ChatGPT yields low accuracy in determining LI-RADS scores based on free-text and structured radiology reports in German language.

Background: To investigate the feasibility of the large language model (LLM) ChatGPT for classifying liver lesions according to the Liver Imaging Reporting and Data System (LI-RADS) based on MRI reports, and to compare classification performance on structured vs. unstructured reports. Methods: LI-RADS classifiable liver lesions were included from German written structured and unstructured MRI reports with report of size, location, and arterial phase contrast enhancement as minimum inclusion requirements. The findings sections of the reports were propagated to ChatGPT (GPT-3.5), which was instructed to determine LI-RADS scores for each classifiable liver lesion. Ground truth was established by two radiologists in consensus. Agreement between ground truth and ChatGPT was assessed with Cohen's kappa. Test-retest reliability was assessed by passing a subset of n = 50 lesions five times to ChatGPT, using the intraclass correlation coefficient (ICC). Results: 205 MRIs from 150 patients were included. The accuracy of ChatGPT at determining LI-RADS categories was poor (53% and 44% on unstructured and structured reports). The agreement to the ground truth was higher (k = 0.51 and k = 0.44), the mean absolute error in LI-RADS scores was lower (0.5 ± 0.5 vs. 0.6 ± 0.7, p < 0.05), and the test-retest reliability was higher (ICC = 0.81 vs. 0.50), in free-text compared to structured reports, respectively, although structured reports comprised the minimum required imaging features significantly more frequently (Chi-square test, p < 0.05). Conclusions: ChatGPT attained only low accuracy when asked to determine LI-RADS scores from liver imaging reports. The superior accuracy and consistency throughout free-text reports might relate to ChatGPT's training process. Clinical relevance statement: Our study indicates both the necessity of optimization of LLMs for structured clinical data input and the potential of LLMs for creating machine-readable labels based on large free-text radiological databases.

Submillisievert Abdominal Photon-Counting CT versus Energy-integrating Detector CT for Urinary Calculi Detection: Impact on Diagnostic Confidence.

Background Contrast-unenhanced abdominal CT is the imaging standard for urinary calculi detection; however, studies comparing photon-counting detector (PCD) CT and energy-integrating detector (EID) CT dose-reduction potentials are lacking. Purpose To compare the radiation dose and image quality of optimized EID CT with those of an experimental PCD CT scan protocol including tin prefiltration in patients suspected of having urinary calculi. Materials and Methods This retrospective single-center study included patients who underwent unenhanced abdominal PCD CT or EID CT for suspected urinary caliculi between February 2022 and March 2023. Signal and noise measurements were performed at three anatomic levels (kidney, psoas, and obturator muscle). Nephrolithiasis and/or urolithiasis presence was independently assessed by three radiologists, and diagnostic confidence was recorded on a five-point scale (1, little to no confidence; 5, complete confidence). Reader agreement was determined by calculating Krippendorff α. Results A total of 507 patients (mean age, 51.7 years ± 17.4 [SD]; 317 male patients) were included (PCD CT group, 229 patients; EID CT group, 278 patients). Readers 1, 2, and 3 detected nephrolithiasis in 129, 127, and 129 patients and 94, 94, and 94 patients, whereas the readers detected urolithiasis in 113, 114, and 114 patients and 152, 153, and 152 patients in the PCD CT and EID CT groups, respectively. Regardless of protocol (PCD CT or EID CT) or calculus localization, near perfect interreader agreement was found (α ≥ 0.99; 95% CI: 0.99, 1). There was no evidence of a difference in reader confidence between PCD CT and EID CT (median confidence, 5; IQR, 5-5; P ≥ .57). The effective doses were 0.79 mSv (IQR, 0.63-0.99 mSv) and 1.39 mSv (IQR, 1.01-1.87 mSv) for PCD CT and EID CT, respectively. Despite the lower radiation exposure, the signal-to-noise ratios at the kidney, psoas, and obturator levels were 30%, 23%, and 17% higher, respectively, in the PCD CT group (P < .001). Conclusion Submillisievert abdominal PCD CT provided high-quality images for the diagnosis of urinary calculi; radiation exposure was reduced by 44% with a higher signal-to-noise ratio than with EID CT and with no evidence of a difference in reader confidence. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Nezami and Malayeri in this issue.

GPT-4 Analysis of MRI Reports in Suspected Myocarditis: A Multicenter Study.

PURPOSE: Diagnosing myocarditis relies on multimodal data including magnetic resonance imaging (MRI), clinical symptoms, and blood values. The correct interpretation and integration of MRI findings requires radiological expertise and knowledge. We aimed to investigate the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model, for report-based medical decision-making in the context of cardiac MRI for suspected myocarditis. METHODS: This retrospective study includes MRI reports from 396 patients with suspected myocarditis and eight centers, respectively. MRI reports and patient data including blood values, age, and further clinical information were provided to GPT-4 and to radiologists with 1 (Resident 1), 2 (Resident 2), and 4 years (Resident 3) of experience in cardiovascular MRI and knowledge of the 2018 Lake Louise Criteria. The final impression of the report regarding the radiological assessment of whether myocarditis is present or not was not provided. The performance of GPT-4 and of the human readers were compared to a consensus reading (two board-certified radiologists with 8 and 10 years of experience in cardiovascular MRI). Sensitivity, specificity, and accuracy were calculated. RESULTS: GPT-4 yielded an accuracy of 83%, sensitivity of 90%, and specificity of 78%, which was comparable to the physician with 1 year of experience (R1: 86%, 90%, 84%, p=.14) and lower than that of more experienced physicians (R2: 89%, 86%, 91%, p=.007 and R3: 91%, 85%, 96%, p<.001). GPT-4 and human readers showed a higher diagnostic performance when results from T1- and T2-mapping sequences were part of the reports, for Residents 1 and Resident 3 with statistical significance (p=.004 and p=.02, respectively). CONCLUSION: GPT-4 yielded good accuracy for diagnosing myocarditis based on MRI reports in a large dataset from multiple centers and therefore holds the potential to serve as a diagnostic decision supporting tool in this capacity, particularly for less experienced physicians. Further studies are required to explore the full potential and elucidate educational aspects of the integration of large language models in medical decision-making.

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

OBJECTIVES: To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). METHODS: This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. RESULTS: DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. CONCLUSION: Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. CLINICAL RELEVANCE STATEMENT: Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. KEY POINTS: Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population.

Material density dual-energy CT images: value added in early diagnosis of peritoneal carcinomatosis : Original research.

OBJECTIVE: To assess the value of material density (MD) images generated from a rapid kilovoltage-switching dual-energy CT (rsDECT) in early detection of peritoneal carcinomatosis (PC). MATERIALS AND METHODS: Thirty patients (60 ± 13 years; 24 women) with PC detected on multiple abdominal DECT scans were included. Four separate DECTs with varying findings of PC from each patient were used for qualitative/quantitative analysis, resulting in a total of 120 DECT scans (n = 30 × 4). Three radiologists independently reviewed DECT images (65 keV alone and 65 keV + MD) for diagnosis of PC (diagnostic confidence, lesion conspicuity, sharpness/delineation and image quality) using a 5-point Likert scale. Quantitative estimation of contrast-to-noise ratio (CNR) was done. Wilcoxon signed-rank test and Odds ratio calculation were used to compare between the two protocols. Inter-observer agreement was evaluated using Kappa coefficient analysis. P values < 0.05 were considered statistically significant. RESULTS: 65 keV + MD images showed a slightly higher sensitivity (89%[95%CI:84,92]) for PC detection compared with 65 keV images alone without statistical significance (84%[95%CI:78,88], p = 0.11) with the experienced reader showing significant improvement (98%[95%CI:93,100] vs. 90%[95%CI:83,94], p = 0.02). On a per-patient basis, use of MD images allowed earlier diagnosis for PC in an additional 13-23% of patients. On sub-group analysis, earlier diagnosis of PC was particularly beneficial in patients with BMI ≤ 29.9 kg/m2. 65 keV + MD images showed higher diagnostic confidence, lesion conspicuity, and lesion sharpness for the experienced reader (p < 0.001). CNR was higher in MD images (1.7 ± 0.5) than 65 keV images (0.1 ± 0.02, p < 0.001). All readers showed moderate interobserver agreement for determining PC by both protocols (κ = 0.58 and κ = 0.47). CONCLUSION: MD images allow earlier and improved detection of PC with the degree of benefit varying based on reader experience and patient body habitus.

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.

Robustness of radiomic features in healthy abdominal parenchyma of patients with repeated examinations on dual-layer dual-energy CT.

OBJECTIVES: Robustness of radiomic features in physiological tissue is an important prerequisite for quantitative analysis of tumor biology and response assessment. In contrast to previous studies which focused on different tumors with mostly short scan-re-scan intervals, this study aimed to evaluate the robustness of radiomic features in cancer-free patients and over a clinically encountered inter-scan interval. MATERIALS AND METHODS: Patients without visible tumor burden who underwent at least two portal-venous phase dual energy CT examinations of the abdomen between May 2016 and January 2020 were included, while macroscopic tumor burden was excluded based upon follow-up imaging for all patients (≥3 months). Further, patients were excluded if no follow-up imaging was available, or if the CT protocol showed deviations between repeated examinations. Circular regions of interest were placed and proofread by two board-certified radiologists (4 years and 5 years experience) within the liver (segments 3 and 6), the psoas muscle (left and right), the pancreatic head, and the spleen to obtain radiomic features from normal-appearing organ parenchyma using PyRadiomics. Radiomic feature robustness was tested using the concordance correlation coefficient with a threshold of 0.75 considered indicative for deeming a feature robust. RESULTS: In total, 160 patients with 480 repeated abdominal CT examinations (range: 2-4 per patient) were retrospectively included in this single-center, IRB-approved study. Considering all organs and feature categories, only 4.58 % (25/546) of all features were robust with the highest rate being found in the first order feature category (20.37 %, 22/108). Other feature categories (grey level co-occurrence matrix, grey level dependence matrix, grey level run length matrix, grey level size zone matrix, and neighborhood gray-tone difference matrix) yielded an overall low percentage of robust features (range: 0.00 %-1.19 %). A subgroup analysis revealed the reconstructed field of view and the X-ray tube current as determinants of feature robustness (significant differences in subgroups for all organs, p < 0.001) as well as the size of the region of interest (no significant difference for the pancreatic head with p = 0.135, significant difference with p < 0.001 for all other organs). CONCLUSION: Radiomic feature robustness obtained from cancer-free subjects with repeated examinations using a consistent protocol and CT scanner was limited, with first order features yielding the highest proportion of robust features.

Coronary Artery Calcium Scoring Using Virtual Versus True Noncontrast Images From Photon-Counting Coronary CT Angiography.

Background Coronary artery calcium scoring (CACS) for coronary artery disease requires true noncontrast (TNC) CT alongside contrast-enhanced coronary CT angiography (CCTA). Photon-counting CT provides an algorithm (PureCalcium) for reconstructing virtual noncontrast images from CCTA specifically for CACS. Purpose To assess CACS differences based on PureCalcium images derived from contrast-enhanced photon-counting CCTA compared with TNC images and evaluate the impact of these differences on the clinically relevant classification of patients into plaque burden groups. Materials and Methods Photon-counting CCTA images acquired between August 2022 and May 2023 were retrospectively identified. Agatston scores were derived from both TNC and PureCalcium images and tested for differences with use of the Wilcoxon signed-rank test. The agreement was assessed with use of equivalence tests, Bland-Altman analysis, and intraclass correlation coefficient. Plaque burden groups were established based on Agatston scores, and agreement was evaluated using weighted Cohen kappa. The dose-length product was analyzed. Results Among 170 patients (mean age, 63 years ± 13 [SD]; 92 male), 111 had Agatston scores higher than 0. Median Agatston scores did not differ between TNC and PureCalcium images (4.8 [IQR, 0-84.4; range, 0.0-2151.8] vs 2.7 [IQR, 0-90.7; range, 0.0-2377.1]; P = .99), with strong correlation (intraclass correlation coefficient, 0.98 [95% CI: 0.97, 0.99]). The equivalence test was inconclusive, with a 95% CI of 0.90, 1.19. Bland-Altman analysis showed wide repeatability limits, indicating low agreement between the two scores. With use of the PureCalcium algorithm, 125 of 170 patients (74%) were correctly classified into plaque burden groups (excellent agreement, κ = 0.88). Patients without plaque burden were misclassified at higher than normal rates (P < .001). TNC image acquisition contributed a mean of 19.7% ± 8.8 of the radiation dose of the entire examination. Conclusion PureCalcium images show potential to replace TNC images for measuring Agatston scores, thereby reducing radiation dose in CCTA. There was strong correlation in calcium scores between TNC and PureCalcium, but limited agreement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Sakuma in this issue.

Case Report: Sudden very late-onset near fatal PD1 inhibitor-associated myocarditis with out-of-hospital cardiac arrest after >2.5 years of pembrolizumab treatment.

Introduction: Immune checkpoint inhibitors have advanced the outcomes of many different types of cancer. A rare but extraordinarily severe complication of these agents resembles immune checkpoint inhibitor-related myocarditis, which typically occurs within the first few weeks after treatment initiation with a mortality of 25%-50%. Case report: A 57-year-old woman had uneventfully received pembrolizumab for metastatic non-small cell lung cancer for over 2.5 years and was admitted after an out-of-hospital cardiac arrest due to ventricular fibrillation. After successful cardiopulmonary resuscitation, the initial diagnostic work-up showed elevated cardiac enzymes and a limited left-ventricular ejection fraction, while coronary angiography did not show relevant stenosis. Despite cardiac MRI being unsuggestive of myocarditis, myocardial biopsies were obtained and histologically confirmed anti-PD-1 antibody-associated myocarditis. After the initiation of prednisone at 1 mg/kg body weight, the patient gradually recovered and was discharged three weeks later with markedly improved cardiac function. Conclusion: This case resembles the first description of a very late onset irMyocarditis, occurring over 2.5 years after the start of treatment. It demonstrates the importance of contemplating that severe immune-related toxicities with a sudden onset clinical presentation may occur even after long uneventful periods of anti-PD-1 immune checkpoint inhibitor treatment. Furthermore, it underlines the critical importance of myocardial biopsies in this setting, especially when cardiac MRI remains inconclusive. Moreover, it demonstrates the necessity and benefits of early immunosuppressive treatment if immune-related myocarditis is considered a differential diagnosis.

Immediate angiographic control after intra-arterial nimodipine administration underestimates the vasodilatory effect.

Intra-arterial nimodipine administration is a widely used rescue therapy for cerebral vasospasm. Although it is known that its effect sets in with delay, there is little evidence in current literature. Our aim was to prove that the maximal vasodilatory effect is underestimated in direct angiographic controls. We reviewed all cases of intra-arterial nimodipine treatment for subarachnoid hemorrhage-related cerebral vasospasm between January 2021 and December 2022. Inclusion criteria were availability of digital subtraction angiography runs before and after nimodipine administration and a delayed run for the most affected vessel at the end of the procedure to decide on further escalation of therapy. We evaluated nimodipine dose, timing of administration and vessel diameters. Delayed runs were performed in 32 cases (19 patients) with a mean delay of 37.6 (± 16.6) min after nimodipine administration and a mean total nimodipine dose of 4.7 (± 1.2) mg. Vessel dilation was more pronounced in delayed vs. immediate controls, with greater changes in spastic vessel segments (n = 31: 113.5 (± 78.5%) vs. 32.2% (± 27.9%), p < 0.0001) vs. non-spastic vessel segments (n = 32: 23.1% (± 13.5%) vs. 13.3% (± 10.7%), p < 0.0001). In conclusion intra-arterially administered nimodipine seems to exert a delayed vasodilatory effect, which should be considered before escalation of therapy.

Automated manufacture of ΔNPM1 TCR-engineered T cells for AML therapy.

Acute myeloid leukemia (AML) is a heterogeneous malignancy that requires further therapeutic improvement, especially for the elderly and for subgroups with poor prognosis. A recently discovered T cell receptor (TCR) targeting mutant nucleophosmin 1 (ΔNPM1) presents an attractive option for the development of a cancer antigen-targeted cellular therapy. Manufacturing of TCR-modified T cells, however, is still limited by a complex, time-consuming, and laborious procedure. Therefore, this study specifically addressed the requirements for a scaled manufacture of ΔNPM1-specific T cells in an automated, closed, and good manufacturing practice-compliant process. Starting from cryopreserved leukapheresis, 2E8 CD8-positive T cells were enriched, activated, lentivirally transduced, expanded, and finally formulated. By adjusting and optimizing culture conditions, we additionally reduced the manufacturing time from 12 to 8 days while still achieving a clinically relevant yield of up to 5.5E9 ΔNPM1 TCR-engineered T cells. The cellular product mainly consisted of highly viable CD8-positive T cells with an early memory phenotype. ΔNPM1 TCR CD8 T cells manufactured with the optimized process showed specific killing of AML in vitro and in vivo. The process has been implemented in an upcoming phase 1/2 clinical trial for the treatment of NPM1-mutated AML.

An anti-CD19/CTLA-4 switch improves efficacy and selectivity of CAR T cells targeting CD80/86-upregulated DLBCL.

Chimeric antigen receptor T cell (CAR T) therapy is a potent treatment for relapsed/refractory (r/r) B cell lymphomas but provides lasting remissions in only ∼40% of patients and is associated with serious adverse events. We identify an upregulation of CD80 and/or CD86 in tumor tissue of (r/r) diffuse large B cell lymphoma (DLBCL) patients treated with tisagenlecleucel. This finding leads to the development of the CAR/CCR (chimeric checkpoint receptor) design, which consists of a CD19-specific first-generation CAR co-expressed with a recombinant CTLA-4-linked receptor with a 4-1BB co-stimulatory domain. CAR/CCR T cells demonstrate superior efficacy in xenograft mouse models compared with CAR T cells, superior long-term activity, and superior selectivity in in vitro assays with non-malignant CD19+ cells. In addition, immunocompetent mice show an intact CD80-CD19+ B cell population after CAR/CCR T cell treatment. The results reveal the CAR/CCR design as a promising strategy for further translational study.

Intra-patient variability of iodine quantification across different dual-energy CT platforms: assessment of normalization techniques.

OBJECTIVES: To investigate intra-patient variability of iodine concentration (IC) between three different dual-energy CT (DECT) platforms and to test different normalization approaches. METHODS: Forty-four patients who underwent portal venous phase abdominal DECT on a dual-source (dsDECT), a rapid kVp switching (rsDECT), and a dual-layer detector platform (dlDECT) during cancer follow-up were retrospectively included. IC in the liver, pancreas, and kidneys and different normalized ICs (NICPV:portal vein; NICAA:abdominal aorta; NICALL:overall iodine load) were compared between the three DECT scanners for each patient. A longitudinal mixed effects analysis was conducted to elucidate the effect of the scanner type, scan order, inter-scan time, and contrast media amount on normalized iodine concentration. RESULTS: Variability of IC was highest in the liver (dsDECT vs. dlDECT 28.96 (14.28-46.87) %, dsDECT vs. rsDECT 29.08 (16.59-62.55) %, rsDECT vs. dlDECT 22.85 (7.52-33.49) %), and lowest in the kidneys (dsDECT vs. dlDECT 15.76 (7.03-26.1) %, dsDECT vs. rsDECT 15.67 (8.86-25.56) %, rsDECT vs. dlDECT 10.92 (4.92-22.79) %). NICALL yielded the best reduction of IC variability throughout all tissues and inter-scanner comparisons, yet did not reduce the variability between dsDECT vs. dlDECT and rsDECT, respectively, in the liver. The scanner type remained a significant determinant for NICALL in the pancreas and the liver (F-values, 12.26 and 23.78; both, p < 0.0001). CONCLUSIONS: We found tissue-specific intra-patient variability of IC across different DECT scanner types. Normalization mitigated variability by reducing physiological fluctuations in iodine distribution. After normalization, the scanner type still had a significant effect on iodine variability in the pancreas and liver. CLINICAL RELEVANCE STATEMENT: Differences in iodine quantification between dual-energy CT scanners can partly be mitigated by normalization, yet remain relevant for specific tissues and inter-scanner comparisons, which should be taken into account at clinical routine imaging. KEY POINTS: • Iodine concentration showed the least variability between scanner types in the kidneys (range 10.92-15.76%) and highest variability in the liver (range 22.85-29.08%). • Normalizing tissue-specific iodine concentrations against the overall iodine load yielded the greatest reduction of variability between scanner types for 2/3 inter-scanner comparisons in the liver and for all (3/3) inter-scanner comparisons in the kidneys and pancreas, respectively. • However, even after normalization, the dual-energy CT scanner type was found to be the factor significantly influencing variability of iodine concentration in the liver and pancreas.

Diagnostic performance and feasibility of dual-layer detector dual-energy CT for characterization of urinary stones in patients of different sizes.

BACKGROUND: Urinary stones are frequently encountered in urology and are typically identified using non-contrast CT scans. Dual-energy CT (DECT) is a valuable imaging technique that produces material-specific images and allows for precise assessment of stone composition by estimating the effective atomic number (Zeff), a capability not achievable with the conventional single-energy CT's attenuation measurement method. PURPOSE: To investigate the diagnostic performance and image quality of dual-layer detector DECT (dlDECT) in characterizing urinary stones in patients of different sizes. METHODS: All consecutive dlDECT examinations with stone protocol and presence of urinary stones between July 2018 and November 2019 were retrospectively evaluated. Two radiologists independently reviewed 120 kVp and color-overlay Zeff images to determine stone composition (reference standard = crystallography) and image quality. The objective analysis included image noise and Zeff values measurement. RESULTS: A total of 739 urinary stones (median size 3.7 mm, range 1-35 mm) were identified on 177 CT examinations from 155 adults (mean age, 57 ± 15 years, 80 men, median weight 82.6 kg, range 42.6-186.9 kg). Using color-overlay Zeff images, the radiologists could subjectively interpret the composition in all stones ≥ 3 mm (n = 491). For stones with available reference standards (n = 74), dlDECT yielded a sensitivity of 80% (95%CI 44-98%) and a specificity of 98% (95%CI 92-100%) in visually discriminating uric acid from non-uric acid stones. Patients weighing > 90 kg and ≤ 90 kg had similar stone characterizability (p = 0.20), with 86% of stones characterized in the > 90 kg group and 87% in the ≤ 90 kg group. All examinations throughout various patients' weights revealed acceptable image quality. A Zeff cutoff of 7.66 accurately distinguished uric acid from non-uric acid stones (AUC = 1.00). Zeff analysis revealed AUCs of 0.78 and 0.91 for differentiating calcium-based stones from other non-uric stones and all stone types, respectively. CONCLUSION: dlDECT allowed accurate differentiation of uric acid and non-uric acid stones among patients with different body sizes with acceptable image quality. CLINICAL IMPACT: The ability to accurately differentiate uric acid stones from non-uric acid stones using color-overlay Zeff images allows for better tailored treatment strategies, helping to choose appropriate interventions and prevent potential complications related to urinary stones in patient care.

Compressed SENSE accelerated 3D single-breath-hold late gadolinium enhancement cardiovascular magnetic resonance with isotropic resolution: clinical evaluation.

Aim: The purpose of this study was to investigate the clinical application of Compressed SENSE accelerated single-breath-hold LGE with 3D isotropic resolution compared to conventional LGE imaging acquired in multiple breath-holds. Material & Methods: This was a retrospective, single-center study including 105 examinations of 101 patients (48.2 ± 16.8 years, 47 females). All patients underwent conventional breath-hold and 3D single-breath-hold (0.96 × 0.96 × 1.1 mm3 reconstructed voxel size, Compressed SENSE factor 6.5) LGE sequences at 1.5 T in clinical routine for the evaluation of ischemic or non-ischemic cardiomyopathies. Two radiologists independently evaluated the left ventricle (LV) for the presence of hyperenhancing lesions in each sequence, including localization and transmural extent, while assessing their scar edge sharpness (SES). Confidence of LGE assessment, image quality (IQ), and artifacts were also rated. The impact of LV ejection fraction (LVEF), heart rate, body mass index (BMI), and gender as possible confounders on IQ, artifacts, and confidence of LGE assessment was evaluated employing ordinal logistic regression analysis. Results: Using 3D single-breath-hold LGE readers detected more hyperenhancing lesions compared to conventional breath-hold LGE (n = 246 vs. n = 216 of 1,785 analyzed segments, 13.8% vs. 12.1%; p < 0.0001), pronounced at subendocardial, midmyocardial, and subepicardial localizations and for 1%-50% of transmural extent. SES was rated superior in 3D single-breath-hold LGE (4.1 ± 0.8 vs. 3.3 ± 0.8; p < 0.001). 3D single-breath-hold LGE yielded more artifacts (3.8 ± 1.0 vs. 4.0 ± 3.8; p = 0.002) whereas IQ (4.1 ± 1.0 vs. 4.2 ± 0.9; p = 0.122) and confidence of LGE assessment (4.3 ± 0.9 vs. 4.3 ± 0.8; p = 0.374) were comparable between both techniques. Female gender negatively influenced artifacts in 3D single-breath-hold LGE (p = 0.0028) while increased heart rate led to decreased IQ in conventional breath-hold LGE (p = 0.0029). Conclusions: In clinical routine, Compressed SENSE accelerated 3D single-breath-hold LGE yields image quality and confidence of LGE assessment comparable to conventional breath-hold LGE while providing improved delineation of smaller LGE lesions with superior scar edge sharpness. Given the fast acquisition of 3D single-breath-hold LGE, the technique holds potential to drastically reduce the examination time of CMR.