Bibliographic record
Abstract
The Center for Translational Neuroimaging of the Brookhaven National Laboratory has been studying the phenomenon of addiction, which has a direct impact on millions of people worldwide. This requires the development of new radiotracers for imaging specific neurotransmitter systems in the brain, and the design and implementation of novel imaging devices to measure the neuroactivity of the brain. The RatCAP, or Rat Conscious Animal Positron Emission Tomography (PET), is a head-mounted miniature PET scanner for brain metabolism imaging of awake rats with minimal mobility restriction to enable correlation with the animal's behavior. The RatCAP detector is based on LSO scintillator crystals and avalanche photodiode (APD) arrays. The design of the RatCAP imposed stringent requirements on the readout electronics. First, due to its size and limited power budget, VLSI of the front-end electronics was mandatory. Second, due to the weak signal to noise ratio from the APD detectors, the analog front-end noise had to be minimized, within the power budget, to provide the best possible timing resolution. Finally, the number of interconnections with the data acquisition system had to be minimal in order to maximize the animal's mobility. This thesis presents the design and implementation of the ASIC for the RatCAP. The final ASIC integrates 32 channels consisting of a charge sensitive preamplifier, programmable gain, a bipolar shaping amplifier, and timing and energy discriminators. A novel 32-to-1 address and timing serial encoder is integrated on-chip to multiplex the acquired data through a single output. The ASIC was realized in 0.18 [mu]m CMOS technology from TSMC, has a size of 3.3 mm × 4.5 mm, and power consumption of 117 mW. The ASIC is fully operational. Noise characterization led to a measured equivalent noise charge of 650 electrons rms with the APD biased at the input. A coincidence timing resolution of 6.7 ns FWHM was measured between two typical LSO-APD-ASIC modules using a 68 Ge source (threshold at 420 keV). An energy resolution of 18.7% FWHM at 511 keV was measured for a 68 Ge source. The ASIC and the technology developed for the RatCAP have opened the door to the realization of many other systems, such as a PET-MRI scanner, and led to the granting of three patents and the publication of numerous scientific presentations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".