Voxel‐by‐voxel correlations of perfusion, substrate, and metabolite signals in dynamic hyperpolarized <sup>13</sup>C imaging
Bibliographic record
Abstract
In this study, a mixture of pyruvic acid and the perfusion agent HP001 was co-polarized for simultaneous assessment of perfusion and metabolism in vivo. The pre-polarized mixture was administered to rats with subcutaneous MDA-MB-231 breast cancer xenografts and imaged using an interleaved sequence with designed spectral-spatial pulses and flyback echo-planar readouts. Voxel-by-voxel signal correlations from 10 animals (15 data sets) were analyzed for tumour, kidney, and muscle regions of interest. The relationship between perfusion and hyperpolarized signal was explored on a voxel-by-voxel basis in various metabolically active tissues, including tumour, healthy kidneys, and skeletal muscle. Positive pairwise correlations between lactate, pyruvate, and HP001 observed in all 10 tumours suggested that substrate delivery was the dominant factor limiting the conversion of pyruvate to lactate in the tumour model used in this study. On the other hand, in cases where conversion is the limiting factor, such as in healthy kidneys, both pyruvate and lactate can act as excellent perfusion markers. In intermediate cases between the two limits, such as in skeletal muscle, some perfusion information may be inferred from the (pyruvate + lactate) signal distribution. Co-administration of pyruvate with a dynamic nuclear polarization (DNP) perfusion agent is an effective approach for distinguishing between slow metabolism and poor perfusion and a practical strategy for lactate signal normalization to account for substrate delivery, especially in cases of rapid pyruvate-to-lactate conversion and in poorly perfused regions with inadequate pyruvate signal-to-noise ratio for reliable determination of the lactate-to-pyruvate ratio. Copyright © 2016 John Wiley & Sons, Ltd.
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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".