Background: The generation of synthetic CT images from MRI scans represents a crucial step toward enabling MRI-only clinical workflows and supporting multi-modal integration in medical imaging, particularly in radiotherapy planning. Despite significant advancements in deep learning models, many current methods still struggle to reconstruct high-density structures, especially bone, and exhibit limited accuracy in density values. This shortcoming is largely attributed to the passage of excessive or noisy features through skip connections in the traditional U-Net architecture, which degrade the quality of information transmitted to the decoder, negatively impacting the clarity of anatomical boundaries and the pixel-wise accuracy of the resulting synthetic image. Methods: In this work, we propose an enhanced 3D U-Net architecture in which the Convolutional Block Attention Module (CBAM) is systematically integrated within each skip connection. The CBAM sequentially applies channel and spatial attention to adaptively reweight encoder feature maps before fusion with the decoder, thereby emphasizing anatomically relevant structures while suppressing irrelevant feature propagation. The model was trained and evaluated on the SynthRAD2023 (Task 1—Brain) MRI–CT dataset. To rigorously assess the contribution of the attention mechanism, a dedicated ablation study was conducted comparing three variants: 3D U-Net with Squeeze-and-Excitation (SE), Coordinate Attention (CA), and the proposed CBAM module. Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross-Correlation (NCC). Results: The ablation study demonstrated that the CBAM-enhanced model consistently outperformed both SE- and CA-based variants across all quantitative metrics. Specifically, the proposed method achieved an MAE of $38.2\pm 5.4$ HU and an RMSE of $51.0\pm 12.0$ HU, representing the lowest reconstruction errors among the evaluated models. In addition, it obtained a PSNR of $29.45\pm 2.10$ dB, SSIM of $0.940\pm 0.031$, and NCC of $0.967\pm 0.015$, indicating superior structural preservation and strong voxel-wise correspondence between synthesized and reference CT volumes. These results confirm that the sequential integration of channel and spatial attention provides a statistically and practically meaningful improvement for high-fidelity MRI-to-CT synthesis. Conclusions: Generating high-resolution brain CT images from brain MRI scans using a 3D U-Net network enhanced with a CBAM module can contribute to supporting the clinical workflow by providing additional diagnostic data without the need for extra radiological examinations, thereby enhancing diagnostic efficiency and reducing radiation exposure. This technique helps reduce patient exposure to radiation and improves accessibility in resource-limited settings. Furthermore, this method is valuable for retrospective studies, surgical planning, and image-guided therapy, where complete multi-modal data may not always be available.

Reliable evaluation of artificial intelligence models for coronavirus disease 2019 (COVID-19) imaging remains challenging because patient-level dependencies, source-related biases, and differences between imaging modalities can lead to overly optimistic performance estimates. This study proposes a leakage-controlled, modality-aware deep ensemble framework for COVID-19 classification using chest X-ray (CXR) and computed tomography (CT) images from the COVID-19 Radiography Database and the SARS-CoV-2 CT-Scan Dataset. Separate evaluation protocols were established for CXR and CT, with duplicate control and available source information incorporated for CXR and patient-level partitioning applied to CT before data augmentation. Four ImageNet-pretrained convolutional neural networks (CNNs)—VGG16, ResNet50, DenseNet169, and InceptionV3—were evaluated, followed by temperature scaling for probability calibration and validation-optimized weighted soft voting for ensemble fusion. The original CT architectural comparison was conducted at the slice level, whereas the final CT evaluation was performed at the patient level. In the final modality-specific held-out evaluation, DenseNet169 achieved an area under the receiver operating characteristic curve (AUROC) of 0.985, an accuracy of 97.32%, and an F1-score of 98.1% on the 2870-image CXR test set. The calibrated weighted ensemble achieved an AUROC of 0.990, an accuracy of 98.43%, and an F1-score of 97.9%, with a Brier score of 0.030 and an expected calibration error (ECE) of 0.023. For CT, DenseNet169 achieved an AUROC of 0.941 and correctly classified 16 of 18 patients (88.89%), whereas the calibrated weighted ensemble achieved an AUROC of 0.969 and correctly classified 17 of 18 patients (94.44%), with a Brier score of 0.070 and an ECE of 0.053. An uncertainty-aware selective-prediction analysis identified 316 of 2870 CXR test images (11.01%) and 2 of 18 CT test patients (11.11%) as low-confidence cases for specialist review. Among the remaining CT cases, 15 of 16 patients were correctly classified (93.75%). Because the CT test cohort contained only 18 patients, the patient-level CT performance and selective-prediction results should be interpreted cautiously as descriptive estimates from this limited cohort. The original pooled evaluation, in which DenseNet169 achieved 98.51% accuracy, is retained only as an exploratory architectural comparison and should not be interpreted as directly comparable with the final modality-specific evaluation. The findings demonstrate that evaluation design, statistical unit, probability calibration, and ensemble fusion materially influence the interpretation of COVID-19 imaging-classification performance. The proposed framework provides leakage-aware and calibrated performance estimates under the evaluated datasets; however, independent multicenter external validation is required before broader claims of clinical generalizability or deployment readiness can be made.