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Record W2981589863 · doi:10.21037/qims.2019.10.03

Chemical exchange saturation transfer magnetic resonance imaging and its main and potential applications in pre-clinical and clinical studies

2019· review· en· W2981589863 on OpenAlexaff
Weiqiang Dou, Chien‐Yuan Lin, Hongyuan Ding, Yong Shen, Carol Dou, Long Qian, Baohong Wen, Bing Wu

Bibliographic record

VenueQuantitative Imaging in Medicine and Surgery · 2019
Typereview
Languageen
FieldMaterials Science
TopicLanthanide and Transition Metal Complexes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMagnetic resonance imagingSaturation (graph theory)Nuclear magnetic resonanceComputer scienceMedicineRadiologyPhysics

Abstract

fetched live from OpenAlex

Abstract: Chemical exchange saturation transfer (CEST) imaging is a novel contrast mechanism, relying on the exchange between mobile protons in amide (-NH), amine (-NH2) and hydroxyl (-OH) groups and bulk water. Due to the targeted protons present in endogenous molecules or exogenous compounds applied externally, CEST imaging can respectively, generate endogenous or exogenous contrast. Nowadays, CEST imaging for endogenous contrast has been explored in pre-clinical and clinical studies. Amide CEST, also called amide proton transfer weighted (APT) imaging, generates CEST effect at 3.5 ppm away from the water signal and has been widely investigated. Given the sensitivity to amide proton concentration and pH level, APT imaging has shown robust performance in the assessment of ischemia, brain tumors, breast and prostate cancer as well as neurodegenerative diseases. With advanced methods proposed, pure APT and Nuclear Overhauser Effect (NOE) mediated CEST effects were separately fitted from original APT signal. Using both effects, early but promising results were obtained for glioma patients in the evaluation of tumor response to therapy and patient survival. Compared to amide CEST, amine CEST is also mobile proton concentration and pH dependent, but has a faster exchange rate between amine protons and water. The resultant CEST effect is usually introduced at 1.8–3 ppm. Glutamate and creatine, as two main metabolites with amine groups for CEST imaging, have been applied to quantitatively assess diseases in the central nervous system and muscle system, respectively. Glycosaminoglycan (Gag) as a representative metabolite with hydroxyl groups has also been measured to evaluate the cartilage of knee or intervertebral discs in CEST MRI. Due to limited frequency difference between hydroxyl protons and water, 7T for better spectral separation is preferred over 3T for GagCEST measurement. The applications of CEST MRI with exogenous contrast agents are still quite limited in clinic. While certain diamagnetic CEST agents, such as dynamic-glucose, have been tried in human for brain tumor or neck cancer assessment, most exogenous agents, i.e., paramagnetic CEST agents, are still tested in the pre-clinical stage, mainly due to potential toxicity. Engineered tissues for tissue regeneration and drug delivery have also shown a great potential in CEST imaging, as many of them, such as hydrogel and polyamide materials, contain mobile protons or can be incorporated with CEST specific chemical compounds. These engineered tissues can thus generate CEST effect in vivo, allowing a possibility to understand the fate of them in vivo longitudinally. Although the CEST MRI with engineered tissues has only been established in early stage, the obtained first evidence is crucial for further optimizing these biomaterials and finally accomplishing the translation into clinical use.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.173
GPT teacher head0.439
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations75
Published2019
Admission routes1
Has abstractyes

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