CMOS Time-Domain Imager for Functional Brain Imaging Using Gated Near-Infrared Spectroscopy
Bibliographic record
Abstract
Non-invasive imaging of brain activity enables novel studies in neuroscience and provides an alternative modality for clinical monitoring applications. Near-infrared spectroscopic imaging (NIRSI) is non-invasive, cheap, portable, and immune to electro-magnetic interference. NIRSI is also superior in terms of spatial or temporal resolution when compared to electro-encephalography (EEG) or magnetic resonance imaging (MRI), respectively. Utilizing the time-domain (TD) technique offers the richest information at the cost of being the most complex[1]. TD NIRS imagers utilize time-correlated single-photon-counting (TCSPC) measurements which require detectors with single-photon sensitivity like single-photon avalanche diodes (SPADs)[2] or Silicon photomultipliers (SiPMs) and very fast time-discriminators like time-discriminator circuits (TDCs). Implementing these circuits in well-established and mature CMOS technologies[3] is advantageous. The main challenges in designing a TD NIRSI sensor deal with four issues: spectral responsivity, noise, fill-factor, and throughput. The spectral responsivity and noise are shaped by the technology process and structure (see figure 2) of the SPAD[4]. The fill-factor is determined by the ancillary circuitry needed to maintain the TD operation of the SPAD[4] (i.e., frontend, gating, and TDC). The throughput is dependent on the readout architecture of the array of pixels. We are proposing a fully integrated scalable array of time-gated actively-quenched SPADs with shared time-gated ring-oscillator-based TDCs following H-tree-based architecture and a 3-transistor active-pixel sensor (3T APS) readout scheme with in-pixel storage capability to be implemented using standard deep sub-micron CMOS technology. For example, using 130 nm CMOS technology SPADs can be built with an area of 50 um2 which exhibit a dark count rate (DCR) of 18 KHz[5]. At a wavelength around 600 nm and an excess bias of 2 V, their photon detection probability (PDP) could reach 22%. These SPADs breakdown at 20 V and could resolve down to 90 ps for wavelengths around 654 nm. With the same technology a 0.04 mm2 TDC can be designed with a resolution or least significant bit (LSB) of 6 ps for a range of 11-bits[6]. Moreover, SPAD structure variations, novel gating schemes, smart resource sharing, and efficient array architecture a CMOS TD NIRSI sensor can be realized to meet requirements of functional human brain imaging applications. References [1] A. Torricelli, D. Contini, A. Pifferi, M. Caffini, R. Re, L. Zucchelli and L. Spinelli, "Time domain functional NIRS imaging for human brain mapping," J NeuroImage, 2013. [2] E. Villella, O. Alonso, A. V. A. Montiel and A. Dieguez, "A low-noise time-gated single-photon detector in HV-CMOS technology for triggered imaging," Sensors and Actuators A: Physical, vol. 201, p. 342, 2013. [3] C. Niclass, M. Soga, H. Matsubara, M. Ogawa and M. Kagami, "A 0.18-um CMOS SoC for a 100-nm-Range 10-Frame/s 200 x 96-Pixel Time-of-Flight Depth Sensor," IEEE Journal of Solid-State Circuits, vol. 49, no. 1, 2014. [4] D. Palubiak, M. El-Desouki, O. Marinov, M. J. Deen and Q. Fang, "High-Speed, Single-Photon Avalanche-Photodiode Imager for Biomedical Applications," IEEE Sensors Journal, vol. 11, no. 10, p. 2401, 2011. [5] E. Webster, L. Grant and R. Henderson, "A High-Performance Single-Photon Avalanche Diode in 130-nm CMOS Imaging Technology," IEEE Electron Device Letters, vol. 33, no. 11, p. 1589, 2012. [6] M. Straayer and M. Perrott, "A multi-path gated ring oscillator TDC with first-order noise shaping," IEEE Journal of Solid-State Circuits, vol. 44, no. 4, p. 1089, 2009. [7] M. Ferrari and V. Quaresima, "A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application," J NeuroImage, vol. 63, p. 921, 2012. [8] L. Braga, L. Gasparini, L. Grant, R. Henderson, N. Massari, M. Perenzoni, D. Stoppa and R. Walker, "A Fully Digital 8 x 16 SiPM Array for PET Applications With Per-Pixel TDCs and Real-Time Energy Output," IEEE Journal of Solid-State Circuits, vol. 49, no. 1, 2014.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".