Yu, Lifeng

Abstract Background Estimating effective dose (E) for CT is often done with dose‐length product (DLP) and effective dose conversion factors, a.k.a. k ‐factors. Previous studies have primarily focused on k ‐factor values derived from certain fixed‐sized phantoms but have not extensively explored all the contributing factors and associated variations in E. Purpose In this study, we investigated the contributing factors and variations of E using k ‐factors as surrogates in adult and pediatric CT exams and provided empirical formula to estimate a comprehensive new set of k ‐factors. Methods Effective dose was estimated using NCICT (National Cancer Institute, USA), including organ and effective dose conversion coefficient library estimated by Monte‐Carlo radiation transport techniques. The k ‐factors were derived as a surrogate of DLP‐normalized E, via E/DLP (unit: mSv/mGy·cm). We examined the variations of effective dose estimates for different phantom and scan parameters represented by the calculations of k ‐factors. This included variations in patient sex, weight, height, kVp, and scan length across typical adult and pediatric CT examinations of anatomical regions such as the head, neck, chest, abdomen, and pelvis. Multivariable regression analyses with/without standardization were conducted to evaluate the significance of the dependency of k ‐factors on the studied parameters. Tube current modulation (TCM) parameters, modeled as a function of patient attenuation using variable modulation strengths, were also incorporated to reflect realistic clinical CT dose variations. Results The variations of DLP‐normalized E were substantially affected by multiple factors, exceeding 100% for adults (weight for adult pelvis) and more than 200% for pediatrics (height for pediatric head). The analysis of standardized regression coefficients revealed that patient weight is the most critical factor, except for head scans in adults, where scan length and patient height are the main determinants. In pediatric CT, both patient weight and height had critical impact. Similarly, for pediatric head CT scans, patient height and scan length are the dominant factors, as observed in adults. Consequently, the k ‐factors have a wide range, from 0.0099 to 0.0357 mSv/mGy·cm for adult chest and from 0.0085 to 0.0363 mSv/mGy·cm for adult abdomen. Empirical formulas to estimate k ‐factors were obtained based on multi‐variable regression with adjusted R square ranging from 0.71 to 0.93. Based on NCICT's TCM model, the use of TCM resulted in an additional average variation of 110% in the k ‐factor compared to non‐TCM results. Conclusions Our study demonstrated the large variations of DLP‐normalized effective dose. Standardized coefficients revealed that patient weight, height and/or scan length were the most crucial determinants. Depending on the specific TCM model used, even greater variations may be observed. This work also provided a practical framework for estimating CT effective dose using the traditional k ‐factor method with updated new conversion factors.

Abstract 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.

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 This article provides an overview of deep-learning–based techniques in CT image reconstruction and processing (referred to as “DLR”), covering technical implementations, performance evaluation, radiation dose reduction, and future perspectives. DLR methods can be categorized into projection-space, projection-to-image-space, image-space, and various hybrid techniques, with applications such as noise reduction, artefact correction, and spatial resolution enhancement. Performance evaluations include phantom-based studies, patient image-based studies, and virtual imaging trials. These evaluation studies demonstrated that DLR can effectively reduce image noise while preserving an image texture like in traditional filtered-backprojection (FBP) images, although the extent of radiation dose reduction varies widely depending on the study and the specific diagnostic task. Challenges remain in low-contrast lesion detection and characterization, where dose reduction may still be less than 50% compared to traditional reconstruction methods. Additionally, the potential for DLR methods to generate false structures or “hallucinations,” especially at low radiation doses, emphasizes the need for effective monitoring and mitigation strategies from both technical and clinical perspectives. Quantitative, accurate, and efficient evaluation techniques, such as virtual image trial-based methods, can be explored to help optimize these algorithms for reducing radiation dose and enhancing diagnostic performance.

Objectives: To determine the feasibility of reduced-dose chest computed tomographic angiography (CTA) with convolutional neural network (CNN) denoising for detecting pulmonary arteriovenous malformations (pAVMs) in children with hereditary hemorrhagic telangiectasia (cwHHT). Methods: Fifteen cwHHT underwent a chest CTA (ie, a controlled “study” dose). Noise was inserted to simulate a quarter dose (QD) exam. Images were reconstructed using iterative reconstruction (IR) and our self-trained CNN denoising model. For each case, 3 sets of images were created: study dose (SD)+IR, QD+IR, and QD+CNN. Two thoracic radiologists independently scored each set to assess quality, spatial resolution, artifacts, and the presence of pAVMs using 4-level ordinal scales. Quantitative assessments of image quality were performed using contrast-to-noise ratios (CNRs) with comparisons made between the experimental conditions. Results: Thirteen of the 15 patients recruited with hereditary hemorrhagic telangiectasia (mean age: 9.3±4.5 y) were positive for pAVM by transthoracic contrast echocardiography. The sensitivities using QD+CNN were 0.85 and 1.00 for readers 1 and 2, respectively. This was compared with 0.69 and 0.84 using QD+IR versus 0.85 and 0.92 for SD+IR. Inter-reader agreement for pAVM detection utilizing QD+CNN was moderate and resulted in kappa=0.59 ( P =0.012). The subjective assessments for QD+CNN were comparable to the SD technique. Regression analysis of reader scores revealed improved quality in QD+CNN versus QD+IR ( P =0.001). Similarly, the QD+CNN condition demonstrated the highest CNRs. Conclusions: Reduced-dose chest CTA with CNN denoising provides a level of sensitivity comparable to standard dose CTA and high CNRs for the detection of pAVMs in cwHHT.

Abstract Objectives To develop a volume of interest (VOI) imaging technique in multi-detector-row helical CT to reduce radiation dose or improve image quality within the VOI. Methods A deep-learning method based on a residual U-Net architecture, named VOI-Net, was developed to correct truncation artifacts in VOI helical CT. Three patient cases, a chest CT of interstitial lung disease and 2 abdominopelvic CT of liver tumour, were used for evaluation through simulation. Results VOI-Net effectively corrected truncation artifacts (root mean square error [RMSE] of 5.97 ± 2.98 Hounsfield Units [HU] for chest, 3.12 ± 1.93 HU, and 3.71 ± 1.87 HU for liver). Radiation dose was reduced by 71% without sacrificing image quality within a 10-cm diameter VOI, compared to a full scan field of view (FOV) of 50 cm. With the same total energy deposited as in a full FOV scan, image quality within the VOI matched that at 350% higher radiation dose. A radiologist confirmed improved lesion conspicuity and visibility of small linear reticulations associated with ground-glass opacity and liver tumour. Conclusions Focusing radiation on the VOI and using VOI-Net in a helical scan, total radiation can be reduced or higher image quality equivalent to those at higher doses in standard full FOV scan can be achieved within the VOI. Advances in knowledge A targeted helical VOI imaging technique enabled by a deep-learning-based artifact correction method improves image quality within the VOI without increasing radiation dose.