relative motion between the magnet and the linac, the rotation of the magnet around the patient can itself introduce possibilities for image distortion that need to be addressed. While there are straightforward techniques in the literature for correcting distortions from gradient nonlinearities and nonuniform magnetic fields during image reconstruction, the correction of distortions related to tissue magnetic susceptibility is more complex. This work investigates the extent of this latter distortion type under the regime of a rotating magnetic field. METHODS: CT images covering patient anatomy in the head, lung, and male pelvic regions were obtained and segmented into components of air, bone, and soft tissue. Each of these three components was assigned bulk magnetic susceptibility values in accordance with those found in the literature. A finite-difference algorithm was then implemented to solve for magnetic field distortion maps should the anatomies be placed in the uniform polarizing field of an MR system. The algorithm was repeated multiple times as the polarizing field was rotated axially about the virtual patient in 15 degrees increments. In this way, a map of maximum distortion, and the range of distortion as the magnetic field is rotated about each anatomical region could be determined. The consequence of these susceptibility distortions in terms of geometric signal shift was calculated for 0.2 T, as well as another low-field system (0.5 T), and a higher field 1.5 T system for comparison, using the assumption of a frequency encoding gradient strength of 5 mT/m. RESULTS: At 0.2 T, the susceptibility-related distortion was limited to less than 0.5 mm given an encoding gradient strength of 5 mT/m or higher. To maintain this same level of geometric accuracy, the 0.5 T system would require a moderately higher minimum gradient strength of 11 mT/m, and at a typical MR field strength of 1.5 T this minimum gradient strength would increase to 33 mT/m. The influence of magnetic susceptibility on mean frequency shift as the field orientation was rotated was also investigated and found to account for less than half a millimeter at 1.5 T, and negligible for low-field systems. CONCLUSIONS: A study of three sites (head, lung, and prostate) that are vulnerable to magnetic susceptibility-related distortions were studied, and showed that in the context of a rotating polarizing magnet, low-field systems can maintain geometric accuracy of 0.5 mm with at most moderate limitations on sequence parameters. This conclusion will likely apply only to endogenous tissues, as implanted materials such as titanium can create field distortions much in excess of what may normally be induced in the body. Items containing such materials (hip prostheses, for example) will require individual scrutiny.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.342
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2010
Admission routes2
Has abstractyes

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