Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study

Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images

This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.

Dose estimation in patients from different protocols of 18F-FDG PET/CT studies and analysis of optimization strategies

This study aimed to evaluate the dose in different protocols of 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) procedure. The retrospective study involves 207 patients with confirmed malignancies who underwent PET/CT. Effective dose (E) from PET was estimated based on injected activity and dose coefficient as per International Commission on Radiation Protection (ICRP) 128. Estimation of E from CT was done utilizing the dose length product (DLP) method and conversion factors as per ICRP 102. There was a significant statistical difference observed in E between different PET/CT protocols (P < .001). E of PET in the whole body (WB) was found to be 4.9 ± 0.9 mSv, whereas mean volume computed tomography dose indexvol, DLP, and E of CT in WB were 7.0 ± 0.2 mGy, 674.3 ± 80.7 mGy.cm, and 10.1 ± 1.2 mSv, respectively. No linear correlation was seen between the size-specific dose estimate and E of CT (r = -0.003; P = .978). The total mean E in WB PET/CT was 17.0 ± 1.7 mSv. CT dose was contributing more than PET dose in all protocols except brain PET/CT. Optimization strategies can be evaluated only if monitored periodically.

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.

Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.

Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model

PURPOSE

Computed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning-based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative reconstruction (IR) methods. However, most comparative studies employ low-dose simulations due to ethical constraints. We used real intraindividual animal scans to investigate the dose-reduction capabilities of a DLD algorithm in comparison to IR.

MATERIALS AND METHODS

Fourteen veterinarian-sedated alive pigs underwent 2 CT scans on the same 3rd generation dual-source scanner with two months between each scan. Four additional scans ensued each time, with mAs reduced to 50 %, 25 %, 10 %, and 5 %. All scans were reconstructed ADMIRE levels 2 (IR2) and a novel DLD algorithm, resulting in 280 datasets. Objective image quality (CT numbers stability, noise, and contrast-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (-1 = inferior, 0 = equal, 1 = superior). The points were averaged for a semiquantitative score, and inter-rater agreement was measured using Spearman's correlation coefficient and adequately corrected mixed-effects modeling analyzed objective and subjective image quality.

RESULTS

Neither dose-reduction nor reconstruction method negatively impacted CT number stability (p > 0.999). In objective image quality assessment, the lowest radiation dose achievable by DLD when comparing noise (p = 0.544) and CNR (p = 0.115) to 100 % IR2 was 25 %. Overall, inter-rater agreement of the subjective image quality ratings was strong (r ≥ 0.69, mean 0.93 ± 0.05, 95 % CI 0.92-0.94; each p < 0.001), and subjective assessments corroborated that DLD at 25 % radiation dose was comparable to 100 % IR2 in image quality, sharpness, and contrast (p ≥ 0.281).

CONCLUSIONS

The DLD algorithm can achieve image quality comparable to the standard IR method but with a significant dose reduction of up to 75%. This suggests a promising avenue for lowering patient radiation exposure without sacrificing diagnostic quality.

Long Axial Field-of-View PET/CT Could Answer Unmet Needs in Gynecological Cancers

Gynecological malignancies currently affect about 3.5 million women all over the world. Imaging of uterine, cervical, vaginal, ovarian, and vulvar cancer still presents several unmet needs when using conventional modalities such as ultrasound, computed tomography (CT), magnetic resonance, and standard positron emission tomography (PET)/CT. Some of the current diagnostic limitations are represented by differential diagnosis between inflammatory and cancerous findings, detection of peritoneal carcinomatosis and metastases <1 cm, detection of cancer-associated vascular complications, effective assessment of post-therapy changes, as well as bone metabolism and osteoporosis assessment. As a result of recent advances in PET/CT instrumentation, new systems now offer a long-axial field-of-view (LAFOV) to image between 106 cm and 194 cm (i.e., total-body PET) of the patient's body simultaneously and feature higher physical sensitivity and spatial resolution compared to standard PET/CT systems. LAFOV PET could overcome the forementioned limitations of conventional imaging and provide valuable global disease assessment, allowing for improved patient-tailored care. This article provides a comprehensive overview of these and other potential applications of LAFOV PET/CT imaging for patients with gynecological malignancies.