Voxelized ray-tracing simulation dedicated to multi-pinhole molecular breast tomosynthesis

Convolutional neural network based attenuation correction for123I-FP-CIT SPECT with focused striatum imaging

SPECT imaging with¹²³I-FP-CIT is used for diagnosis of neurodegenerative disorders like Parkinson's disease. Attenuation correction (AC) can be useful for quantitative analysis of¹²³I-FP-CIT SPECT. Ideally, AC would be performed based on attenuation maps (μ-maps) derived from perfectly registered CT scans. Suchμ-maps, however, are most times not available and possible errors in image registration can induce quantitative inaccuracies in AC corrected SPECT images. Earlier, we showed that a convolutional neural network (CNN) based approach allows to estimate SPECT-alignedμ-maps for full brain perfusion imaging using only emission data. Here we investigate the feasibility of similar CNN methods for axially focused¹²³I-FP-CIT scans. We tested our approach on a high-resolution multi-pinhole prototype clinical SPECT system in a Monte Carlo simulation study. Three CNNs that estimateμ-maps in a voxel-wise, patch-wise and image-wise manner were investigated. As the added value of AC on clinical¹²³I-FP-CIT scans is still debatable, the impact of AC was also reported to check in which cases CNN based AC could be beneficial. AC using the ground truthμ-maps (GT-AC) and CNN estimatedμ-maps (CNN-AC) were compared with the case when no AC was done (No-AC). Results show that the effect of using GT-AC versus CNN-AC or No-AC on striatal shape and symmetry is minimal. Specific binding ratios (SBRs) from localized regions show a deviation from GT-AC≤2.5% for all three CNN-ACs while No-AC systematically underestimates SBRs by 13.1%. A strong correlation (r≥0.99) was obtained between GT-AC based SBRs and SBRs from CNN-ACs and No-AC. Absolute quantification (in kBq ml^-1) shows a deviation from GT-AC within 2.2% for all three CNN-ACs and of 71.7% for No-AC. To conclude, all three CNNs show comparable performance in accurateμ-map estimation and¹²³I-FP-CIT quantification. CNN-estimatedμ-map can be a promising substitute for CT-basedμ-map.

Design and system evaluation of a dual-panel portable PET (DP-PET)

BACKGROUND

Integrated whole-body PET/MR technology continues to mature and is now extensively used in clinical settings. However, due to the special design architecture, integrated whole-body PET/MR comes with a few inherent limitations. Firstly, whole-body PET/MR lacks sensitivity and resolution for focused organs. Secondly, broader clinical access of integrated PET/MR has been significantly restricted due to its prohibitively high cost. The MR-compatible PET insert is an independent and removable PET scanner which can be placed within an MRI bore. However, the mobility and configurability of all existing MR-compatible PET insert prototypes remain limited.

METHODS

An MR-compatible portable PET insert prototype, dual-panel portable PET (DP-PET), has been developed for simultaneous PET/MR imaging. Using SiPM, digital readout electronics, novel carbon fiber shielding, phase-change cooling, and MRI compatible battery power, DP-PET was designed to achieve high-sensitivity and high-resolution with compatibility with a clinical 3-T MRI scanner. A GPU-based reconstruction method with resolution modeling (RM) has been developed for the DP-PET reconstruction. We evaluated the system performance on PET resolution, sensitivity, image quality, and the PET/MR interference.

RESULTS

The initial results reveal that the DP-PET prototype worked as expected in the MRI bore and caused minimal compromise to the MRI image quality. The PET performance was measured to show a spatial resolution ≤ 2.5 mm (parallel to the detector panels), maximum sensitivity = 3.6% at the center of FOV, and energy resolution = 12.43%. MR pulsing introduces less than 2% variation to the PET performance measurement results.

CONCLUSIONS

We developed a MR-compatible PET insert prototype and performed several studies to begin to characterize the performance of the proposed DP-PET. The results showed that the proposed DP-PET performed well in the MRI bore and would cause little influence on the MRI images. The Derenzo phantom test showed that the proposed reconstruction method could obtain high-quality images using DP-PET.

Automatic attenuation map estimation from SPECT data only for brain perfusion scans using convolutional neural networks

In clinical brain SPECT, correction for photon attenuation in the patient is essential to obtain images which provide quantitative information on the regional activity concentration per unit volume (kBq.[Formula: see text]). This correction generally requires an attenuation map ([Formula: see text] map) denoting the attenuation coefficient at each voxel which is often derived from a CT or MRI scan. However, such an additional scan is not always available and the method may suffer from registration errors. Therefore, we propose a SPECT-only-based strategy for [Formula: see text] map estimation that we apply to a stationary multi-pinhole clinical SPECT system (G-SPECT-I) for ^99mTc-HMPAO brain perfusion imaging. The method is based on the use of a convolutional neural network (CNN) and was validated with Monte Carlo simulated scans. Data acquired in list mode was used to employ the energy information of both primary and scattered photons to obtain information about the tissue attenuation as much as possible. Multiple SPECT reconstructions were performed from different energy windows over a large energy range. Locally extracted 4D SPECT patches (three spatial plus one energy dimension) were used as input for the CNN which was trained to predict the attenuation coefficient of the corresponding central voxel of the patch. Results show that Attenuation Correction using the Ground Truth [Formula: see text] maps (GT-AC) or using the CNN estimated [Formula: see text] maps (CNN-AC) achieve comparable accuracy. This was confirmed by a visual assessment as well as a quantitative comparison; the mean deviation from the GT-AC when using the CNN-AC is within 1.8% for the standardized uptake values in all brain regions. Therefore, our results indicate that a CNN-based method can be an automatic and accurate tool for SPECT attenuation correction that is independent of attenuation data from other imaging modalities or human interpretations about head contours.

Characterization of a multi-pinhole molecular breast tomosynthesis scanner

In recent years, breast imaging using radiolabelled molecules has attracted significant interest. Our group has proposed a multi-pinhole molecular breast tomosynthesis (MP-MBT) scanner to obtain 3D functional molecular breast images at high resolutions. After conducting extensive optimisation studies using simulations, we here present a first prototype of MP-MBT and evaluate its performance using physical phantoms. The MP-MBT design is based on two opposing gamma cameras that can image a lightly compressed pendant breast. Each gamma camera consists of a 250 × 150 mm² detector equipped with a collimator with multiple pinholes focusing on a line. The NaI(Tl) gamma detector is a customised design with 3.5 mm intrinsic spatial resolution and high spatial linearity near the edges due to a novel light-guide geometry and the use of square PMTs. A volume-of-interest is scanned by translating the collimator and gamma detector together in a sequence that optimises count yield from the scan region. Derenzo phantom images showed that the system can reach 3.5 mm resolution for a clinically realistic ^99mTc activity concentration in an 11-minute scan, while in breast phantoms the smallest spheres visible were 6 mm in diameter for the same scan time. To conclude, the experimental results of the novel MP-MBT scanner showed that the setup had sub-centimetre breast tumour detection capability which might facilitate 3D molecular breast cancer imaging in the future.