Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning

External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data

BACKGROUND

A reduction of dose and/or acquisition duration of PET examinations is desirable in terms of radiation protection, patient comfort and throughput, but leads to decreased image quality due to poorer image statistics. Recently, different deep-learning based methods have been proposed to improve image quality of low-count PET images. For example, one such approach allows the generation of AI-enhanced PET images (AI-PET) based on ultra-low count PET/CT scans. The performance of this algorithm has so far only been clinically evaluated on patient data featuring limited scan statistics and unknown actual activity concentration. Therefore, this study investigates the performance of this deep-learning algorithm using PET measurements of a phantom resembling different lesion sizes and count statistics (from ultra-low to high) to understand the capabilities and limitations of AI-based post processing for improved image quality in ultra-low count PET imaging.

METHODS

A previously trained pix2pixHD Generative Adversarial Network was evaluated. To this end, a NEMA PET body phantom filled with two sphere-to-background activity concentration ratios (4:1 and 10:1) and two attenuation scenarios to investigate the effects of obese patients was scanned in list mode. Images were reconstructed with 13 different acquisition durations ranging from 5 s up to 900 s. Image noise, recovery coefficients, SUV-differences, image quality measurement metrics such as the Structural Similarity Index Metric, and the contrast-to-noise-ratio were assessed. In addition, the benefits of the deep-learning network over Gaussian smoothing were investigated.

RESULTS

The presented AI-algorithm is very well suitable for denoising ultra-low count PET images and for restoring structural information, but increases image noise in ultra-high count PET scans. The generated AI-PET scans strongly underestimate SUV especially in small lesions with a diameter ≤ 17 mm, while quantitative measures of large lesions ≥ 37 mm in diameter were accurately recovered. In ultra-low count or low contrast images, the AI algorithm might not be able to recognize small lesions ≤ 13 mm in diameter. In comparison to standardized image post-processing using a Gaussian filter, the deep-learning network is better suited to improve image quality, but at the same time degrades SUV accuracy to a greater extent than post-filtering and quantitative SUV accuracy varies for different lesion sizes.

CONCLUSIONS

Phantom-based validation of AI-based algorithms allows for a detailed assessment of the performance, limitations, and generalizability of deep-learning based algorithms for PET image enhancement. Here it was confirmed that the AI-based approach performs very well in denoising ultra-low count PET images and outperforms traditional Gaussian post-filtering. However, there are strong limitations in terms of quantitative accuracy and detectability of small lesions.

Automatic Brain Segmentation for PET/MR Dual-Modal Images Through a Cross-Fusion Mechanism

The precise segmentation of different brain regions and tissues is usually a prerequisite for the detection and diagnosis of various neurological disorders in neuroscience. Considering the abundance of functional and structural dual-modality information for positron emission tomography/magnetic resonance (PET/MR) images, we propose a novel 3D whole-brain segmentation network with a cross-fusion mechanism introduced to obtain 45 brain regions. Specifically, the network processes PET and MR images simultaneously, employing UX-Net and a cross-fusion block for feature extraction and fusion in the encoder. We test our method by comparing it with other deep learning-based methods, including 3DUXNET, SwinUNETR, UNETR, nnFormer, UNet3D, NestedUNet, ResUNet, and VNet. The experimental results demonstrate that the proposed method achieves better segmentation performance in terms of both visual and quantitative evaluation metrics and achieves more precise segmentation in three views while preserving fine details. In particular, the proposed method achieves superior quantitative results, with a Dice coefficient of 85.73% 0.01%, a Jaccard index of 76.68% 0.02%, a sensitivity of 85.00% 0.01%, a precision of 83.26% 0.03% and a Hausdorff distance (HD) of 4.4885 14.85%. Moreover, the distribution and correlation of the SUV in the volume of interest (VOI) are also evaluated (PCC > 0.9), indicating consistency with the ground truth and the superiority of the proposed method. In future work, we will utilize our whole-brain segmentation method in clinical practice to assist doctors in accurately diagnosing and treating brain diseases.

High-temporal-resolution dynamic PET imaging based on a kinetic-induced voxel filter

Objective. Dynamic positron emission tomography (dPET) is an important molecular imaging technology that is used for the clinical diagnosis, staging, and treatment of various human cancers. Higher temporal imaging resolutions are desired for the early stages of radioactive tracer metabolism. However, images reconstructed from raw data with shorter frame durations have lower image signal-to-noise ratios (SNRs) and unexpected spatial resolutions.Approach. To address these issues, this paper proposes a kinetic-induced voxel filtering technique for processing noisy and distorted dPET images. This method extracts the inherent motion information contained in the target PET image and effectively uses this information to construct an image filter for each PET image frame. To ensure that the filtered image remains undistorted, we integrate and reorganize the information from each frame along the temporal dimension. In addition, our method applies repeated filtering operations to the image to produce optimal denoising results.Main results. The effectiveness of the proposed method is validated on both simulated and clinical dPET data, with quantitative evaluations of dynamic images and pharmacokinetic parameter maps calculated via the peak SNR and mean structural similarity index measure. Compared with the state-of-the-art methods, our method achieves superior results in both qualitative and quantitative imaging scenarios.Significance. It exhibits commendable performance and high interpretability and is demonstrated to be both effective and feasible in high-temporal-resolution dynamic PET imaging tasks.

A generative whole-brain segmentation model for positron emission tomography images

PURPOSE

Whole-brain segmentation via positron emission tomography (PET) imaging is crucial for advancing neuroscience research and clinical medicine, providing essential insights into biological metabolism and activity within different brain regions. However, the low resolution of PET images may have limited the segmentation accuracy of multiple brain structures. Therefore, we propose a generative multi-object segmentation model for brain PET images to achieve automatic and accurate segmentation.

METHODS

In this study, we propose a generative multi-object segmentation model for brain PET images with two learning protocols. First, we pretrained a latent mapping model to learn the mapping relationship between PET and MR images so that we could extract anatomical information of the brain. A 3D multi-object segmentation model was subsequently proposed to apply whole-brain segmentation to MR images generated from integrated latent mapping models. Moreover, a custom cross-attention module based on a cross-attention mechanism was constructed to effectively fuse the functional information and structural information. The proposed method was compared with various deep learning-based approaches in terms of the Dice similarity coefficient, Jaccard index, precision, and recall serving as evaluation metrics.

RESULTS

Experiments were conducted on real brain PET/MR images from 120 patients. Both visual and quantitative results indicate that our method outperforms the other comparison approaches, achieving 75.53% ± 4.26% Dice, 66.02% ± 4.55% Jaccard, 74.64% ± 4.15% recall and 81.40% ± 2.30% precision. Furthermore, the evaluation of the SUV distribution and correlation assessment in the regions of interest demonstrated consistency with the ground truth. Additionally, clinical tolerance rates, which are determined by the tumor background ratio, have confirmed the ability of the method to distinguish highly metabolic regions accurately from normal regions, reinforcing its clinical applicability.

CONCLUSION

For automatic and accurate whole-brain segmentation, we propose a novel 3D generative multi-object segmentation model for brain PET images, which achieves superior model performance compared with other deep learning methods. In the future, we will apply our whole-brain segmentation method to clinical practice and extend it to other multimodal tasks.

Total-Body PET/CT: A Role of Artificial Intelligence?

The purpose of this paper is to provide an overview of the cutting-edge applications of artificial intelligence (AI) technology in total-body positron emission tomography/computed tomography (PET/CT) scanning technology and its profound impact on the field of medical imaging. The introduction of total-body PET/CT scanners marked a major breakthrough in medical imaging, as their superior sensitivity and ultralong axial fields of view allowed for high-quality PET images of the entire body to be obtained in a single scan, greatly enhancing the efficiency and accuracy of diagnoses. However, this advancement is accompanied by the challenges of increasing data volumes and data complexity levels, which pose severe challenges for traditional image processing and analysis methods. Given the excellent ability of AI technology to process massive and high-dimensional data, the combination of AI technology and ultrasensitive PET/CT can be considered a complementary match, opening a new path for rapidly improving the efficiency of the PET-based medical diagnosis process. Recently, AI technology has demonstrated extraordinary potential in several key areas related to total-body PET/CT, including radiation dose reductions, dynamic parametric imaging refinements, quantitative analysis accuracy improvements, and significant image quality enhancements. The accelerated adoption of AI in clinical practice is of particular interest and is directly driven by the rapid progress made by AI technologies in terms of interpretability; i.e., the decision-making processes of algorithms and models have become more transparent and understandable. In the future, we believe that AI technology will fundamentally reshape the use of PET/CT, not only playing a more critical role in clinical diagnoses but also facilitating the customization and implementation of personalized healthcare solutions, providing patients with safer, more accurate, and more efficient healthcare experiences.

Total Body PET/CT: Clinical Value and Future Aspects of Quantification in Static and Dynamic Imaging

Total body (TB) Positron Emission Tomography (PET) / Computed Tomography (CT) scanners have revolutionized nuclear medicine by enabling whole-body imaging in a single bed position.1 This review assesses the physical and clinical value of TB-PET/CT, with a focus on the advancements in both static and dynamic imaging, as well as the evolving quantification techniques. The significantly enhanced sensitivity of TB scanners can reduce radiation exposure and scan time, offering improved patient comfort and making it particularly useful for pediatric imaging and various other scenarios. Shorter scan times also decrease motion artifacts, leading to higher-quality images and better diagnostic accuracy. Dynamic PET imaging with TB scanners extends these advantages by capturing temporal changes in tracer uptake over time, providing real-time insights into both structural and functional assessment, and promoting the ability to monitor disease progression and treatment response. We also present CT-free attenuation correction methods that utilize the increased sensitivity of TB-PET as a potential improvement for dynamic TB-PET protocols. In static imaging, emerging quantification techniques such as dual-tracer PET using TB scanners allow imaging of two biological pathways, simultaneously, for a more comprehensive assessment of disease. In addition, positronium imaging, a novel technique utilizing positronium lifetime measurements, is introduced as a promising aspect for providing structural information alongside functional quantification. Finally, the potential of expanding clinical applications with the increased sensitivity of TB-PET/CT scanners is discussed.

Assessment of image-derived input functions from small vessels for patlak parametric imaging using total-body PET/CT

PURPOSE

The image-derived input function (IDIF) from the descending aorta has demonstrated performance comparable to arterial blood sampling while avoiding its invasive nature in parametric imaging. However, in conventional PET, large vessels may not always be within the imaging field of view (FOV). This study aims to evaluate the efficacy of dynamic parametric Ki imaging using image-derived input functions (IDIFs) extracted from various arteries, facilitated by total-body PET/CT.

METHOD

Twenty-three participants underwent a 60-minute total-body [18F]FDG PET scan. Data from each subject were used to reconstruct both total-body PET images and short-axis field-of-view PET images at different bed positions, each with a 25 cm axial field-of-view (AFOV). Partial volume correction (PVC) was performed using the blurred Van Cittert iterative deconvolution. IDIFs extracted from the descending aorta, carotid artery, abdominal aorta, and iliac artery were employed for Patlak analysis. The resulting Ki images were compared using quantification indicators and subjective assessment. Linear regression analysis was conducted to examine the correlation of Ki values among IDIFs in normal organ and lesion regions of interest (ROIs).

RESULT

High similarities were observed in Ki images derived from the IDIFs from the descending aorta and other arteries, with a median structural similarity index measure (SSIM) above 0.98 and a median peak signal-to-noise ratio (PSNR) above 37dB. Linear regression analysis revealed strong correlations in Ki values (r² > 0.88) between the descending aorta and the three alternative vessels, with slopes of the linear fits close to 1. No significant difference in lesion detectability among IDIFs was found, as assessed visually and using metrics such as tumor-to-background ratio (TBR) and contrast-to-noise ratio (CNR) (P < 0.05).

CONCLUSION

IDIFs from smaller vessels can reliably reconstruct parametric Ki images without compromising lesion detectability, providing clinically relevant information.

Realization of high-end PET devices that assist conventional PET devices in improving image quality via diffusion modeling

PURPOSE

This study aimed to implement high-end positron emission tomography (PET) equipment to assist conventional PET equipment in improving image quality via a distribution learning-based diffusion model.

METHODS

A diffusion model was first trained on a dataset of high-quality (HQ) images acquired by a high-end PET device (uEXPLORER scanner), and the quality of the conventional PET images was later improved on the basis of this trained model built on null-space constraints. Data from 180 patients were used in this study. Among them, 137 patients who underwent total-body PET/computed tomography scans via a uEXPLORER scanner at the Sun Yat-sen University Cancer Center were retrospectively enrolled. The datasets of 50 of these patients were used to train the diffusion model. The remaining 87 cases and 43 PET images acquired from The Cancer Imaging Archive were used to quantitatively and qualitatively evaluate the proposed method. The nonlocal means (NLM) method, UNet and a generative adversarial network (GAN) were used as reference methods.

RESULTS

The incorporation of HQ imaging priors derived from high-end devices into the diffusion model through network training can enable the sharing of information between scanners, thereby pushing the limits of conventional scanners and improving their imaging quality. The quantitative results showed that the diffusion model based on null-space constraints produced better and more stable results than those of the methods based on NLM, UNet and the GAN and is well suited for cross-center and cross-device imaging.

CONCLUSION

A diffusion model based on null-space constraints is a flexible framework that can effectively utilize the prior information provided by high-end scanners to improve the image quality of conventional scanners in cross-center and cross-device scenarios.

Learning CT-free attenuation-corrected total-body PET images through deep learning

OBJECTIVES

Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning.

METHODS

Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements.

RESULTS

The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images.

CONCLUSION

Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices.

CLINICAL RELEVANCE STATEMENT

The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up.

KEY POINTS

• CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious. • The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations. • The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.

The Role of Total-Body PET in Drug Development and Evaluation: Status and Outlook

Total-body PET, an emerging technique, enables high-quality simultaneous total-body dynamic PET acquisition and accurate kinetic analysis. It has the potential to facilitate the study of multiple tracers while minimizing radiation dose and improving tracer-specific imaging. This advancement holds promise for enhancing the development and clinical evaluation of drugs, particularly radiopharmaceuticals. Multiple clinical trials are using a total-body PET scanner to explore existing and innovative radiopharmaceuticals. However, challenges persist, along with the opportunities, with regard to the use of total-body PET in drug development and evaluation. Specifically, considerations relate to the role of total-body PET in clinical pharmacologic evaluations and its integration into the theranostic paradigm. In this review, state-of-the-art total-body PET and its potential roles in pharmaceutical research are explored.

Clinical Implementation of Total-Body PET in China

Total-body (TB) PET/CT is a groundbreaking tool that has brought about a revolution in both clinical application and scientific research. The transformative impact of TB PET/CT in the realms of clinical practice and scientific exploration has been steadily unfolding since its introduction in 2018, with implications for its implementation within the health care landscape of China. TB PET/CT's exceptional sensitivity enables the acquisition of high-quality images in significantly reduced time frames. Clinical applications have underscored its effectiveness across various scenarios, emphasizing the capacity to personalize dosage, scan duration, and image quality to optimize patient outcomes. TB PET/CT's ability to perform dynamic scans with high temporal and spatial resolution and to perform parametric imaging facilitates the exploration of radiotracer biodistribution and kinetic parameters throughout the body. The comprehensive TB coverage offers opportunities to study interconnections among organs, enhancing our understanding of human physiology and pathology. These insights have the potential to benefit applications requiring holistic TB assessments. The standard topics outlined in The Journal of Nuclear Medicine were used to categorized the reviewed articles into 3 sections: current clinical applications, scan protocol design, and advanced topics. This article delves into the bottleneck that impedes the full use of TB PET in China, accompanied by suggested solutions.

Positron Emission Tomography/Computed Tomography Imaging in Therapeutic Clinical Trials in Alzheimer's Disease: An Overview of the Current State of the Art of Research

The integration of positron emission tomography/computed tomography (PET/CT) has revolutionized the landscape of Alzheimer's disease (AD) research and therapeutic interventions. By combining structural and functional imaging, PET/CT provides a comprehensive understanding of disease pathology and response to treatment assessment. PET/CT, particularly with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG), facilitates the visualization of glucose metabolism in the brain, enabling early diagnosis, staging, and monitoring of neurodegenerative disease progression. The advent of amyloid and tau PET imaging has further propelled the field forward, offering invaluable tools for tracking pathological hallmarks, assessing treatment response, and predicting clinical outcomes. While some therapeutic interventions targeting amyloid plaque load showed promising results with the reduction of cerebral amyloid accumulation over time, others failed to demonstrate a significant impact of anti-amyloid agents for reducing the amyloid plaques burden in AD brains. Tau PET imaging has conversely fueled the advent of disease-modifying therapeutic strategies in AD by supporting the assessment of neurofibrillary tangles of tau pathology deposition over time. Looking ahead, PET imaging holds immense promise for studying additional targets such as neuroinflammation, cholinergic deficit, and synaptic dysfunction. Advances in radiotracer development, dedicated brain PET/CT scanners, and Artificial Intelligence-powered software are poised to enhance the quality, sensitivity, and diagnostic power of molecular neuroimaging. Consequently, PET/CT remains at the forefront of AD research, offering unparalleled opportunities for unravelling the complexities of the disease and advancing therapeutic interventions, although it is not yet enough alone to allow patients' recruitment in therapeutic clinical trials.