Development of a cost-effective detector for an MRI compatible brainPET
Bibliographic record
Abstract
Following the development of a Magnetic Resonance Imaging (MRI) compatible small animal Positron Emission Tomography (PET) insert, the University of Manitoba PET lab aims to develop an MRI compatible brainPET designed to retrofit into the Siemens Magnetom 7 T brain MRI. The purpose of this device is to perform simultaneous PET/MRI brain imaging for identification and diagnosis of brain and neck tumours, as well as neurodegenerative diseases. This thesis focuses on finding a suitable detector block through experimentation to be replicated 64 times to make up 4 rings of a 35 cm diameter brainPET insert. Experiments are conducted with a detector block and timing pick-off detector setup to capture 511 keV annihilation photon pairs emitted by a 22Na source, using the PETsys TOFPET2 ASIC for signal readout and digitization. The detector block is a Dual-Layer Offset (DLO) array of 2.4 mm pitch Lutetium-Yttrium Oxyorthosilicate (LYSO) scintillator crystals optically coupled to a sparse array of 3 mm Silicon Photomultiplier (SiPM) photosensors, with a 21 % fill factor. Motivating the low fill factor is the reduced cost of the detector, however it leads to scintillation light loss over the dead space on the photosensor board. To minimize light loss, reflective materials are placed on the board and tested, including Enhanced Specular Reflector (ESR), and white Polylactic Acid (PLA). The best result was with the PLA reflector, with a per-crystal average energy resolution of (18.5/19.0)% at 511 keV and Coincidence Timing Resolution (CTR) of (0.83/0.82)ns FWHM for the top/bottom layers of the scintillator. Lightguides promoting crystal-photosensor lightsharing were tested by calculating the resolvability of scintillator crystal positions measured by the detector. Of the lightguides tested, the 1.5 mm and 2.0 mm thicknesses produced the best crystal resolvability. Data processing techniques used for analysis are discussed, and parameters are tested by their effect on the results. Systematic timing offsets are discovered, and it is determined that there likely is not a DC offset in the TOFPET2 ASIC signal amplitude measurement.
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 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".