Jieqing Jiao

Joint parametric reconstruction and motion correction framework for dynamic PET data.

In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithms computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction residual with noisy data in the simulation-based validation and reduces the bias of the direct estimation of the kinetic parameter of interest. A preliminary evaluation on clinical brain data using [18F]Choline shows improved contrast for regions of high activity. The proposed method is based on a data-driven kinetic modelling method and is directly applicable to reversible and irreversible PET tracers, covering a range of clinical applications.

Direct Parametric Reconstruction With Joint Motion Estimation/Correction for Dynamic Brain PET Data

Direct reconstruction of parametric images from raw photon counts has been shown to improve the quantitative analysis of dynamic positron emission tomography (PET) data. However it suffers from subject motion which is inevitable during the typical acquisition time of 1-2 hours. In this work we propose a framework to jointly estimate subject head motion and reconstruct the motion-corrected parametric images directly from raw PET data, so that the effects of distorted tissue-to-voxel mapping due to subject motion can be reduced in reconstructing the parametric images with motion-compensated attenuation correction and spatially aligned temporal PET data. The proposed approach is formulated within the maximum likelihood framework, and efficient solutions are derived for estimating subject motion and kinetic parameters from raw PET photon count data. Results from evaluations on simulated [11C]raclopride data using the Zubal brain phantom and real clinical [18F]florbetapir data of a patient with Alzheimers disease show that the proposed joint direct parametric reconstruction motion correction approach can improve the accuracy of quantifying dynamic PET data with large subject motion.Direct reconstruction of parametric images from raw photon counts has been shown to improve the quantitative analysis of dynamic positron emission tomography (PET) data. However it suffers from subject motion which is inevitable during the typical acquisition time of 1-2 hours. In this work we propose a framework to jointly estimate subject head motion and reconstruct the motion-corrected parametric images directly from raw PET data, so that the effects of distorted tissue-to-voxel mapping due to subject motion can be reduced in reconstructing the parametric images with motion-compensated attenuation correction and spatially aligned temporal PET data. The proposed approach is formulated within the maximum likelihood framework, and efficient solutions are derived for estimating subject motion and kinetic parameters from raw PET photon count data. Results from evaluations on simulated [11C]raclopride data using the Zubal brain phantom and real clinical [18F]florbetapir data of a patient with Alzheimers disease show that the proposed joint direct parametric reconstruction motion correction approach can improve the accuracy of quantifying dynamic PET data with large subject motion.

Effect of scatter correction when comparing attenuation maps: Application to brain PET/MR

In PET imaging, attenuation and scatter corrections are an essential requirement to accurately quantify the radionuclide uptake. In the context of PET/MR scanners, obtaining the attenuation information can be challenging. Various authors have quantified the effect of an imprecise attenuation map on the reconstructed PET image but its influence on scatter correction has usually been ignored. In this paper, we investigate the effects of imperfect attenuation maps (μmaps) on the scatter correction in a simulation setting. We focused our study on three μmaps: the reference μmap derived from a CT image, and two MR-based methods. Two scatter estimation strategies were implemented: a μmap-specific scatter estimation and an ideal scatter estimation relying only on the reference CT μmap. The scatter estimation used the Single Scatter Simulation algorithm with tail-fitting. The results show that, for FDG brain PET, regardless of the μmap used in the reconstruction, the difference on PET images between μmap-specific and ideal scatter estimations is small (less than 1%). More importantly, the relative error between attenuation correction methods does not change depending on the scatter estimation method included in the simulation and reconstruction process. This means that the effect of errors in the μmap on the PET image is dominated by the attenuation correction, while the scatter estimate is relatively unaffected. Therefore, while scatter correction improves reconstruction accuracy, it is unnecessary to include scatter in the simulation when comparing different attenuation correction methods for brain PET/MR.

High throughput CUDA implementation of accurate geometric modelling for iterative reconstruction of PET data

An approach to high throughput and high accuracy modelling of the geometric component of PET acquisition for the Siemens Biograph mMR PET/MR scanner is presented. The geometric components calculated in forward and back-projections are computationally expensive, however, they are inherently parallel and therefore, they are suitable for implementation on parallel computing platforms such as CUDA, consequently permitting more accurate and computationally involved system models. The key aspects of this work are: (1) accurate modelling of the geometric component of each tube of response (TOR) by tracing multiple lines for each TOR to account for the varying sensitivity along and across the TOR; (2) decomposition of the calculations into transaxial and axial components allowing the use of ray and voxel-driven methods optimal for forward and back-projection, respectively. Such decomposition also allows keeping exact correspondence between the ray and voxel-driven methods for forward and backprojection. (3) Due to the large axial field of view and the high number of crystal rings (64), the core ray-tracing is taking place in the axial dimension by projecting the transaxial calculations on each direct and oblique sinogram element. (4) Axially oriented arrangement of the images and sinograms in the GPU memory enables more efficient use of the L2 cache and together with point (3) it leads to more optimal performance even without the use of shared memory. Currently, for the Biograph mMR scanner geometry the projections are calculated within two seconds.

ASL-incorporated pharmacokinetic modelling of PET data with reduced acquisition time: Application to amyloid imaging

Pharmacokinetic analysis of Positron Emission Tomography (PET) data typically requires at least one hour of image acquisition, which poses a great disadvantage in clinical practice. In this work, we propose a novel approach for pharmacokinetic modelling with significantly reduced PET acquisition time, by incorporating the blood flow information from simultaneously acquired arterial spin labelling (ASL) magnetic resonance imaging (MRI). A relationship is established between blood flow, measured by ASL, and the transfer rate constant from plasma to tissue of the PET tracer, leading to modified PET kinetic models with ASL-derived flow information. Evaluation on clinical amyloid imaging data from an Alzheimer’s disease (AD) study shows that the proposed approach with the simplified reference tissue model can achieve amyloid burden estimation from 30 min [\(^{18}\)F]florbetapir PET data and 5 min simultaneous ASL MR data, which is comparable with the estimation from 60 min PET data (mean error\(\,=-0.03\)). Conversely, standardised uptake value ratio (SUVR), the alternative measure from the data showed a positive bias in areas of higher amyloid burden (mean error\(\,=0.07\)).

Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors

Positron emission tomography (PET) reconstruction is an ill-posed inverse problem which typically involves fitting a high-dimensional forward model of the imaging process to noisy, and sometimes undersampled photon emission data. To improve the image quality, prior information derived from anatomical images of the same subject has been previously used in the penalised maximum likelihood (PML) method to regularise the model complexity and selectively smooth the image on a voxel basis in PET reconstruction. In this work, we propose a novel perspective of incorporating the prior information by exploring the sparse property of natural images. Instead of a regular voxel grid, the sparse image representation jointly determined by the prior image and the PET data is used in reconstruction to leverage between the image details and smoothness, and this prior is integrated into the PET forward model and has a closed-form expectation maximisation (EM) solution. Simulations show that the proposed approach achieves improved bias versus variance trade-off and higher contrast recovery than the current state-of-the-art methods, and preserves the image details better. Application to clinical PET data shows promising results.

Short acquisition time PET quantification using MRI-based pharmacokinetic parameter synthesis

Positron Emission Tomography (PET) with pharmacokinetic (PK) modelling is a quantitative molecular imaging technique, however the long data acquisition time is prohibitive in clinical practice. An approach has been proposed to incorporate blood flow information from Arterial Spin Labelling (ASL) Magnetic Resonance Imaging (MRI) into PET PK modelling to reduce the acquisition time. This requires the conversion of cerebral blood flow (CBF) maps, measured by ASL, into the relative tracer delivery parameter (\(R_1\)) used in the PET PK model. This was performed regionally using linear regression between population \(R_1\) and ASL values. In this paper we propose a novel technique to synthesise \(R_1\) maps from ASL data using a database with both \(R_1\) and CBF maps. The local similarity between the candidate ASL image and those in the database is used to weight the propagation of \(R_1\) values to obtain the optimal patient specific \(R_1\) map. Structural MRI data is also included to provide information within common regions of artefact in ASL data. This methodology is compared to the linear regression technique using leave one out analysis on 32 subjects. The proposed method significantly improves regional \(R_1\) estimation (\(p<0.001\)), reducing the error in the pharmacokinetic modelling. Furthermore, it allows this technique to be extended to a voxel level, increasing the clinical utility of the images.

Image reconstruction of mMR PET data using the open source software STIR

Simultaneous PET and MR acquisitions have now become possible with the new hybrid Biograph Molecular MR (mMR) scanner from Siemens. The purpose of this work is to create a platform for mMR 3D and 4D PET image reconstruction which would be freely accessible to the community as well as fully adjustable in order to obtain optimal images for a given research task in PET imaging. The proposed platform is envisaged to prove useful in developing novel and robust image bio-markers which could then be adapted for use on the mMR scanner. STIR (Software for Tomographic Image Reconstruction [1]), an open source C++ library, has been used as a basis for this platform. However, a number of practical issues have to be addressed before useful reconstructed images can be obtained. Many of the practical issues have been addressed and reconstructions of two datasets (phantom and human brain) are demonstrated. The reconstruction pipeline involves the following: Conversion of the Siemens Dicom files to the STIR interfile format. Histogramming the emission data. Component-based normalisation. Estimation and correction for attenuation and scatter using atlas based µ-maps [2]. Correction for randoms. Iterative image reconstruction ([3–5]). Two datasets were used: uniform cylinder phantom and FDG-PET human brain scan. Figure ​Figure11 shows the OSEM reconstruction using the of-line version of the Siemens Healthcare reconstruction software which was made available for this project (top); the STIR reconstruction (middle); and the µ-map including the bed component (bottom). Figure 1 Image reconstruction of the phantom using Siemens proprietary software (top) and STIR (middle). Bottom: the MRI derived µ-map. The reconstruction of the FDG human brain dataset is presented in Figure ​Figure22 using Siemens proprietary software (top) and the proposed STIR pipeline (bottom). Figure 2 Image reconstruction of the human brain FDG-PET dataset using Siemens proprietary software (top) and STIR (bottom). It was demonstrated that STIR image reconstruction of PET mMR data is possible paving the way to more advanced models included in the pipeline of 4D PET reconstruction.

4-D PET joint image reconstruction/non-rigid motion estimation with limited MRI prior information

Motion compensated gated PET image reconstruction methods include joint-reconstruction (JR) and indirect reconstruction (IR) with pre-estimated motion from MRI (MRI-IR). JR suffers from poor PET data quality whereas MRI-IR requires high-quality MRI volumes at each gate. We propose a penalised maximum-likelihood approach combining JR and MRI-IR. Our method is referred to as minimal MRI prior JR (MP-JR). The M gates data are stored in g = [g1; …; gM] where gm is the measurement vector at gate m. Each gm is a Poisson distributed vector of parameter where P is the projector, W(αm) is the m-th motion of parameter αm, rm is the m-th average random/scatter vector and f is the activity at m = 1. JR is achieved with (1). 1 MRI-IR is achieved by solving (2) 2 MP-JR is achieved with (3). 3 The first term accounts for PET data, whereas the second term accounts for MRI motion information from subset S. The last term controls temporal smoothness. We tested each method on 9 PET FDG volumes generated from a real dynamic MRI sequence. Tumours were added to the activity distribution (invisible in the MRI). The gates subset S for MP-JR contains the reference gate, end-inspiration and end-expiration. Reconstruction profiles 1 show that MRI-IR improves edges visible in the MRI but degrades the tumours. On the contrary, JR performs well on tumours, but the edges are poorly reconstructed. MP-JR appears to perform well on both organ edges and tumours. Figure 1 Reconstruction profiles: (a) across tumours; (b) across liver. MP-JR seems to perform well where both JR and MRI-IR under-perform. This is due to the fact that MP-JR relies on both MRI and PET data. In addition, results tend to show that with temporal smoothing on B-spline parameters, a subset of MRI volumes provides sufficient information.

Short Acquisition Time PET/MR Pharmacokinetic Modelling Using CNNs

Standard quantification of Positron Emission Tomography (PET) data requires a long acquisition time to enable pharmacokinetic (PK) model fitting, however blood flow information from Arterial Spin Labelling (ASL) Magnetic Resonance Imaging (MRI) can be combined with simultaneous dynamic PET data to reduce the acquisition time. Due the difficulty of fitting a PK model to noisy PET data with limited time points, such ‘fixed-\(R_1\)’ techniques are constrained to a 30 min minimum acquisition, which is intolerable for many patients. In this work we apply a deep convolutional neural network (CNN) approach to combine the PET and MRI data. This permits shorter acquisition times as it avoids the noise sensitive voxelwise PK modelling and facilitates the full modelling of the relationship between blood flow and the dynamic PET data. This method is compared to three fixed-\(R_1\) PK methods, and the clinically used standardised uptake value ratio (SUVR), using 60 min dynamic PET PK modelling as the gold standard. Testing on 11 subjects participating in a study of pre-clinical Alzheimer’s Disease showed that, for 30 min acquisitions, all methods which combine the PET and MRI data have comparable performance, however at shorter acquisition times the CNN approach has a significantly lower mean square error (MSE) compared to fixed-\(R_1\) PK modelling (\(p=0.001\)). For both acquisition windows, SUVR had a significantly higher MSE than the CNN method (\(p\le 0.003\)). This demonstrates that combining simultaneous PET and MRI data using a CNN can result in robust PET quantification within a scan time which is tolerable to patients with dementia.