PET in cardiology

Stability of radiomic features from positron emission tomography images: a phantom study comparing advanced reconstruction algorithms and ordered subset expectation maximization

In this study, we compared the repeatability and reproducibility of radiomic features obtained from positron emission tomography (PET) images according to the reconstruction algorithm used-advanced reconstruction algorithms, such as HYPER iterative (IT), HYPER deep learning reconstruction (DLR), and HYPER deep progressive reconstruction (DPR), or traditional Ordered Subset Expectation Maximization (OSEM)-to understand the potential variations and implications of using advanced reconstruction techniques in PET-based radiomics. We used a heterogeneous phantom with acrylic spherical beads (4- or 8-mm diameter) filled with 18F. PET images were acquired and reconstructed using OSEM, IT, DLR, and DPR. Original and wavelet radiomic features were calculated using SlicerRadiomics. Radiomic feature repeatability was assessed using the Coefficient of Variance (COV) and intraclass correlation coefficient (ICC), and inter-acquisition time reproducibility was assessed using the concordance correlation coefficient (CCC). For the 4- and 8-mm diameter beads phantom, the proportion of radiomic features with a COV < 10% was equivocal or higher for the advanced reconstruction algorithm than for OSEM. ICC indicated that advanced methods generally outperformed OSEM in repeatability, except for the original features of the 8-mm beads phantom. In the inter-acquisition time reproducibility analysis, the combinations of 3 and 5 min exhibited the highest reproducibility in both phantoms, with IT and DPR showing the highest proportion of radiomic features with CCC > 0.8. Advanced reconstruction methods provided enhanced stability of radiomic features compared with OSEM, suggesting their potential for optimal image reconstruction in PET-based radiomics, offering potential benefits in clinical diagnostics and prognostics.

A comprehensive review of the role of bone marrow biopsy and PET-CT in the evaluation of bone marrow involvement in adults newly diagnosed with DLBCL

Diffuse large B cell lymphoma (DLBCL) is one of the most prevalent subtypes of non-Hodgkin lymphoma (NHL) and is known for commonly infiltrating extra-nodal sites. The involvement of the bone marrow by lymphoma cells significantly impacts the staging, treatment, and prognosis among the extra-nodal sites in DLBCL. Bone marrow biopsy has been considered the standard diagnostic procedure for detecting bone marrow involvement. However, advancements in imaging techniques, such as positron emission tomography-computed tomography (PET-CT), have shown an improved ability to detect bone marrow involvement, making the need for bone marrow biopsy debatable. This review aims to emphasize the importance of bone marrow evaluation in adult patients newly diagnosed with DLBCL and suggest an optimal diagnostic approach to identify bone marrow involvement in these patients.

Virtual high-count PET image generation using a deep learning method

PURPOSE

Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images.

METHODS

The training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHC images.

RESULTS

The cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significant difference.

CONCLUSIONS

A DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinical diagnosis.

Routine 18F-FDG PET/CT does not detect inflammation in the left atrium in patients with atrial fibrillation

Increasing evidence supports a role of inflammation in the development of atrial fibrillation (AF). However, direct evidence of persistent inflammatory activity in the atria of AF patients is scarce. In this study, we used 18-Fluor-Deoxyglucose positron emission tomography computed tomography (¹⁸F-FDG PET/CT) to determine atrial inflammation in patients with and without AF. Retrospectively, ¹⁸F-FDG PET/CT scans were analyzed. 37 patients with a history of AF were compared to an age and sex matched control group with no history of AF. Standardized uptake values were obtained in the atrial walls, in the left ventricular wall, and in the right ventricular blood pool, respectively. Target to background ratios (TBR) were determined in the atrial and left ventricular walls and compared between the two groups. TBR values of the left atrial wall were slightly but not significantly higher in patients with AF (1.21 ± 0.27) compared to those without AF (1.14 ± 0.29; p = 0.85). Likewise, a weak but not significant difference was observed in signal intensities in the right atrial wall between patients in the AF (1.14 ± 0.45) and the control group (0.96 ± 0.2; p = 0.41). TBR values of the left ventricular myocardium did not differ between the groups; no significant correlation was found between the TBR in the left and right atrial wall and blood glucose levels. ¹⁸F-FDG PET/CT performed under routine conditions did not detect a significant difference in inflammatory activity in the left or right atrium between patients with and without AF. Contrary to previous reports, these results therefore do not clearly support a role for ongoing atrial inflammation in patients with AF. Prospective clinical studies using myocardial glucose uptake suppression strategies may be helpful to clarify these issues.