A practical way to improve contrast-to-noise ratio and quantitation for statistical-based iterative reconstruction in whole-body PET imaging

Influences on PET Quantification and Interpretation

Various factors have been identified that influence quantitative accuracy and image interpretation in positron emission tomography (PET). Through the continuous introduction of new PET technology—both imaging hardware and reconstruction software—into clinical care, we now find ourselves in a transition period in which traditional and new technologies coexist. The effects on the clinical value of PET imaging and its interpretation in routine clinical practice require careful reevaluation. In this review, we provide a comprehensive summary of important factors influencing quantification and interpretation with a focus on recent developments in PET technology. Finally, we discuss the relationship between quantitative accuracy and subjective image interpretation.

Study of two-layer tapered depth of interaction PET detector

Improving detection efficiency in small animal PET scanners without degrading spatial resolution is one of the main problems of these scanners. Commercial small animal PET scanners use different methods to achieve desirable levels of sensitivity and spatial resolution. GE Healthcare eXplore VISTA PET scanner uses double layer (LYSO-GSO) depth-of-interaction (DOI) capable cuboid detector modules. In this work, the design of GE Healthcare eXplore VISTA PET scanner is improved using tapered detector geometry instead of cuboid geometry. Using tapered detector geometry, the gaps between adjacent modules are filled and the sensitive volume has increased about 11.5%. The new designed PET scanner sensitivity and spatial resolution are studied for different crystal layer configurations (LYSO-GSO and GSO-LYSO with different thicknesses). As expected, average sensitivity over FOV is improved. Spatial resolution is slightly degraded but it is still uniform over FOV.

PET imaging of occult tumours by temporal integration of tumour-acidosis signals from pH-sensitive 64Cu-labelled polymers

Owing to the diversity of cancer types and the spatiotemporal heterogeneity of tumour signals, high-resolution imaging of occult malignancy is challenging. ¹⁸F-fluorodeoxyglucose positron emission tomography allows for near-universal cancer detection, yet in many clinical scenarios it is hampered by false positives. Here, we report a method for the amplification of imaging contrast in tumours via the temporal integration of the imaging signals triggered by tumour acidosis. This method exploits the catastrophic disassembly, at the acidic pH of the tumour milieu, of pH-sensitive positron-emitting neutral copolymer micelles into polycationic polymers, which are then internalized and retained by the cancer cells. Positron emission tomography imaging of the ⁶⁴Cu-labelled polymers detected small occult tumours (10-20 mm³) in the brain, head, neck and breast of mice at much higher contrast than ¹⁸F-fluorodeoxyglucose, ¹¹C-methionine and pH-insensitive ⁶⁴Cu-labelled nanoparticles. We also show that the pH-sensitive probes reduce false positive detection rates in a mouse model of non-cancerous lipopolysaccharide-induced inflammation. This macromolecular strategy for integrating tumour acidosis should enable improved cancer detection, surveillance and staging.

Detective quantum efficiency (DQE) in PET scanners: A simulation study

The aim of the present study is to introduce the detective quantum efficiency (DQE) for the image quality assessment of positron emission tomography (PET) scanners. For this purpose, a thin layer chromatography (TLC) plane source was simulated using a previously validated, scanner and source geometry, Monte Carlo (MC) model. The model was developed with the Geant4 application for tomographic emission (GATE) MC package and reconstructed images obtained with the software for tomographic image reconstruction (STIR), with cluster computing. The GE Discovery ST PET scanner was simulated by using a previously validated code. A plane source consisting of a TLC plate, was simulated by a layer of silica gel on aluminum (Al) foil substrate, immersed in 18F-FDG bath solution (1MBq). Image quality was assessed in terms of the modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) in order to obtain the detective quantum efficiency (DQE). MTF curves were estimated from transverse reconstructed images of the plane source, whereas the NNPS data were estimated from the corresponding coronal images. Images were reconstructed by the maximum likelihood estimation ordered subsets maximum a posteriori one step late (MLE)-OS-MAP-OSL algorithm, by using various subsets 1-21) and iterations 1-20). MTF values were found to increase up to the 12th iteration whereas remain almost constant thereafter. However, the range of the increase in the MTF is limited as the number of subsets increases. The noise levels were found to increase with the corresponding increase of both the number of iterations and subsets. The maximum NNPS value (0.517mm²) was observed for the 420 MLEM-equivalent iterations reconstructed image at 0cycles/mm. Finally DQE values were found to increase for spatial frequencies up to 0.038cycles/mm and to decrease thereafter with the corresponding increase in both number of iterations and subsets. The maximum DQE value (0.48 at 0.038cycles/mm) was obtained for the 8 MLEM-equivalent iterations image. The simulated PET evaluation method based on the TLC plane source can be useful in the quality control and in the further development of PET and SPECT scanners though GATE simulations.

Effect of tomographic operator inaccuracies and respiratory motion on PET/CT lung nodule images smearing

BACKGROUND

In thoracic PET/computed tomography (CT) imaging, uptake foci usually appear smeared because of postreconstruction smoothing and respiratory motion.

OBJECTIVE

The aim of the present study was to assess the respective contributions of the reconstruction process and respiratory motion on PET/CT images.

MATERIALS AND METHODS

Thirty-one pulmonary lesions were studied. Free-breathing PET/CT acquisitions were followed by a 10-min respiratory-gated PET/CT acquisition. Four different reconstructions were performed by combining two different tomographic operators (TOs) (i.e. the geometric clinical system matrix or a system matrix including the detector response) and taking account (or not) of respiratory motion using a previously developed 'CT-based' technique. For each reconstruction method, lesion segmentation was performed with an adaptive threshold. Next, we computed the volume differences between each reconstruction. Finally, we applied a multiple linear model to compute the relative contributions of TO-based and CT-based respiratory compensation to lesion volume.

RESULTS

The three groups, combining the reconstruction methods and the respiratory compensation (or not), differed significantly in terms of the volume differences. For all lesions, the full linear model yielded a regression coefficient R of 76.10%. The partial R values were 65.58 and 10.52% for the detector response operator and the CT-based method, respectively. For lesions in the upper/middle lobes, blurring was mainly because of TO (partial R=78.53%), whereas, for lower lobe lesions, smearing was mainly because of respiratory motion (partial R=56.76%).

CONCLUSION

Our results showed that image reconstruction, by TO accuracy, was the main explanatory factor for lesion smearing when considering the chest as a whole. Respiration had a major impact on the lower lobes.

Influence of reconstruction settings on the performance of adaptive thresholding algorithms for FDG-PET image segmentation in radiotherapy planning

The purpose of this study was to analyze the behavior of a contouring algorithm for PET images based on adaptive thresholding depending on lesions size and target‐to‐background (TB) ratio under different conditions of image reconstruction parameters. Based on this analysis, the image reconstruction scheme able to maximize the goodness of fit of the thresholding algorithm has been selected. A phantom study employing spherical targets was designed to determine slice‐specific threshold (TS) levels which produce accurate cross‐sectional areas. A wide range of TB ratio was investigated. Multiple regression methods were used to fit the data and to construct algorithms depending both on target cross‐sectional area and TB ratio, using various reconstruction schemes employing a wide range of iteration number and amount of postfiltering Gaussian smoothing. Analysis of covariance was used to test the influence of iteration number and smoothing on threshold determination.

The degree of convergence of ordered‐subset expectation maximization (OSEM) algorithms does not influence TS determination. Among these approaches, the OSEM at two iterations and eight subsets with a 6–8 mm post‐reconstruction Gaussian three‐dimensional filter provided the best fit with a coefficient of determination R2=0.90 for cross‐sectional areas ≤ 133 mm² and R2=0.95 for cross‐sectional areas > 133 mm². The amount of post‐reconstruction smoothing has been directly incorporated in the adaptive thresholding algorithms. The feasibility of the method was tested in two patients with lymph node FDG accumulation and in five patients using the bladder to mimic an anatomical structure of large size and uniform uptake, with satisfactory results.

Slice‐specific adaptive thresholding algorithms look promising as a reproducible method for delineating PET target volumes with good accuracy.

PACS numbers: 87.57.nm, 87.55.D‐, 87.57.uk

Preliminary clinical applications of a device-dedicated whole-body positron emission tomography reconstruction method: impact on standardized uptake values

BACKGROUND

¹⁸F-fluorodeoxyglucose positron emission tomography has proven relevance in oncological diagnosis, staging and follow-up. The standardized uptake value (SUV) is one of the most widely used semi-quantitative criteria in PET imaging. However, factors such as noise and image resolution affect the measurement of the SUV. We reported earlier that a device-dedicated projector [attenuation-weighted ordered-subsets expectation maximization detector response (AW-OSEM DR), based on point-source measurements] introduces less noise than a geometrical model (AW-OSEM).

OBJECTIVE

The aim of this study was to investigate the AW-OSEM DR method's impact on SUV measurements under clinical conditions.

METHODS

We first performed a bias analysis to assess the accuracy of the quantitation for the two reconstruction methods as a function of target size and the number of iterations, with 14 acquisitions of the NEMA IEC/2001 phantom. We then used each method to calculate the maximum and average SUVs, respectively for 32 lesions.

RESULTS

For all spheres and all iterations, the bias was significantly lower with AW-OSEM DR than with AW-OSEM (P=0.012). Moreover, a paired Student's t-test showed significant intermethod differences for maximum SUV and average SUV (both P<0.001) in cancer patients. Conversely, the two methods did not differ significantly in terms of the mean SUV and signal-to-noise ratio calculated in the liver for each patient (P=0.5 and 0.08, respectively).

CONCLUSION

Phantom and patient studies were performed to quantify the effects of AW-OSEM DR on PET images. The phantom study highlighted the fact that our method produces more accurate images in terms of the SUV, which is an essential quality for ensuring correct diagnosis, follow-up and treatment planning.

Evaluation of the spatial dependence of the point spread function in 2D PET image reconstruction using LOR-OSEM

PURPOSE

The use of positron emission tomography (PET) imaging has proved beneficial in the staging and diagnosis of several cancer disease sites. Additional applications of PET imaging in treatment planning and the evaluation of treatment response are limited by the relatively low spatial resolution of PET images. Including point spread function (PSF) information in the system matrix (SM) of iterative reconstruction techniques has been shown to produce improved spatial resolution in PET images.

METHODS

In this study, the authors sampled the spatially variant PSF at over 6000 locations in the field of view for a General Electric Discovery ST PET/CT (General Electric Healthcare, Waukesha, WI) scanner in 2D acquisition mode. The authors developed PSF blurred SMs based on different combinations of the radial, depth, and azimuthal spatial dependencies to test the overall spatial dependence of the PSF on image quality. The PSF blurred SMs were included in a LOR-OSEM reconstruction algorithm and used for image reconstruction of geometric phantoms. The authors also examined the effect of sampling density on PSF characterization to design a more efficient sampling scheme.

RESULTS

The authors found that depth dependent change in the amplitude of the detector response was the most important factor affecting image quality. A SM created from a PSF that introduced r (perpendicular to the LOR), d (parallel to the LOR), or r and d dependent blurring across the radial lines of response led to visually identifiable improvements in spatial resolution and contrast in reconstructed images compared to images reconstructed with a purely geometric SM with no PSF blurring. Images reconstructed using a SM with r and d dependent blurring across the radial lines of response showed improved spatial resolution and contrast-noise ratios compared to images reconstructed with a SM that had only r dependent blurring. Additionally, the authors determined that the PSF could be adequately characterized with roughly 85% fewer samples through the use of a better optimized sampling scheme.

CONCLUSIONS

PET image reconstruction using a SM made from an accurately characterized PSF that accounts for r and d dependencies results in improved spatial resolution and contrast-noise relations, which may aid in lesion boundary detection for treatment planning or quantitative assessment of treatment response.