Magnetic resonance spectroscopy findings of brain olfactory areas in patients with COVID-19-related anosmia: A preliminary comparative study.

Objectives: 2019 novel coronavirus disease (COVID-19) infection is commonly associated with olfactory dysfunctions, but the basic pathogenesis of these complications remains controversial. This study seeks to evaluate the value of magnetic resonance spectroscopy (MRS) in determining the molecular neurometabolite alterations within the main brain olfactory areas in patients with COVID-19-related anosmia. Methods: In a cross-sectional study, seven patients with persistent COVID-19-related anosmia (mean age: 29.57 years) and seven healthy volunteers (mean age: 27.28 years) underwent MRS in which N-acetyl-aspartate (NAA), choline (Cho), creatine (Cr), and their ratios were measured in the anterior cingulate cortex, dorsolateral prefrontal cortex, orbitofrontal cortex (OFC), insular cortex, and ventromedial prefrontal cortex. Data were analyzed using TARQUIN software (version 4.3.10), and the results were compared with an independent sample t-test and nonparametric Mann-Whitney test based on the normality of the MRS data distribution. Results: The mean duration of anosmia before imaging was 8.5 months in COVID-19-related anosmia group. MRS analysis elucidated a significant association between MRS findings within OFC and COVID-19-related anosmia (P disease < 0.01), and NAA was among the most important neurometabolites (P interaction = 0.006). Reduced levels of NAA (P < 0.001), Cr (P < 0.001) and NAA/Cho ratio (P = 0.007) within OFC characterize COVID-19-related anosmia. Conclusions: This study emphasizes that MRS can be illuminating in COVID-19-related anosmia and indicates a possible association between central nervous system impairment and persistent COVID-19-related anosmia.

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.

BACKGROUND: Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. METHODS: Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. RESULTS: Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). CONCLUSION: Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study.

Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameterßwas varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration.Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, variousßvalues (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV).Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasingß, peaking atß= 550. The CNR for allß, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images withß= 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration.Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

Optimization of the Acquisition Time and Injected Dose of 18 F-Fluorodeoxyglucose Based on Patient Specifications for High-Sensitive Positron Emission Tomography/Computed Tomography Scanner.

Background This study was aimed to optimize the fluorodeoxyglucose (FDG)-administered dose and scan time based on patient specifications using a highly sensitive five-ring bismuth germanium oxide (BGO)-based positron emission tomography/computed tomography (PET/CT) scanner (Discovery IQ). Methods We retrospectively analyzed 101 whole-body 18 F-FDG PET/CT images. Patient data were reconstructed using ordered subset expectation maximization with resolution recovery algorithms (OSEM + SharpIR). Signal-to-noise ratio (SNR) was calculated for each patient, standardized to SNR norm , and plotted against three body index parameters (weight, body mass index, and lean body mass). Two professional physicians blindly examined image quality at different patient time per bed positions to determine the minimum acceptable quality. To select images of acceptable quality, the noise index parameter was also measured. A new dose-time product (DTP) was established for each patient, and a predicted injected dose was assumed. Results We found an almost linear association between patient weight and normalized SNR, and patient weight had the highest R 2 in the fitting. The redesigned DTP can reduce results by approximately 74 and 38% compared with ordinary DTP for 80- and 160-s scan durations. The new dose regimen formula was found to be DTP = c/t × m 1.24 , where m is the patient weight, t is the scan time per bed position, and c is 1.8 and 4.3 for acceptable and higher confidence states, respectively, in Discovery IQ PET/CT. Conclusion Patient weight is the best clinical parameter for the implementation of 18 F-FDG PET/CT image quality assessment. A new dose-time regimen based on body weight was proposed for use in highly sensitive five-ring BGO PET-CT scanners to significantly reduce the injection dose and scan times while maintaining sufficient image quality for diagnosis.

Harmonization based on quantitative analysis of standardized uptake value variations across PET/CT scanners: a multicenter phantom study.

OBJECTIVES: This study aimed to measure standardized uptake value (SUV) variations across different PET/computed tomography (CT) scanners to harmonize quantification across systems. METHODS: We acquired images using the National Electrical Manufacturers Association International Electrotechnical Commission phantom from three PET/CT scanners operated using routine imaging protocols at each site. The SUVs of lesions were assessed in the presence of reference values by a digital reference object (DRO) and recommendations by the European Association of Nuclear Medicine (EANM/EARL) to measure inter-site variations. For harmonization, Gaussian filters with tuned full width at half maximum (FWHM) values were applied to images to minimize differences in SUVs between reference and images. Inter-site variation of SUVs was evaluated in both pre- and postharmonization situations. Test-retest analysis was also carried out to evaluate repeatability. RESULTS: SUVs from different scanners became significantly more consistent, and inter-site differences decreased for SUV mean , SUV max and SUV peak from 17.3, 20.7, and 15.5% to 4.8, 4.7, and 2.7%, respectively, by harmonization ( P values <0.05 for all). The values for contrast-to-noise ratio in the smallest lesion of the phantom verified preservation of image quality following harmonization (>2.8%). CONCLUSIONS: Harmonization significantly lowered variations in SUV measurements across different PET/CT scanners, improving reproducibility while preserving image quality.