Bratt, Alex K

Abstract Background Photon‐counting‐detector (PCD) CT systems offer ultra‐high spatial resolution, yet the visual spatial resolution on clinical images often constrained by large pixel size, yielding resolutions below system capabilities. While reducing pixel size and using sharp kernels enhance visual spatial resolution, it increases noise, compromising image quality. Purpose To investigate the combined effects of pixel size and reconstruction kernels on visual spatial resolution using phantom and clinical images and to develop a dedicated high‐resolution deep convolutional neural network (HR‐CNN) to better utilize the intrinsic high spatial resolution of PCD‐CT in clinical imaging. Methods The relationship between spatial resolution, reconstruction kernel, and pixel size was investigated to identify strategies for utilizing the full spatial resolution potential of PCD‐CT. To overcome the increased noise associated with high‐resolution settings, a dedicated HR‐CNN was developed to push the limit of spatial resolution in routine PCD‐CT exams. The HR‐CNN was trained using patient exams acquired with ultra‐high‐resolution (UHR) mode and reconstructed with a 150‐mm field of view (FOV), matrix size of 1024×1024 (0.15‐mm pixel size) and sharpest quantitative kernel (Qr89). The impact of FOV, kernel, and denoising on spatial resolution was studied using bar‐pattern phantoms and a pilot clinical evaluation including 5 patients with interstitial lung diseases. Two thoracic radiologists evaluated 4 different FOV/reconstruction conditions: (1) FOV‐410/Qr56‐Iterative reconstruction (IR), (2) FOV‐410/Qr89‐IR, (3) FOV‐150/Qr89‐IR, and (4) FOV‐150/Qr89‐HR‐CNN in terms of overall image quality, noise, visual spatial resolution, and overall preference. Results With a FOV of 410 mm, the Qr89 sharp kernel displayed bar‐patterns up to 14 lp/cm, not much higher than the routine lung kernel Qr56. When the FOV was reduced to 150 mm, Qr89‐IR allowed for the visualization of line pair patterns ranging from 18 to 20 lp/cm, with 20 lp/cm being moderately discernible. The application of Qr89‐HR‐CNN yielded further improvement, enabling the display of line pair patterns as high as 20–22 lp/cm. In patient cases, both radiologists consistently ranked the FOV‐150 images processed with HR‐CNN as superior across metrics including overall image quality, noise reduction, visual spatial resolution, and overall preference. The HR‐CNN reduced the noise in patients’ images by 93.0 ± 0.6% and 44.9 ± 5.3% in comparison with the original FBP and IR images, respectively. Conclusions The spatial resolution of PCD‐CT is not maximized in routine practice due to the large FOV and high noise levels at sharp kernels. The proposed HR‐CNN denoising method, along with small pixel size, may allow the high spatial resolution toward the system limit to be implemented in practice, which is beneficial in the diagnosis of many diseases, including interstitial lung disease.

Abstract Objectives Large language models (LLMs) offer potential in natural language processing tasks in healthcare. Due to the need for high accuracy, understanding their limitations is essential. The purpose of this study was to evaluate the performance of LLMs in classifying radiology reports for the presence of pulmonary embolism (PE) under various conditions, including different prompt designs and data perturbations. Materials and Methods In this retrospective, institutional review board approved study, we evaluated 3 Google’s LLMs including Gemini-1.5-Pro, Gemini-1.5-Flash-001, and Gemini-1.5-Flash-002, in classifying 11 999 pulmonary CT angiography radiology reports for PE. Ground truth labels were determined by concordance between a computer vision-based PE detection (CVPED) algorithm and multiple LLM runs under various configurations. Discrepancies between algorithms’ classifications were aggregated and manually reviewed. We evaluated the effects of prompt design, data perturbations, and repeated analyses across geographic cloud regions. Performance metrics were calculated. Results Of 11 999 reports, 1296 (10.8%) were PE-positive. Accuracy across LLMs ranged between 0.953 and 0.996. The highest recall rate for a prompt modified after a review of the misclassified cases (up to 0.997). Few-shot prompting improved recall (up to 0.99), while chain-of-thought generally degraded performance. Gemini-1.5-Flash-002 demonstrated the highest robustness against data perturbations. Geographic cloud region variability was minimal for Gemini-1.5+-Pro, while the Flash models showed stable performance. Discussion and Conclusion LLMs demonstrated high performance in classifying radiology reports, though results varied with prompt design and data quality. These findings underscore the need for systematic evaluation and validation of LLMs for clinical applications, particularly in high-stakes scenarios.