Radiology-Artificial Intelligence最新文献

Purpose To develop methods for creating pseudo-contrast-enhanced US (CEUS) by using an enhanced generative adversarial network and evaluate its ability to assess tumor ablation effectiveness. Materials and Methods This retrospective study included 1030 patients who underwent thyroid nodule ablation across seven centers from January 2020 to April 2023. A generative adversarial network-based model was developed for direct pseudo-CEUS generation from B-mode US and tested on thyroid, breast, and liver ablation datasets. The reliability of pseudo-CEUS was assessed using structural similarity index (SSIM), color histogram correlation, and mean absolute percentage error against real CEUS. Additionally, a subjective evaluation system was devised to validate its clinical value. The Wilcoxon signed rank test was employed to analyze differences in the data. Results The study included 1030 patients (mean age, 46.9 years ± 12.5 [SD]; 799 female and 231 male patients). For internal test set 1, the mean SSIM was 0.89 ± 0.05, while across external test sets 1-6, mean SSIM values ranged from 0.84 ± 0.08 to 0.88 ± 0.04. Subjective assessments affirmed the method's stability and near-realistic performance in evaluating ablation effectiveness. The thyroid ablation datasets had an average identification score of 0.49 (0.5 indicates indistinguishability), while the similarity average score for all datasets was 4.75 out of 5. Radiologists' assessments of residual blood supply were nearly consistent, with no differences in defining ablation zones between real and pseudo-CEUS. Conclusion The pseudo-CEUS method demonstrated high similarity to real CEUS in evaluating tumor ablation effectiveness. Keywords: Ablation Techniques, Ultrasound, Computer Applications-Virtual Imaging Supplemental material is available for this article. © RSNA, 2025.

Purpose To construct and evaluate the performance of a machine learning model for bone segmentation using whole-body CT images. Materials and Methods In this retrospective study, whole-body CT scans (from June 2010 to January 2018) from 90 patients (mean age, 61 years ± 9 [SD]; 45 male, 45 female) with multiple myeloma were manually segmented using 60 labels and subsegmented into cortical and trabecular bone. Segmentations were verified by board-certified radiology and nuclear medicine physicians. The impacts of isotropy, resolution, multiple labeling schemes, and postprocessing were assessed. Model performance was assessed on internal and external test datasets (362 scans) and benchmarked against the TotalSegmentator segmentation model. Performance was assessed using Dice similarity coefficient (DSC), normalized surface distance (NSD), and manual inspection. Results Skellytour achieved consistently high segmentation performance on the internal dataset (DSC: 0.94, NSD: 0.99) and two external datasets (DSC: 0.94, 0.96; NSD: 0.999, 1.0), outperforming TotalSegmentator on the first two datasets. Subsegmentation performance was also high (DSC: 0.95, NSD: 0.995). Skellytour produced finely detailed segmentations, even in low-density bones. Conclusion The study demonstrates that Skellytour is an accurate and generalizable bone segmentation and subsegmentation model for CT data; it is available as a Python package via GitHub (https://github.com/cpwardell/Skellytour). Keywords: CT, Informatics, Skeletal-Axial, Demineralization-Bone, Comparative Studies, Segmentation, Supervised Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Khosravi and Rouzrokh in this issue.

Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/generalized autocalibrating partial parallel acquisition scans from clinical trials for glioblastoma (trial 1, May 2014-October 2018) and major depressive disorder (trial 2, 2022-2023). The training dataset included 685 000 spectra from 20 participants (60 scans) in trial 1. The testing dataset included 115 000 spectra from five participants (13 scans) in trial 1 and 145 000 spectra from seven participants (16 scans) in trial 2. A comparative analysis was performed between NNFit and a widely used parametric-modeling spectral quantitation method (FITT). Metabolite maps generated by each method were compared using the structural similarity index measure (SSIM) and linear correlation coefficient (R²). Radiation treatment volumes for glioblastoma based on metabolite maps were compared using the Dice coefficient and a two-tailed t test. Results Mean SSIMs and R² values for trial 1 test set data were 0.91 and 0.90 for choline, 0.93 and 0.93 for creatine, 0.93 and 0.93 for N-acetylaspartate, 0.80 and 0.72 for myo-inositol, and 0.59 and 0.47 for glutamate plus glutamine. Mean values for trial 2 test set data were 0.95 and 0.95, 0.98 and 0.97, 0.98 and 0.98, 0.92 and 0.92, and 0.79 and 0.81, respectively. The treatment volumes had a mean Dice coefficient of 0.92. The mean processing times were 90.1 seconds for NNFit and 52.9 minutes for FITT. Conclusion A deep learning approach to spectral quantitation offers performance similar to that of conventional quantification methods for EPSI data, but with faster processing at short TE. Keywords: MR Spectroscopy, Neural Networks, Brain/Brain Stem Supplemental material is available for this article. © RSNA, 2025.

Purpose To investigate whether the computational effort of three-dimensional CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, Tucker decomposition was applied to the convolutional kernels of the TotalSegmentator model, an nnU-Net model trained on a comprehensive CT dataset for automatic segmentation of 117 anatomic structures. The proposed approach reduced the floating-point operations and memory required during inference, offering an adjustable trade-off between computational efficiency and segmentation quality. This study used the publicly available TotalSegmentator dataset containing 1228 segmented CT scans and a test subset of 89 CT scans and used various downsampling factors to explore the relationship between model size, inference speed, and segmentation accuracy. Segmentation performance was evaluated using the Dice score. Results The application of Tucker decomposition to the TotalSegmentator model substantially reduced the model parameters and floating-point operations across various compression ratios, with limited loss in segmentation accuracy. Up to 88.17% of the model's parameters were removed, with no evidence of differences in performance compared with the original model for 113 of 117 classes after fine-tuning. Practical benefits varied across different graphics processing unit architectures, with more distinct speedups on less powerful hardware. Conclusion The study demonstrated that post hoc network compression via Tucker decomposition presents a viable strategy for reducing the computational demand of medical image segmentation models without substantially impacting model accuracy. Keywords: Deep Learning, Segmentation, Network Compression, Convolution, Tucker Decomposition Supplemental material is available for this article. © RSNA, 2025.

Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a two-dimensional (2D) single-channel convolutional neural network (CNN) with multiplanar MRI sections showed the potential to detect fetuses with VM. This study examines the diagnostic performance of a deep learning-based fetal brain age prediction model to distinguish fetuses with VM (n = 317) from typically developing fetuses (n = 183), the severity of VM, and the presence of associated CNS abnormalities. The predicted age difference (PAD) was measured by subtracting the predicted brain age from the gestational age in fetuses with VM and typical development. PAD and absolute value of PAD (AAD) were compared between VM and typically developing fetuses. In addition, PAD and AAD were compared between subgroups by VM severity and the presence of associated CNS abnormalities in VM. Fetuses with VM showed significantly larger AAD than typically developing fetuses (P < .001), and fetuses with severe VM showed larger AAD than those with moderate VM (P = .004). Fetuses with VM and associated CNS abnormalities had significantly lower PAD than fetuses with isolated VM (P = .005). These findings suggest that fetal brain age prediction using the 2D single-channel CNN method has the clinical ability to assist in identifying not only the enlargement of the ventricles but also the presence of associated CNS abnormalities. Keywords: MR-Fetal (Fetal MRI), Brain/Brain Stem, Fetus, Supervised Learning, Machine Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms Supplemental material is available for this article. ©RSNA, 2025.