Standard OSEM Research Articles

A comparison of the image quality of full-time myocardial perfusion SPECT vs wide beam reconstruction half-time and half-dose SPECT

ObjectivesWide Beam Reconstruction (WBR) (UltraSPECT, Ltd) uses resolution recovery and noise modeling to cope with decreased SPECT count statistics. Because WBR processing reconstructs half the usual SPECT count statistics, we postulate that image quality equivalent to a full-time acquisition can be achieved in either half the time or with half the radiopharmaceutical activity. MethodsIn 156 consecutive patients (pts) rest and 8-frame gated post-stress myocardial perfusion SPECT was performed following 333-444 and 1184-1480 MBq (9-12 and 32-40 mCi) Tc-99m sestamibi injections, respectively, with full-time (rest = 14 min; stress = 12.3 min) acquisitions processed with OSEM and also separate “half-time” acquisitions processed with WBR. A subsequent group of 160 consecutive pts matched in gender, weight, and chest circumference received “half-dose” rest and stress injections 214.6 ± 22.2 and 647.5 ± 92.5 MBq (5.8 ± 0.6 and 17.5 ± 2.5 mCi) with full-time SPECT acquisitions. Image quality (1 = poor to 5 = excellent) was judged by myocardial count density and uniformity, endocardial edge definition, perfusion defect delineation, right ventricular visualization, and background noise. ResultsMean image quality for rest, stress, and post-stress gated images were 3.6 ± 0.7, 3.8 ± 0.7, and 3.9 ± 1.0, respectively, for “full-time OSEM; 3.7 ± 0.8, 4.0 ± 0.7, and 4.8 ± 0.4 for “half-time” WBR; and 4.3 ± 0.8, 4.6 ± 0.6, and 4.7 ± 0.6 for “half-dose” WBR. “Half-time” and “half-dose” WBR image quality were both superior to standard full-time OSEM (P’s < .001). There was no significant difference between the summed stress and rest scores for “full-time” OSEM vs “half-time” WBR in 82 patients with perfusion defects. ConclusionsBoth “half-time” and “half-dose” WBR provide myocardial perfusion SPECT quality superior to full-time OSEM, with an associated decrease in scan acquisition time and patient radiation exposure, respectively.

Partial volume correction in SPECT reconstruction with OSEM

SPECT images suffer from poor spatial resolution, which leads to partial volume effects due to cross-talk between different anatomical regions. By utilising high-resolution structural images (CT or MRI) it is possible to compensate for these effects. Traditional partial volume correction (PVC) methods suffer from various limitations, such as correcting a single region only, returning only regional mean values, or assuming a stationary point spread function (PSF). We recently presented a novel method in which PVC was combined with the reconstruction process in order to take into account the distance dependent PSF in SPECT, which was based on filtered backprojection (FBP) reconstruction. We now present a new method based on the iterative OSEM algorithm, which has advantageous noise properties compared to FBP. We have applied this method to a series of 10 brain SPECT studies performed on healthy volunteers using the DATSCAN tracer. T1-weighted MRI images were co-registered to the SPECT data and segmented into 33 anatomical regions. The SPECT data were reconstructed using OSEM, and PVC was applied in the projection domain at each iteration. The correction factors were calculated by forward projection of a piece-wise constant image, generated from the segmented MRI. Images were also reconstructed using FBP and standard OSEM with and without resolution recovery (RR) for comparison. The images were evaluated in terms of striatal contrast and regional variability (CoV). The mean striatal contrast obtained with OSEM, OSEM-RR and OSEM-PVC relative to FBP were 1.04, 1.42 and 1.53, respectively, and the mean striatal CoV values are 1.05, 1.53, 1.07. Both OSEM-RR and OSEM-PVC results in images with significantly higher contrast as compared to FBP or OSEM, but OSEM-PVC avoids the increased regional variability of OSEM-RR due to improved structural definition.

AB-OSEM reconstruction for improved Patlak kinetic parameter estimation: a simulation study

The non-negativity constraint inherently present in OSEM reconstruction successfully reduces the standard deviation in cold regions but at the cost of introducing a positive bias, especially at low iteration numbers. For low-count data, as often encountered in short-duration frames in dynamic imaging protocols, it has been shown that it can be advantageous (in terms of bias in the reconstructed image) to remove the non-negativity constraint. In this work two competing algorithms that do not impose non-negativity in the reconstructed image are investigated: NEG-ML and AB-OSEM. It was found that the AB-OSEM reconstruction outperformed the NEG-ML reconstruction. The AB-OSEM algorithm was then further developed to allow a forward model that includes randoms and scatter background terms. In addition to static reconstruction the current analysis was extended to consider the important case of kinetic parameter estimation from dynamic PET data. Simulation studies (comparing OSEM, FBP and AB-OSEM) showed that the positive bias obtained with OSEM reconstruction can be avoided in both static and parametric imaging through use of a negative lower bound in AB-OSEM reconstruction (i.e. by lifting the implicit non-negativity constraint of OSEM). When quantification tasks are considered, the overall error in the estimates (composed of both bias and standard deviation) is often of primary concern. An important finding of this work is that in most cases the activity concentration and the kinetic parameters obtained from images reconstructed using AB-OSEM showed a lower overall root mean squared error compared to the popular choices of either OSEM or FBP reconstruction for both cold and warm regions. As such, AB-OSEM should be preferred instead of the standard OSEM and FBP reconstructions when kinetic parameter estimation is considered. Finally, this work shows example parametric images from the high-resolution research tomograph obtained using the different reconstruction methods.

PET imaging for the quantification of biologically heterogeneous tumours: measuring the effect of relative position on image-based quantification of dose-painting targets

Quantitative imaging of tumours represents the foundation of customized therapies and adaptive patient care. As such, we have investigated the effect of patient positioning errors on the reproducibility of images of biologically heterogeneous tumours generated by a clinical PET/CT system. A commercial multi-slice PET/CT system was used to acquire 2D and 3D PET images of a phantom containing multiple spheres of known volumes and known radioactivity concentrations and suspended in an aqueous medium. The spheres served as surrogates for sub-tumour regions of biological heterogeneities with dimensions of 5–15 mm. Between image acquisitions, a motorized-arm was used to reposition the spheres in 1 mm intervals along either the radial or the axial direction. Images of the phantom were reconstructed using typical diagnostic reconstruction techniques, and these images were analysed to characterize and model the position-dependent changes in contrast recovery. A simulation study was also conducted to investigate the effect of patient position on the reproducibility of PET imaging of biologically heterogeneous head and neck (HN) tumours. For this simulation study, we calculated the changes in image intensity values that would occur with changes in the relative position of the patients at the time of imaging. PET images of two HN patients were used to simulate an imaging study that incorporated set-up errors that are typical for HN patients. One thousand randomized positioning errors were investigated for each patient. As a result of the phantom study, a position-dependent trend was identified for measurements of contrast recovery of small objects. The peak contrast recovery occurred at radial and axial positions that coincide with the centre of the image voxel. Conversely, the minimum contrast recovery occurred when the object was positioned at the edges of the image voxel. Changing the position of high contrast spheres by one-half the voxel dimension lead to errors in the measurement of contrast recovery values which were larger than 30%. However, the magnitudes of the errors were found to depend on the size of the sphere and method of image reconstruction. The error values from standard OSEM images of the 5 mm diameter sphere were 20–35%, and for the 10 mm diameter sphere were 5–10%. The position-dependent variation of contrast recovery can result in changes in spatial distribution within images of heterogeneous tumours. In experiments simulating random set-up errors during imaging of two HN patients, the expectation value of the correlation was ∼1.0 for these tumours; however, Pearson correlation coefficient values as low as 0.8 were observed. Moreover, variations within the images can drastically change the delineation of biological target volumes. The errors in target delineation were more prominent in very heterogeneous tumours. As an example, in a pair of images with a correlation of 0.8, there was a 36% change in the volume of the dose-painting target delineated at 50%-of-max-SUV (ROI50%). The results of these studies indicate that the contrast recovery and spatial distributions of tracer within PET images are susceptible to changes in the position of the patient/tumour at the time of imaging. As such, random set-up errors in HN patients can result in reduced correlation between subsequent image-studies of the same tumour.

Quantitative simultaneous cardiac SPECT using MC‐JOSEM

Simultaneous rest cardiac SPECT imaging has the potential to replace current clinical ‐Sestamibi rest/stress imaging and therefore has great potential in the case of patients with chest pain presenting to the emergency department. Separation of images of these two radionuclides is difficult, however, because their emission energies are close. The authors previously developed a fast Monte Carlo (MC)‐based joint ordered‐subset expectation maximization (JOSEM) iterative reconstruction algorithm (MC‐JOSEM), which simultaneously compensates for scatter and cross talk as well as detector response within the reconstruction algorithm. In this work, the authors evaluated the performance of MC‐JOSEM in a realistic population of studies using cardiac phantom data on a Siemens e.cam system using a standard cardiac protocol. The authors also compared the performance of MC‐JOSEM for estimation tasks to that of two other methods: standard OSEM using photopeak energy windows without scatter correction (NSC‐OSEM) and standard OSEM using a Compton‐scatter energy window for scatter correction (SC‐OSEM). For each radionuclide the authors separately acquired high‐count projections of radioactivity in the myocardium wall, liver, and soft tissue background compartments of a water‐filled torso phantom, and they generated synthetic projections of various dual‐radionuclide activity distributions. Images of different combinations of myocardium wall/background activity concentration ratios for each radionuclide were reconstructed by NSC‐OSEM, SC‐OSEM, and MC‐JOSEM. For activity estimation in the myocardium wall, MC‐JOSEM always produced the best relative bias and relative standard deviation compared with NSC‐OSEM and SC‐OSEM for all the activity combinations. On average, the relative biases after 100 iterations were 8.1% for and 3.7% for with MC‐JOSEM, 39.4% for and 23.7% for with NSC‐OSEM, and 20.9% for with SC‐OSEM. The relative standard deviations after 30 iterations were 0.7% for and 1.0% for with MC‐JOSEM, as compared to 1.1% for and 1.2% for with NSC‐OSEM and 1.3% for with SC‐OSEM. Finally, the authors compared the relative standard deviation after 30 iterations with the minimum theoretical variance on activity estimation, the Cramer–Rao lower bound (CRB), and with the biased CRB. The measured precision was larger than the biased bound values by factors of 2–4, suggesting that further improvement could be made to the method.

Fast Monte Carlo based joint iterative reconstruction for simultaneous SPECT imaging

Simultaneous 99mTC/ 123I SPECT allows the assessment of two physiological functions under identical conditions. The separation of these radionuclides is difficult, however, because their energies are close. Most energy-window-based scatter correction methods do not fully model either physical factors or patient-specific activity and attenuation distributions. We have developed a fast Monte Carlo (MC) simulation-based multiple-radionuclide and multiple-energy joint ordered-subset expectation-maximization (JOSEM) iterative reconstruction algorithm, MC-JOSEM. MC-JOSEM simultaneously corrects for scatter and cross talk as well as detector response within the reconstruction algorithm. We evaluated MC-JOSEM for simultaneous brain profusion (99mTc-HMPAO) and neurotransmission (123I-altropane) SPECT. MC simulations of 99mTc and 123I studies were generated separately and then combined to mimic simultaneous 99mTc/ 123I SPECT. All the details of photon transport through the brain, the collimator, and detector, including Compton and coherent scatter, septal penetration, and backscatter from components behind the crystal, were modeled. We reconstructed images from simultaneous dual-radionuclide projections in three ways. First, we reconstructed the photopeak-energy-window projections (with an asymmetric energy window for 1231) using the standard ordered-subsets expectation-maximization algorithm (NSC-OSEM). Second, we used standard OSEM to reconstruct 99mTc photopeak-energy-window projections, while including an estimate of scatter from a Compton-scatter energy window (SC-OSEM). Third, we jointly reconstructed both 99mTc and 123I images using projection data associated with two photo-peak energy windows and an intermediate-energy window using MC-JOSEM. For 15 iterations of reconstruction, the bias and standard deviation of 99mTc activity estimates in several brain structures were calculated for NSC-OSEM, SC-OSEM, and MC-JOSEM, using images reconstructed from primary (unscattered) photons as a reference. Similar calculations were performed for 123I images for NSC-OSEM and MC-JOSEM. For 123I images, dopamine binding potential (BP) at equilibrium and its signal-to-noise ratio (SNR) were also calculated. Our results demonstrate that MC-JOSEM performs better than NSC- and SC-OSEM for quantitation tasks. After 15 iterations of reconstruction, the relative bias of 99mTc activity estimates in the thalamus, striata, white matter, and gray matter volumes from MC-JOSEM ranged from -2.4% to 1.2%, while the same estimates for NSC-OSEM (SC-OSEM) ranged from 20.8% to 103.6% (7.2% to 41.9%). Similarly, the relative bias of 123I activity estimates from 15 iterations of MC-JOSEM in the striata and background ranged from -1.4% to 2.9%, while the same estimates for NSC-OSEM ranged from 1.6% to 10.0%. The relative standard deviation of 99mTc activity estimates from MC-JOSEM ranged from 1.1% to 4.8% versus 1.2% to 6.7% (1.2% to 5.9%) for NSC-OSEM (SC-OSEM). The relative standard deviation of 123I activity estimates using MC-JOSEM ranged from 1.1% to 1.9% versus 1.5% to 2.7% for NSC-OSEM. Using the 123I dopamine BP obtained from the reconstruction produced by primary photons as a reference, the result for MC-JOSEM was 50.5% closer to the reference than that of NSC-OSEM after 15 iterations. The SNR for dopamine BP was 23.6 for MC-JOSEM as compared to 18.3 for NSC-OSEM.

Preliminary evaluation of the tomographic performance of the mediSPECT small animal imaging system

We report on the tests of a prototype (MediSPECT) system developed at University & INFN Napoli, for Single Photon Emission Computed Tomography (SPECT) imaging on small animals with a small Field of View (FoV) and high spatial resolution. MediSPECT is a SPECT imaging system based on a 1-mm-thick CdTe pixel detector, bump-bonded to the Medipix2 CMOS readout circuit operating in single-photon counting. The CdTe detector has 256×256 square array of pixels arranged with a 55 μm pitch, for a sensitive area of 14×14 mm 2. In its present version, this system implements a single detector head, mounted on a rotating gantry. For preliminary testing and calibration of the acquisition equipment and image reconstruction algorithms, 90 projections of a γ-ray point source ( 109Cd) through a single pinhole (diameter 0.4 mm; radius of rotation about 2.5 cm; focal length about 4.5 cm) were acquired for 20 min each in a step-and-shoot mode. Capillaries, 800 μm in diameter, were arranged in a Y-shape to form a more complex phantom ( 125I, 1 mm pinhole diameter, 45 projections, each acquired for 25 min). Images were reconstructed with a custom algorithm implementing standard OS-EM with center of rotation correction and spatial resolution of 0.2 mm over a FoV of 2 mm was obtained.