Novel Deep Learning Denoising Enhances Image Quality and Lowers Radiation Exposure in Interventional Bronchial Artery Embolization Cone Beam CT.

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.

Image Quality and Quantitative PET Parameters of Low-Dose [18F]FDG PET in a Long Axial Field-of-View PET/CT Scanner.

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.

Definitions of Abnormal Breast Size and Asymmetry: A Cohort Study of 400 Women.

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 .

Background enhancement in contrast-enhanced spectral mammography (CESM): are there qualitative and quantitative differences between imaging systems?

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.

Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy.

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.

AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging.

(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.

Early Tumor Size Reduction of at least 10% at the First Follow-Up Computed Tomography Can Predict Survival in the Setting of Advanced Melanoma and Immunotherapy.

RATIONALE AND OBJECTIVES: Early tumor size reduction (TSR) has been explored as a prognostic factor for survival in patients with advanced melanoma in clinical trials. The purpose of this analysis is to validate, in a routine clinical milieu, the predictive capacity of TSR by 10% for overall survival (OS) and progression-free survival (PFS) and to compare its predictive performance with the RECIST 1.1 criteria. MATERIALS AND METHODS: This retrospective study was approved by the local ethics committee. A total of 152 patients with both CT before immunotherapy initiation and at first response evaluation after immunotherapy initiation were included. Prior to statistical analysis, treatment response was trichotomized as follows: Complete response and/or partial response, stable disease and progressive disease. Furthermore, response was dichotomized regarding TSR (TSR ≥ 10% and TSR < 10%). Kaplan-Meier survival estimates, Cox regression and Harrel's concordance index (C-index) were computed for prediction of overall survival and progression-free survival. RESULTS: Tumor size reduction by at least 10% significantly differentiated between patients with increased survival from the ones with decreased survival (median OS: TSR ≥ 10%: 2137 days vs. TSR < 10%: 263 days) (p < 0.001) (median PFS: TSR ≥ 10%: 590 days vs. TSR < 10%: 11 days) (p < 0.001). RECIST 1.1. criteria had a slightly higher C-index for overall survival reflecting a slight superior predictive capacity (RECIST: 0.69 vs TSR: 0.64) but a similar predictive capacity regarding progression-free survival (both: 0. 63). CONCLUSION: Early tumor size reduction serves as a simple-to-use metric which can be implemented on the first follow-up CT. Tumor size reduction by at least 10% can be considered an additional biomarker predictive of overall survival and progression-free survival in routine clinical care and not only in the context of clinical trials in patients with advanced melanoma undergoing immunotherapy. Nevertheless, RECIST-based criteria should remain the main tool of treatment response assessment until results of prospective studies validating the TSR method are available.

Clinical Evaluation of an Abbreviated Contrast-Enhanced Whole-Body MRI for Oncologic Follow-Up Imaging.

Over the last decades, overall survival for most cancer types has increased due to earlier diagnosis and more effective treatments. Simultaneously, whole-body MRI-(WB-MRI) has gained importance as a radiation free staging alternative to computed tomography. The aim of this study was to evaluate the diagnostic confidence and reproducibility of a novel abbreviated 20-min WB-MRI for oncologic follow-up imaging in patients with melanoma. In total, 24 patients with melanoma were retrospectively included in this institutional review board-approved study. All patients underwent three consecutive staging examinations via WB-MRI in a clinical 3 T MR scanner with an abbreviated 20-min protocol. Three radiologists independently evaluated the images in a blinded, random order regarding image quality (overall image quality, organ-based image quality, sharpness, noise, and artifacts) and regarding its diagnostic confidence on a 5-point-Likert-Scale (5 = excellent). Inter-reader agreement and reproducibility were assessed. Overall image quality and diagnostic confidence were rated to be excellent (median 5, interquartile range [IQR] 5-5). The sharpness of anatomic structures, and the extent of noise and artifacts, as well as the assessment of lymph nodes, liver, bone, and the cutaneous system were rated to be excellent (median 5, IQR 4-5). The image quality of the lung was rated to be good (median 4, IQR 4-5). Therefore, our study demonstrated that the novel accelerated 20-min WB-MRI protocol is feasible, providing high image quality and diagnostic confidence with reliable reproducibility for oncologic follow-up imaging.

A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.

BACKGROUND: To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy. MATERIAL AND METHODS: A total of 140 consecutive patients with melanoma (58 female, 63±16 years) for whom baseline DECT tumor load assessment revealed stage IV and who were subsequently treated with immunotherapy were included. Best response was determined using the clinical reports (81 responders: 27 complete response, 45 partial response, 9 stable disease). Individual lesion response was classified manually analogous to RECIST 1.1 through 1291 follow-up examinations on a total of 776 lesions (6.7±7.2 per patient). The patients were sorted chronologically into a study and a validation cohort (each n=70). The baseline DECT was examined using specialized tumor segmentation prototype software, and radiomic features were analyzed for response predictors. Significant features were selected using univariate statistics with Bonferroni correction and multiple logistic regression. The area under the receiver operating characteristic curve of the best subset was computed (AUROC). For each combination (SECT/DECT and patient response/lesion response), an individual random forest classifier with 10-fold internal cross-validation was trained on the study cohort and tested on the validation cohort to confirm the predictive performance. RESULTS: We performed manual RECIST 1.1 response analysis on a total of 6533 lesions. Multivariate statistics selected significant features for patient response in SECT (min. brightness, R²=0.112, padj. ≤0.001) and DECT (textural coarseness, R²=0.121, padj. ≤0.001), as well as lesion response in SECT (mean absolute voxel intensity deviation, R²=0.115, padj. ≤0.001) and DECT (iodine uptake metrics, R²≥0.12, padj. ≤0.001). Applying the machine learning models to the validation cohort confirmed the additive predictive power of DECT (patient response AUROC SECT=0.5, DECT=0.75; lesion response AUROC SECT=0.61, DECT=0.85; p<0.001). CONCLUSION: The new method of DECT-specific radiomic analysis provides a significant additive value over SECT radiomics approaches for response prediction in patients with metastatic melanoma preceding immunotherapy, especially on a lesion-based level. As mixed tumor response is not uncommon in metastatic melanoma, this lends a powerful tool for clinical decision-making and may potentially be an essential step toward individualized medicine.

Simulated Radiation Dose Reduction in Whole-Body CT on a 3rd Generation Dual-Source Scanner: An Intraindividual Comparison.

To evaluate the effect of radiation dose reduction on image quality and diagnostic confidence in contrast-enhanced whole-body computed tomography (WBCT) staging. We randomly selected March 2016 for retrospective inclusion of 18 consecutive patients (14 female, 60 ± 15 years) with clinically indicated WBCT staging on the same 3rd generation dual-source CT. Using low-dose simulations, we created data sets with 100, 80, 60, 40, and 20% of the original radiation dose. Each set was reconstructed using filtered back projection (FBP) and Advanced Modeled Iterative Reconstruction (ADMIRE®, Siemens Healthineers, Forchheim, Germany) strength 1-5, resulting in 540 datasets total. ADMIRE 2 was the reference standard for intraindividual comparison. The effective radiation dose was calculated using commercially available software. For comparison of objective image quality, noise assessments of subcutaneous adipose tissue regions were performed automatically using the software. Three radiologists blinded to the study evaluated image quality and diagnostic confidence independently on an equidistant 5-point Likert scale (1 = poor to 5 = excellent). At 100%, the effective radiation dose in our population was 13.3 ± 9.1 mSv. At 20% radiation dose, it was possible to obtain comparably low noise levels when using ADMIRE 5 (p = 1.000, r = 0.29). We identified ADMIRE 3 at 40% radiation dose (5.3 ± 3.6 mSv) as the lowest achievable radiation dose with image quality and diagnostic confidence equal to our reference standard (p = 1.000, r > 0.4). The inter-rater agreement for this result was almost perfect (ICC ≥ 0.958, 95% CI 0.909-0.983). On a 3rd generation scanner, it is feasible to maintain good subjective image quality, diagnostic confidence, and image noise in single-energy WBCT staging at dose levels as low as 40% of the original dose (5.3 ± 3.6 mSv), when using ADMIRE 3.

Effects of radiation dose reduction on diagnostic performance of 3rd generation Dual Source CT pulmonary angiography.

PURPOSE: To evaluate the effects of radiation dose reduction on diagnostic accuracy and image quality of pulmonary angiography CT (CTPA) in adults with suspected pulmonary embolism (PE). MATERIAL & METHODS: 52 consecutive patients received CTPA for suspected PE. Realistic low-Dose CT simulations were generated using an offline software (ReconCT, Siemens Healthineers, Forchheim, Germany), as either filter back projections (FBP) or iterative reconstruction as ADMIRE (strength 3 or 5) with 25 %, 50 % and 75 % of the original dose. To assess image quality (overall image quality, noise, artifacts, and sharpness) and diagnostic confidence, a five-point scale was used. Patient-based and segment-based diagnostic accuracy was calculated for Low-dose computed tomography (LDCT)-reconstruction with original dose CTPA as a standard of reference. Furthermore, effective radiation doses were calculated using a commercially available dose management platform (Radimetrics, Bayer HealthCare, Leverkusen, Germany). RESULTS: Among 52 patients, a total of 15 patients (28.8 %) had acute pulmonary artery embolism. The median dose-length product and effective dose for all 52 scans were 291.1 ±â€¯210.1 mGy⋅cm and 5.8 ±â€¯3.4 mSv. Overall subjective image quality was highest for ADMIRE 5 with 75 % and lowest for FBP with 25 % of the original dose (median [interquartile range]:5 [5] vs. 3 [2-3], p < 0.001. Patient-based diagnostic accuracy was perfect for all iteratively reconstructed data sets (ADMIRE 3 and 5) (sensitivity: 100 %, negative predictive value [NPV]: 100 %). LDCT data sets with FBP had perfect diagnostic accuracy at 50 % and 75 % of the original dose, which however decreased at 25 % of the original dose (sensitivity: 93 %; [NPV]: 97 %). Segment-based diagnostic accuracy was high for ADMIRE 3 and 5 down to 25 % dose reduction (sensitivity: 90.4 % specificity: 99.5 %) and lowest for FBP with 25 % dose reduction (sensitivity: 84.6 %, specificity: 98.9 %). Inter-class correlation regarding the detection of PE was almost perfect at all doses and recons (ICC: 96.1-1.0). Thus, accurate diagnosis for PE was possible for ADMIRE 3 and 5 datasets with 25 % of the original dose (1.45 mSv) and for FBP with 50 % of the original dose (2.9 mSv). CONCLUSION: Our findings indicate that radiation dose reduction down to 25 % (1.45 mSv) of the original data via iterative reconstruction algorithms on a 3rd generation Dual Source CT (DSCT) scanner maintained the diagnostic accuracy and image quality for the assessment of PE in CTPA.