A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET

Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT

In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. For image quality enhancement in low-dose PET and CT, we propose a novel theoretical adversarial and variational deep neural network (DNN) framework relying on expectation maximization (EM) based learning, termed adversarial EM (AdvEM). AdvEM proposes an encoder-decoder architecture with a multiscale latent space, and generalized-Gaussian models enabling datum-specific robust statistical modeling in latent space and image space. The model robustness is further enhanced by including adversarial learning in the training protocol. Unlike typical variational-DNN learning, AdvEM proposes latent-space sampling from the posterior distribution, and uses a Metropolis-Hastings scheme. Unlike existing schemes for PET or CT image enhancement which train using pairs of low-dose images with their corresponding normal-dose versions, we propose a semi-supervised AdvEM (ssAdvEM) framework that enables learning using a small number of normal-dose images. AdvEM and ssAdvEM enable per-pixel uncertainty estimates for their outputs. Empirical analyses on real-world PET and CT data involving many baselines, out-of-distribution data, and ablation studies show the benefits of the proposed framework.

Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist

The widespread use of Chat Generative Pre-trained Transformer (known as ChatGPT) and other emerging technology that is powered by generative artificial intelligence (GenAI) has drawn attention to the potential ethical issues they can cause, especially in high-stakes applications such as health care, but ethical discussions have not yet been translated into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (eg, images) for research and practical purposes, which resolve some ethical issues and expose others. We did a scoping review of the ethical discussions on GenAI in health care to comprehensively analyse gaps in the research. To reduce the gaps, we have developed a checklist for comprehensive assessment and evaluation of ethical discussions in GenAI research. The checklist can be integrated into peer review and publication systems to enhance GenAI research and might be useful for ethics-related disclosures for GenAI-powered products and health-care applications of such products and beyond.

Deep denoiser prior driven relaxed iterated Tikhonov method for low-count PET image restoration

Objective. Low-count positron emission tomography (PET) imaging is an efficient way to promote more widespread use of PET because of its short scan time and low injected activity. However, this often leads to low-quality PET images with clinical image reconstruction, due to high noise and blurring effects. Existing PET image restoration (IR) methods hinder their own restoration performance due to the semi-convergence property and the lack of suitable denoiser prior.Approach. To overcome these limitations, we propose a novel deep plug-and-play IR method called Deep denoiser Prior driven Relaxed Iterated Tikhonov method (DP-RI-Tikhonov). Specifically, we train a deep convolutional neural network denoiser to generate a flexible deep denoiser prior to handle high noise. Then, we plug the deep denoiser prior as a modular part into a novel iterative optimization algorithm to handle blurring effects and propose an adaptive parameter selection strategy for the iterative optimization algorithm.Main results. Simulation results show that the deep denoiser prior plays the role of reducing noise intensity, while the novel iterative optimization algorithm and adaptive parameter selection strategy can effectively eliminate the semi-convergence property. They enable DP-RI-Tikhonov to achieve an average quantitative result (normalized root mean square error, structural similarity) of (0.1364, 0.9574) at the stopping iteration, outperforming a conventional PET IR method with an average quantitative result of (0.1533, 0.9523) and a state-of-the-art deep plug-and-play IR method with an average quantitative result of (0.1404, 0.9554). Moreover, the advantage of DP-RI-Tikhonov becomes more obvious at the last iteration. Experiments on six clinical whole-body PET images further indicate that DP-RI-Tikhonov successfully reduces noise intensity and recovers fine details, recovering sharper and more uniform images than the comparison methods.Significance. DP-RI-Tikhonov's ability to reduce noise intensity and effectively eliminate the semi-convergence property overcomes the limitations of existing methods. This advancement may have substantial implications for other medical IR.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model

PURPOSE

Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients.

METHODS

We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs.

RESULTS

In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario.

CONCLUSIONS

We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement

PURPOSE

Technological advances in instruments have greatly promoted the development of positron emission tomography (PET) scanners. State-of-the-art PET scanners such as uEXPLORER can collect PET images of significantly higher quality. However, these scanners are not currently available in most local hospitals due to the high cost of manufacturing and maintenance. Our study aims to convert low-quality PET images acquired by common PET scanners into images of comparable quality to those obtained by state-of-the-art scanners without the need for paired low- and high-quality PET images.

METHODS

In this paper, we proposed an improved CycleGAN (IE-CycleGAN) model for unpaired PET image enhancement. The proposed method is based on CycleGAN, and the correlation coefficient loss and patient-specific prior loss were added to constrain the structure of the generated images. Furthermore, we defined a normalX-to-advanced training strategy to enhance the generalization ability of the network. The proposed method was validated on unpaired uEXPLORER datasets and Biograph Vision local hospital datasets.

RESULTS

For the uEXPLORER dataset, the proposed method achieved better results than non-local mean filtering (NLM), block-matching and 3D filtering (BM3D), and deep image prior (DIP), which are comparable to Unet (supervised) and CycleGAN (supervised). For the Biograph Vision local hospital datasets, the proposed method achieved higher contrast-to-noise ratios (CNR) and tumor-to-background SUVmax ratios (TBR) than NLM, BM3D, and DIP. In addition, the proposed method showed higher contrast, SUVmax, and TBR than Unet (supervised) and CycleGAN (supervised) when applied to images from different scanners.

CONCLUSION

The proposed unpaired PET image enhancement method outperforms NLM, BM3D, and DIP. Moreover, it performs better than the Unet (supervised) and CycleGAN (supervised) when implemented on local hospital datasets, which demonstrates its excellent generalization ability.

Sedation-free pediatric [18F]FDG imaging on totalbody PET/CT with the assistance of artificial intelligence

PURPOSE

While sedation is routinely used in pediatric PET examinations to preserve diagnostic quality, it may result in side effects and may affect the radiotracer's biodistribution. This study aims to investigate the feasibility of sedation-free pediatric PET imaging using ultra-fast total-body (TB) PET scanners and deep learning (DL)-based attenuation and scatter correction (ASC).

METHODS

This retrospective study included TB PET (uExplorer) imaging of 35 sedated pediatric patients under four years old to determine the minimum effective scanning time. A DL-based ASC method was applied to enhance PET quantification. Both quantitative and qualitative assessments were conducted to evaluate the image quality of ultra-fast DL-ASC PET. Five non-sedated pediatric patients were subsequently used to validate the proposed approach.

RESULTS

Comparisons between standard 300-second and ultra-fast 15-second imaging, CT-ASC and DL-ASC ultra-fast 15-second images, as well as DL-ASC ultra-fast 15-second images in non-sedated and sedated patients, showed no significant differences in qualitative scoring, lesion detectability, and quantitative Standard Uptake Value (SUV) (P = ns).

CONCLUSIONS

This study demonstrates that pediatric PET imaging can be effectively performed without sedation by combining ultra-fast imaging techniques with a DL-based ASC. This advancement in sedation-free ultra-fast PET imaging holds potential for broader clinical adoption.

Deep learning-based PET image denoising and reconstruction: a review

This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.

A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.

Artificial Intelligence for PET and SPECT Image Enhancement

Nuclear medicine imaging modalities such as PET and SPECT are confounded by high noise levels and low spatial resolution, necessitating postreconstruction image enhancement to improve their quality and quantitative accuracy. Artificial intelligence (AI) models such as convolutional neural networks, U-Nets, and generative adversarial networks have shown promising outcomes in enhancing PET and SPECT images. This review article presents a comprehensive survey of state-of-the-art AI methods for PET and SPECT image enhancement and seeks to identify emerging trends in this field. We focus on recent breakthroughs in AI-based PET and SPECT image denoising and deblurring. Supervised deep-learning models have shown great potential in reducing radiotracer dose and scan times without sacrificing image quality and diagnostic accuracy. However, the clinical utility of these methods is often limited by their need for paired clean and corrupt datasets for training. This has motivated research into unsupervised alternatives that can overcome this limitation by relying on only corrupt inputs or unpaired datasets to train models. This review highlights recently published supervised and unsupervised efforts toward AI-based PET and SPECT image enhancement. We discuss cross-scanner and cross-protocol training efforts, which can greatly enhance the clinical translatability of AI-based image enhancement tools. We also aim to address the looming question of whether the improvements in image quality generated by AI models lead to actual clinical benefit. To this end, we discuss works that have focused on task-specific objective clinical evaluation of AI models for image enhancement or incorporated clinical metrics into their loss functions to guide the image generation process. Finally, we discuss emerging research directions, which include the exploration of novel training paradigms, curation of larger task-specific datasets, and objective clinical evaluation that will enable the realization of the full translation potential of these models in the future.

PET image denoising based on denoising diffusion probabilistic model

PURPOSE

Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic model (DDPM) was a distribution learning-based model, which tried to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising.

METHODS

Under the DDPM framework, one way to perform PET image denoising was to provide the PET image and/or the prior image as the input. Another way was to supply the prior image as the network input with the PET image included in the refinement steps, which could fit for scenarios of different noise levels. 150 brain [[Formula: see text]F]FDG datasets and 140 brain [[Formula: see text]F]MK-6240 (imaging neurofibrillary tangles deposition) datasets were utilized to evaluate the proposed DDPM-based methods.

RESULTS

Quantification showed that the DDPM-based frameworks with PET information included generated better results than the nonlocal mean, Unet and generative adversarial network (GAN)-based denoising methods. Adding additional MR prior in the model helped achieved better performance and further reduced the uncertainty during image denoising. Solely relying on MR prior while ignoring the PET information resulted in large bias. Regional and surface quantification showed that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference achieved the best performance.

CONCLUSION

DDPM-based PET image denoising is a flexible framework, which can efficiently utilize prior information and achieve better performance than the nonlocal mean, Unet and GAN-based denoising methods.

Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images

Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.

["New Modalities in Cancer Imaging and Therapy" XVth edition of the workshop organized by the network "Tumor Targeting, Imaging, Radiotherapies" of the Cancéropôle Grand-Ouest, 5-8 October 2022, France]

The fifteenth edition of the international workshop organized by the "Tumour Targeting and Radiotherapies network" of the Cancéropôle Grand-Ouest focused on the latest advances in internal and external radiotherapy from different disciplinary angles: chemistry, biology, physics, and medicine. The workshop covered several deliberately diverse topics: the role of artificial intelligence, new tools for imaging and external radiotherapy, theranostic aspects, molecules and contrast agents, vectors for innovative combined therapies, and the use of alpha particles in therapy.

Long-axial field-of-view PET/CT: perspectives and review of a revolutionary development in nuclear medicine based on clinical experience in over 7000 patients

Recently introduced long-axial field-of-view (LAFOV) PET/CT systems represent one of the most significant advancements in nuclear medicine since the advent of multi-modality PET/CT imaging. The higher sensitivity exhibited by such systems allow for reductions in applied activity and short duration scans. However, we consider this to be just one small part of the story: Instead, the ability to image the body in its entirety in a single FOV affords insights which standard FOV systems cannot provide. For example, we now have the ability to capture a wider dynamic range of a tracer by imaging it over multiple half-lives without detrimental image noise, to leverage lower radiopharmaceutical doses by using dual-tracer techniques and with improved quantification. The potential for quantitative dynamic whole-body imaging using abbreviated protocols potentially makes these techniques viable for routine clinical use, transforming PET-reporting from a subjective analysis of semi-quantitative maps of radiopharmaceutical uptake at a single time-point to an accurate and quantitative, non-invasive tool to determine human function and physiology and to explore organ interactions and to perform whole-body systems analysis. This article will share the insights obtained from 2 years' of clinical operation of the first Biograph Vision Quadra (Siemens Healthineers) LAFOV system. It will also survey the current state-of-the-art in PET technology. Several technologies are poised to furnish systems with even greater sensitivity and resolution than current systems, potentially with orders of magnitude higher sensitivity. Current barriers which remain to be surmounted, such as data pipelines, patient throughput and the hindrances to implementing kinetic analysis for routine patient care will also be discussed.

Synthesis and Bioevaluation of Novel Technetium-99m-Labeled Complexes with Norfloxacin HYNIC Derivatives for Bacterial Infection Imaging

To seek a novel ^99mTc-labeled quinolone derivative for bacterial infection SPECT imaging that aims to lower nontarget organ uptake, a novel norfloxacin 6-hydrazinoicotinamide (HYNIC) derivative (HYNICNF) was designed and synthesized. It was radiolabeled with different coligands, such as tricine, trisodium triphenylphosphine-3,3',3″-trisulfonate (TPPTS), sodium triphenylphosphine-3-monosulfonate (TPPMS), and ethylenediamine-N,N'-diacetic acid (EDDA), to obtain three ^99mTc-labeled norfloxacin HYNIC complexes, namely, [^99mTc]Tc-tricine-TPPTS-HYNICNF, [^99mTc]Tc-tricine-TPPMS-HYNICNF, and [^99mTc]Tc-EDDA-HYNICNF. These complexes were purified (RCP > 95%) and evaluated in vitro and in vivo for targeting bacteria. All three complexes are hydrophilic, maintain good stability, and specifically bind Staphylococcus aureus in vitro. The biodistribution in mice with bacterial infection demonstrated that [^99mTc]Tc-EDDA-HYNICNF showed a higher abscess uptake and lower nontarget organ uptake and was able to distinguish bacterial infection and sterile inflammation. Single photon emission computed tomography (SPECT) image study in bacterial infection mice showed there was a visible accumulation in the infection site, suggesting that [^99mTc]Tc-EDDA-HYNICNF is a potential radiotracer for bacterial infection imaging.

Combined [68 Ga]Ga-PSMA-11 and low-dose 2-[18F]FDG PET/CT using a long-axial field of view scanner for patients referred for [177Lu]-PSMA-radioligand therapy

PURPOSE

Performing 2-[¹⁸F]FDG PET/CT in addition to a PSMA-ligand PET/CT can assist in the detection of lesions with low PSMA expression and may help in prognostication and identification of patients who likely benefit from PSMA-radioligand therapy (PSMA-RLT). However, the cost and time needed for a separate PET/CT examination might hinder its routine implementation. In this communication, we present our initial experiences with additional low-dose 2-[¹⁸F]FDG PET/CT as part of a dual-tracer and same-day imaging protocol which exploits the higher sensitivity exhibited by long-axial field-of-view (LAFOV) and total-body PET/CT systems and demonstrates its feasibility.

METHODS

Fourteen patients referred for evaluation for PSMA-RLT received [⁶⁸ Ga]Ga-PSMA-11 PET/CT at 1 h p.i. with a standard activity of 150 MBq and an additional low-dose 2-[¹⁸F]FDG PET/CT with 40 MBq 1 h thereafter using a long-axial field-of-view PET/CT system in a single sitting and as per institutional protocol. Scans were scrutinized by two experienced nuclear medicine physicians for mismatch findings.

RESULTS

The combined protocol identified additional lesions with low or absent PSMA-expression but high FDG-avidity in 1/14 (7%) patients. The protocol was easily implemented and well tolerated by all patients.

CONCLUSION

Additional low-dose 2-[¹⁸F]FDG-PET/CT is feasible as part of a same-day imaging protocol and can help reveal lesions of low PSMA avidity as part of therapy assessment for [¹⁷⁷Lu]-PSMA radioligand therapy and demonstrates higher sensitivity compared to [⁶⁸ Ga]Ga-PSMA-11 PET/CT alone in some patients.

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

The future of PSMA PET and WB MRI as next-generation imaging tools in prostate cancer

Radiolabelled prostate-specific membrane antigen (PSMA)-based PET-CT has been shown in numerous studies to be superior to conventional imaging in the detection of nodal or distant metastatic lesions. ⁶⁸Ga-PSMA PET-CT is now recommended by many guidelines for the detection of biochemically relapsed disease after radical local therapy. PSMA radioligands can also function as radiotheranostics, and Lu-PSMA has been shown to be a potential new line of treatment for metastatic castration-resistant prostate cancer. Whole-body (WB) MRI has been shown to have a high diagnostic performance in the detection and monitoring of metastatic bone disease. Prospective, randomized, multicentre studies comparing ⁶⁸Ga-PSMA PET-CT and WB MRI for pelvic nodal and metastatic disease detection are yet to be performed. Challenges for interpretation of PSMA include tracer trapping in non-target tissues and also urinary excretion of tracers, which confounds image interpretation at the vesicoureteral junction. Additionally, studies have shown how long-term androgen deprivation therapy (ADT) affects PSMA expression and could, therefore, reduce tracer uptake and visibility of PSMA⁺ lesions. Furthermore, ADT of short duration might increase PSMA expression, leading to the PSMA flare phenomenon, which makes the accurate monitoring of treatment response to ADT with PSMA PET challenging. Scan duration, detection of incidentalomas and presence of metallic implants are some of the major challenges with WB MRI. Emerging data support the wider adoption of PSMA PET and WB MRI for diagnosis, staging, disease burden evaluation and response monitoring, although their relative roles in the standard-of-care management of patients are yet to be fully defined.

A clinical trial of super-stable homogeneous lipiodol-nanoICG formulation-guided precise fluorescent laparoscopic hepatocellular carcinoma resection

BACKGROUND

Applying traditional fluorescence navigation technologies in hepatocellular carcinoma is severely restricted by high false-positive rates, variable tumor differentiation, and unstable fluorescence performance.

RESULTS

In this study, a green, economical and safe nanomedicine formulation technology was developed to construct carrier-free indocyanine green nanoparticles (nanoICG) with a small uniform size and better fluorescent properties without any molecular structure changes compared to the ICG molecule. Subsequently, nanoICG dispersed into lipiodol via a super-stable homogeneous intermixed formulation technology (SHIFT&nanoICG) for transhepatic arterial embolization combined with fluorescent laparoscopic hepatectomy to eliminate the existing shortcomings. A 52-year-old liver cancer patient was recruited for the clinical trial of SHIFT&nanoICG. We demonstrate that SHIFT&nanoICG could accurately identify and mark the lesion with excellent stability, embolism, optical imaging performance, and higher tumor-to-normal tissue ratio, especially in the detection of the microsatellite lesions (0.4 × 0.3 cm), which could not be detected by preoperative imaging, to realize a complete resection of hepatocellular carcinoma under fluorescence laparoscopy in a shorter period (within 2 h) and with less intraoperative blood loss (50 mL).

CONCLUSIONS

This simple and effective strategy integrates the diagnosis and treatment of hepatocellular carcinoma, and thus, it has great potential in various clinical applications.

Nobiletin prevents amyloid β1-40-induced cognitive impairment via inhibition of neuroinflammation and oxidative/nitrosative stress

Alzheimer's disease (AD) is presented as an age-related neurodegenerative disease with multiple cognitive deficits and amyloid β (Aβ) accumulation is the most important involved factor in its development. Nobiletin is a bioflavonoid isolated from citrus fruits peels with anti-inflammatory and anti-oxidative activity as well as anti-dementia property that has shown potency to ameliorate intracellular and extracellular Ab. The aim of the present study was to assess protective effect of nobiletin against Aβ_1-40-induced cognitive impairment as a consistent model of AD. After bilateral intrahippocampal (CA1 subfield) injection of Aβ_1-40, rats were treated with nobiletin (10 mg/kg/day; p.o.) from stereotaxic surgery day (day 0) till day + 7. Cognition function was evaluated in a battery of behavioral tasks at week 3 with final assessment of hippocampal oxidative stress and inflammation besides Nissl staining and 3-nitrotyrosine (3-NT) immunohistochemistry. Analysis of behavioral data showed notable and significant improvement of alternation in Y maze test, discrimination ratio in novel object recognition task, and step through latency in passive avoidance test in nobiletin-treated Aβ group. Additionally, nobiletin treatment was associated with lower hippocampal levels of MDA and ROS and partial reversal of SOD activity and also improvement of Nrf2 with no significant effect on GSH and catalase. Furthermore, nobiletin attenuated hippocampal neuroinflammation in Aβ group as shown by lower tissue levels of TLR4, NF-kB, and TNFa. Histochemical findings showed that nobiletin prevents CA1 neuronal loss in Nissl staining in addition to its alleviation of 3-nitrotyrosine (3-NT) immunoreactivity as a marker of nitrosative stress. Collectively, these findings indicated neuroprotective and anti-dementia potential of nobiletin that is partly attributed to its anti-oxidative, anti-nitrosative, and anti-inflammatory property associated with proper modulation of TLR4/NF-kB/Nrf2 pathways.

Assessment of malignancy and PSMA expression of uncertain bone foci in [18F]PSMA-1007 PET/CT for prostate cancer-a single-centre experience of PET-guided biopsies

Purpose

Uncertain focal bone uptake (UBU) with intensive radiopharmaceutical avidity are frequently observed in patients undergoing [¹⁸F]PSMA-1007 PET/CT for the detection of prostate cancer (PC). Such foci can pose diagnostic conundrums and risk incorrect staging. The aim of this short communication is to share the results of PET-guided biopsies of such foci.

Methods

A retrospective analysis revealed 10 patients who were referred to our department for PET-guided biopsy of UBU visible in a previous [¹⁸F]PSMA-1007 PET/CT. [¹⁸F]-PSMA-1007 PET-guided biopsy was conducted for 11 PSMA-avid bone foci in these 10 patients. The biopsy materials were analysed for tissue typing, and immunohistochemistry (IHC) was performed for prostate-specific-membrane-antigen (PSMA) expression. The scans were analysed by two experienced physicians in a consensus read for clinical characteristics and radiopharmaceutical uptake of foci.

Results

One out of 11 (9.1%) of the foci biopsied was confirmed as bone metastasis of PC with intense PSMA-expression, while 10/11 (90.9%) foci were revealed to be unremarkable bone tissue without evidence of PSMA expression at IHC. Amongst all bone foci assessed by biopsy, eight were visually classified as being at high risk of malignancy in the PET/CT (SUVmean 12.0 ± 8.1; SUVmax 18.8 ± 13.1), three as equivocal (SUVmean 4.6 ± 2.1; SUVmax 7.2 ± 3.0) and zero as low risk. No UBU had any CT correlate.

Conclusions

This cohort biopsy revealed that a small but relevant number of UBU are true metastases. For those confirmed as benign, no PSMA expression at IHC was observed, suggesting a non-PSMA mediated cause for intensive [¹⁸F]PSMA-1007 uptake of which the reason remains unclear. Readers must interpret such foci with caution in order to reduce the risk of erroneous staging and subsequent treatment. PET-guided biopsy, particularly in the absence of morphological changes in the CT, can be a useful method to clarify such foci.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05745-5.

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.

18F- or 177Lu-labeled bivalent ligand of fibroblast activation protein with high tumor uptake and retention

PURPOSE

Fibroblast activation protein (FAP) has become a promising cancer-related target for diagnosis and therapy. The aim of this study was to develop a bivalent FAP ligand for both diagnostic PET imaging and endoradiotherapy.

METHODS

We synthesized a bivalent FAP ligand (ND-bisFAP) and labeled it with ¹⁸F or ¹⁷⁷Lu. FAP-positive A549-FAP cells were used to study competitive binding to FAP, cellular internalization, and efflux properties in vitro. Micro-PET imaging with [¹⁸F]AlF-ND-bisFAPI was conducted in mice bearing A549-FAP or U87MG tumors. Biodistribution and therapeutic efficacy of [¹⁷⁷Lu]Lu-ND-bisFAPI were conducted in mice bearing A549-FAP tumors.

RESULTS

The FAP binding affinity of ND-bisFAPI is 0.25 ± 0.05 nM, eightfold higher in potency than the monomeric DOTA-FAPI-04 (IC₅₀ = 2.0 ± 0.18 nM). In A549-FAP cells, ND-bisFAPI showed specific uptake, a high internalized fraction, and slow cellular efflux. Compared to the monomeric [¹⁸F]AlF-FAPI-42, micro-PET imaging with [¹⁸F]AlF-ND-bisFAPI showed higher specific tumor uptake and retention for at least 6 h. Biodistribution studies showed that [¹⁷⁷Lu]Lu-ND-bisFAPI had higher tumor uptake than [¹⁷⁷Lu]Lu-FAPI-04 at the 24, 72, 120, and 168 h time points (all P < 0.01). [¹⁷⁷Lu]Lu-ND-bisFAPI delivered fourfold higher radiation than [¹⁷⁷Lu]Lu-FAPI-04 to A549-FAP tumors. For the endoradiotherapy study, 37 MBq of [¹⁷⁷Lu]Lu-ND-bisFAPI significantly reduced tumor growth compared to the same dose of [¹⁷⁷Lu]Lu-FAPI-04. Half of the dose of [¹⁷⁷Lu]Lu-ND-bisFAPI (18.5 MBq) has comparable median survival as 37 MBq of [¹⁷⁷Lu]Lu-FAPI-04 (37 vs 36 days).

CONCLUSION

The novel bivalent FAP ligand was developed as a theranostic radiopharmaceutical and showed promising properties including higher tumor uptake and retention compared to the established radioligands [¹⁸F]AlF-FAPI-42 and [¹⁷⁷Lu]Lu-FAPI-04. Preliminary experiments with ¹⁸F- or ¹⁷⁷Lu-labeled ND-bisFAPI showed promising imaging properties and favorable anti-tumor responses.

Exploration of the total-body PET/CT reconstruction protocol with ultra-low 18F-FDG activity over a wide range of patient body mass indices

Purpose

The purpose of this study was to investigate the image quality and diagnostic performance of different reconstructions over a wide range of patient body mass indices (BMIs) obtained by total-body PET/CT with ultra-low ¹⁸F-FDG activity (0.37 MBq/kg).

Methods

A total of 63 patients who underwent total-body PET/CT with ultra-low activity (0.37 MBq/kg) ¹⁸F-FDG were enrolled. Patients were grouped by their BMIs. Images were reconstructed with the following two algorithms: the ordered subset expectation maximization (OSEM) algorithm (2, 3 iterations), both with time of flight (TOF) and point spread function (PSF) corrections (hereinafter referred as OSEM2, OSEM3) and HYPER Iterative algorithm (β-values of 0.3, 0.4, 0.5, 0.6) embedded TOF and PSF technologies (hereinafter referred as HYPER0.3, HYPER0.4, HYPER0.5 and HYPER0.6, respectively). Subjective image quality was assessed by two experienced nuclear medicine physicians according to the Likert quintile, including overall image quality, image noise and lesion conspicuity. The standard deviation (SD) and signal-to-noise ratio (SNR) of the liver, and maximum standard uptake value (SUV_max), peak standard uptake value (SUV_peak), tumour background ratio (T/N) and the largest diameter of lesions were quantitatively analysed by a third reader who did not participate in the subjective image assessment.

Results

Increased noise was associated with increased BMI in all reconstruction groups. Significant differences occurred in the liver SNR among BMI categories of OSEM reconstructions (P < 0.001) but no difference was seen in the HYPER Iterative reconstructions between any of the BMI categories (P > 0.05). With the increase in BMI, overall image quality and image noise scores decreased significantly in all reconstructions, but there was no statistically significant difference of lesion conspicuity. The overall image quality score of the obese group was not qualified (score = 2.7) in OSEM3, while the others were qualified. The lesion conspicuity scores were significantly higher in HYPER Iterative reconstructions and lower in OSEM2 than in OSEM3 (all P < 0.05). The values of SUV_max, SUV_peak and T/N in HYPER0.3, HYPER0.4 and HYPER0.5 were higher than those in OSEM3. In different reconstructions, there was a correlation between lesion size (median, 1.55 cm; range, 0.7–11.0 cm) and SUV_peak variation rate compared to OSEM3 (r = 0.388, − 0.515, − 0.495, − 0.464, and − 0.423, respectively, and all P < 0.001).

Conclusion

Considering the image quality and lesion analysis in ¹⁸F-FDG total-body PET/CT with ultra-low activity injection, OSEM reconstructions with 3 iterations meet the clinical requirements in patients with BMI < 30. In patients with BMI ≥ 30, it is recommended that the HYPER Iterative algorithm (β-value of 0.3–0.5) be used to ensure consistent visual image quality and quantitative assessment.

Intraoperative fluorescence molecular imaging accelerates the coming of precision surgery in China

Purpose

China has the largest cancer population globally. Surgery is the main choice for most solid cancer patients. Intraoperative fluorescence molecular imaging (FMI) has shown its great potential in assisting surgeons in achieving precise resection. We summarized the typical applications of intraoperative FMI and several new trends to promote the development of precision surgery.

Methods

The academic database and NIH clinical trial platform were systematically evaluated. We focused on the clinical application of intraoperative FMI in China. Special emphasis was placed on a series of typical studies with new technologies or high-level evidence. The emerging strategy of combining FMI with other modalities was also discussed.

Results

The clinical applications of clinically approved indocyanine green (ICG), methylene blue (MB), or fluorescein are on the rise in different surgical departments. Intraoperative FMI has achieved precise lesion detection, sentinel lymph node mapping, and lymphangiography for many cancers. Nerve imaging is also exploring to reduce iatrogenic injuries. Through different administration routes, these fluorescent imaging agents provided encouraging results in surgical navigation. Meanwhile, designing new cancer-specific fluorescent tracers is expected to be a promising trend to further improve the surgical outcome.

Conclusions

Intraoperative FMI is in a rapid development in China. In-depth understanding of cancer-related molecular mechanisms is necessary to achieve precision surgery. Molecular-targeted fluorescent agents and multi-modal imaging techniques might play crucial roles in the era of precision surgery.

Processing speed dysfunction is associated with functional corticostriatal circuit alterations in childhood epilepsy with centrotemporal spikes: a PET and fMRI study

Purpose

Epilepsy with centrotemporal spikes (ECTS) is the most common epilepsy syndrome in children and usually presents with cognitive dysfunctions. However, little is known about the processing speed dysfunction and the associated neuroimaging mechanism in ECTS. This study aims to investigate the brain functional abnormality of processing speed dysfunction in ECTS patients by using the ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI).

Methods

This prospective study recruited twenty-eight ECTS patients who underwent the ¹⁸F-FDG PET, rs-fMRI, and neuropsychological examinations. Twenty children with extracranial tumors were included as PET controls, and 20 healthy children were recruited as MRI controls. The PET image analysis investigated glucose metabolism by determining standardized uptake value ratio (SUVR). The MRI image analysis explored abnormal functional connectivity (FC) within the cortical–striatal circuit through network-based statistical (NBS) analysis. Correlation analysis was performed to explore the relationship between SUVR, FC, and processing speed index (PSI).

Results

Compared with healthy controls, ECTS patients showed normal intelligence quotient but significantly decreased PSI (P = 0.04). PET analysis showed significantly decreased SUVRs within bilateral caudate, putamen, pallidum, left NAc, right rostral middle frontal gyrus, and frontal pole of ECTS patients (P < 0.05). Rs-fMRI analysis showed absolute values of 20 FCs were significantly decreased in ECTS patients compared with MRI controls, which connected 16 distinct ROIs. The average SUVR of right caudate and the average of 20 FCs were positively correlated with PSI in ECTS patients (P = 0.034 and P = 0.005, respectively).

Conclusion

This study indicated that ECTS patients presented significantly reduced PSI, which is closely associated with decreased SUVR and FC of cortical–striatal circuit. Caudate played an important role in processing speed dysfunction.

Clinical trial registration

NCT04954729; registered on July 8, 2021, public site, https://clinicaltrials.gov/ct2/show/NCT04954729

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05740-w.