Performance of a deep learning enhancement method applied to PET images acquired with a reduced acquisition time.

BACKGROUND: This study aims to evaluate the performance of a deep learning enhancement method in PET images reconstructed with a shorter acquisition time, and different reconstruction algorithms. The impact of the enhancement on clinical decisions was also assessed. MATERIAL AND METHODS: Thirty-seven subjects underwent clinical whole-body [18F]FDG PET/CT exams with an acquisition time of 1.5 min per bed position. PET images were reconstructed with the OSEM algorithm using 66% counts (imitating 1 min/bed acquisition time) and 100% counts (1.5 min/bed). Images reconstructed from 66% counts were subsequently enhanced using the SubtlePET™ (SP) deep-learning-based software, (Subtle Medical, USA) - with two different software versions (SP1 and SP2). Additionally, images obtained with 66% counts were reconstructed with QClear™ (GE, USA) algorithm and enhanced with SP2. Volumes of interest (VOI) of the lesions and reference VOIs in the liver, brain, bladder, and mediastinum were drawn on OSEM images and copied on SP images. Quantitative SUVmax values per VOI of OSEM or QClear™ and AI-enhanced 'shortened' acquisitions were compared. RESULTS: Two hundred and fifty-two VOIs were identified (37 for each reference region, and 104 for the lesions) for OSEM, SP1, SP2, and QClear™ images AI-enhanced with SP2. SUVmax values on SP1 images were lower than standard OSEM, but on SP2 differences were smaller (average difference for SP1 11.6%, for SP2 -4.5%). For images reconstructed with QClear™, SUVmax values were higher (average +8.9%, median 6.1%, SD 18.9%). For small lesions with SUVmax values range 2.0 to 4.0 decrease of measured SUVmax was much less significant with SP2 (for liver average -6.5%, median -5.6% for lesions average -5.6%, median - 6.0, SD 5.2%) and showed the best correlation with original OSEM. While no artifacts and good general diagnostic confidence were found in AI-enhanced images, SP1, the images were not equal to the original OSEM - some lesions were hard to spot. SP2 produced images with almost the same quality as the original 1.5 min/bed OSEM reconstruction. CONCLUSIONS: The studied deep learning enhancement method can be used to accelerate PET acquisitions without compromising quantitative SUVmax values. AI-based algorithms can enhance the image quality of accelerated PET acquisitions, enabling the dose reduction to the patients and improving the cost-effectiveness of PET/CT imaging.

Interference of COVID-19 Vaccination With PET/CT Leads to Unnecessary Additional Imaging in a Patient With Metastatic Cutaneous Melanoma-Case Report.

The COVID-19 pandemic has widely influenced oncological imaging mainly by presenting unexpected pulmonary and mediastinal lesions. The ongoing global program of vaccination has led to incidental diagnosis of axillary lymphadenopathy. We present a case of increased accumulation of 18F-FDG in an axillary lymph node in a PET/CT scan performed in a 43-year-old female patient with metastatic melanoma. The scan was performed 4 days after the AZD1222 vaccination. The occurrence of lymphadenopathy was verified with another PET/CT scan scheduled one month later. This case report presents a possible misinterpretation of PET/CT images caused by the recent COVID-19 vaccination. To avoid distress of the patient and unnecessary oncological diagnostics to verify the findings, we recommend avoiding scheduling PET/CT shortly after vaccination.

A natriuretic peptide from Arabidopsis thaliana (AtPNP-A) can modulate catalase 2 activity.

Analogues of vertebrate natriuretic peptides (NPs) present in plants, termed plant natriuretic peptides (PNPs), comprise a novel class of hormones that systemically affect salt and water balance and responses to plant pathogens. Several lines of evidence indicate that Arabidopsis thaliana PNP (AtPNP-A) affects cellular redox homeostasis, which is also typical for the signaling of its vertebrate analogues, but the molecular mechanism(s) of this effect remains elusive. Here we report identification of catalase 2 (CAT2), an antioxidant enzyme, as an interactor of AtPNP-A. The full-length AtPNP-A recombinant protein and the biologically active fragment of AtPNP-A bind specifically to CAT2 in surface plasmon resonance (SPR) analyses, while a biologically inactive scrambled peptide does not. In vivo bimolecular fluorescence complementation (BiFC) showed that CAT2 interacts with AtPNP-A in chloroplasts. Furthermore, CAT2 activity is lower in homozygous atpnp-a knockdown compared with wild type plants, and atpnp-a knockdown plants phenocopy CAT2-deficient plants in their sensitivity to elevated H2O2, which is consistent with a direct modulatory effect of the PNP on the activity of CAT2 and hence H2O2 homeostasis. Our work underlines the critical role of AtPNP-A in modulating the activity of CAT2 and highlights a mechanism of fine-tuning plant responses to adverse conditions by PNPs.

Pre-treatment 2D and 3D dosimetric verification of volumetric arc therapy. A correlation study between gamma index passing rate and clinical dose volume histogram.

OBJECTIVES: To evaluate methods for the pre-treatment verification of volumetric modulated arc therapy (VMAT) based on the percentage gamma passing rate (%GP) and its correlation and sensitivity with percentage dosimetric errors (%DE). METHODS: A total of 25 patients with prostate cancer and 15 with endometrial cancer were analysed. The %GP values of 2D and 3D verifications with different acceptance criteria (1%/1 mm, 2%/2 mm, and 3%/3 mm) were obtained using OmniPro and Compass. The %DE was calculated using a planned dose volume histogram (DVH) created in Monaco's treatment planning system (TPS), which relates radiation dose to tissue and the patient's predicted dose volume histogram in Compass. Statistical correlation between %GP and %DE was verified using Pearson's correlation coefficient. Sensitivity was calculated based on the receiver operating characteristics (ROC) curve. Plans were calculated using Collapsed Cone Convolution and the Monte Carlo algorithm. RESULTS: The t-test results of the planned and estimated DVH showed that the mean values were comparable (P > 0.05). For the 3%/3 mm criterion, the average %GP was acceptable for the prostate and endometrial cancer groups, with average rates of 99.68 ± 0.49% and 99.03 ± 0.59% for 2D and 99.86 ± 0.39% and 99.53 ± 0.44% for 3D, respectively. The number of correlations was poor for all analysed data. The mean Pearson's R-values for prostate and endometrial cancer were < 0.45 and < 0.43, respectively. The area under the ROC curve for the prostate and endometrial cancer groups, was lower than 0.667. CONCLUSIONS: Analysis of the %GP versus %DE values revealed only weak correlations between 2D and 3D verifications. DVH results obtained using the Compass system will be helpful in confirming that the analysed plans respect dosimetric constraints.