Cervical spondylosis, a complex and prevalent condition, demands precise and efficient diagnostic techniques for accurate assessment. While MRI offers detailed visualization of cervical spine anatomy, manual interpretation remains labor-intensive and prone to error. To address this, we developed an innovative AI-assisted Expert-based Diagnosis System that automates both segmentation and diagnosis of cervical spondylosis using MRI. Leveraging multi-center datasets of cervical MRI images from patients with cervical spondylosis, our system features a pathology-guided segmentation model capable of accurately segmenting key cervical anatomical structures. The segmentation is followed by an expert-based diagnostic framework that automates the calculation of critical clinical indicators. Our segmentation model achieved an impressive average Dice coefficient exceeding 0.90 across four cervical spinal anatomies and demonstrated enhanced accuracy in herniation areas. Diagnostic evaluation further showcased the system's precision, with the lowest mean average errors (MAE) for the C2-C7 Cobb angle and the Maximum Spinal Cord Compression (MSCC) coefficient. In addition, our method delivered high accuracy, precision, recall, and F1 scores in herniation localization, K-line status assessment, T2 hyperintensity detection, and Kang grading. Comparative analysis and external validation demonstrate that our system outperforms existing methods, establishing a new benchmark for segmentation and diagnostic tasks for cervical spondylosis.

Osteoporotic vertebral compression fractures (OVCFs) are prevalent in the elderly population, typically assessed on computed tomography (CT) scans by evaluating vertebral height loss. This assessment helps determine the fracture's impact on spinal stability and the need for surgical intervention. However, the absence of pre-fracture CT scans and standardized vertebral references leads to measurement errors and inter-observer variability, while irregular compression patterns further challenge the precise grading of fracture severity. While deep learning methods have shown promise in aiding OVCFs screening, they often lack interpretability and sufficient sensitivity, limiting their clinical applicability. To address these challenges, we introduce a novel vertebra synthesis-height loss quantification-OVCFs grading framework. Our proposed model, HealthiVert-GAN, utilizes a coarse-to-fine synthesis network designed to generate pseudo-healthy vertebral images that simulate the pre-fracture state of fractured vertebrae. This model integrates three auxiliary modules that leverage the morphology and height information of adjacent healthy vertebrae to ensure anatomical consistency. Additionally, we introduce the Relative Height Loss of Vertebrae (RHLV) as a quantification metric, which divides each vertebra into three sections to measure height loss between pre-fracture and post-fracture states, followed by fracture severity classification using a Support Vector Machine (SVM). Our approach achieves state-of-the-art classification performance on both the Verse2019 dataset and in-house dataset, and it provides cross-sectional distribution maps of vertebral height loss. This practical tool enhances diagnostic accuracy in clinical settings and assisting in surgical decision-making.

Novel multimode thermal therapy by freezing before radio-frequency heating has achieved a desirable therapeutic effect in liver cancer. Compared with surgical resection, ablation treatment has a relatively high risk of tumor recurrence. To monitor tumor progression after ablation, we developed a novel survival analysis framework for survival prediction and efficacy assessment. We extracted preoperative and postoperative MRI radiomics features and vision transformer-based deep learning features. We also combined the immune features extracted from peripheral blood immune responses using flow cytometry and routine blood tests before and after treatment. We selected features using random survival forest and improved the deep Cox mixture (DCM) for survival analysis. To properly accommodate multitype input features, we proposed a self-adapted fully connected layer for locally and globally representing features. We evaluated the method using our clinical dataset. Of note, the immune features rank the highest feature importance and contribute significantly to the prediction accuracy. The results showed a promising C$^{\mathit{td}}$-index of 0.885 $\pm$ 0.040 and an integrated Brier score of 0.041 $\pm$ 0.014, which outperformed state-of-the-art method combinations of survival prediction. For each patient, individual survival probability was accurately predicted over time, which provided clinicians with trustable prognosis suggestions.

Computed tomography angiography (CTA) is a powerful tool for the diagnosis of vascular diseases and its radiation dose is widely concerned. Limited scan within vessel region, deemed as region-of-interest (ROI), is a particularly apt dose reduction strategy for CTA since clinical assessment mainly depends on vessel structures. However, insufficient raw data may induce noise and artifacts in images reconstructed by conventional analytic and iterative methods. In this paper, we introduced an ultra-low-dose scan protocol for CTA, by modifying the contrast-enhanced stage with a low-mA, few-view ROI scan. Accordingly, a previous-stage-based ROI reconstruction method (PSBROI) was proposed with weighted projections and voxels and a dual-dictionary learning (DDL) strategy. Experiments showed that under the ultra-low-dose scan protocol, the proposed method performed better in artifact removal, noise suppression and structure preservation within ROI than the conventional methods. The ultra-low-dose scan with 30 mAs, 60 projection views and 34.1 mm ROI size could reduce dose to about 0.305% of normal dose.

Repeated CT scans are known to increase the risk of cancer; thus, it is paradoxical to use multiple follow-up CT scans to monitor the development of a lung nodule and conduct early treatment of the nodule. In the case of a solitary lung nodule, regional scanning and region of interest (ROI) reconstruction are likely to restore the internal area at the nodule. A limited-range few-view CT is proposed in this paper for lung nodule follow-ups with extremely reduced X-radiation. For a planned scanning of an ROI, where a solitary lung nodule is positioned, a limited-range few-view CT can be employed, and thus, less tissue is exposed to X-radiation per view. An ROI reconstruction method is also proposed that makes full use of the former standard lung scan. The experimental results show that the nodule size and shape are preserved. In the case of a 40-mm ROI, the number of exposed X-rays can be reduced by 99.6% for a circular scan and 99.9% for a 3-D scan.

Spectral computed tomography (CT) has attracted considerable attention because of its energy-resolving capability in identifying and discriminating materials. The use of a narrow energy bin can improve energy resolution. However, a narrow energy bin has high noise ratio, which degrades the imaging quality of spectral CT. To address this problem, this study exploits the structure correlations of images in the energy domain and proposed two types of united iterative reconstruction (UIR) algorithms. One type uses the well-reconstructed broad-spectrum image, with all available photons, as a constraint, whereas the other type uses a pseudo narrow-energy image, which is estimated with the use of our proposed structure-coupling (SC) method, as a constraint. The SC method utilizes local structures to connect images that are reconstructed with broad-spectrum and narrow-energy CT datasets. Given a broad-spectrum image, the SC method can accurately estimate its corresponding narrow-energy image. Results show that UIR algorithms significantly outperform conventional iterative reconstruction algorithms for narrow-energy image reconstruction in spectral CT. Among the UIR algorithms, SC-UIR yields the best results.