Diagnostic and Interventional Imaging最新文献

英文中文

Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis 利用沙普利加法解释可解释性分析预测乳腺癌分子亚型的多参数磁共振成像模型

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-05-01 DOI: 10.1016/j.diii.2024.01.004

Yao Huang , Xiaoxia Wang , Ying Cao , Mengfei Li , Lan Li , Huifang Chen , Sun Tang , Xiaosong Lan , Fujie Jiang , Jiuquan Zhang

Purpose

The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis.

Material and methods

Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis.

Results

A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25–75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478–0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681–0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630–0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717–0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217–0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively.

Conclusion

Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.

材料与方法在2019年2月至2022年1月期间招募了接受治疗前磁共振成像（包括超快动态对比增强磁共振成像、磁共振波谱、弥散峰度成像和体细胞内不连贯运动）的乳腺癌患者。收集了13个语义特征和13个多参数特征，并选择其中的关键特征，利用逐步逻辑回归法开发了预测乳腺癌分子亚型（管腔A型、管腔B型、三阴性和HER2富集）的机器学习模型。建立了语义模型和多参数模型，并基于五个机器学习分类器进行了比较。结果共有 188 名妇女（平均年龄为 53 ± 11 [标准差]岁；年龄范围：25-75 岁）参加了研究，并进一步分为训练队列（131 名妇女）和验证队列（57 名妇女）。在五种机器学习分类器中，XGBoost 表现出良好的预测性能。在验证队列中，语义模型的接收者操作特征曲线下面积（AUC）从HER2富集亚型的0.693（95%置信区间[CI]：0.478-0.839）到HER2富集亚型的0.764（95%置信区间[CI]：0.对于管腔 A 亚型，其 AUC 为 0.771（95% CI：0.630-0.888），而对于三阴性亚型，其 AUC 为 0.857（95% CI：0.717-0.957）。语义模型和多参数模型之间的 AUC 并无显著差异（P 范围：0.217-0.640）。SHAP分析显示，较低的iAUC、较高的峰度、较低的D*和较低的峰度分别是管腔A型、管腔B型、三阴性乳腺癌和HER2富集亚型的显著特征。

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引用次数: 0

Lung fibrosis is uncommon in primary Sjögren's disease: A retrospective analysis of computed tomography features in 77 patients 肺纤维化在原发性斯约恩病中并不常见：对 77 例患者计算机断层扫描特征的回顾性分析。

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-05-01 DOI: 10.1016/j.diii.2024.01.003

Grégoire Martin de Frémont , Alessandra Monaya , Guillaume Chassagnon , Samir Bouam , Emma Canniff , Pascal Cohen , Marion Casadevall , Luc Mouthon , Véronique Le Guern , Marie-Pierre Revel

Purpose

The purpose of this study was to describe lung abnormalities observed on computed tomography (CT) in patients meeting the 2016 American College of Rheumatology/European League Against Rheumatism (EULAR) classification criteria for primary Sjögren's disease (pSD).

Materials and methods

All patients with pSD seen between January 2009 and December 2020 in the day care centre of our National Reference Center for rare systemic autoimmune diseases, who had at least one chest CT examination available for review and for whom the cumulative EULAR Sjögren's Syndrome Disease Activity Index (cumESSDAI) could be calculated were retrospectively evaluated. CT examinations were reviewed, together with clinical symptoms and pulmonary functional results.

Results

Seventy-seven patients (73 women, four men) with a median age of 51 years at pSD diagnosis (age range: 17–79 years), a median follow-up time of 6 years and a median cumESSDAI of 7 were included. Sixty-six patients (86%) had anti-SSA antibodies. Thirty-three patients (33/77; 43%) had respiratory symptoms, without significant alteration in pulmonary function tests. Forty patients (40/77; 52%) had abnormal lung CT findings of whom almost half of them had no respiratory symptoms. Abnormalities on chest CT were more frequently observed in patients with anti-SSA positivity and a history of lymphoma. Air cysts (28/77; 36%) and mosaic perfusion (35/77; 35%) were the predominant abnormalities, whereas lung fibrosis was observed in five patients (5/77; 6%).

Conclusion

More than half of patients with pSD have abnormal CT findings, mainly air cysts and mosaic perfusion, indicative of small airways disease, whereas lung fibrosis is rare, observed in less than 10% of such patients.

目的：本研究旨在描述符合2016年美国风湿病学会/欧洲抗风湿联盟（EULAR）原发性斯约格伦病（ppSD）分类标准的患者在计算机断层扫描（CT）中观察到的肺部异常：回顾性评估了2009年1月至2020年12月期间在国家罕见系统性自身免疫疾病参考资料中心日间护理中心就诊的所有原发性斯琼氏综合征患者，这些患者至少有一次胸部CT检查可供复查，并且可以计算累积EULAR斯琼氏综合征疾病活动指数（cumESSDAI）。对CT检查结果、临床症状和肺功能结果进行了复查：共纳入 77 名患者（73 名女性，4 名男性），诊断为 pSD 时的中位年龄为 51 岁（年龄范围：17-79 岁），中位随访时间为 6 年，中位 cumESSDAI 为 7。66名患者（86%）有抗SSA抗体。33名患者（33/77；43%）有呼吸道症状，肺功能检查无明显变化。40名患者（40/77；52%）的肺部CT结果异常，其中近一半患者没有呼吸道症状。抗-SSA 阳性和有淋巴瘤病史的患者更常出现胸部 CT 异常。气囊（28/77；36%）和镶嵌灌注（35/77；35%）是主要的异常现象，而在五名患者中观察到肺纤维化（5/77；6%）：结论：半数以上的 pSD 患者 CT 检查结果异常，主要是气囊和镶嵌灌注，表明存在小气道疾病，而肺纤维化很少见，只有不到 10% 的此类患者观察到肺纤维化。

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引用次数: 0

Breast cancer molecular subtype prediction: Improving interpretability of complex machine-learning models based on multiparametric-MRI features using SHapley Additive exPlanations (SHAP) methodology 乳腺癌分子亚型预测：使用 SHapley Additive exPlanations (SHAP) 方法提高基于多参数磁共振成像特征的复杂机器学习模型的可解释性。

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-05-01 DOI: 10.1016/j.diii.2024.01.008

Amandine Crombé, Masako Kataoka

引用次数: 0

Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study. 使用能量积分或光子计数探测器双源 CT 的腹部图像质量和剂量降低：一项模型研究。

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-05-01 DOI: 10.1016/j.diii.2024.05.002

Joël Greffier, D. Dabli, S. Faby, Maxime Pastor, Cédric Croisille, Fabien de Oliveira, Julien Erath, J. Beregi

引用次数: 0

ChatGPT in radiology: A systematic review of performance, pitfalls, and future perspectives 放射学中的 ChatGPT：对性能、陷阱和未来前景的系统回顾。

IF 4.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2024-04-27 DOI: 10.1016/j.diii.2024.04.003

Pedram Keshavarz , Sara Bagherieh , Seyed Ali Nabipoorashrafi , Hamid Chalian , Amir Ali Rahsepar , Grace Hyun J. Kim , Cameron Hassani , Steven S. Raman , Arash Bedayat

Purpose

The purpose of this study was to systematically review the reported performances of ChatGPT, identify potential limitations, and explore future directions for its integration, optimization, and ethical considerations in radiology applications.

Materials and methods

After a comprehensive review of PubMed, Web of Science, Embase, and Google Scholar databases, a cohort of published studies was identified up to January 1, 2024, utilizing ChatGPT for clinical radiology applications.

Results

Out of 861 studies derived, 44 studies evaluated the performance of ChatGPT; among these, 37 (37/44; 84.1%) demonstrated high performance, and seven (7/44; 15.9%) indicated it had a lower performance in providing information on diagnosis and clinical decision support (6/44; 13.6%) and patient communication and educational content (1/44; 2.3%). Twenty-four (24/44; 54.5%) studies reported the proportion of ChatGPT's performance. Among these, 19 (19/24; 79.2%) studies recorded a median accuracy of 70.5%, and in five (5/24; 20.8%) studies, there was a median agreement of 83.6% between ChatGPT outcomes and reference standards [radiologists’ decision or guidelines], generally confirming ChatGPT's high accuracy in these studies. Eleven studies compared two recent ChatGPT versions, and in ten (10/11; 90.9%), ChatGPTv4 outperformed v3.5, showing notable enhancements in addressing higher-order thinking questions, better comprehension of radiology terms, and improved accuracy in describing images. Risks and concerns about using ChatGPT included biased responses, limited originality, and the potential for inaccurate information leading to misinformation, hallucinations, improper citations and fake references, cybersecurity vulnerabilities, and patient privacy risks.

Conclusion

Although ChatGPT's effectiveness has been shown in 84.1% of radiology studies, there are still multiple pitfalls and limitations to address. It is too soon to confirm its complete proficiency and accuracy, and more extensive multicenter studies utilizing diverse datasets and pre-training techniques are required to verify ChatGPT's role in radiology.

目的：本研究旨在系统回顾已报道的 ChatGPT 性能，识别潜在的局限性，并探索其在放射学应用中的整合、优化和伦理考虑的未来方向：在对PubMed、Web of Science、Embase和Google Scholar数据库进行全面审查后，确定了截至2024年1月1日利用ChatGPT进行临床放射学应用的已发表研究：在得出的 861 项研究中，44 项研究对 ChatGPT 的性能进行了评估；其中 37 项（37/44；84.1%）表现出较高的性能，7 项（7/44；15.9%）表示在提供诊断信息和临床决策支持（6/44；13.6%）以及患者交流和教育内容（1/44；2.3%）方面性能较低。有 24 项研究（24/44；54.5%）报告了 ChatGPT 的性能比例。其中，19 项研究（19/24；79.2%）记录的准确率中位数为 70.5%，5 项研究（5/24；20.8%）的 ChatGPT 结果与参考标准（放射科医生的决定或指南）的一致性中位数为 83.6%，总体上证实了 ChatGPT 在这些研究中的高准确率。有 11 项研究比较了两个最新的 ChatGPT 版本，其中 10 项研究（10/11；90.9%）发现 ChatGPTv4 的表现优于 v3.5，这表明 ChatGPTv4 在解决高阶思维问题、更好地理解放射学术语以及提高图像描述准确性方面都有显著提升。使用 ChatGPT 的风险和顾虑包括：回答有偏差、原创性有限、信息不准确可能导致误报、幻觉、引用不当和虚假引用、网络安全漏洞和患者隐私风险：虽然 ChatGPT 在 84.1% 的放射学研究中显示出其有效性，但仍有许多隐患和局限性需要解决。要确认其完全熟练和准确还为时尚早，需要利用不同的数据集和预培训技术进行更广泛的多中心研究，以验证 ChatGPT 在放射学中的作用。

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引用次数: 0

Differentiation between adrenocortical carcinoma and lipid-poor adrenal adenoma using a multiparametric MRI-based diagnostic algorithm. 利用基于多参数磁共振成像的诊断算法区分肾上腺皮质癌和贫脂肾上腺腺瘤。

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-04-03 DOI: 10.1016/j.diii.2024.03.005

Carmelia Oloukoi, Anthony Dohan, Martin Gaillard, Christine Hoeffel, Lionel Groussin-Rouiller, Jérome Bertherat, Anne Jouinot, Guillaume Assié, David Fuks, Mathilde Sibony, Philippe Soyer, Anne-Sophie Jannot, Maxime Barat

Purpose: The purpose of this study was to evaluate the capabilities of multiparametric magnetic resonance imaging (MRI) in differentiating between lipid-poor adrenal adenoma (LPAA) and adrenocortical carcinoma (ACC).

Materials and methods: Patients of two centers who underwent surgical resection of LPAA or ACC after multiparametric MRI were retrospectively included. A training cohort was used to build a diagnostic algorithm obtained through recursive partitioning based on multiparametric MRI variables, including apparent diffusion coefficient and chemical shift signal ratio (i.e., tumor signal intensity index). The diagnostic performances of the multiparametric MRI-based algorithm were evaluated using a validation cohort, alone first and then in association with adrenal tumor size using a cut-off of 4 cm. Performances of the diagnostic algorithm for the diagnosis of ACC vs. LPAA were calculated using pathology as the reference standard.

Results: Fifty-four patients (27 with LPAA and 27 with ACC; 37 women; mean age, 48.5 ± 13.3 [standard deviation (SD)] years) were used as the training cohort and 61 patients (24 with LPAA and 37 with ACC; 47 women; mean age, 49 ± 11.7 [SD] years) were used as the validation cohort. In the validation cohort, the diagnostic algorithm yielded best accuracy for the diagnosis of ACC vs. LPAA (75%; 46/61; 95% CI: 55-88) when used without lesion size. Best sensitivity was obtained with the association of the diagnostic algorithm with tumor size (96%; 23/24; 95% CI: 80-99). Best specificity was obtained with the diagnostic algorithm used alone (76%; 28/37; 95% CI: 60-87).

Conclusion: A multiparametric MRI-based diagnostic algorithm that includes apparent diffusion coefficient and tumor signal intensity index helps discriminate between ACC and LPAA with high degrees of specificity and accuracy. The association of the multiparametric MRI-based diagnostic algorithm with adrenal lesion size helps maximize the sensitivity of multiparametric MRI for the diagnosis of ACC.

目的：本研究旨在评估多参数磁共振成像（MRI）在区分贫脂性肾上腺腺瘤（LPAA）和肾上腺皮质癌（ACC）方面的能力：回顾性纳入两个中心在多参数磁共振成像后接受手术切除LPAA或ACC的患者。根据多参数磁共振成像变量，包括表观弥散系数和化学位移信号比（即肿瘤信号强度指数），利用训练队列建立递归分区诊断算法。首先使用验证队列评估了基于多参数磁共振成像算法的诊断性能，然后以 4 厘米为临界值，评估了该算法与肾上腺肿瘤大小的关联性。以病理学作为参考标准，计算了该诊断算法对 ACC 和 LPAA 的诊断效果：54名患者（27名患有LPAA，27名患有ACC；37名女性；平均年龄（48.5 ± 13.3 [标准差（SD）]岁）被用作训练队列，61名患者（24名患有LPAA，37名患有ACC；47名女性；平均年龄（49 ± 11.7 [标准差（SD）]岁）被用作验证队列。在验证队列中，在不考虑病变大小的情况下，诊断算法对 ACC 与 LPAA 的诊断准确率最高（75%；46/61；95% CI：55-88）。诊断算法与肿瘤大小相关时，灵敏度最高（96%；23/24；95% CI：80-99）。单独使用诊断算法的特异性最佳（76%；28/37；95% CI：60-87）：结论：基于磁共振成像的多参数诊断算法包括表观弥散系数和肿瘤信号强度指数，有助于区分ACC和LPAA，特异性和准确性都很高。基于多参数 MRI 的诊断算法与肾上腺病变大小的关联有助于最大限度地提高多参数 MRI 诊断 ACC 的灵敏度。

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引用次数: 0

Ultra-low dose chest CT for the diagnosis of pulmonary arteriovenous malformation in patients with hereditary hemorrhagic telangiectasia. 超低剂量胸部 CT 诊断遗传性出血性毛细血管扩张症患者的肺动静脉畸形。

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-04-01 DOI: 10.1016/j.diii.2024.03.006

Jean-Etienne Delpon, J. Greffier, Hugo Lacombe, Apolline Barbe, Morgane Bouin, Fabien de Oliveira, Adeline Mansuy, L. Delagrange, Anne-Emmanuelle Fargeton, J. Beregi, Vincent Cottin, S. Dupuis-Girod, S. Si-Mohamed

引用次数: 0

Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study 心脏CT图像上两种深度学习图像重建算法的比较：一项体模研究。

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-03-01 DOI: 10.1016/j.diii.2023.10.004

Joël Greffier , Maxime Pastor , Salim Si-Mohamed , Cynthia Goutain-Majorel , Aude Peudon-Balas , Mourad Zoubir Bensalah , Julien Frandon , Jean-Paul Beregi , Djamel Dabli

Purpose

The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol.

Materials and methods

Acquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d’) was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter).

Results

Noise magnitude values were lower with PIQE than with AiCE (−13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f₅₀) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f₅₀ values were found to be close. Whatever the dose and DLR level, d’ values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d’ values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels.

Conclusion

Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.

目的：本研究的目的是根据心脏计算机断层扫描（CT）协议中的剂量水平，比较Precise IQ Engine（PIQE）和Advanced intelligent Clear IQ Engine（AiCE）算法在图像质量方面的性能。材料和方法：使用CT ACR 464体模在三个剂量水平（体积CT剂量指数：7.1/5.2/3.1mGy）下进行采集，使用前瞻性心脏CT方案。使用AiCE和PIQE三个级别（轻度、标准和重度）重建原始数据。计算了骨和丙烯酸嵌件的噪声功率谱（NPS）和基于任务的传递函数（TTF）。计算可检测性指数（d'）以模拟冠状动脉管腔（350 Hounsfield单位，直径4mm）和非钙化斑块（40 Hounsfield单元，直径2mm）的可检测性，标准水平为-20.4±4.0[SD]%，强水平为-32.6±2.6[SD]百分比）。与AiCE相比，PIQE的平均NPS空间频率向更高的频率移动（轻度水平为21.9±3.5[SD]%，标准水平为20.1±3.0[SD]%-强水平为12.5±3.5[SD]%）。对于丙烯酸嵌件，50%（f50）值的TTF值随着PIQE向比AiCE更高的频率移动，但对于骨嵌件，f50值接近。无论剂量和DLR水平如何，PIQE的两种模拟心脏病变的d’值都高于AiCE。对于模拟冠状动脉管腔，轻度的所有剂量水平的d'值平均好35.1±9.3（SD）%，标准的为43.2±5.0（SD），强的为62.6±1.2（SD）。结论：与AiCE相比，PIQE降低了噪声，提高了空间分辨率、噪声纹理和模拟心脏病变的可检测性。PIQE在心脏CT采集中似乎具有更大的剂量减少潜力。

{"title":"Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study","authors":"Joël Greffier , Maxime Pastor , Salim Si-Mohamed , Cynthia Goutain-Majorel , Aude Peudon-Balas , Mourad Zoubir Bensalah , Julien Frandon , Jean-Paul Beregi , Djamel Dabli","doi":"10.1016/j.diii.2023.10.004","DOIUrl":"10.1016/j.diii.2023.10.004","url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol.</p></div><div><h3>Materials and methods</h3><p>Acquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d’) was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter).</p></div><div><h3>Results</h3><p>Noise magnitude values were lower with PIQE than with AiCE (−13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f<sub>5</sub><sub>0</sub>) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f<sub>50</sub> values were found to be close. Whatever the dose and DLR level, d’ values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d’ values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels.</p></div><div><h3>Conclusion</h3><p>Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CT features of isolated pulmonary amyloid light-chain (AL) amyloidosis 孤立性肺淀粉样轻链（AL）淀粉样变性的 CT 特征

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-03-01 DOI: 10.1016/j.diii.2024.02.002

Adrien Lecomte, Nicole Olejarz, Emma Canniff

引用次数: 0

Spectral photon-counting CT: Hype or hope for colorectal peritoneal metastases imaging? 光谱光子计数 CT：大肠腹膜转移成像是炒作还是希望？

IF 5.5 2区医学 Q1 Medicine

Diagnostic and Interventional Imaging

Pub Date : 2024-03-01 DOI: 10.1016/j.diii.2024.01.002

Rémi Grange, Salim Si-Mohamed, Vahan Kepenekian, Sara Boccalini, Olivier Glehen, Pascal Rousset

引用次数: 0

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