From 2D PET to 3D PET: Issues of Data Representation and Image Reconstruction

PET/MR imaging of inflammation in atherosclerosis

Myocardial infarction, stroke, mental disorders, neurodegenerative processes, autoimmune diseases, cancer and the human immunodeficiency virus impact the haematopoietic system, which through immunity and inflammation may aggravate pre-existing atherosclerosis. The interplay between the haematopoietic system and its modulation of atherosclerosis has been studied by imaging the cardiovascular system and the activation of haematopoietic organs via scanners integrating positron emission tomography and resonance imaging (PET/MRI). In this Perspective, we review the applicability of integrated whole-body PET/MRI for the study of immune-mediated phenomena associated with haematopoietic activity and cardiovascular disease, and discuss the translational opportunities and challenges of the technology.

An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET

PURPOSE

Deep learning is an emerging reconstruction method for positron emission tomography (PET), which can tackle complex PET corrections in an integrated procedure. This paper optimizes the direct PET reconstruction from sinogram on a long axial field of view (LAFOV) PET.

METHODS

This paper proposes a novel deep learning architecture to reduce the biases during direct reconstruction from sinograms to images. This architecture is based on an encoder-decoder network, where the perceptual loss is used with pre-trained convolutional layers. It is trained and tested on data of 80 patients acquired from recent Siemens Biograph Vision Quadra long axial FOV (LAFOV) PET/CT. The patients are randomly split into a training dataset of 60 patients, a validation dataset of 10 patients, and a test dataset of 10 patients. The 3D sinograms are converted into 2D sinogram slices and used as input to the network. In addition, the vendor reconstructed images are considered as ground truths. Finally, the proposed method is compared with DeepPET, a benchmark deep learning method for PET reconstruction.

RESULTS

Compared with DeepPET, the proposed network significantly reduces the root-mean-squared error (NRMSE) from 0.63 to 0.6 (p < 0.01) and increases the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) from 0.93 to 0.95 (p < 0.01) and from 82.02 to 82.36 (p < 0.01), respectively. The reconstruction time is approximately 10 s per patient, which is shortened by 23 times compared with the conventional method. The errors of mean standardized uptake values (SUVmean) for lesions between ground truth and the predicted result are reduced from 33.5 to 18.7% (p = 0.03). In addition, the error of max SUV is reduced from 32.7 to 21.8% (p = 0.02).

CONCLUSION

The results demonstrate the feasibility of using deep learning to reconstruct images with acceptable image quality and short reconstruction time. It is shown that the proposed method can improve the quality of deep learning-based reconstructed images without additional CT images for attenuation and scattering corrections. This study demonstrated the feasibility of deep learning to rapidly reconstruct images without additional CT images for complex corrections from actual clinical measurements on LAFOV PET. Despite improving the current development, AI-based reconstruction does not work appropriately for untrained scenarios due to limited extrapolation capability and cannot completely replace conventional reconstruction currently.

Guide to Plant-PET Imaging Using 11CO2

Due to its high sensitivity and specificity for tumor detection, positron emission tomography (PET) has become a standard and widely used molecular imaging technique. Given the popularity of PET, both clinically and preclinically, its use has been extended to study plants. However, only a limited number of research groups worldwide report PET-based studies, while we believe that this technique has much more potential and could contribute extensively to plant science. The limited application of PET may be related to the complexity of putting together methodological developments from multiple disciplines, such as radio-pharmacology, physics, mathematics and engineering, which may form an obstacle for some research groups. By means of this manuscript, we want to encourage researchers to study plants using PET. The main goal is to provide a clear description on how to design and execute PET scans, process the resulting data and fully explore its potential by quantification via compartmental modeling. The different steps that need to be taken will be discussed as well as the related challenges. Hereby, the main focus will be on, although not limited to, tracing 11CO2 to study plant carbon dynamics.

PET/CT Myocardial Perfusion Imaging Acquisition and Processing: Ten Tips and Tricks to Help You Succeed

PURPOSE OF REVIEW

Positron emission tomography (PET) is a leading non-invasive modality for the diagnosis of coronary artery disease due to its diagnostic accuracy and high image quality. With the latest advances in PET systems, clinicians are able to assess for myocardial ischemia and myocardial blood flow while exposing patients to extremely low radiation doses. This review will focus on the basics of acquisition and processing of hybrid PET/CT systems from appropriate patient selection to common artifacts and pitfalls.

RECENT FINDINGS

The continued development of hybrid PET/CT technology is producing scanners with exquisite sensitivity capable of generating high-quality images while exposing patients to low radiation doses. List mode acquisition is an essential component in all modern PET/CT scanners allowing simultaneous dynamic and ECG-gated imaging without lengthening scan duration. Various PET radiotracers are currently being developed but rubidium-82 and 13N-ammonia remain the most commonly used perfusion radiotracers. The development of mini 13N-ammonia cyclotrons is a promising tool that should increase access to this radiotracer. Misregistration, attenuation from extra-cardiac activity, and patient motion are the most common causes of artifacts during perfusion imaging. Techniques to automatically realign images and correct respiratory or patient motion artifacts continue to evolve. Despite the continuous evolution of PET imaging techniques, basic knowledge of scan parameters, acquisition techniques, and post processing tools remains essential to ensure high-quality images are produced and artifacts are recognized and corrected. Future research should focus on optimizing scanners to allow for shorter scan protocols and lower radiation exposure as well as continue developing techniques to minimize and correct for motion and misregistration artifacts.

Influence of PET reconstruction para meters on the TrueX algorithm

With the increasing use of functional imaging in modern radiotherapy (RT) and the envisaged automated integration of PET into target definition, the need for reliable quantification of PET is growing. Reconstruction algorithms in new PET scanners employ pointspread-function (PSF) based resolution recovery, however, their impact on PET quantification still requires thorough investigation. Patients, material, methods: Measurements were performed on a Siemens PET/CT using an IEC phantom filled with varying activity. Data were reconstructed using the OSEM (Gauss filter) and the PSF TrueX (Gauss and Allpass filter) algorithm with all available products of iterations (i) and subsets (ss). The recovery coeffcient (RC) and threshold defining the real sphere volume were determined for all settings and compared to the clinical standard (4i21ss). PET acquisitions of eight lung patients were reconstructed using all algorithms with 4i21ss. Volume size and tracer uptake were determined with different segmentation methods. Results: The threshold for the TrueX was lower (up to 40%) than for the OSEM. The RC for the different algorithms and filters varied. TrueX was more sensitive to permutations of i and ss and only the RC of the OSEM stabilised with increasing number. For patient scans the difference of the volume and activity between TrueX and OSEM could be reduced by applying an adapted threshold and activity correction. Conclusion: The TrueX algorithm results in excellent diagnostic image quality, however, guidelines for native algorithms have to be extended for PSF based reconstruction methods. For appropriate tumour delineation, for the TrueX a lower threshold than the 42% recommended for the OSEM is necessary. These filter dependent thresholds have to be verified for different scanners prior to using them in multicenter trials.

PET based volume segmentation with emphasis on the iterative TrueX algorithm

PURPOSE

To assess the influence of reconstruction algorithms for positron emission tomography (PET) based volume quantification. The specifically detected activity in the threshold defined volume was investigated for different reconstruction algorithms as a function of volume size and signal to background ratio (SBR), especially for volumes smaller than 1ml. Special attention was given to the Siemens specific iterative reconstruction algorithm TrueX.

METHODS

Measurements were performed with a modified in-house produced IEC body phantom on a Siemens Biograph 64 True Point PET/CT scanner (Siemens, Medical Systems) for six different SBRs (2.1, 3.8, 4.9, 6.7, 8.9, 9.4 and without active background (BG)). The phantom consisted of a water-filled cavity with built-in plastic spheres (0.27, 0.52, 1.15, 2.57, 5.58 and 11.49ml). The following reconstruction algorithms available on the Siemens Syngo workstation were evaluated: Iterative OSEM (OSEM) (4 iterations, 21 subsets), iterative TrueX (TrueX) (4 iterations, 21 subsets) and filtered backprojection (FBP). For the threshold based volume segmentation the software Rover (ABX, Dresden) was used.

RESULTS

For spheres larger than 2.5ml a constant threshold (standard deviation (SD) 10%) level was found for a given SBR and reconstruction algorithm and therefore a mean threshold for the largest three spheres was calculated. This threshold could be approximated by a function inversely proportional to the SBR. The threshold decreased with increasing SBR for all sphere sizes. For the OSEM algorithm the threshold for small spheres with 0.27, 0.52 and 1.15ml varied between 17% and 44% (depending on sphere size). The threshold for the TrueX algorithm was substantially lower (up to 17%) than for the OSEM algorithm for all sphere sizes. The maximum activity in a specific volume yielded the true activity for the OSEM algorithm when using a SBR independent correction factor C, which depended on sphere size. For the largest three volumes a constant factor C=1.10±0.03 was found. For smaller volumes, C increased exponentially due to the partial volume effect. For the TrueX algorithm the maximum activity overestimated the true activity.

CONCLUSION

The threshold values for PET based target volume segmentation increased with increasing sphere size for all tested algorithms. True activity values of spheres in the phantom could be extracted using experimentally determined correction factors C. The TrueX algorithm has to be used carefully for quantitative comparison (e.g. follow-up) and multicenter studies.

Multimodality imaging of atherosclerotic plaque activity and composition using FDG-PET/CT and MRI in carotid and femoral arteries

PURPOSE

To evaluate the relationship between atherosclerotic plaque inflammation, as assessed by FDG-positron emission tomography/computed tomography (FDG-PET/CT), and plaque morphology and composition, as assessed by magnetic resonance imaging (MRI), in the carotid and femoral arteries.

MATERIALS AND METHODS

Sixteen patients underwent FDG-PET/CT and MRI (T2-weighted (T2W) and proton density weighted (PDW)) of the carotid and femoral arteries. For every image slice, two observers determined the corresponding regions of the FDG-PET/CT and MRI image sets by matching CT and T2W axial images. Each plaque was then classified into one of three groups according to the CT appearance and T2W/PDW signal: (1) collagen, (2) lipid-necrotic core and (3) calcium. Arterial FDG uptake was measured for each plaque and normalized to vein FDG activity to produce a blood-normalized artery activity called the target to background ratio (TBR). The vessel wall thickness (VWT), the vessel wall area and the total vessel wall area were measured from the T2W MR images.

RESULTS

The TBR value was higher in the lipid-necrotic core group compared to the collagen and calcium groups, (p<0.001). The lipid-necrotic core group demonstrated a significant TBR variation according to the median of the VWT (TBR=1.26+/-0.25 vs. 1.50+/-0.12). There was no correlation with other morphological MR parameters.

CONCLUSIONS

This study demonstrates the complementary value of non-invasive FDG-PET/CT and MR imaging for the evaluation of atherosclerotic plaque composition and activity. Lipid-rich plaques are more inflamed than either calcified or collagen-rich plaques.

Relationships among regional arterial inflammation, calcification, risk factors, and biomarkers: a prospective fluorodeoxyglucose positron-emission tomography/computed tomography imaging study

BACKGROUND

Fluorodeoxyglucose positron-emission tomography (FDG PET) imaging of atherosclerosis has been used to quantify plaque inflammation and to measure the effect of plaque-stabilizing drugs. We explored how atherosclerotic plaque inflammation varies across arterial territories and how it relates to arterial calcification. We also tested the hypotheses that the degree of local arterial inflammation measured by PET is correlated with the extent of systemic inflammation and presence of risk factors for vascular disease.

METHODS AND RESULTS

Forty-one subjects underwent vascular PET/computed tomography imaging with FDG. All had either vascular disease or multiple risk factors. Forty subjects underwent carotid imaging, 27 subjects underwent aortic, 24 subjects iliac, and 13 subjects femoral imaging. Thirty-three subjects had a panel of biomarkers analyzed. We found strong associations between FDG uptake in neighboring arteries (left versus right carotid, r=0.91, P<0.001; ascending aorta versus aortic arch, r=0.88, P<0.001). Calcification and inflammation rarely overlapped within arteries (carotid artery FDG uptake versus calcium score, r=-0.42, P=0.03). Carotid artery FDG uptake was greater in those with a history of coronary artery disease (target-to-

BACKGROUND

<0.01) and in males versus females (target-to-

BACKGROUND

<0.05). Similar findings were also noted in the aorta and iliac arteries. Subjects with the highest levels of FDG uptake also had the greatest concentrations of inflammatory biomarkers (descending aorta target-to-

BACKGROUND

=0.53, P=0.01; carotid target-to-

BACKGROUND

=0.50, P=0.01). Nonsignificant positive trends were seen between FDG uptake and levels of interleukin-18, fibrinogen, and C-reactive protein. Finally, we found that the atheroprotective biomarker adiponectin was negatively correlated with the degree of arterial inflammation in the descending aorta (r=-0.49, P=0.03).

CONCLUSIONS

This study shows that FDG PET imaging can increase our knowledge of how atherosclerotic plaque inflammation relates to calcification, serum biomarkers, and vascular risk factors. Plaque inflammation and calcification rarely overlap, supporting the theory that calcification represents a late, burnt-out stage of atherosclerosis. Inflammation in one arterial territory is associated with inflammation elsewhere, and the degree of local arterial inflammation is reflected in the blood levels of several circulating biomarkers. We suggest that FDG PET imaging could be used as a surrogate marker of both atherosclerotic disease activity and drug effectiveness. Prospective, event-driven studies are now underway to determine the role of this technique in clinical risk prediction.

Atherosclerosis inflammation imaging with 18F-FDG PET: carotid, iliac, and femoral uptake reproducibility, quantification methods, and recommendations

Atherosclerosis imaging with 18F-FDG PET is useful for tracking inflammation within plaque and monitoring the response to drug therapy. Short-term reproducibility of this technique in peripheral artery disease has not been assessed, and the optimal method of 18F-FDG quantification is still debated. We imaged 20 patients with vascular disease using 18F-FDG PET twice, 14 d apart, and used these data to assess reproducibility measures and compare 2 methods of 18F-FDG uptake measurement. We also reviewed the literature on quantification methods to determine the optimal measures of arterial 18F-FDG uptake for future studies.

METHODS

Twenty patients with vascular disease underwent PET/CT of the iliac, femoral, and carotid arteries 90 min after 18F-FDG administration. In 19 patients, repeat testing was performed at 2 wk. Coregistration and attenuation correction were performed with CT. Vessel 18F-FDG uptake was measured as both the mean and maximum blood-normalized standardized uptake value (SUV), known as the target-to-background ratio (TBR). We assessed interscan, interobserver, and intraobserver agreement.

RESULTS

Nineteen patients completed both imaging sessions. The carotid and peripheral arteries all have excellent short-term reproducibility of the 18F-FDG signal, with intraclass correlation coefficients all greater than 0.8 for all measures of reproducibility. Both mean and maximum TBR measurements for quantifying 18F-FDG uptake are equally reproducible. 18F-FDG uptake was significantly higher in the carotid arteries than in both iliac and femoral vessels (P < 0.001 for both).

CONCLUSION

We found that both mean and maximum TBR in the carotid, iliac, and femoral arteries were highly reproducible. We suggest the mean TBR be used for tracking systemic arterial therapies, whereas the maximum TBR is optimal for detecting and monitoring local, plaque-based therapy.