Monte Carlo is an important and well established research tool used in emission tomography. While used extensively in research applications, these techniques are not typically implemented clinically due to their low detection efficiency and long acquisition times. In order to make this computational tool faster, the variance reduction technique known as convolution-based forced detection (CFD) has been implemented into the SIMIND MC code (CFD-SIMIND) by our group. Briefly, at each site of interaction within the object, photons are forced to travel in a direction perpendicular to the detector and are then convolved with a distance dependent blurring kernel specific to that collimator and photon energy. A similar CFD method has already been implemented as an option in the SIMIND Monte Carlo program. The study presented in Chapter 2 performs a comparison between a well established, non-VRT Monte Carlo program, GATE, with our accelerated CFD-SIMIND. The intent of this work is to establish if CFD-SIMIND can either replace or be used in conjunction with GATE in order to gain significant reduction in simulation times for low and medium energy isotopes. A number of simulation studies were performed using point sources in air and water, along with the 3D XCAT phantom and a rectangular sheet source for Tc-99m with low and medium energy collimator and In-111 with medium energy collimator. A comparison in the projection domain was then performed in terms of spatial resolution, sensitivity, image profiles and energy spectra. The study has shown percent differences of between 3−5% in sensitivity between CFD-SIMIND and GATE with mean universal image quality index value of 0.994 ± 0.009 and spatial resolution within 0.2 mm of each other. CFD-SIMIND offers a significant reduction in simulation time by a factor of 5−6 orders of magnitude compared to GATE. This acceleration time is useful for many applications. This study also provides an objective tool that can help to determine if CFD-SIMIND can be used in place of GATE in order to achieve images of sufficient quality within a reduced time and at much lower computational cost.
Simultaneous multi-isotope SPECT imaging has a number of applications in cardiac, brain and cancer imaging. The major concern however, is the significant crosstalk contamination due to photon scatter between the different isotopes. The second study
(Chapter 3) focuses on a method of downscatter compensation between two isotopes iii
in simultaneous dual isotope SPECT acquisition applied to cancer imaging using Tc-99m and In-111. We have developed an iterative image reconstruction technique that simulates the photon down-scatter from one isotope into the acquisition window of a second isotope. Our approach uses CFD-SIMIND for the forward projection step in an iterative reconstruction algorithm. The MC estimated scatter contamination of a radionuclide contained in a given projection view is then used to compensate for the photon contamination in the acquisition window of other nuclide. We use a modified ordered subset-expectation maximization (OS-EM) algorithm named simultaneous ordered subset-expectation maximization (Sim-OSEM), to perform this step. In this study, we have undertaken a number of simulation tests and phantom studies to verify this approach. The proposed reconstruction technique was also evaluated by reconstruction of experimentally acquired phantom data. Reconstruction using Sim- OSEM showed very promising results in terms of contrast recovery and uniformity of object background compared to alternative reconstruction methods implementing alternative scatter correction schemes (i.e., triple energy window or separately acquired projection data). In this study the evaluation is based on the quality of reconstructed images and activity estimated using Sim-OSEM. In order to quantitate the possible improvement in spatial resolution and signal to noise ratio (SNR) observed in this study, further simulation and experimental studies are required.
It is perceived that in simultaneous dual-isotope breast SPECT studies using 123I-labelled Z-MIVE and Tc-99m sestamibi, I-123 labelled Z-MIVE not only detects the presence of estrogen receptor (ER) but, also thought to complement Tc-99m sestamibi in differentiating between benign and malignant breast lesions for patients with breast cancer (Chapter 4). The major concern in simultaneous Tc-99m/I-123 SPECT is the significant crosstalk contamination between the different isotopes used. The current study focuses on a method of crosstalk (downscatter and spillover) compensation between two isotopes with data acquired using Thallium activated Sodium Iodide (NaI(Tl)) detector (Energy resolution 9.8% at 140 keV ) and Cadmium Zinc Telluride (CZT) detector (Energy resolution 5% 140 keV ) respectively. The study uses Sim- OSEM for crosstalk compensation between the isotopes. We have undertaken a number of simulation studies using our modeled breast phantom to verify this approach. Reconstruction using Sim-OSEM showed very promising results in terms of crosstalk and scatter compensation and uniformity of background. In our case images obtained using Sim-OSEM were comparable or even better than the images reconstructed from separately acquired projection data using analytical attenuation based reconstruction. This may be due to better small angle scatter compensation in case of Sim-OSEM as CFD-SIMIND based MC forward projector was used.
Compensation of the image degradation effects (i.e. attenuation, scatter and collimator-detector response) is necessary for an accurate quantification in SPECT imaging. We have previously proposed an accelerated Monte Carlo (MC) based iterative SPECT reconstruction algorithm that accurately corrects for attenuation and scatter once provided with attenuation information (Chapters 3 and 4). This algorithm uses SIMIND MC program accelerated through the implementation of a variance reduction technique known as, convolution forced detection (CFD), (CFD-SIMIND). With ever increasing number of hybrid SPECT/CT systems, CT-based attenuation correction is becoming a standard clinical protocol. This co-registered CT image with SPECT data can also be used to incorporate anatomical information as a prior into a maximum a-posteriori (MAP) SPECT image reconstruction algorithm. The study presented in Chapter 5 proposes a MAP reconstruction algorithm that includes CFD-SIMIND as a forward projector and a CT-image as an anatomical prior (CFD-AMAP) for simultaneous compensation of scatter and attenuation and, enhancement of spatial resolution during reconstruction. We have performed a number of simulation and experimental studies to elaborate the advantages of CFD-AMAP. These studies show an accurate quantification (within ±5% and ±8% for simulation and experimental studies respectively) accompanied by a significant reduction in coefficient of variation (CoV ). This reduction of CoV results in an improved boundary delineation and the Gibbs artifact compensation. However, this compensation comes at the cost of loss of an overall contrast in the reconstructed images due to a more uniform distribution of estimated activity over the regions of interest (ROI’s).
Further studies with more complex phantoms and real patient data, task-based ROC studies, improvement in CFD-SIMIND in terms of speed and use of better Bayesian image reconstruction algorithms are needed to elaborate on the strengths and weaknesses of this proposed MC based forward projector and to pave the way for CFD-SIMIND based image reconstruction algorithms from research to clinic. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20876 |
| Date | January 2017 |
| Creators | Karamat, Muhammad Irfan |
| Contributors | Farncombe, Troy, Medical Physics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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