Guillem Pratx

GPU computing in medical physics: a review.

The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction, dose calculation and treatment plan optimization, and image processing.

Fast, Accurate and Shift-Varying Line Projections for Iterative Reconstruction Using the GPU

List-mode processing provides an efficient way to deal with sparse projections in iterative image reconstruction for emission tomography. An issue often reported is the tremendous amount of computation required by such algorithm. Each recorded event requires several back- and forward line projections. We investigated the use of the programmable graphics processing unit (GPU) to accelerate the line-projection operations and implement fully-3D list-mode ordered-subsets expectation-maximization for positron emission tomography (PET). We designed a reconstruction approach that incorporates resolution kernels, which model the spatially-varying physical processes associated with photon emission, transport and detection. Our development is particularly suitable for applications where the projection data is sparse, such as high-resolution, dynamic, and time-of-flight PET reconstruction. The GPU approach runs more than 50 times faster than an equivalent CPU implementation while image quality and accuracy are virtually identical. This paper describes in details how the GPU can be used to accelerate the line projection operations, even when the lines-of-response have arbitrary endpoint locations and shift-varying resolution kernels are used. A quantitative evaluation is included to validate the correctness of this new approach.

X-Ray Luminescence Computed Tomography via Selective Excitation: A Feasibility Study

X-ray luminescence computed tomography (XLCT) is proposed as a new molecular imaging modality based on the selective excitation and optical detection of X-ray-excitable phosphor nanoparticles. These nano-sized particles can be fabricated to emit near-infrared (NIR) light when excited with X-rays, and, because because both X-rays and NIR photons propagate long distances in tissue, they are particularly well suited for in vivo biomedical imaging. In XLCT, tomographic images are generated by irradiating the subject using a sequence of programmed X-ray beams, while sensitive photo-detectors measure the light diffusing out of the subject. By restricting the X-ray excitation to a single, narrow beam of radiation, the origin of the optical photons can be inferred regardless of where these photons were detected, and how many times they scattered in tissue. This study presents computer simulations exploring the feasibility of imaging small objects with XLCT, such as research animals. The accumulation of 50 nm phosphor nanoparticles in a 2-mm-diameter target can be detected and quantified with subpicomolar sensitivity using less than 1 cGy of radiation dose. Provided sufficient signal-to-noise ratio, the spatial resolution of the system can be made as high as needed by narrowing the beam aperture. In particular, 1 mm spatial resolution was achieved for a 1-mm-wide X-ray beam. By including an X-ray detector in the system, anatomical imaging is performed simultaneously with molecular imaging via standard X-ray computed tomography (CT). The molecular and anatomical images are spatially and temporally co-registered, and, if a single-pixel X-ray detector is used, they have matching spatial resolution.

Synthesis and Radioluminescence of PEGylated Eu3+-doped Nanophosphors as Bioimaging Probes

Lanthanide-doped nanophosphors have received significant attention for use in biological sensing and imaging due to their unique optical properties. Much like semiconductor quantum dots (QDs), these luminescent nanocrystals offer several advantages over conventional organic fluorophores, including high photochemical stability, large Stokes shift, and tunable fluorescence emission. [1] Up-conversion nanophosphors, which are capable of absorbing two or more low-energy photons to emit a higher-energy photon, also exhibit favorable characteristics such as long fluorescence lifetimes, no photoblinking, and reduced autofluorescence. [2] The recent development of lanthanide-doped nanophosphors that function in the near-infrared (NIR) spectral range optimal for optical transmission through biological tissues (650–900 nm) has attracted great interest towards in vivo bioim-aging probes. [3–5] Alternatively, high-energy radiation, currently employed in medical imaging modalities, such as X-ray computed tomography (CT) or positron emission tomo graphy (PET), may also be used to excite NIR-emitting radioluminescent nanophosphors (RLNPs) for bioimaging.

Tomographic molecular imaging of x-ray-excitable nanoparticles

X-ray luminescence computed tomography (XLCT) is proposed as a new dual molecular/anatomical imaging modality. XLCT is based on the selective excitation and optical detection of x-ray-excitable nanoparticles. As a proof of concept, we built a prototype XLCT system and imaged near-IR-emitting Gd(2)O(2)S:Eu phosphors in various phantoms. Imaging in an optically diffusive medium shows that imaging performance is not affected by optical scatter; furthermore, the linear response of the reconstructed images suggests that XLCT is capable of quantitative imaging.

Intraoperative Imaging of Tumors Using Cerenkov Luminescence Endoscopy: A Feasibility Experimental Study

Cerenkov luminescence imaging (CLI) is an emerging new molecular imaging modality that is relatively inexpensive, easy to use, and has high throughput. CLI can image clinically available PET and SPECT probes using optical instrumentation. Cerenkov luminescence endoscopy (CLE) is one of the most intriguing applications that promise potential clinical translation. We developed a prototype customized fiberscopic Cerenkov imaging system to investigate the potential in guiding minimally invasive surgical resection. Methods: All experiments were performed in a dark chamber. Cerenkov luminescence from 18F-FDG samples containing decaying radioactivity was transmitted through an optical fiber bundle and imaged by an intensified charge-coupled device camera. Phantoms filled with 18F-FDG were used to assess the imaging spatial resolution. Finally, mice bearing subcutaneous C6 glioma cells were injected intravenously with 18F-FDG to determine the feasibility of in vivo imaging. The tumor tissues were exposed, and CLI was performed on the mouse before and after surgical removal of the tumor using the fiber-based imaging system and compared with a commercial optical imaging system. Results: The sensitivity of this particular setup was approximately 45 kBq (1.21 μCi)/300 μL. The 3 smallest sets of cylindric holes in a commercial SPECT phantom were identifiable via this system, demonstrating that the system has a resolution better than 1.2 mm. Finally, the in vivo tumor imaging study demonstrated the feasibility of using CLI to guide the resection of tumor tissues. Conclusion: This proof-of-concept study explored the feasibility of using fiber-based CLE for the detection of tumor tissue in vivo for guided surgery. With further improvements of the imaging sensitivity and spatial resolution of the current system, CLE may have a significant application in the clinical setting in the near future.

Hybrid x-ray/optical luminescence imaging: characterization of experimental conditions.

PURPOSE The feasibility of x-ray luminescence imaging is investigated using a dual-modality imaging system that merges x-ray and optical imaging. This modality utilizes x-ray activated nanophosphors that luminesce when excited by ionizing photons. By doping phosphors with lanthanides, which emit light in the visible and near infrared range, the luminescence is suitable for biological applications. This study examines practical aspects of this new modality including phosphor concentration, light emission linearity, detector damage, and spectral emission characteristics. Finally, the contrast produced by these phosphors is compared to that of x-ray fluoroscopy. METHODS Gadolinium and lanthanum oxysulfide phosphors doped with terbium (green emission) or europium (red emission) were studied. The light emission was imaged in a clinical x-ray scanner with a cooled CCD camera and a spectrophotometer; dose measurements were determined with a calibrated dosimeter. Using these properties, in addition to luminescence efficiency values found in the literature for a similar phosphor, minimum concentration calculations are performed. Finally, a 2.5 cm agar phantom with a 1 cm diameter cylindrical phosphor-filled inclusion (diluted at 10 mg/ml) is imaged to compare x-ray luminescence contrast with x-ray fluoroscopic contrast at a superficial location. RESULTS Dose to the CCD camera in the chosen imaging geometry was measured at less than 0.02 cGy/s. Emitted light was found to be linear with dose (R(2)= 1) and concentration (R(2)= 1). Emission peaks for clinical x-ray energies are less than 3 nm full width at half maximum, as expected from lanthanide dopants. The minimum practical concentration necessary to detect luminescent phosphors is dependent on dose; it is estimated that subpicomolar concentrations are detectable at the surface of the tissue with typical mammographic doses, with the minimum detectable concentration increasing with depth and decreasing with dose. In a reflection geometry, x-ray luminescence had nearly a 430-fold greater contrast to background than x-ray fluoroscopy. CONCLUSIONS X-ray luminescence has the potential to be a promising new modality for enabling molecular imaging within x-ray scanners. Although much work needs to be done to ensure biocompatibility of x-ray exciting phosphors, the benefits of this modality, highlighted in this work, encourage further study.

Fully 3D list-mode time-of-flight PET image reconstruction on GPUs using CUDA.

PURPOSE List-mode processing is an efficient way of dealing with the sparse nature of positron emission tomography (PET) data sets and is the processing method of choice for time-of-flight (ToF) PET image reconstruction. However, the massive amount of computation involved in forward projection and backprojection limits the application of list-mode reconstruction in practice, and makes it challenging to incorporate accurate system modeling. METHODS The authors present a novel formulation for computing line projection operations on graphics processing units (GPUs) using the compute unified device architecture (CUDA) framework, and apply the formulation to list-mode ordered-subsets expectation maximization (OSEM) image reconstruction. Our method overcomes well-known GPU challenges such as divergence of compute threads, limited bandwidth of global memory, and limited size of shared memory, while exploiting GPU capabilities such as fast access to shared memory and efficient linear interpolation of texture memory. Execution time comparison and image quality analysis of the GPU-CUDA method and the central processing unit (CPU) method are performed on several data sets acquired on a preclinical scanner and a clinical ToF scanner. RESULTS When applied to line projection operations for non-ToF list-mode PET, this new GPU-CUDA method is >200 times faster than a single-threaded reference CPU implementation. For ToF reconstruction, we exploit a ToF-specific optimization to improve the efficiency of our parallel processing method, resulting in GPU reconstruction >300 times faster than the CPU counterpart. For a typical whole-body scan with 75 × 75 × 26 image matrix, 40.7 million LORs, 33 subsets, and 3 iterations, the overall processing time is 7.7 s for GPU and 42 min for a single-threaded CPU. Image quality and accuracy are preserved for multiple imaging configurations and reconstruction parameters, with normalized root mean squared (RMS) deviation less than 1% between CPU and GPU-generated images for all cases. CONCLUSIONS A list-mode ToF OSEM library was developed on the GPU-CUDA platform. Our studies show that the GPU reformulation is considerably faster than a single-threaded reference CPU method especially for ToF processing, while producing virtually identical images. This new method can be easily adapted to enable more advanced algorithms for high resolution PET reconstruction based on additional information such as depth of interaction (DoI), photon energy, and point spread functions (PSFs).

Investigation of X-ray Fluorescence Computed Tomography (XFCT) and K-Edge Imaging

This work provides a comprehensive Monte Carlo study of X-ray fluorescence computed tomography (XFCT) and K-edge imaging system, including the system design, the influence of various imaging components, the sensitivity and resolution under various conditions. We modified the widely used EGSnrc/DOSXYZnrc code to simulate XFCT images of two acrylic phantoms loaded with various concentrations of gold nanoparticles and Cisplatin for a number of XFCT geometries. In particular, reconstructed signal as a function of the width of the detector ring, its angular coverage and energy resolution were studied. We found that XFCT imaging sensitivity of the modeled systems consisting of a conventional X-ray tube and a full 2-cm-wide energy-resolving detector ring was 0.061% and 0.042% for gold nanoparticles and Cisplatin, respectively, for a dose of ~10cGy. Contrast-to-noise ratio (CNR) of XFCT images of the simulated acrylic phantoms was higher than that of transmission K-edge images for contrast concentrations below 0.4%.

Bayesian reconstruction of photon interaction sequences for high-resolution PET detectors

Realizing the full potential of high-resolution positron emission tomography (PET) systems involves accurately positioning events in which the annihilation photon deposits all its energy across multiple detector elements. Reconstructing the complete sequence of interactions of each photon provides a reliable way to select the earliest interaction because it ensures that all the interactions are consistent with one another. Bayesian estimation forms a natural framework to maximize the consistency of the sequence with the measurements while taking into account the physics of gamma-ray transport. An inherently statistical method, it accounts for the uncertainty in the measured energy and position of each interaction. An algorithm based on maximum a posteriori (MAP) was evaluated for computer simulations. For a high-resolution PET system based on cadmium zinc telluride detectors, 93.8% of the recorded coincidences involved at least one photon multiple-interactions event (PMIE). The MAP estimate of the first interaction was accurate for 85.2% of the single photons. This represents a two-fold reduction in the number of mispositioned events compared to minimum pair distance, a simpler yet efficient positioning method. The point-spread function of the system presented lower tails and higher peak value when MAP was used. This translated into improved image quality, which we quantified by studying contrast and spatial resolution gains.