Estimation of the Image Quality in Emission Tomography: Application to Optimization of SPECT System Design

In Emission Tomography the design of the Imaging System has a great influence on the quality of the output image. Optimisation of the system design is a difficult problem due to the computational complexity and to the challenges in its mathematical formulation. In order to compare different system designs, an efficient and effective method to calculate the Image Quality is needed. In this thesis the statistical and deterministic methods for the calculation of the uncertainty in the reconstruction are presented. In the deterministic case, the Fisher Information Matrix (FIM) formalism can be employed to characterize such uncertainty. Unfortunately, computing, storing and inverting the FIM is not feasible with 3D imaging systems. In order to tackle the problem of the computational load in calculating the inverse of the FIM a novel approximation, that relies on a sub-sampling of the FIM, is proposed. The FIM is calculated over a subset of voxels arranged in a grid that covers the whole volume. This formulation reduces the computational complexity in inverting the FIM but nevertheless accounts for the global interdependence between the variables, for the acquisition geometry and for the object dependency. Using this approach, the noise properties as a function of the system geometry parameterisation were investigated for three different cases. In the first study, the design of a parallel-hole collimator for SPECT is optimised. The new method can be applied to evaluating problems like trading-off collimator resolution and sensitivity. In the second study, the reconstructed image quality was evaluated in the case of truncated projection data; showing how the subsampling approach is very accurate for evaluating the effects of missing data. Finally, the noise properties of a D-SPECT system were studied for varying acquisition protocols; showing how the new method is well-suited to problems like optimising adaptive data sampling schemes

CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions

Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular (LV) coverage in a single breath-hold. However, 3D imaging remains limited to anisotropic resolution and long reconstruction times. Recently deep learning has shown promising results for computationally efficient reconstructions of highly accelerated 2D CINE imaging. In this work, we propose a novel 4D (3D + time) deep learning-based reconstruction network, termed 4D CINENet, for prospectively undersampled 3D Cartesian CINE imaging. CINENet is based on (3 + 1)D complex-valued spatio-temporal convolutions and multi-coil data processing. We trained and evaluated the proposed CINENet on in-house acquired 3D CINE data of 20 healthy subjects and 15 patients with suspected cardiovascular disease. The proposed CINENet network outperforms iterative reconstructions in visual image quality and contrast (+ 67% improvement). We found good agreement in LV function (bias ± 95% confidence) in terms of end-systolic volume (0 ± 3.3 ml), end-diastolic volume (- 0.4 ± 2.0 ml) and ejection fraction (0.1 ± 3.2%) compared to clinical gold-standard 2D CINE, enabling single breath-hold isotropic 3D CINE in less than 10 s scan and ~ 5 s reconstruction time

Laboratory equipment

System designs in single photon emission tomography (SPECT) can be evaluated based on the fundamental trade-off between bias and variance that can be achieved in the reconstruction of emission tomograms. This trade off can be derived analytically using the Cramer-Rao type bounds, which imply the calculation and the inversion of the Fisher information matrix (FIM). The inverse of the FIM expresses the uncertainty associated to the tomogram, enabling the comparison of system designs. However, computing, storing and inverting the FIM is not practical with 3-D imaging systems. In order to tackle the problem of the computational load in calculating the inverse of the FIM, a method based on the calculation of the local impulse response and the variance, in a single point, from a single row of the FIM, has been previously proposed for system design. However this approximation (circulant approximation) does not capture the global interdependence between the variables in shift-variant systems such as SPECT, and cannot account e.g., for data truncation or missing data. Our new formulation relies on subsampling the FIM. The FIM is calculated over a subset of voxels arranged in a grid that covers the whole volume. Every element of the FIM at the grid points is calculated exactly, accounting for the acquisition geometry and for the object. This new formulation reduces the computational complexity in estimating the uncertainty, but nevertheless accounts for the global interdependence between the variables, enabling the exploration of design spaces hindered by the circulant approximation. The graphics processing unit accelerated implementation of the algorithm reduces further the computation times, making the algorithm a good candidate for real-time optimization of adaptive imaging systems. This paper describes the subsampled FIM formulation and implementation details. The advantages and limitations of the new approximation are explored, in comparison with the circulant approximation, in the context of design optimization of a parallel-hole collimator SPECT system and of an adaptive imaging system (similar to the commercially available D-SPECT)

Collimator design in SPECT, an optimisation tool

Traditional gamma camera collimators have been based on a standard parallel design, usually with parallel holes. The collimator determines the resolution of the camera, but is also the determining factor of the number of counts detected. The uncertainty about the origin of the detected photons is modelled by a Point Spread Function (PSF), which, in literature, is normally assumed to be a Gaussian function, whose full width at half maximum (FWHM), is determined by the collimator's aperture. In this paper, these traditional assumptions will be abandoned. From related work [6] it appears possible to improve resolution recovery based reconstruction by considering different shapes of PSF. A novel collimator geometry has been then proposed, that does not imply necessarily parallel holes, but could involve a polygonal shape of the septa. To model the geometrical and septal penetration component of the point source response function a ray-tracing algorithm has been coded. In addition we developed an optimisation method that, given a target PSF, determines the optimum collimator geometry using a genetic algorithm.6 page(s

Point spread function optimization in SPECT

In this paper we propose a novel method for collimator design in single photon emission tomography (SPECT). The challenge here is to find a practical collimator design that allows good recovery and good sensitivity. Instead of working on the collimator's shape, the problem is addressed by optimizing the point spread function (PSF) with respect to the performance of the reconstruction algorithm in terms of resolution modelling. The optimization is based on an object-dependent cost function that takes into account bother recovery coefficient (RC) and sensitivity. Therefore, for each object considered a different "optimal" PSF is expected. Once a PSF is obtained, we assess its performances by plotting the coefficient of variation (COV) versus the recovery coefficient (RC) at each iteration of a maximum likelihood maximization expectation (MLEM) algorithm. We performed our experiments on two-dimensional (2-D) geometric phantoms, in order to investigate the relationship between the optimal PSF and the object geometrical properties, as well as on a 2-D brain activity phantom. We show that the optimized PSF's lead to resolution models that improve both image resolution and signal to noise ratio.5 page(s

Comparison of the clinical performance of upper abdominal PET/DCE-MRI with and without concurrent respiratory motion correction (MoCo)

Purpose: To compare the clinical performance of upper abdominal PET/DCE-MRI with and without concurrent respiratory motion correction (MoCo). Methods: MoCo PET/DCE-MRI of the upper abdomen was acquired in 44 consecutive oncologic patients and compared with non-MoCo PET/MRI. SUVmax and MTV of FDG-avid upper abdominal malignant lesions were assessed on MoCo and non-MoCo PET images. Image quality was compared between MoCo DCE-MRI and non-MoCo CE-MRI, and between fused MoCo PET/MRI and fused non-MoCo PET/MRI images. Results: MoCo PET resulted in higher SUVmax (10.8 ± 5.45) than non-MoCo PET (9.62 ± 5.42) and lower MTV (35.55 ± 141.95 cm 3 ) than non-MoCo PET (38.11 ± 198.14 cm 3 ; p < 0.005 for both). The quality of MoCo DCE-MRI images (4.73 ± 0.5) was higher than that of non-MoCo CE-MRI images (4.53±0.71; p = 0.037). The quality of fused MoCo-PET/MRI images (4.96 ± 0.16) was higher than that of fused non-MoCo PET/MRI images (4.39 ± 0.66; p < 0.005). Conclusion: MoCo PET/MRI provided qualitatively better images than non-MoCo PET/MRI, and upper abdominal malignant lesions demonstrated higher SUVmax and lower MTV on MoCo PET/MRI

Staging performance of whole-body DWI, PET/CT and PET/MRI in invasive ductal carcinoma of the breast

The aim of the present study was to evaluate the performance of whole-body diffusion-weighted imaging (WB-DWI), whole-body positron emission tomography with computed tomography (WB-PET/CT), and whole-body positron emission tomography with magnetic resonance imaging (WB-PET/MRI) in staging patients with untreated invasive ductal carcinoma of the breast. Fifty-one women with newly diagnosed invasive ductal carcinoma of the breast underwent WB-DWI, WB-PET/CT and WB-PET/MRI before treatment. A radiologist and a nuclear medicine physician reviewed in consensus the images from the three modalities and searched for occurrence, number and location of metastases. Final staging, according to each technique, was compared. Pathology and imaging follow-up were used as the reference. WB-DWI, WB-PET/CT and WB-PET/MRI correctly and concordantly staged 33/51 patients: stage IIA in 7 patients, stage IIB in 8 patients, stage IIIC in 4 patients and stage IV in 14 patients. WB-DWI, WB-PET/CT and WB-PET/MRI incorrectly and concordantly staged 1/51 patient as stage IV instead of III A. Discordant staging was reported in 17/51 patients. WB-PET/MRI resulted in improved staging when compared to WB-PET/CT (50 correctly staged on WB-PET/MRI vs. 38 correctly staged on WB-PET/CT; McNemar's test; P<0.01). Comparing the performance of WB-PET/MRI and WB-DWI (43 correct) did not reveal a statistically significant difference (McNemar test, P=0.14). WB-PET/MRI is more accurate in the initial staging of breast cancer than WB-DWI and WB-PET/CT, however, the discrepancies between WB-PET/MRI and WB-DWI were not statistically significant. When available. WB-PET/MRI should be considered for staging patient with invasive ductal breast carcinoma