Lifeng Yu

Objective: Accurate lung volumetry is essential for donor-recipient size matching in lung transplantation. This study evaluated the impact of radiation dose reduction on the robustness of AI-based whole-lung segmentation, 5-lobe segmentation, and automated tissue classification. Methods: Fifty cases from the Cancer Image Archive (TCIA) Low-dose CT Image and Projection Data set were analyzed. A reduced-dose data set at 10% of the routine dose for each case was generated using validated projection-domain noise insertion. Whole-lung and 5-lobe segmentation were performed using the Lung CT Segmenter module with the lungmask deep learning model in 3D Slicer. Segmentation accuracy was quantified using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), and maximum Hausdorff distance (HDmax). Automated tissue classification and volumetry were performed using the Lung CT Analyzer module. Results: Whole-lung segmentation demonstrated excellent consistency between full-dose and 10% dose images (mean DSC 0.997±0.002, HD95 0.73±0.26 mm). In contrast, 5-lobe segmentation showed substantially lower accuracy and greater variability, even at routine dose. Automated tissue classification revealed a mean decrease in functional (inflated) lung volume of 17.12%±5.69% in the right lung and 17.15%±5.56% in the left lung, accompanied by marked increases in emphysema, infiltrated, and collapsed volumes. Conclusion: AI-based whole-lung segmentation remains highly accurate at 10% of the routine dose, supporting its potential for lung transplant volumetry. However, tissue classification and 5-lobe segmentation exhibited notable degradation, highlighting limitations of existing tools at ultra-low dose levels.

Photon-counting detector CT (PCD-CT) directly detects individual photons and measures their energies, enabling the acquisition of multi-energy data from a single x-ray source with higher spatial resolution, thereby allowing visualization of smaller arteries and veins and other important imaging features not seen at conventional CT. It also substantially reduces calcium and stent blooming artifacts. Appropriate patient selection and task-based optimization of acquisition, reconstruction, and interpretation are critical to realize these benefits. Vascular imaging examinations for which early evidence shows that PCD-CT demonstrates higher diagnostic performance compared to conventional CT include coronary CT angiography (CTA) for evaluation of partially calcified plaques or stents, peripheral runoff CTA, detection of cerebrospinal fluid-venous fistulas, and head CTA for evaluation of small intracranial vessels and aneurysms. Current limitations include excessive image noise with the sharpest reconstruction kernels and challenges when adapting conventional CT protocols (eg, requiring sharper kernels, thinner slices, higher matrices, tailored scan modes) to achieve time-efficient image reconstruction and hanging protocols. New solutions and directions will include deep-learning-based image noise reduction, improved spectral separation, strategic triage of specific patient populations to PCD-CT systems, and subspecialty clinical practice recommendations for this new technology.

Deep learning reconstruction (DLR) methods can generate false anatomical structures (“hallucinations”) by interpreting noise or artifacts, especially at low radiation doses. This study assessed how dose reduction affects hallucinations in two DLR algorithms (DLR1 and DLR2) for high-resolution chest CT scans, specifically for interstitial lung disease (ILD). Full-dose projection data from a photon-counting detector CT scanner were simulated at 25% and 8% dose levels, with 8% approaching chest X-ray doses. Thirty image series (5 patients × 3 doses × 2 DLRs) were independently assessed by four thoracic radiologists under two references: routine reconstruction with a medium-sharp kernel (Qr56-IR3) and the sharpest quantitative kernel (Qr89-IR4). Images were rated on 5-point Likert scales for hallucination presence. DLR2 showed superior hallucination suppression versus DLR1 at 25% and 8% doses (p < 0.01), with comparable hallucination scores at full dose. Radiation dose reduction induces hallucinations in DLR images, with effects depending on algorithm. DLR2 consistently minimized hallucinations better than DLR1 at reduced doses.

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.

4D parathyroid CT is a multiphase protocol used in the setting of hyperparathyroidism to detect parathyroid adenomas, largely identified by their attenuation patterns relative to the thyroid and enlarged non-lymphoid soft tissue in the central compartment. This technique has previously been described utilizing traditional energy integrating detector-CT. We present our protocol using photon counting detector-CT as a means of using increased spatial and contrast resolution to help identify parathyroid adenomas. ABBREVIATIONS: PCD-CT= Photon Counting Detector-CT, EID-CT = Energy Integrating Detector-CT

Photon counting detector CT is a relatively new technology that is becoming increasingly prevalent. In recent years, photon counting CT has been shown to have benefits in numerous neuroradiologic clinical applications, including temporal bone imaging, detection of spinal CSF leaks, and CT angiography of the head, neck, and spine. New uses for photon counting detector CT continue to emerge, capitalizing on the high spatial resolution and other advantages of this technology to improve patient care. One potential application in the spine is the detection and localization of the artery of Adamkiewicz, which provides the dominant supply to the inferior portion of the anterior spinal artery. Localization of this artery is sometimes desired prior to surgeries requiring ligation of the foraminal neurovascular complex. Traditional energy integrating detector CT is useful but imperfect for detection of this relatively small and transiently opacifying vessel. The improved spatial resolution and spectral information obtained with photon counting CT may help improve detection of the artery of Adamkiewicz. In this clinical report, we outline a protocol for imaging the artery of Adamkiewicz with photon counting detector CT and describe a comparative analysis of three different reconstruction techniques in six consecutive patients who underwent this exam. We found that an intermediate sharp vascular kernel (Bv60) reconstructed with a low energy threshold outperformed other conditions, including a sharper kernel and a low keV virtual monoenergetic reconstruction. ABBREVIATIONS: AKA = artery of Adamkiewicz or great radiculomedullary artery; CNR = contrast-to-noise ratio; EID = energy integrating detector; PCD = photon counting detector QIR = quantum iterative reconstruction; T3D = low energy threshold; UHR = ultra-high resolution