Il Jun Ahn

Motion-compensated PET image reconstruction with respiratory-matched attenuation correction using two low-dose inhale and exhale CT images

Positron emission tomography (PET) is widely used for diagnosis and follow up assessment of radiotherapy. However, thoracic and abdominal PET suffers from false staging and incorrect quantification of the radioactive uptake of lesion(s) due to respiratory motion. Furthermore, respiratory motion-induced mismatch between a computed tomography (CT) attenuation map and PET data often leads to significant artifacts in the reconstructed PET image. To solve these problems, we propose a unified framework for respiratory-matched attenuation correction and motion compensation of respiratory-gated PET. For the attenuation correction, the proposed algorithm manipulates a 4D CT image virtually generated from two low-dose inhale and exhale CT images, rather than a real 4D CT image which significantly increases the radiation burden on a patient. It also utilizes CT-driven motion fields for motion compensation. To realize the proposed algorithm, we propose an improved region-based approach for non-rigid registration between body CT images, and we suggest a selection scheme of 3D CT images that are respiratory-matched to each respiratory-gated sinogram. In this work, the proposed algorithm was evaluated qualitatively and quantitatively by using patient datasets including lung and/or liver lesion(s). Experimental results show that the method can provide much clearer organ boundaries and more accurate lesion information than existing algorithms by utilizing two low-dose CT images.

An Effective Post-Filtering Framework for 3-D PET Image Denoising Based on Noise and Sensitivity Characteristics

Positron emission tomography (PET) images usually suffer from a noticeable amount of statistical noise. In order to reduce this noise, a post-filtering process is usually adopted. However, the performance of this approach is limited because the denoising process is mostly performed on the basis of the Gaussian random noise. It has been reported that in a PET image reconstructed by the expectation-maximization (EM), the noise variance of each voxel depends on its mean value, unlike in the case of Gaussian noise. In addition, we observe that the variance also varies with the spatial sensitivity distribution in a PET system, which reflects both the solid angle determined by a given scanner geometry and the attenuation information of a scanned object. Thus, if a post-filtering process based on the Gaussian random noise is applied to PET images without consideration of the noise characteristics along with the spatial sensitivity distribution, the spatially variant non-Gaussian noise cannot be reduced effectively. In the proposed framework, to effectively reduce the noise in PET images reconstructed by the 3-D ordinary Poisson ordered subset EM (3-D OP-OSEM), we first denormalize an image according to the sensitivity of each voxel so that the voxel mean value can represent its statistical properties reliably. Based on our observation that each noisy denormalized voxel has a linear relationship between the mean and variance, we try to convert this non-Gaussian noise image to a Gaussian noise image. We then apply a block matching 4-D algorithm that is optimized for noise reduction of the Gaussian noise image, and reconvert and renormalize the result to obtain a final denoised image. Using simulated phantom data and clinical patient data, we demonstrate that the proposed framework can effectively suppress the noise over the whole region of a PET image while minimizing degradation of the image resolution.

GPU-based fast projection-backprojection algorithm for 3-D PET image reconstruction

Iterative image reconstruction algorithms based on a stochastic model of emission tomography have been widely studied, because they can provide better image quality than analytic reconstruction algorithms. However, their long reconstruction time has been a major bottle neck for further developments of high resolution PET scanners and their applications. In recent years, there have been several attempts to reduce the PET image reconstruction time by using a graphic processing unit (GPU). To obtain high computational performance on a massive parallel GPU, however, global memory coalescing and branching diversity are to be carefully considered, which are not considered in most existing GPU-based algorithms. To increase global memory coalescing, we propose the image-rotation-based (IR-based) projection and frame-rotation-based (FR-based) backprojection schemes. We then successfully incorporate the geometrical symmetry property into the proposed schemes to reduce the branching diversity. Thereby, we effectively reduce the total image reconstruction time from many hours to a few seconds. Experimental results show that the proposed algorithm reduces the computation time by a factor of about 539 compared with a CPU-based straightforward implementation.

Super-Resolution Reconstruction of 3D PET Images Using Two Respiratory-Phase Low-Dose CT Images

Positron emission tomography (PET) images usually suffer from incorrect quantification of the radioactive uptake of small lesions due to low spatial resolution. To improve the spatial resolution, we previously proposed super-resolution (SR) algorithms based on wobble scanning. The proposed algorithms, however, require mechanical motion of the patient bed or a system gantry for wobble scanning. In this paper, we propose a framework for SR reconstruction of 3D PET images based on the use of respiratory motion rather than wobble motion. As in the conventional protocol of PET imaging, gated list-mode PET data are acquired in a free breathing condition. In addition, we acquire two low-dose CT images in a breath-hold manner at exhale and inhale phases, without increasing the radiation burden to a patient. Using the two low-dose CT images, we estimate the 4D motion vector field (MVF) and correspondingly generate a virtual 4D CT image that are matched to the 4D PET image. The 3D CT images have much better spatial resolution than PET images and therefore the corresponding estimated 3D MVFs can be considered reliable for PET SR reconstruction. We then estimate space-variant point spread functions (PSFs) in the imaging field of view using a minimum number of PSFs obtained through Monte-Carlo simulations. Finally, SR reconstruction is performed by incorporating the estimated 3D MVFs and space-variant PSFs. In the SR reconstruction, to avoid the resolution degradation in the evenly spaced parallel-beam rebinning and to reduce the computational time on the graphics processing unit, we introduce a parallel-friendly spanned line of response reconstruction technique based on fan-beam reordering. The proposed framework is evaluated via Monte-Carlo simulations with the digital XCAT phantom and via experiments with several patient datasets including moving lung lesions. Both the simulation and experiment results show that the proposed framework provides much clearer organ boundaries as well as more accurate quantitative lesion information than the conventional methods, with a considerable reduction of computational time.

LOR-Based Reconstruction for Super-Resolved 3D PET Image on GPU

Positron emission tomography (PET) images usually suffer from low spatial resolution mainly because of the finite dimension of crystals. To improve the spatial resolution based on wobble scanning, we previously proposed a sinogram-based super-resolution (SR) algorithm using a space-variant blur matrix. However, the algorithm may cause unwanted resolution loss owing to an inevitable interpolation process for preparing evenly spaced projections. In this article, we propose a novel one-step line of response (LOR)-based SR framework for 3D PET images. In the framework, we efficiently determine a large number of space-variant point spread functions (PSFs) in the image space by using the scanner symmetries and the proposed PSF interpolation scheme based on nonrigid registration. Furthermore, to minimize the resolution degradation in the evenly spaced parallel-beam rebinning and to reduce the computational time in the graphics processing unit (GPU) implementation, we introduce parallel-friendly LOR reconstruction based on cone-beam reordering. We then obtain a high resolution image via a one-step super-resolved 3D PET image reconstruction with the determined PSFs. The proposed framework provides noticeable improvement on the spatial resolution of PET images with a considerable reduction of computational time.

Post-filtering of PET image based on noise characteristic and spatial sensitivity distribution

Positron emission tomography (PET) images suffer from a noticeable amount of statistical noise. In order to reduce the noise, a post smoothing process is usually adopted in the conventional PET systems. However, its performance is limited because the process is mostly based on a Gaussian random noise which is quite distinct from the noise of PET images. It has been reported that noise variance of each voxel is proportional to the square of the mean value in a PET image reconstructed by expectation-maximization (EM). In addition, we also observe that the variance varies with the spatial sensitivity distribution in a PET system. Based on those properties, we determine a unique formula representing a relationship between the mean and variance for a given PET system. Meanwhile, a block matching 3D (or 4D) algorithm is known as the state of the art in Gaussian noise reduction. To effectively apply it for noise reduction of PET images, we first perform a noise characteristic conversion from the PET image noise to Gaussian random noise using a pre-determined relationship. We then apply a block matching 4D (BM4D) algorithm and reconvert the result. Using the Monte Carlo simulation, we demonstrate that proposed algorithm can effectively reduce the noise in the whole image region while minimizing the image resolution degradation.

Phased attenuation correction and respiratory motion compensation of pet image by using a ct images and multiple respiratory-phase mr images

Respiratory motion blurs positron emission tomography (PET) images in thorax and abdominal regions, and can cause attenuation-related errors in quantitative parameters. In addition, this motion can cause localization error and disappearance of tumor(s) near the diaphragm. Respiratory gated PET imaging, or 4D PET imaging, is an attractive solution. For 4D PET, a surrogate measurement of the patients breathing is made during the scan, and based on this measurement the list-mode PET data are sorted and reconstructed into multiple images. In several studies, it has been reported that 4D PET imaging recovers tumor volume more accurately than 3D PET imaging, and yields more accurate standard uptake values. In spite of its promise, 4D PET images suffer from statistical noise because only a fraction of the acquired data can be used in each image. Moreover, they also suffer from inconsistent attenuation correction due to mismatches between PET and CT images. In this paper, we propose a novel framework for phased attenuation correction and respiratory motion compensation for a 3D PET image by using a 3D CT image and multiple respiratory-phase 3D MR images. For phased attenuation correction, we generate and utilize 4D CT image obtained via non-rigid registrations among a 3D CT image and multiple respiratory-phase 3D MR images. A 3D PET image at a selected respiratory phase is then obtained by adding attenuation corrected multiple 3D PET images through non-rigid transformation. Experimental result for a clinical dataset shows that the proposed algorithm can provide much clearer organ boundary with improved quality than the conventional method in a 3D PET image.

Motion compensated 4D PET-CT-MR image generation for respiratory synchronized multi-modal image display

Positron emission tomography (PET) image has been widely used for early detection of malignant lesion(s) and its treatment, because it can provide functional information. Since computed tomography (CT) and magnetic resonance (MR) images can provide high resolution anatomical information, their simultaneous display with PET image may improve the clinical value of PET-image-based applications. Meanwhile, temporal information regarding anatomical changes due to respiration may be important in the radiation therapy planning of a thoracic or abdominal region. To provide such important information, we propose a framework for motion compensated 4D PET-CT-MR image generation. To realize the framework, we present a MR-driven motion compensation algorithm for respiratory-gated 4D PET imaging. We then introduce a respiratory-synchronized multi-modal image generation method. We expect that the respiratory-synchronized multi-modal image display can be useful for clinicians to efficiently utilize the temporal information as well as the complementary spatial information in their applications.

LOR-based reconstruction for super-resolved 3D PET image

PET images usually suffer from low spatial resolution due to positron range, photon non-collinearity, scatters inside scintillating crystals, finite dimension of crystals, and so on. To improve the spatial resolution based on wobble scanning, we previously proposed a sinogram-based super-resolution (SR) algorithm based on a space-variant blur matrix. However, the algorithm may cause unwanted resolution loss due to an inevitable interpolation process for preparing even-spaced sinograms. In this paper, we propose a novel and efficient one-step line of response (LOR) based SR framework for 3D PET images. In the framework, we efficiently determine a large number of space-variant PSFs in an image space by using the scanner symmetries and the proposed PSF interpolation scheme based on non-rigid registration. We then obtain a HR image via one-step super-resolved 3D PET image reconstruction with the determined PSFs. Furthermore, we reduce the computational time of GPU-based reconstruction by introducing a parallel-friendly cone-beam based LOR system matrix. The proposed framework provides noticeable improvement on the spatial resolution of PET images with a considerable reduction of computational time.

PET image reconstruction based on several respiratory-phase low-dose CT images

Positron emission tomography (PET) can provide functional information in the human body. However, thoracic and abdominal PET images suffer from attenuation-related errors due to respiratory motion as well as blurs due to respiratory motion itself. In our previous study, to obtain respiratory-gated PET images, we proposed a novel method for respiratory phase matched attenuation correction (AC) and motion compensation (MC). That method utilizes a virtual 4D CT image generated via non-rigid registrations among a 3D CT image and a couple of respiratory-phase 3D MR images. In this paper, for respiratory phase matched attenuation correction and motion compensation, we propose to use a couple of respiratory-phase low-dose CT images rather than MR images. Experimental result for a clinical dataset shows that the proposed method can also provide much clearer organ boundaries as well as more accurate lesion information than the conventional method in a 3D PET image.