Impact of Photon Counting Detector CT Derived Virtual Monoenergetic Images on the Diagnosis of Pulmonary Embolism

Purpose: To assess the impact of virtual-monoenergetic-image (VMI) energies on the diagnosis of pulmonary embolism (PE) in photon-counting-detector computed-tomography (PCD-CT). Methods: Eighty patients (median age 60.4 years) with suspected PE were retrospectively included. Scans were performed on PCD-CT in the multi-energy mode at 120 kV. VMIs from 40-70 keV in 10 keV intervals were reconstructed. CT-attenuation was measured in the pulmonary trunk and the main branches of the pulmonary artery. Signal-to-noise (SNR) ratio was calculated. Two radiologists evaluated subjective-image-quality (noise, vessel-attenuation and sharpness; five-point-Likert-scale, non-diagnostic-excellent), the presence of hardening artefacts and presence/visibility of PE. Results: Signal was highest at the lowest evaluated VMI (40 keV; 1053.50 HU); image noise was lowest at the highest VMI (70 keV; 15.60 HU). Highest SNR was achieved at the lowest VMI (p < 0.05). Inter-reader-agreement for subjective analysis was fair to excellent (k = 0.373-1.000; p < 0.001). Scores for vessel-attenuation and sharpness were highest at 40 keV (both:5, range 4/3-5; k = 1.000); scores for image-noise were highest at 70 keV (4, range 3-5). The highest number of hardening artifacts were reported at 40 keV (n = 22; 28%). PE-visualization was rated best at 50 keV (4.7; range 4-5) and decreased with increasing VMI-energy (r = -0.558; p < 0.001). Conclusions: While SNR was best at 40 keV, subjective PE visibility was rated highest at 50 keV, potentially owing to the lower image noise and hardening artefacts

Quantitative evaluation of aortic valve regurgitation in 4D flow cardiac magnetic resonance: at which level should we measure?

PURPOSE To find the best level to measure aortic flow for quantification of aortic regurgitation (AR) in 4D flow CMR. METHODS In 27 congenital heart disease patients with AR (67% male, 31 ± 16 years) two blinded observers measured antegrade, retrograde, net aortic flow volumes and regurgitant fractions at 6 levels in 4D flow: (1) below the aortic valve (AV), (2) at the AV, (3) at the aortic sinus, (4) at the sinotubular junction, (5) at the level of the pulmonary arteries (PA) and (6) below the brachiocephalic trunk. 2D phase contrast (2DPC) sequences were acquired at the level of PA. All patients received prior transthoracic echocardiography (TTE) with AR severity grading according to a recommended multiparametric approach. RESULTS After assigning 2DPC measurements into AR grading, agreement between TTE AR grading and 2DPC was good (κ = 0.88). In 4D flow, antegrade flow was similar between the six levels (p = 0.87). Net flow was higher at level 1-2 than at levels 3-6 (p < 0.05). Retrograde flow and regurgitant fraction at level 1-2 were lower compared to levels 3-6 (p < 0.05). Reproducibility (inter-reader agreement: ICC 0.993, 95% CI 0.986-0.99; intra-reader agreement: ICC 0.982, 95%CI 0.943-0.994) as well as measurement agreement between 4D flow and 2DPC (ICC 0.994; 95%CI 0.989 - 0.998) was best at the level of PA. CONCLUSION For estimating severity of AR in 4D flow, best reproducibility along with best agreement with 2DPC measurements can be expected at the level of PA. Measurements at AV or below AV might underestimate AR

Three-dimensional Whole-Heart Cardiac MRI Sequence for Measuring Trabeculation in Left Ventricular Noncompaction

PURPOSE To compare three-dimensional (3D) whole-heart MRI with isotropic submillimeter resolution with standard two-dimensional (2D) cine MRI in measuring the bilayered myocardium in left ventricular noncompaction (LVNC). MATERIALS AND METHODS Twenty-four patients with LVNC (mean age, 42 years ± 16 [SD]) were retrospectively enrolled between October 2011 and July 2020. Compacted myocardium (CM) and noncompacted myocardium (NCM) were measured in long axis (Petersen approach) and short axis (Jacquier approach) at 3D whole-heart and 2D cine MRI by two independent readers. Image quality (1 = excellent, 2 = adequate, 3 = nondiagnostic), considering discrimination between NCM and CM and CM and adjacent tissue, was evaluated. Pearson, Spearman, and intraclass correlation tests were used as statistical tests. RESULTS In long-axis measurements, the correlation between both sequences was moderate to strong for CM (Pearson, 0.66-0.79; Spearman, 0.61-0.68) and strong to very strong for NCM (Pearson, 0.90-0.97; Spearman, 0.77-0.91). Intraclass correlation coefficient (ICC) in 3D whole-heart MRI was 0.90 (95% CI: 0.78, 0.95) for CM and 0.94 (95% CI: 0.84, 0.97) for NCM, while ICC in 2D cine MRI was 0.77 (95% CI: 0.55, 0.89) for CM and 0.87 (95% CI: 0.72, 0.94) for NCM. Short-axis CM and NCM measurements had a strong to very strong correlation between both sequences (Pearson, 0.86-0.98; Spearman, 0.82-0.98). ICC in 3D whole-heart MRI was 0.96 (95% CI: 0.94, 0.99) for CM and 0.98 (95% CI: 0.97, 0.99) for NCM, while ICC in 2D cine MRI was 0.82 (95% CI: 0.63, 0.92) for CM and 0.87 (95% CI: 0.72, 0.94) for NCM. 3D whole-heart MRI demonstrated higher image quality than did 2D cine MRI (P < .001). CONCLUSION 3D whole-heart MRI revealed higher image quality, with better structure discrimination and interobserver agreement in LVNC measurements, compared with standard 2D cine images.Keywords: MR Imaging, Cardiac, Cardiovascular Magnetic Resonance, Left Ventricular Noncompaction, Free-breathing Imaging Technique Supplemental material is available for this article. © RSNA, 2022See also the commentary by Jensen and Petersen in this issue

Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification

Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features. To address this, we propose a machine learning approach that utilizes CLIP, a multimodal (image and text) self-supervised model, for ILD classification. We extensively integrate zero-shot CLIP throughout our workflow, starting from the initial extraction of image patches from volumetric CT scans and proceeding to ILD classification using "patch montages". Furthermore, we investigate how domain adaptive pretraining (DAPT) CLIP with task-specific images (CT "patch montages" extracted with ILD-specific prompts for CLIP) and/or text (lung-specific sections of radiology reports) affects downstream ILD classification performance. By leveraging CLIP-extracted "patch montages" and DAPT, we achieve strong zero-shot ILD classification results, including an AUROC of 0.893, without the need for any labeled training data. This work highlights the versatility and potential of multimodal models like CLIP for medical image classification tasks where labeled data is scarce.Comment: 11 pages, 11 figure

Photon-Counting Detector CT Angiography for Endoleak Detection After Endovascular Aortic Repair: Triphasic CT With True Noncontrast Versus Biphasic CT With Virtual Noniodine Imaging

OBJECTIVES: The aim of this study was to compare image quality and endoleak detection after endovascular abdominal aortic aneurysm repair between a triphasic computed tomography (CT) with true noncontrast (TNC) and a biphasic CT with virtual noniodine (VNI) images on photon-counting detector CT (PCD-CT). MATERIALS AND METHODS: Adult patients after endovascular abdominal aortic aneurysm repair who received a triphasic examination (TNC, arterial, venous phase) on a PCD-CT between August 2021 and July 2022 were retrospectively included. Endoleak detection was evaluated by 2 blinded radiologists on 2 different readout sets (triphasic CT with TNC-arterial-venous vs biphasic CT with VNI-arterial-venous). Virtual noniodine images were reconstructed from the venous phase. The radiologic report with additional confirmation by an expert reader served as reference standard for endoleak presence. Sensitivity, specificity, and interreader agreement (Krippendorf α) were calculated. Image noise was assessed subjectively in patients using a 5-point scale and objectively calculating the noise power spectrum in a phantom. RESULTS: One hundred ten patients (7 women; age, 76 ± 8 years) with 41 endoleaks were included. Endoleak detection was comparable between both readout sets with a sensitivity and specificity of 0.95/0.84 (TNC) versus 0.95/0.86 (VNI) for reader 1 and 0.88/0.98 (TNC) versus 0.88/0.94 (VNI) for reader 2. Interreader agreement for endoleak detection was substantial (TNC: 0.716, VNI: 0.756). Subjective image noise was comparable between TNC and VNI (4; IQR [4, 5] vs 4; IQR [4, 5], P = 0.44). In the phantom, noise power spectrum peak spatial frequency was similar between TNC and VNI (both f peak = 0.16 mm -1 ). Objective image noise was higher in TNC (12.7 HU) as compared with VNI (11.5 HU). CONCLUSIONS: Endoleak detection and image quality were comparable using VNI images in biphasic CT as compared with TNC images in triphasic CT offering the possibility to reduce scan phases and radiation exposure

Diagnostic performance of 3D cardiac magnetic resonance perfusion in elderly patients for the detection of coronary artery disease as compared to fractional flow reserve

OBJECTIVES: In patients of advanced age, the feasibility of myocardial ischemia testing might be limited by age-related comorbidities and falling compliance abilities. Therefore, we aimed to test the accuracy of 3D cardiac magnetic resonance (CMR) stress perfusion in the elderly population as compared to reference standard fractional flow reserve (FFR). METHODS: Fifty-six patients at age 75 years or older (mean age 79 ± 4 years, 35 male) underwent 3D CMR perfusion imaging and invasive coronary angiography with FFR in 5 centers using the same study protocol. The diagnostic accuracy of CMR was compared to a control group of 360 patients aged below 75 years (mean age 61 ± 9 years, 262 male). The percentage of myocardial ischemic burden (MIB) relative to myocardial scar burden was further analyzed using semi-automated software. RESULTS: Sensitivity, specificity, and positive and negative predictive values of 3D perfusion CMR deemed similar for both age groups in the detection of hemodynamically relevant (FFR 0.05 all). While MIB was larger in the elderly patients (15% ± 17% vs. 9% ± 13%), the diagnostic accuracy of 3D CMR perfusion was high in both elderly and non-elderly populations to predict pathological FFR (AUC: 0.906 and 0.866). CONCLUSIONS: 3D CMR perfusion has excellent diagnostic accuracy for the detection of hemodynamically relevant coronary stenosis, independent of patient age. KEY POINTS • The increasing prevalence of coronary artery disease in elderly populations is accompanied with a larger ischemic burden of the myocardium as compared to younger individuals. • 3D cardiac magnetic resonance perfusion imaging predicts pathological fractional flow reserve in elderly patients aged ≥ 75 years with high diagnostic accuracy. • Ischemia testing with 3D CMR perfusion imaging has similarly high accuracy in the elderly as in younger patients and it might be particularly useful when other non-invasive techniques are limited by aging-related comorbidities and falling compliance abilities

RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, and clinical text) and via prompting (zero-shot, in-context learning) or parameter-efficient fine-tuning (prefix tuning, LoRA). Our results on the MIMIC-III dataset consistently demonstrate best performance by maximally adapting to the task via pretraining on clinical text and parameter-efficient fine-tuning on RRS examples. Importantly, this method fine-tunes a mere 0.32% of parameters throughout the model, in contrast to end-to-end fine-tuning (100% of parameters). Additionally, we study the effect of in-context examples and out-of-distribution (OOD) training before concluding with a radiologist reader study and qualitative analysis. Our findings highlight the importance of domain adaptation in RRS and provide valuable insights toward developing effective natural language processing solutions for clinical tasks.Comment: 12 pages, 9 figure

Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine.Comment: 23 pages, 22 figure