Purpose: Time-of-flight (TOF) been successfully implemented in whole body PET, significantly improving clinical performance. However, TOF has not been a priority in development of dedicated brain PET systems due the relatively small size of the human head, where coincidence timing resolution (CTR) below 200 ps is necessary to arrive at substantial performance improvements. The Brain PET (BET) consortium is developing a PET detector block with ultrafast CTR, high sensitivity and high spatial resolution (X, Y, depth of interaction, DOI) that provides a pathway to significantly improved brain PET. Methods: We have implemented analytical and Monte Carlo models of scintillation photons transport in scintillator segments with the trans-axial cross-section equal or smaller than 3x3 mm2 . Results: The signal amplitude and timing of W mm x W mm x L mm scintillators (1 mm<W<3 mm, 5 mm <L< 30 mm) are strongly influenced by sidewall surface polish and external reflector. Highly polished surfaces provide nearly perfect total internal reflection (TIR), enabling the ultrafast timing performance to be relatively independent of scintillator crosssection. The signal amplitude in such a configuration does not depend on DOI. However, the differential signal from top and bottom SiPM in the dual-ended readout can be used to determine DOI. Using TIR alone, the average of the photon detection times at the top and bottom SiPMs provides a good estimation of the gamma ray absorption time. Averaging ~10 photons starting from 3rd photon produces the shortest CTR for SPTR=50 ps. Conclusions: We established that the advanced silicon photomultiplier designs with high single photon detection efficiency (QE=60%) and high single photon timing resolution (SPTR =50 ps) are critical for achieving ultrafast TOF-PET performance with CTR ~50 ps and ~4 mm DOI resolution.
Using analytical and Monte Carlo modeling, we explored performance of a lightweight wearable helmet-shaped brain positron emission tomography (PET), or BET camera, based on thin-film digital Geiger avalanche photodiode arrays with Lutetium-yttrium oxyorthosilicate (LYSO) or LaBr3 scintillators for imaging in vivo human brain function of freely moving and acting subjects. We investigated a spherical cap BET and cylindrical brain PET (CYL) geometries with 250-mm diameter. We also considered a clinical whole-body (WB) LYSO PET/CT scanner. The simulated energy resolutions were 10.8% (LYSO) and 3.3% (LaBr3), and the coincidence window was set at 2 ns. The brain was simulated as a water sphere of uniform F-18 activity with a radius of 100 mm. We found that BET achieved >40% better noise equivalent count (NEC) performance relative to the CYL and >800% than WB. For 10-mm-thick LaBr3 equivalent mass systems, LYSO (7-mm thick) had ∼40% higher NEC than LaBr3. We found that 1×1×3 mm scintillator crystals achieved ∼1.1 mm full-width-half-maximum spatial resolution without parallax errors. Additionally, our simulations showed that LYSO generally outperformed LaBr3 for NEC unless the timing resolution for LaBr3 was considerably smaller than that presently used for LYSO, i.e., well below 300 ps.
Purpose: To explore, by means of analytical and Monte Carlo modeling, performance of a novel lightweight and low-cost wearable helmet-shaped Brain PET (BET) camera based on thin-film digital Geiger Avalanche Photo Diode (dGAPD) with LSO and LaBr3 scintillators for imaging in vivo human brain processes for freely moving and acting subjects responding to various stimuli in any environment.
Methods: We performed analytical and Monte Carlo modeling PET performance of a spherical cap BET device and cylindrical brain PET (CYL) device, both with 25 cm diameter and the same total mass of LSO scintillator. Total mass of LSO in both the BET and CYL systems is about 32 kg for a 25 mm thick scintillator, and 13 kg for 10 mm thick scintillator (assuming an LSO density of 7.3 g/ml). We also investigated a similar system using an LaBr3 scintillator corresponding to 22 kg and 9 kg for the 25 mm and 10 mm thick systems (assuming an LaBr3 density of 5.08 g/ml). In addition, we considered a clinical whole body (WB) LSO PET/CT scanner with 82 cm ring diameter and 15.8 cm axial length to represent a reference system. BET consisted of distributed Autonomous Detector Arrays (ADAs) integrated into Intelligent Autonomous Detector Blocks (IADBs). The ADA comprised of an array of small LYSO scintillator volumes (voxels with base a×a: 1.0 ≤ a ≤ 2.0 mm and length c: 3.0 ≤ c ≤ 6.0 mm) with 5–65 μm thick reflective layers on its five sides and sixth side optically coupled to the matching array of dGAPDs and processing electronics with total thickness of 50 μm. Simulated energy resolution was 10.8% and 3.3% for LSO and LaBr3 respectively and the coincidence window was set at 2 ns. The brain was simulated as a sphere of uniform F-18 activity with diameter of 10 cm embedded in a center of water sphere with diameter of 10 cm.
Results: Analytical and Monte Carlo models showed similar results for lower energy window values (458 keV versus 445 keV for LSO, and 492 keV versus 485 keV for LaBr3), and for the relative performance of system sensitivity. Monte Carlo results further showed that the BET geometry had >50% better noise equivalent count (NEC) performance relative to the CYL geometry, and >1100% better performance than a WB geometry for 25 mm thick LSO and LaBr3. For 10 mm thick LaBr3 equivalent mass systems LSO (7 mm thick) performed ~40% higher NEC than LaBr3. Analytic and Monte Carlo simulations also showed that 1×1×3 mm scintillator crystals can achieve ~1.2 mm FWHM spatial resolution.
Conclusions: This study shows that a spherical cap brain PET system can provide improved NEC while preserving spatial resolution when compared to an equivalent dedicated cylindrical PET brain camera and shows greatly improved PET performance relative to a conventional whole body PET/CT. In addition, our simulations show that LSO will generally outperform LaBr3 for NEC unless the timing resolution for LaBr3 is considerably smaller than presently used for LSO, i.e. well below 300 ps.
Wavelet transforms have been successfully applied in many fields of image processing. Yet, to our knowledge, they have never been directly incorporated to the objective function in Emission Computed Tomography (ECT) image reconstruction. Our aim has been to investigate if the ℓ1-norm of non-decimated discrete cosine transform (DCT) coefficients of the estimated radiotracer distribution could be effectively used as the regularization term for the penalized-likelihood (PL) reconstruction, where a regularizer is used to enforce the image smoothness in the reconstruction. In this study, the ℓ1-norm of 2D DCT wavelet decomposition was used as a regularization term. The Preconditioned Alternating Projection Algorithm (PAPA), which we proposed in earlier work to solve penalized likelihood (PL) reconstruction with non-differentiable regularizers, was used to solve this optimization problem. The DCT wavelet decompositions were performed on the transaxial reconstructed images. We reconstructed Monte Carlo simulated SPECT data obtained for a numerical phantom with Gaussian blobs as hot lesions and with a warm random lumpy background. Reconstructed images using the proposed method exhibited better noise suppression and improved lesion conspicuity, compared with images reconstructed using expectation maximization (EM) algorithm with Gaussian post filter (GPF). Also, the mean square error (MSE) was smaller, compared with EM-GPF. A critical and challenging aspect of this method was selection of optimal parameters. In summary, our numerical experiments demonstrated that the ℓ1-norm of discrete cosine transform (DCT) wavelet frame transform DCT regularizer shows promise for SPECT image reconstruction using PAPA method.