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Perfusion Imaging With Magnetic Resonance Imaging

2004· article· en· W2316286129 on OpenAlex
Timothy P. L. Roberts

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTopics in Magnetic Resonance Imaging · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsPerfusionPerfusion scanningMagnetic resonance imagingCerebral blood flowMedicineBlood flowCerebral perfusion pressureBiomedical engineeringComputer scienceRadiologyCardiology

Abstract

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It is an undisputed fact that perfusion of the tissue bed is an important aspect of the characterization of tissue viability. Noninvasive assessments of tissue perfusion provide indices of a physiologically specific nature, helping us build up comprehensive characterizations of tissue status: to improve diagnosis, to offer prognosis and, importantly, to select appropriate treatment strategies and then to monitor the efficacy of such treatments. Recent developments in the pharmaceutical industry have shown considerable promise and consequent emphasis in the evolution of thrombolytic agents for treatment of embolic stroke and anti-angiogenic agents for the treatment of cancer. Noninvasive imaging of perfusion provides the perfect partner for assessment and optimization of these treatment strategies. This issue brings together theoretical and clinical insights from experts in the field of perfusion MRI. In the first article, Østergaard describes the mathematics of deconvolution, the process whereby previously qualitative or semi-quantitative descriptions of contrast agent-induced signal changes have evolved into quantitative estimates of physiologically relevant parameters, cerebral blood volume, mean transit time, and indeed cerebral blood flow. Next, Golay et al. describe an alternative approach to quantitative flow measurement, namely, using a “magnetic bolus” or arterial spin labeling (ASL). Theory is presented to show how this too can lead to quantitation of cerebral blood flow. While there remains considerable development to be achieved with ASL, it is noteworthy that the absence of a physical contrast agent offers the scope for rapidly repeated perfusion assessments, leading to potential new applications where dynamic assessment of perfusion is a necessity. Further, by combination with BOLD contrast, ASL approaches begin to give insight into other physiologic parameters of tissue such as the metabolic rate of oxygen consumption, previously the domain of positron emission tomography. In contrast to the theoretical underpinnings of these two articles, the third by Rowley and Roberts discusses the practical implementation of perfusion MRI into neuroradiologic protocols as well as providing evidence for its utility in a range of clinical situations. Perhaps most excitingly is the emerging role of physiologically specific MRI (such as perfusion MRI) in contributing to patient selection for novel thrombolytic treatments when presenting with acute stroke. Such an individualized or “data-driven” approach to treatment offers considerable potential over the standard “time since onset” restriction currently in place. While the articles so far have emphasized perfusion of the brain, Padhani and Dzik-Jurasz in the next article discuss the imaging of perfusion in body applications, particularly oncology. This is an exciting and emerging field with technical as well as practical considerations. For example, the absence of an analog of the blood–brain barrier leads to considerable contrast agent extravasation when clinically approved agents are used. This places technical limitations on the interpretation of kinetic modeling. Other restrictions may be imposed by the body itself: abdominal imaging is commonly performed in “breath-hold” to avoid motion-related artifacts (and to avoid image to image misregistration, which would confound parameter mapping). However, maximum breath-hold durations (20–30 seconds) may not allow full capturing of the contrast agent bolus transit. Nonetheless, the imaging of tumor perfusion outside the brain may be of considerable clinical impact in determining, guiding, and monitoring treatment. Finally, in the last article Kassner and Roberts discuss extensions of perfusion imaging to more fully characterize vascular function and integrity. Two emerging applications related to perfusion are introduced: 1) measurement of cerebrovascular reactivity (CVR), the responsiveness of blood vessels to vasoactive challenges (and an indication of their ability to autoregulate); and 2) measurement of microvascular permeability (ie, the extravasation of contrast medium indicative of blood–brain barrier disturbance). The latter in particular has shown utility in identifying angiogenic activity in regions of tumors and may be a physiologically specific measure of the efficacy of, for example, anti-angiogenic pharmaceuticals. The opportunities for application of CVR and permeability assessment promise to expand as our ability to quantify these aspects of vascular integrity improves. In summary, this issue provides a timely insight into the state of perfusion MRI. Once an experimental laboratory tool, the technique has rapidly emerged as clinically routine. Once qualitative, this issue indicates that quantitative estimation of cerebrovascular parameters can be obtained. Once restricted to the brain, these techniques are now being used in the body; and once limited to considerations of flow and volume, new approaches are offering insight into additional characterization of vascular function and integrity. Perfusion MRI represents one component of the class of physiologically specific imaging techniques, which are becoming integral to the new radiology, with applications extending beyond diagnosis but into patient stratification, therapy guidance and treatment efficacy monitoring, by offering physiologically specific interpretation and thus physiologically specific characterization of the tissues under investigation.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.282
Teacher spread0.273 · 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