ObjectiveThe aim of this study was to investigate the effects of the total variation regularized expectation maximization (TVREM) reconstruction on improving 68Ga-DOTA-TATE PET/CT images compared to the ordered subset expectation maximization (OSEM) reconstruction.MethodA total of 17 patients with neuroendocrine tumors who underwent clinical 68Ga-DOTA-TATE PET/CT were involved in this study retrospectively. The PET images were acquired with either 3 min-per-bed (min/bed) acquisition time and reconstructed with OSEM (2 iterations, 20 subsets, and a 3.2-mm Gaussian filter) and TVREM (seven penalization factors = 0.01, 0.07, 0.14, 0.21, 0.28, 0.35, and 0.42) for 2 and 3 min-per-bed (min/bed) acquisition time using list-mode. The SUVmean of the liver, background variability (BV), signal-to-noise ratios (SNR), SUVmax of the lesions and tumor-to-background ratios (TBR) were measured. The mean percentage difference in the SNR and TBR between TVREM with difference penalization factors and OSEM was calculated. Qualitative image quality was evaluated by two experienced radiologists using a 5-point score scale (5-excellent, 1-poor).ResultsIn total, 63 lesions were analyzed in this study. The SUVmean of the liver did not differ significantly between TVREM and OSEM. The BV of all TVREM groups was lower than OSEM groups (all p < 0.05), and the BV of TVREM 2 min/bed group with penalization factor of 0.21 was considered comparable to OSEM 3 min/bed group (p = 0.010 and 0.006). The SNR, SUVmax and TBR were higher for all TVREM groups compared to OSEM groups (all p < 0.05). The mean percentage difference in the SNR and TBR was larger for small lesions (<10 mm) than that for medium (≥10 mm but < 20 mm) and large lesions (≥20 mm). The highest image quality score was given to TVREM 2 min/bed group with penalization factor of 0.21 (3.77 ± 0.26) and TVREM 3 min/bed group with penalization factor of 0.35 (3.77 ± 0.26).ConclusionTVREM could reduce image noise, improve the SNR, SUVmax and TBR of the lesions, and has the potential to preserves the image quality with shorter acquisition time.

Background Epicardial adipose tissue (EAT) is known as an important imaging indicator for cardiovascular risk stratification. The present study aimed to determine whether the EAT volume (EV) and mean EAT attenuation (mEA) measured by non-contrast routine chest CT (RCCT) could be more consistent with those measured by coronary CT angiography (CCTA) by adjusting the threshold of fatty attenuation. Methods In total, 83 subjects who simultaneously underwent CCTA and RCCT were enrolled. EV and mEA were quantified by CCTA using a threshold of (N30) (− 190 HU, − 30 HU) as a reference and measured by RCCT using thresholds of N30, N40 (− 190 HU, − 40 HU), and N45 (− 190 HU, − 45 HU). The correlation and agreement of EAT metrics between the two imaging modalities and differences between patients with coronary plaques (plaque ( +)) and without plaques (plaque ( −)) were analyzed. Results EV obtained from RCCT showed very strong correlation with the reference (r = 0.974, 0.976, 0.972 (N30, N40, N45), P < 0.001), whereas mEA showed a moderate correlation (r = 0.516, 0.500, 0.477 (N30, N40, N45), P < 0.001). Threshold adjustment was able to reduce the bias of EV, while increase the bias of mEA. Data obtained by CCTA and RCCT both demonstrated a significantly larger EV in the plaque ( +) group than in the plaque ( −) group (P < 0.05). A significant difference in mEA was shown only by RCCT using a threshold of N30 (plaque ( +) vs ( −): − 80.0 ± 4.4 HU vs − 78.0 ± 4.0 HU, P = 0.030). The mEA measured on RCCT using threshold of N40 and N45 showed no significant statistical difference between the two groups (P = 0.092 and 0.075), which was consistent with the result obtained on CCTA (P = 0.204). Conclusion Applying more negative threshold, the consistency of EV measurements between the two techniques improves and a consistent result can be obtained when comparing EF measurements between groups, although the bias of mEA increases. Threshold adjustment is necessary when measuring EF with non-contrast RCCT.

Objective To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules. Materials and methods CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep‐learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction (HIR) technique. The AIIR data were divided into a training (n = 96) and a validation set (n = 22), and the HIR data were set as the test set (n = 118). Extracted texture features were compared using the Mann‐Whitney U test and t‐test. The diagnostic performance of the classification model was analyzed with the receiver operating characteristic curve (ROC), the area under ROC (AUC), sensitivity, specificity, and accuracy. Results Among the obtained 68 texture features, 51 (75.0%) were not influenced by the change of reconstruction algorithm (p > 0.05). Forty‐four features were significantly different between benign and malignant nodules (p < 0.05) for the AIIR dataset, which were selected to build the classification model. The accuracy and AUC of the classification model were 92.3% and 0.91 (95% confidence interval [CI], 0.74−0.90) with the validation set, which were 80.0% and 0.80 (95% CI, 0.68−0.86) with the test set. Conclusion With the CT texture analysis model trained with deep‐learning reconstruction (AIIR) images showing favorable diagnostic accuracy in discriminating benign and malignant pulmonary nodules, it also has certain applicability to the iterative reconstruction (HIR) images.

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