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Background: Attenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners. Methods: Two hundred and sixty patients who underwent 99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-ACµ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (µ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-ACµ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-ACµ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated µ-maps from (c/e)DL-ACµ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods. Results: The NMSE for Chang's method, DL-ACµ, DL-AC, cDL-ACµ, cDL-AC, eDL-ACµ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-ACµ are closer to CT-AC, Followed by DL-AC, eDL-ACµ, cDL-ACµ, cDL-AC, eDL-AC, Chang's method, and NAC. Conclusion: All DL-based AC methods are superior to Chang-AC. DL-ACµ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99mTc-TRODAT-1 brain SPECT.
RESUMO
BACKGROUND/AIM: Oral squamous cell carcinoma (OSCC) has limited treatment options. This study investigated imipramine, a tricyclic antidepressant, as a potential therapy for OSCC using a SAS-bearing xenograft animal model. MATERIALS AND METHODS: The SAS-bearing xenograft model evaluated imipramine's impact on tumor growth. The control group received no treatment, while the imipramine-treated group received regular doses. Tumor growth, confirmed by imaging, and histological analysis assessed size and weight. Imipramine's effects on apoptosis, epithelial-to-mesenchymal transition (EMT), and transcription factors (AKT, ERK, STAT3) were analyzed. RESULTS: Imipramine significantly suppressed tumor growth within 6 days of treatment, with sustained activity. Computer tomography (CT) scans and histology confirmed reduced size and weight by imipramine. Imipramine induced apoptosis via caspase-dependent/-independent pathways, inhibited EMT, and down-regulated phosphorylated AKT, ERK, and STAT3. CONCLUSION: Imipramine shows promise as an effective OSCC therapy, inhibiting tumor growth, inducing apoptosis, and inhibiting EMT. Its impact on transcription factors and modulation of the AKT/ERK/STAT3 pathway suggest a multifaceted approach.
