J. L. Herraiz

Performance evaluation of SiPM detectors for PET imaging in the presence of magnetic fields

The multi-pixel photon counter (MPPC) or silicon photo-multiplier (SiPM), recently introduced as a solid-state photodetector, consists of an array of Geiger-mode photodiodes (microcells). It is a promising device for PET thanks to its potential for high photon detection efficiency (PDE) and immunity to high magnetic fields. It is also very easy to use, with simple electronic read-out, high gain and small size. In this work we evaluate the performance of three 1 × 1 mm2 and one 6 × 6 mm2 (2 × 2 array) SiPMs offered by Hamamatsu for their use in PET. We examine the dependence of the energy resolution and the gain of these devices on the thermal and reverse bias when coupled to LYSO scintillator crystals. We find that the 400 and 1600 microcells models and the 2 × 2 array are suitable for small size crystals, like those employed in high resolution small animal scanners. The good performance of these devices up to 7 Tesla has also been confirmed.

Positron range estimations with PeneloPET

Technical advances towards high resolution PET imaging try to overcome the inherent physical limitations to spatial resolution. Positrons travel in tissue until they annihilate into the two gamma photons detected. This range is the main detector-independent contribution to PET imaging blurring. To a large extent, it can be remedied during image reconstruction if accurate estimates of positron range are available. However, the existing estimates differ, and the comparison with the scarce experimental data available is not conclusive. In this work we present positron annihilation distributions obtained from Monte Carlo simulations with the PeneloPET simulation toolkit, for several common PET isotopes ((18)F, (11)C, (13)N, (15)O, (68)Ga and (82)Rb) in different biological media (cortical bone, soft bone, skin, muscle striated, brain, water, adipose tissue and lung). We compare PeneloPET simulations against experimental data and other simulation results available in the literature. To this end the different positron range representations employed in the literature are related to each other by means of a new parameterization for positron range profiles. Our results are generally consistent with experiments and with most simulations previously reported with differences of less than 20% in the mean and maximum range values. From these results, we conclude that better experimental measurements are needed, especially to disentangle the effect of positronium formation in positron range. Finally, with the aid of PeneloPET, we confirm that scaling approaches can be used to obtain universal, material and isotope independent, positron range profiles, which would considerably simplify range correction.

Parallel transmit pulse design for patients with deep brain stimulation implants

Specific absorption rate (SAR) amplification around active implantable medical devices during diagnostic MRI procedures poses a potential risk for patient safety. In this study, we present a parallel transmit (pTx) strategy that can be used to safely scan patients with deep brain stimulation (DBS) implants.

GPU-Based Fast Iterative Reconstruction of Fully 3-D PET Sinograms

This work presents a graphics processing unit (GPU)-based implementation of a fully 3-D PET iterative reconstruction code, FIRST (Fast Iterative Reconstruction Software for [PET] Tomography), which was developed by our group. We describe the main steps followed to convert the FIRST code (which can run on several CPUs using the message passing interface [MPI] protocol) into a code where the main time-consuming parts of the reconstruction process (forward and backward projection) are massively parallelized on a GPU. Our objective was to obtain significant acceleration of the reconstruction without compromising the image quality or the flexibility of the CPU implementation. Therefore, we implemented a GPU version using an abstraction layer for the GPU, namely, CUDA C. The code reconstructs images from sinogram data, and with the same System Response Matrix obtained from Monte Carlo simulations than the CPU version. The use of memory was optimized to ensure good performance in the GPU. The code was adapted for the VrPET small-animal PET scanner. The CUDA version is more than 70 times faster than the original code running in a single core of a high-end CPU, with no loss of accuracy.

Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies

Attenuation correction in hybrid PET/MR scanners is still a challenging task. This paper describes a methodology for synthesizing a pseudo-CT volume from a single T1-weighted volume, thus allowing us to create accurate attenuation correction maps. Methods: We propose a fast pseudo-CT volume generation from a patient-specific MR T1-weighted image using a groupwise patch-based approach and an MRI–CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel to the patches of all MR images in the database that lie in a certain anatomic neighborhood. The pseudo-CT volume is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a graphical processing unit (GPU). Results: We evaluated our method both qualitatively and quantitatively for PET/MR correction. The approach performed successfully in all cases considered. We compared the SUVs of the PET image obtained after attenuation correction using the patient-specific CT volume and using the corresponding computed pseudo-CT volume. The patient-specific correlation between SUV obtained with both methods was high (R2 = 0.9980, P < 0.0001), and the Bland–Altman test showed that the average of the differences was low (0.0006 ± 0.0594). A region-of-interest analysis was also performed. The correlation between SUVmean and SUVmax for every region was high (R2 = 0.9989, P < 0.0001, and R2 = 0.9904, P < 0.0001, respectively). Conclusion: The results indicate that our method can accurately approximate the patient-specific CT volume and serves as a potential solution for accurate attenuation correction in hybrid PET/MR systems. The quality of the corrected PET scan using our pseudo-CT volume is comparable to having acquired a patient-specific CT scan, thus improving the results obtained with the ultrashort-echo-time–based attenuation correction maps currently used in the scanner. The GPU implementation substantially decreases computational time, making the approach suitable for real applications.

SAR reduction in 7T C-spine imaging using a "dark modes" transmit array strategy.

Local specific absorption rate (SAR) limits many applications of parallel transmit (pTx) in ultra high‐field imaging. In this Note, we introduce the use of an array element, which is intentionally inefficient at generating spin excitation (a “dark mode”) to attempt a partial cancellation of the electric field from those elements that do generate excitation. We show that adding dipole elements oriented orthogonal to their conventional orientation to a linear array of conventional loop elements can lower the local SAR hotspot in a C‐spine array at 7 T.

GPU acceleration of a fully 3D Iterative Reconstruction Software for PET using CUDA

A CUDA implementation of the existing software FIRST (Fast Iterative Reconstruction Software for (PET) Tomography) is presented. This implementation uses consumer graphics processing units (GPUs) to accelerate the compute-intensive parts of the reconstruction: forward and backward projection. FIRST was originally developed in FORTRAN, and it has been migrated to C language to be used with NVIDIA C for CUDA, as well as for a straightforward implementation and performance comparison between the C versions of the code running on the CPU and on the GPU. We measured the execution time of the CUDA version compared to the fastest available CPU. The CUDA implementation includes a loop re-ordering and an optimized memory allocation, which improves even more the performance of the reconstruction on the GPUs.

Statistical reconstruction methods in PET: resolution limit, noise, edge artifacts and considerations for the design of better scanners

Small animal positron emission tomography (PET) scanners are being increasingly used as a basic measurement tool in modern biomedical research. The new designs and technologies of these scanners and the modern reconstruction methods have allowed to reach high spatial resolution and sensitivity. Despite their successes, some important issues remain to be addressed in high resolution PET imaging. First, iterative reconstruction methods like maximum likelihood-expectation maximization (MLEM) are known to recover resolution, but also to create noisy images and edge artifacts if some kind of regularization is not imposed. Second, the limit of resolution achievable by iterative methods on high resolution scanners is not quantitatively understood. Third, the use of regularization methods like Sieves or maximum a posteriori (MAP) requires the determination of the optimal values of several adjustable parameter that may be object-dependent. In this work we review these problems in high resolution PET and establish that the origin of them is more related with the physical effects involved in the emission and detection of the radiation during the acquisition than with the kind of iterative reconstruction method chosen. These physical effects (positron range, non-collinearity, scatter inside the object and inside the detector materials) cause that the tube of response (TOR) that connects the voxels with a line of response (LOR) is rather thick. This implies that the higher frequencies of the patient organ structures are not recorded by the scanner and therefore cannot be recovered during the reconstruction. As iterations grow, ML-EM algorithms try to recover higher frequencies in the image. Once that a certain critic frequency is reached, this only maximizes high frequency noise. Using frequency response analyses techniques, we determine the maximum achievable resolution, before edge artifacts spoil the quality of the image, for a particular scanner as a function of the thickness of the TOR, and independently of the reconstruction method employed. With the same techniques, we can deduce well defined stopping criteria for reconstructions methods. Also, criteria for the highest number of subsets which should be used and how the design of the scanners can be optimized when statistical reconstruction methods are employed, is established.

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation.

MRI‐based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1‐weighted volume.

Tissue-Dependent and Spatially-Variant Positron Range Correction in 3D PET

Positron range (PR) is a significant factor that limits PET image resolution, especially with some radionuclides currently used in clinical and preclinical studies such as 82Rb, 124I and 68Ga. The use of an accurate model of the PR in the image reconstruction may minimize its impact on the image quality. Nevertheless, PR distributions are difficult to model, as they may be different at each voxel and direction, depending on the materials that the positron flies through. Several approximated methods have been proposed, considering only one or several propagating media without taking into account boundaries effects. In some regions, like lungs or trachea, these methods may not be accurate enough and yield artifacts. In this work, we present an efficient method to accurately incorporate spatially-variant PR corrections. The method is based on pre-computing voxel-dependent PR kernels using a CT or a manually segmented image, and a model of the dependence of the PR on each material derived from Monte Carlo simulations. The images are convoluted with these kernels in the forward-projection step of the iterative reconstruction algorithm. This implementation of the algorithm adds a modest overhead to the overall reconstruction time and it obtains artifact-free PR-corrected images, even when the activity is concentrated at tissue boundaries with extreme changes of density. We verified the method with the preclinical Argus PET/CT scanner, but it can be also applied to other scanners and improve the image quality in clinical PET studies using isotopes with large PR.