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OBJECTIVE: To evaluate the impact of the digital mammography imaging system on overall background enhancement on recombined contrast-enhanced spectral mammography (CESM) images, the overall background enhancement of two different mammography systems was compared. METHODS: In a retrospective single-center study, CESM images of n = 129 female patients who underwent CESM between 2016 and 2019 were analyzed independently by two radiologists. Two mammography machines of different manufacturers were compared qualitatively using a Likert-scale from 1 (minimal) to 4 (marked overall background enhancement) and quantitatively by placing a region of interest and measuring the intensity enhancement. Lesion conspicuity was analyzed using a Likert-scale from 1 (lesion not reliably distinguishable) to 5 (excellent lesion conspicuity). A multivariate regression was performed to test for potential biases on the quantitative results. RESULTS: Significant differences in qualitative background enhancement measurements between machines A and B were observed for both readers (p = 0.003 and p < 0.001). The quantitative evaluation showed significant differences in background enhancement with an average difference of 75.69 (99%-CI [74.37, 77.02]; p < 0.001). Lesion conspicuity was better for machine A for the first and second reader respectively (p = 0.009 and p < 0.001). The factor machine was the only influencing factor (p < 0.001). The factors contrast agent, breast density, age, and menstrual cycle could be excluded as potential biases. CONCLUSION: Mammography machines seem to significantly influence overall background enhancement qualitatively and quantitatively; thus, an impact on diagnostic accuracy appears possible. KEY POINTS: ⢠Overall background enhancement on CESM differs between different vendors qualitatively and quantitatively. ⢠Our retrospective single-center study showed consistent results of the qualitative and quantitative data analysis of overall background enhancement. ⢠Lesion conspicuity is higher in cases of lower background enhancement on CESM.
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Neoplasias da Mama , Mamografia , Humanos , Feminino , Estudos Retrospectivos , Mamografia/métodos , Meios de Contraste/farmacologia , Densidade da Mama , Projetos de Pesquisa , Neoplasias da Mama/diagnóstico por imagem , Sensibilidade e EspecificidadeRESUMO
BACKGROUND: Macromastia, micromastia and breast asymmetry have an impact on health and quality of life. However, there is scarce information addressing breast size and asymmetry frequency distribution in reference populations. OBJECTIVE: The current study aims to identify factors that influence breast size and symmetry and classifies abnormal breast sizes and breast asymmetries in an adult German population. METHODS: Breast base dimensions, breast volume, symmetry, and other breast anthropometric parameters of 400 German female patients were determined in a retrospective review of the MRI archives at our institution. Professional medical MRI-segmentation software was used for volume measurement. RESULTS: A total of 400 Patients were retrospectively enrolled. The patients had a mean age of 50 ± 12 years (min: 24; max: 82), mean BMI of 25.0 ± 5.0 (min: 14.7, max: 45.6), and a mean total breast volume of 976 ml (right: 973 ml, min: 64, max: 4777; left: 979 ml, min: 55, max: 4670). The strongest correlation of breast volume was observed with BMI (r = 0.834, p < 0.001), followed by breast base width (r = 0.799, p < 0.001). Smaller breasts have higher breast volume asymmetry ratios (r = - 0.124, p < 0.014). For a BMI between 18.5 and 24.9 kg/m2, micromastia is defined by breast volumes below 250 ml (5th percentile) and macromastia by volumes above 1250 ml (95th percentile). Abnormal breast volume asymmetry (< 5th and > 95th percentile) is equivalent to an absolute difference of approximately 25% relative to the smallest side (bidirectional asymmetry ratio 5th percentile - 19%; 95th percentile 26%). CONCLUSION: This study provides normative data of German women, as well as selected size-for-BMI percentiles and asymmetry ratio percentiles. The normative data may help to establish transparent and objective coverage criteria for health insurances. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Mama/anormalidades , Hipertrofia , Mamoplastia , Adulto , Feminino , Humanos , Pessoa de Meia-Idade , Estudos de Coortes , Estudos Retrospectivos , Mamoplastia/métodos , Qualidade de Vida , Resultado do Tratamento , EstéticaRESUMO
BACKGROUND. Lower extremity external fixators have complex geometries that induce pronounced metal artifact on CT. Iterative metal artifact reduction (iMAR) algorithms help reduce such artifact, although no dedicated iMAR preset exists for external fixators. OBJECTIVE. The purpose of our study was to compare iMAR presets for CT examinations in terms of quantitative metal artifact burden and subjective image quality in patients with external fixators for complex lower extremity fractures. METHODS. This retrospective study included 72 CT examinations in 56 patients (20 women, 36 men; mean age, 56 ± 18 [SD] years) with lower extremity external fixators (regular, hybrid, or monotube). Examinations were reconstructed without iMAR (hereafter referred to as "noMAR") and with three iMAR presets (iMARspine, iMARhip, iMARextremity). A radiology resident quantified metal artifact burden using software. Two radiology residents independently assessed overall image quality and diagnostic confidence using 4-point scales (4 = excellent [highest quality or highest confidence]). Techniques were compared using Bonferroni-corrected post hoc tests. Interreader agreement was assessed by intraclass correlation coefficients (ICCs). A post hoc multinomial regression model was used for predicting overall image quality. RESULTS. Mean quantitative metal artifact burden was 100,816 ± 45,558 for noMAR, 88,889 ± 44,028 for iMARspine, 82,295 ± 41,983 for iMARhip, and 81,956 ± 41,890 for iMARextremity. Overall image quality yielded an ICC of 0.94 or greater. Using pooled reader data, median overall image quality score for the regular fixator was 2 (noMAR), 3 (iMARspine and iMARhip), and 4 (iMARextremity); for the hybrid fixator, 1 (noMAR), 2 (iMARspine), and 3 (iMARhip and iMARextremity); and for the monotube fixator, 2 (noMAR), 3 (iMARspine and iMARhip), and 4 (iMARextremity). Metal artifact burden was lower and overall image quality was higher (p < .05) for iMARhip and iMARextremity than noMAR and iMARspine for all fixators (aside from image quality of iMARhip and iMARextremity vs iMARspine for regular fixators) but were not different (all, p > .05) between iMARhip and iMARextremity. Median diagnostic confidence was 4 for all fixators and reconstructions. Independent predictors of overall quality relative to noMAR were iMARspine (odds ratio [OR] = 1.92-5.51), iMARhip (OR = 5.56-31.10), and iMARextremity (OR = 7.07-38.21). All iMAR presets introduced new reconstruction artifacts for all examinations for both readers. CONCLUSION. For the three fixator types, iMARhip and iMARextremity achieved greatest metal artifact burden reduction and highest subjective image quality, although both introduced new reconstruction artifacts. CLINICAL IMPACT. CT using the two identified iMAR presets may facilitate perioperative management of external fixators.
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Artefatos , Fixadores Externos , Fixação de Fratura/métodos , Fraturas Ósseas/diagnóstico por imagem , Fraturas Ósseas/terapia , Extremidade Inferior/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Extremidade Inferior/lesões , Masculino , Metais , Pessoa de Meia-Idade , Estudos Retrospectivos , Adulto JovemRESUMO
PURPOSE: To assess the diagnostic accuracy of ChatGPT-4V in interpreting a set of four chest CT slices for each case of COVID-19, non-small cell lung cancer (NSCLC), and control cases, thereby evaluating its potential as an AI tool in radiological diagnostics. MATERIALS AND METHODS: In this retrospective study, 60 CT scans from The Cancer Imaging Archive, covering COVID-19, NSCLC, and control cases were analyzed using ChatGPT-4V. A radiologist selected four CT slices from each scan for evaluation. ChatGPT-4V's interpretations were compared against the gold standard diagnoses and assessed by two radiologists. Statistical analyses focused on accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), along with an examination of the impact of pathology location and lobe involvement. RESULTS: ChatGPT-4V showed an overall diagnostic accuracy of 56.76%. For NSCLC, sensitivity was 27.27% and specificity was 60.47%. In COVID-19 detection, sensitivity was 13.64% and specificity of 64.29%. For control cases, the sensitivity was 31.82%, with a specificity of 95.24%. The highest sensitivity (83.33%) was observed in cases involving all lung lobes. The chi-squared statistical analysis indicated significant differences in Sensitivity across categories and in relation to the location and lobar involvement of pathologies. CONCLUSION: ChatGPT-4V demonstrated variable diagnostic performance in chest CT interpretation, with notable proficiency in specific scenarios. This underscores the challenges of cross-modal AI models like ChatGPT-4V in radiology, pointing toward significant areas for improvement to ensure dependability. The study emphasizes the importance of enhancing these models for broader, more reliable medical use.
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BACKGROUND: Neck computed tomography (NCT) is essential for diagnosing suspected neck tumors and abscesses, but radiation exposure can be an issue. In conventional reconstruction techniques, limiting radiation dose comes at the cost of diminished diagnostic accuracy. Therefore, this study aimed to evaluate the effects of an AI-based denoising post-processing software solution in low-dose neck computer tomography. MATERIALS AND METHODS: From 01 September 2023 to 01 December 2023, we retrospectively included patients with clinically suspected neck tumors from the same single-source scanner. The scans were reconstructed using Advanced Modeled Iterative Reconstruction (Original) at 100% and simulated 50% and 25% radiation doses. Each dataset was post-processed using a novel denoising software solution (Denoising). Three radiologists with varying experience levels subjectively rated image quality, diagnostic confidence, sharpness, and contrast for all pairwise combinations of radiation dose and reconstruction mode in a randomized, blinded forced-choice setup. Objective image quality was assessed using ROI measurements of mean CT numbers, noise, and a contrast-to-noise ratio (CNR). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. RESULTS: At each radiation dose level, pairwise comparisons showed significantly lower image noise and higher CNR for Denoising than for Original (p < 0.001). In subjective analysis, image quality, diagnostic confidence, sharpness, and contrast were significantly higher for Denoising than for Original at 100 and 50 % (p < 0.001). However, there were no significant differences in the subjective ratings between Original 100 % and Denoising 25 % (p = 0.906). CONCLUSIONS: The investigated denoising algorithm enables diagnostic-quality neck CT images with radiation doses reduced to 25% of conventional levels, significantly minimizing patient exposure.
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PURPOSE: Computed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning-based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative reconstruction (IR) methods. However, most comparative studies employ low-dose simulations due to ethical constraints. We used real intraindividual animal scans to investigate the dose-reduction capabilities of a DLD algorithm in comparison to IR. MATERIALS AND METHODS: Fourteen veterinarian-sedated alive pigs underwent 2 CT scans on the same 3rd generation dual-source scanner with two months between each scan. Four additional scans ensued each time, with mAs reduced to 50 %, 25 %, 10 %, and 5 %. All scans were reconstructed ADMIRE levels 2 (IR2) and a novel DLD algorithm, resulting in 280 datasets. Objective image quality (CT numbers stability, noise, and contrast-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (-1 = inferior, 0 = equal, 1 = superior). The points were averaged for a semiquantitative score, and inter-rater agreement was measured using Spearman's correlation coefficient and adequately corrected mixed-effects modeling analyzed objective and subjective image quality. RESULTS: Neither dose-reduction nor reconstruction method negatively impacted CT number stability (p > 0.999). In objective image quality assessment, the lowest radiation dose achievable by DLD when comparing noise (p = 0.544) and CNR (p = 0.115) to 100 % IR2 was 25 %. Overall, inter-rater agreement of the subjective image quality ratings was strong (r ≥ 0.69, mean 0.93 ± 0.05, 95 % CI 0.92-0.94; each p < 0.001), and subjective assessments corroborated that DLD at 25 % radiation dose was comparable to 100 % IR2 in image quality, sharpness, and contrast (p ≥ 0.281). CONCLUSIONS: The DLD algorithm can achieve image quality comparable to the standard IR method but with a significant dose reduction of up to 75%. This suggests a promising avenue for lowering patient radiation exposure without sacrificing diagnostic quality.
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Aprendizado Profundo , Humanos , Animais , Suínos , Doses de Radiação , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Modelos AnimaisRESUMO
RATIONALE AND OBJECTIVES: To determine the impact on acquisition time reduction and image quality of a deep learning (DL) reconstruction for accelerated diffusion-weighted imaging (DWI) of the pelvis at 1.5 T compared to standard DWI. MATERIALS AND METHODS: A total of 55 patients (mean age, 61 ± 13 years; range, 27-89; 20 men, 35 women) were consecutively included in this retrospective, monocentric study between February and November 2022. Inclusion criteria were (1) standard DWI (DWIS) in clinically indicated magnetic resonance imaging (MRI) at 1.5 T and (2) DL-reconstructed DWI (DWIDL). All patients were examined using the institution's standard MRI protocol according to their diagnosis including DWI with two different b-values (0 and 800 s/mm2) and calculation of apparent diffusion coefficient (ADC) maps. Image quality was qualitatively assessed by four radiologists using a visual 5-point Likert scale (5 = best) for the following criteria: overall image quality, noise level, extent of artifacts, sharpness, and diagnostic confidence. The qualitative scores for DWIS and DWIDL were compared with the Wilcoxon signed-rank test. RESULTS: The overall image quality was evaluated to be significantly superior in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .05). The extent of noise was evaluated to be significantly less in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .001). No significant differences were found regarding artifacts, lesion detectability, sharpness of organs, and diagnostic confidence (P > .05). Acquisition time for DWIS was 2:06 minutes, and simulated acquisition time for DWIDL was 1:12 minutes. CONCLUSION: DL image reconstruction improves image quality, and simulation results suggest that a reduction in acquisition time for diffusion-weighted MRI of the pelvis at 1.5 T is possible.
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Aprendizado Profundo , Masculino , Humanos , Feminino , Pessoa de Meia-Idade , Idoso , Estudos Retrospectivos , Razão Sinal-Ruído , Reprodutibilidade dos Testes , Imagem de Difusão por Ressonância Magnética/métodos , Pelve/diagnóstico por imagem , Artefatos , Imageamento por Ressonância MagnéticaRESUMO
OBJECTIVE: Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-DixonDL). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). METHODS: This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed. RESULTS: Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-DixonDL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-DixonDL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-DixonDL (P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-DixonDL technique (P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-DixonDL. Interreader agreement between VIBE-Dixon and VIBE-DixonDL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXONDL was observed in both the precontrast (P = 0.025) and postcontrast images (P < 0.001). Also, an increase of splenic SNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.34 and P = 0.003, respectively). Similarly, an increase of pancreas CNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.557 and P = 0.026, respectively). CONCLUSIONS: The prospectively accelerated, DL-enhanced VIBE with Dixon fat suppression was clinically feasible. It enabled a 52% reduction in breath-hold time and provided superior image quality, diagnostic confidence, and pancreatic lesion conspicuity. This technique might be especially useful for patients with limited breath-hold capacity.
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RATIONALE AND OBJECTIVES: To compare a conventional T1 volumetric interpolated breath-hold examination (VIBE) with SPectral Attenuated Inversion Recovery (SPAIR) fat saturation and a deep learning (DL)-reconstructed accelerated VIBE sequence with SPAIR fat saturation achieving a 50 % reduction in breath-hold duration (hereafter, VIBE-SPAIRDL) in terms of image quality and diagnostic confidence. MATERIALS AND METHODS: This prospective study enrolled consecutive patients referred for upper abdominal MRI from November 2023 to December 2023 at a single tertiary center. Patients underwent upper abdominal MRI with acquisition of non-contrast and gadobutrol-enhanced conventional VIBE-SPAIR (fourfold acceleration, acquisition time 16 s) and VIBE-SPAIRDL (sixfold acceleration, acquisition time 8 s) on a 1.5 T scanner. Image analysis was performed by four readers, evaluating homogeneity of fat suppression, perceived signal-to-noise ratio (SNR), edge sharpness, artifact level, lesion detectability and diagnostic confidence. A statistical power analysis for patient sample size estimation was performed. Image quality parameters were compared by a repeated measures analysis of variance, and interreader agreement was assessed using Fleiss' κ. RESULTS: Among 450 consecutive patients, 45 patients were evaluated (mean age, 60 years ± 15 [SD]; 27 men, 18 women). VIBE-SPAIRDL acquisition demonstrated superior SNR (P < 0.001), edge sharpness (P < 0.001), and reduced artifacts (P < 0.001) with substantial to almost perfect interreader agreement for non-contrast (κ: 0.70-0.91) and gadobutrol-enhanced MRI (κ: 0.68-0.87). No evidence of a difference was found between conventional VIBE-SPAIR and VIBE-SPAIRDL regarding homogeneity of fat suppression, lesion detectability, or diagnostic confidence (all P > 0.05). CONCLUSION: Deep learning reconstruction of VIBE-SPAIR facilitated a reduction of breath-hold duration by half, while reducing artifacts and improving image quality. SUMMARY: Deep learning reconstruction of prospectively accelerated T1 volumetric interpolated breath-hold examination for upper abdominal MRI enabled a 50 % reduction in breath-hold time with superior image quality. KEY RESULTS: 1) In a prospective analysis of 45 patients referred for upper abdominal MRI, accelerated deep learning (DL)-reconstructed VIBE images with spectral fat saturation (SPAIR) showed better overall image quality, with better perceived signal-to-noise ratio and less artifacts (all P < 0.001), despite a 50 % reduction in acquisition time compared to conventional VIBE. 2) No evidence of a difference was found between conventional VIBE-SPAIR and accelerated VIBE-SPAIRDL regarding lesion detectability or diagnostic confidence.
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PET/CT scanners with a long axial field-of-view (LAFOV) provide increased sensitivity, enabling the adjustment of imaging parameters by reducing the injected activity or shortening the acquisition time. This study aimed to evaluate the limitations of reduced [18F]FDG activity doses on image quality, lesion detectability, and the quantification of lesion uptake in the Biograph Vision Quadra, as well as to assess the benefits of the recently introduced ultra-high sensitivity mode in a clinical setting. A number of 26 patients who underwent [18F]FDG-PET/CT (3.0 MBq/kg, 5 min scan time) were included in this analysis. The PET raw data was rebinned for shorter frame durations to simulate 5 min scans with lower activities in the high sensitivity (HS) and ultra-high sensitivity (UHS) modes. Image quality, noise, and lesion detectability (n = 82) were assessed using a 5-point Likert scale. The coefficient of variation (CoV), signal-to-noise ratio (SNR), tumor-to-background ratio (TBR), and standardized uptake values (SUV) including SUVmean, SUVmax, and SUVpeak were evaluated. Subjective image ratings were generally superior in UHS compared to the HS mode. At 0.5 MBq/kg, lesion detectability decreased to 95% (HS) and to 98% (UHS). SNR was comparable at 1.0 MBq/kg in HS (5.7 ± 0.6) and 0.5 MBq/kg in UHS (5.5 ± 0.5). With lower doses, there were negligible reductions in SUVmean and SUVpeak, whereas SUVmax increased steadily. Reducing the [18F]FDG activity to 1.0 MBq/kg (HS/UHS) in a LAFOV PET/CT provides diagnostic image quality without statistically significant changes in the uptake parameters. The UHS mode improves image quality, noise, and lesion detectability compared to the HS mode.
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OBJECTIVES: In interventional bronchial artery embolization (BAE), periprocedural cone beam CT (CBCT) improves guiding and localization. However, a trade-off exists between 6-second runs (high radiation dose and motion artifacts, but low noise) and 3-second runs (vice versa). This study aimed to determine the efficacy of an advanced deep learning denoising (DLD) technique in mitigating the trade-offs related to radiation dose and image quality during interventional BAE CBCT. MATERIALS AND METHODS: This study included BMI-matched patients undergoing 6-second and 3-second BAE CBCT scans. The dose-area product values (DAP) were obtained. All datasets were reconstructed using standard weighted filtered back projection (OR) and a novel DLD software. Objective image metrics were derived from place-consistent regions of interest, including CT numbers of the Aorta and lung, noise, and contrast-to-noise ratio. Three blinded radiologists performed subjective assessments regarding image quality, sharpness, contrast, and motion artifacts on all dataset combinations in a forced-choice setup (-1 = inferior, 0 = equal; 1 = superior). The points were averaged per item for a total score. Statistical analysis ensued using a properly corrected mixed-effects model with post hoc pairwise comparisons. RESULTS: Sixty patients were assessed in 30 matched pairs (age 64 ± 15 years; 10 female). The mean DAP for the 6 s and 3 s runs was 2199 ± 185 µGym² and 1227 ± 90 µGym², respectively. Neither low-dose imaging nor the reconstruction method introduced a significant HU shift (p ≥ 0.127). The 3 s-DLD presented the least noise and superior contrast-to-noise ratio (CNR) (p < 0.001). While subjective evaluation revealed no noticeable distinction between 6 s-DLD and 3 s-DLD in terms of quality (p ≥ 0.996), both outperformed the OR variants (p < 0.001). The 3 s datasets exhibited fewer motion artifacts than the 6 s datasets (p < 0.001). CONCLUSIONS: DLD effectively mitigates the trade-off between radiation dose, image noise, and motion artifact burden in regular reconstructed BAE CBCT by enabling diagnostic scans with low radiation exposure and inherently low motion artifact burden at short examination times.
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OBJECTIVES: CT low-dose simulation methods have gained significant traction in protocol development, as they lack the risk of increased patient exposure. However, in-vivo validations of low-dose simulations are as uncommon as prospective low-dose image acquisition itself. Therefore, we investigated the extent to which simulated low-dose CT datasets resemble their real-dose counterparts. MATERIALS AND METHODS: Fourteen veterinarian-sedated alive pigs underwent three CT scans on the same third generation dual-source scanner with 2 months between each scan. At each time, three additional scans ensued, with mAs reduced to 50%, 25%, and 10%. All scans were reconstructed using wFBP and ADMIRE levels 1-5. Matching low-dose datasets were generated from the 100% scans using reconstruction-based and DICOM-based simulations. Objective image quality (CT numbers stability, noise, and signal-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (-1=inferior, 0=equal, 1=superior). The points were averaged for a semiquantitative score, and inter-rater-agreement was measured using Spearman's correlation coefficient. A structural similarity index (SSIM) analyzed the voxel-wise similarity of the volumes. Adequately corrected mixed-effects analysis compared objective and subjective image quality. Multiple linear regression with three-way interactions measured the contribution of dose, reconstruction mode, simulation method, and rater to subjective image quality. RESULTS: There were no significant differences between objective and subjective image quality of reconstruction-based and DICOM-based simulation on all dose levels (p≥0.137). However, both simulation methods produced significantly lower objective image quality than real-dose images below 25% mAs due to noise overestimation (p<0.001; SSIM≤89±3). Overall, inter-rater-agreement was strong (r≥0.68, mean 0.93±0.05, 95% CI 0.92-0.94; each p<0.001). In regression analysis, significant decreases in subjective image quality were observed for lower radiation doses (b ≤ -0.387, 95%CI -0.399 to -0.358; p<0.001) but not for reconstruction modes, simulation methods, raters, or three-way interactions (p≥0.103). CONCLUSION: Simulated low-dose CT datasets are subjectively and objectively indistinguishable from their real-dose counterparts down to 25% mAs, making them an invaluable tool for efficient low-dose protocol development.
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Interpretação de Imagem Radiográfica Assistida por Computador , Tomografia Computadorizada por Raios X , Animais , Suínos , Estudos Prospectivos , Doses de Radiação , Tomografia Computadorizada por Raios X/métodos , Simulação por Computador , Imagens de Fantasmas , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , AlgoritmosRESUMO
Due to its high morbidity and mortality, myocardial infarction is the leading cause of death worldwide. Against this background, rapid diagnosis is of immense importance. Especially in case of an atypical course, the correct diagnosis may be delayed and thus lead to increased mortality rates. In this report, we present a complex case of acute coronary syndrome. A triple-rule-out CT examination was performed in dual-energy CT (DECT) mode. While pulmonary artery embolism and aortic dissection could be ruled out with conventional CT series, the presence of anterior wall infarction was only detectable on DECT reconstructions. Subsequently, adequate and rapid therapy was then initiated leading to survival of the patient.
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(1) Background: To evaluate the effects of an AI-based denoising post-processing software solution in low-dose whole-body computer tomography (WBCT) stagings; (2) Methods: From 1 January 2019 to 1 January 2021, we retrospectively included biometrically matching melanoma patients with clinically indicated WBCT staging from two scanners. The scans were reconstructed using weighted filtered back-projection (wFBP) and Advanced Modeled Iterative Reconstruction strength 2 (ADMIRE 2) at 100% and simulated 50%, 40%, and 30% radiation doses. Each dataset was post-processed using a novel denoising software solution. Five blinded radiologists independently scored subjective image quality twice with 6 weeks between readings. Inter-rater agreement and intra-rater reliability were determined with an intraclass correlation coefficient (ICC). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. Multiple linear regression measured the contribution of "Radiation Dose", "Scanner", "Mode", "Rater", and "Timepoint" to image quality. Consistent regions of interest (ROI) measured noise for objective image quality; (3) Results: With good-excellent inter-rater agreement and intra-rater reliability (Timepoint 1: ICC ≥ 0.82, 95% CI 0.74-0.88; Timepoint 2: ICC ≥ 0.86, 95% CI 0.80-0.91; Timepoint 1 vs. 2: ICC ≥ 0.84, 95% CI 0.78-0.90; all p ≤ 0.001), subjective image quality deteriorated significantly below 100% for wFBP and ADMIRE 2 but remained good-excellent for the post-processed images, regardless of input (p ≤ 0.002). In regression analysis, significant increases in subjective image quality were only observed for higher radiation doses (≥0.78, 95%CI 0.63-0.93; p < 0.001), as well as for the post-processed images (≥2.88, 95%CI 2.72-3.03, p < 0.001). All post-processed images had significantly lower image noise than their standard counterparts (p < 0.001), with no differences between the post-processed images themselves. (4) Conclusions: The investigated AI post-processing software solution produces diagnostic images as low as 30% of the initial radiation dose (3.13 ± 0.75 mSv), regardless of scanner type or reconstruction method. Therefore, it might help limit patient radiation exposure, especially in the setting of repeated whole-body staging examinations.
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(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed interventions and peri-procedural cbCT were included. The unenhanced mask run and the contrast-enhanced fill run of the cbCT were reconstructed using weighted filtered back projection. Additionally, each dataset was post-processed using a novel denoising software solution. Place-consistent regions of interest measured signal-to-noise ratio (SNR) per dataset. Corrected mixed-effects analysis with BMI subgroup analyses compared objective image quality. Multiple linear regression measured the contribution of "Radiation Dose", "Body-Mass-Index", and "Mode" to SNR. Two radiologists independently rated diagnostic confidence. Inter-rater agreement was measured using Spearman correlation (r); (3) SNR was significantly higher in the denoised datasets than in the regular datasets (p < 0.001). Furthermore, BMI subgroup analysis showed significant SNR deteriorations in the regular datasets for higher patient BMI (p < 0.001), but stable results for denoising (p > 0.999). In regression, only denoising contributed positively towards SNR (0.6191; 95%CI 0.6096 to 0.6286; p < 0.001). The denoised datasets received overall significantly higher diagnostic confidence grades (p = 0.010), with good inter-rater agreement (r ≥ 0.795, p < 0.001). In a subgroup analysis, diagnostic confidence deteriorated significantly for higher patient BMI (p < 0.001) in the regular datasets but was stable in the denoised datasets (p ≥ 0.103).; (4) AI denoising can significantly enhance image quality in interventional cone-beam CT and effectively mitigate diagnostic confidence deterioration for rising patient BMI.
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Inteligência Artificial , Tomografia Computadorizada de Feixe Cônico , Tomografia Computadorizada de Feixe Cônico/métodos , Humanos , Razão Sinal-RuídoRESUMO
Purpose: The purpose of this study was to test the technical feasibility and the impact on the image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T abdominopelvic MR imaging. Methods: 44 patients who underwent abdominopelvic MRI were retrospectively included, of which 4 had to be subsequently excluded. After the acquisition of the conventional volume interpolated breath-hold examination (VIBEStd), images underwent postprocessing, using a deep learning-based iterative denoising super-resolution reconstruction algorithm for partial Fourier acquisitions (VIBESR). Image analysis of 40 patients with a mean age of 56 years (range 18−84 years) was performed qualitatively by two radiologists independently using a Likert scale ranging from 1 to 5, where 5 was considered the best rating. Results: Image analysis showed an improvement of image quality, noise, sharpness of the organs and lymph nodes, and sharpness of the intestine for pre- and postcontrast images in VIBESR compared to VIBEStd (each p < 0.001). Lesion detectability was better for VIBESR (p < 0.001), while there were no differences concerning the number of lesions. Average acquisition time was 16 s (±1) for the upper abdomen and 15 s (±1) for the pelvis for VIBEStd, and 15 s (±1) for the upper abdomen and 14 s (±1) for the pelvis for VIBESR. Conclusion: This study demonstrated the technical feasibility of a deep learning-based super-resolution algorithm including partial Fourier technique in abdominopelvic MR images and illustrated a significant improvement of image quality, noise, and sharpness while reducing TA.
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PURPOSE: To investigate reduction of radiation exposure in unenhanced CT in suspicion of renal calculi using a tin-filtered high tube voltage protocol compared to a standard low-dose protocol without spectral shaping. MATERIALS AND METHODS: A phantom study using 7 human renal calculi was performed to test both protocols. 120 consecutive unenhanced CT examinations performed due to suspicion of renal calculi were included in this retrospective, monocentric study. 60 examinations were included with the standard-dose protocol (SP) (100âkV/130âmAs), whereas another 60 studies were included using a low-dose protocol (LD) applying spectral shaping with tin filtration of high tube voltages (Sn150âkV/80âmAs). Image quality was assessed by two radiologists in consensus blinded to technical parameters using an equidistant Likert scale ranging from 1-5 with 5 being the highest score. Quantitative image quality was assessed using regions of interest in abdominal organs, muscles, and adipose tissue to analyze image noise and signal-to-noise ratios (SNR). Commercially available dosimetry software was used to determine and compare effective dose (ED) and size-specific dose estimates (SSDEmean). RESULTS: All seven renal calculi of the phantom could be detected with both protocols. There was no difference regarding calcluli size between the two protocols except for the smallest one. The smallest concretion measured 1.5âmm in LD and 1.0âmm in SP (ground truth 1.5âmm). CTDIvol was 3.36âmGy in LD (DLP: 119.3 mGycm) and 8.27âmGy in SP (DLP: 293.6 mGycm). The mean patient age in SP was 47â±â17 years and in LD 49â±â13 years. Ureterolithiasis was found in 33 cases in SP and 32 cases in LD. The median concretion size was 3âmm in SP and 4âmm in LD. The median ED in LD was 1.3âmSv (interquartile range (IQR) 0.3âmSv) compared to 2.3âmSv (IQR 0.9âmSv) in SP (pâ<â0.001). The SSDEmean of LD was also significantly lower compared to SP with 2.4âmGy (IQR 0.4âmGy) vs. 4.8âmGy (IQR 2.3âmGy) (pâ<â0.001). The SNR was significantly lower in LD compared to SP (pâ<â0.001). However, there was no significant difference between SP and LD regarding the qualitative assessment of image quality with a median of 4 (IQR 1) for both groups (pâ=â0.648). CONCLUSION: Tin-filtered unenhanced abdominal CT for the detection of renal calculi using high tube voltages leads to a significant reduction of radiation exposure and yields high diagnostic image quality without a significant difference compared to the institution's standard of care low-dose protocol without tin filtration. KEY POINTS: · Tin-filtered CT for the detection of renal calculi significantly reduces radiation dose.. · The application of tin filtration provides comparable diagnostic image quality to that of SP protocols.. · An increase in image noise does not hamper diagnostic image quality.. CITATION FORMAT: · Gassenmaier S, Winkelmann MT, Magnus J etâal. Low-Dose CT for Renal Calculi Detection Using Spectral Shaping of High Tube Voltage. Fortschr Röntgenstr 2022; 194: 1012â-â1019.
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Cálculos Renais , Tomografia Computadorizada por Raios X , Adulto , Humanos , Pessoa de Meia-Idade , Doses de Radiação , Estudos Retrospectivos , EstanhoRESUMO
(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT). (2) Methods: 100 consecutive pediatric thorax ULDCT were included and reconstructed using weighted filtered back projection (wFBP), iterative reconstruction (ADMIRE 2), and AI denoising (PixelShine). Place-consistent noise measurements were used to compare objective image quality. Eight blinded readers independently rated the subjective image quality on a Likert scale (1 = worst to 5 = best). Each reader wrote a semiquantitative report to evaluate disease severity using a severity score with six common pathologies. The time to diagnosis was measured for each reader to compare the possible workflow benefits. Properly corrected mixed-effects analysis with post-hoc subgroup tests were used. Spearman's correlation coefficient measured inter-reader agreement for the subjective image quality analysis and the severity score sheets. (3) Results: The highest noise was measured for wFBP, followed by ADMIRE 2, and PixelShine (76.9 ± 9.62 vs. 43.4 ± 4.45 vs. 34.8 ± 3.27 HU; each p < 0.001). The highest subjective image quality was measured for PixelShine, followed by ADMIRE 2, and wFBP (4 (4−5) vs. 3 (4−5) vs. 3 (2−4), each p < 0.001) with good inter-rater agreement (r ≥ 0.790; p ≤ 0.001). In diagnostic accuracy analysis, there was a good inter-rater agreement between the severity scores (r ≥ 0.764; p < 0.001) without significant differences between severity score items per reconstruction mode (F (5.71; 566) = 0.792; p = 0.570). The shortest time to diagnosis was measured for the PixelShine datasets, followed by ADMIRE 2, and wFBP (2.28 ± 1.56 vs. 2.45 ± 1.90 vs. 2.66 ± 2.31 min; F (1.000; 99.00) = 268.1; p < 0.001). (4) Conclusions: AI denoising significantly improves image quality in pediatric thorax ULDCT without compromising the diagnostic confidence and reduces the time to diagnosis substantially.
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Interpretação de Imagem Radiográfica Assistida por Computador , Tomografia Computadorizada por Raios X , Inteligência Artificial , Criança , Humanos , Doses de Radiação , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tórax , Tomografia Computadorizada por Raios X/métodos , Fluxo de TrabalhoRESUMO
BACKGROUND: This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six and twelve months, in stage-IV malignant melanoma patients undergoing immunotherapy with PD-1 checkpoint inhibitors and CTLA-4 checkpoint inhibitors. METHODS: A random forest model using clinical parameters (demographic variables and tumor markers = baseline model) was compared to a random forest model using clinical parameters and radiomics (extended model) via repeated 5-fold cross-validation. For this purpose, the baseline computed tomographies of 262 stage-IV malignant melanoma patients treated at a tertiary referral center were identified in the Central Malignant Melanoma Registry, and all visible metastases were three-dimensionally segmented (n = 6404). RESULTS: The extended model was not significantly superior compared to the baseline model for survival prediction after six and twelve months (AUC (95% CI): 0.664 (0.598, 0.729) vs. 0.620 (0.545, 0.692) and AUC (95% CI): 0.600 (0.526, 0.667) vs. 0.588 (0.481, 0.629), respectively). The extended model was not significantly superior compared to the baseline model for response prediction after three months (AUC (95% CI): 0.641 (0.581, 0.700) vs. 0.656 (0.587, 0.719)). CONCLUSIONS: The study indicated a potential, but non-significant, added value of radiomics for six-month and twelve-month survival prediction of stage-IV melanoma patients undergoing immunotherapy.