Search

Rationale and Objectives: Magnetic resonance imaging (MRI) of the hand and wrist is a routine MRI examination and takes about 15-20 minutes, which can lead to problems resulting from the relatively long scan time, such as decreased image quality due to motion artifacts and lower patient throughput. The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the hand and wrist regarding image quality, visualization of anatomy, and diagnostic performance concerning common pathologies., Materials and Methods: Twenty-one patients (mean age: 43 ± 19 [19-85] years, 10 men, 11 female) were prospectively enrolled in this study between October 2020 and June 2021. Each participant underwent two MRI protocols: first, standard fully sampled TSE sequences reconstructed with a standard GRAPPA reconstruction (TSE S ) and second, prospectively undersampled TSE sequences using a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSE DL ). Both protocols were acquired consecutively in one examination. Two experienced MSK-imaging radiologists qualitatively evaluated the images concerning image quality, noise, edge sharpness, artifacts, and diagnostic confidence, as well as the delineation of anatomical structures (triangular fibrocartilage complex, tendon of the extensor carpi ulnaris muscle, extrinsic and intrinsic ligaments, median nerve, cartilage) using a five-point Likert scale and assessed common pathologies. Wilcoxon signed-rank test and kappa statistics were performed to compare the sequences., Results: Overall image quality, artifacts, delineation of anatomical structures, and diagnostic confidence of TSE DL were rated to be comparable to TSE S (p > 0.05). Additionally, TSE DL showed decreased image noise (4.90, median 5, IQR 5-5) compared to TSE S (4.52, median 5, IQR 4-5, p < 0.05) and improved edge sharpness (TSE DL : 4.10, median 4, IQR 3.5-5; TSE S : 3.57, median 4, IQR 3-4; p < 0.05). Inter- and intrareader agreement was substantial to almost perfect (κ = 0.632-1.000) for the detection of common pathologies. Time of acquisition could be reduced by more than 60% with the protocol using TSE DL ., Conclusion: Compared to TSE S, TSE DL provided decreased noise and increased edge sharpness, equal image quality, delineation of anatomical structures, detection of pathologies, and diagnostic confidence. Therefore, TSE DL may be clinically relevant for hand and wrist imaging, as it reduces examination time by more than 60%, thus increasing patient comfort and patient throughput., (Copyright © 2023. Published by Elsevier Inc.)

Objectives: Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSE DL ) for routine clinical hip MRI at 1.5 and 3 T in terms of feasibility, image quality, and diagnostic performance., Material and Methods: In this prospective, monocentric study, TSE DL was implemented clinically and evaluated in 14 prospectively enrolled patients undergoing a clinically indicated hip MRI at 1.5 and 3T between October 2020 and May 2021. Each patient underwent two examinations: For the first exam, we used standard sequences with generalized autocalibrating partial parallel acquisition reconstruction (TSE S ). For the second exam, we implemented prospectively undersampled TSE sequences with DL reconstruction (TSE DL ). Two radiologists assessed the TSE DL and TSE S regarding image quality, artifacts, noise, edge sharpness, diagnostic confidence, and delineation of anatomical structures using an ordinal five-point Likert scale (1 = non-diagnostic; 2 = poor; 3 = moderate; 4 = good; 5 = excellent). Both sequences were compared regarding the detection of common pathologies of the hip. Comparative analyses were conducted to assess the differences between TSE DL and TSE S ., Results: Compared with TSE S , TSE DL was rated to be significantly superior in terms of image quality ( p ≤ 0.020) with significantly reduced noise ( p ≤ 0.001) and significantly improved edge sharpness ( p = 0.003). No difference was found between TSE S and TSE DL concerning the extent of artifacts, diagnostic confidence, or the delineation of anatomical structures ( p > 0.05). Example acquisition time reductions for the TSE sequences of 52% at 3 Tesla and 70% at 1.5 Tesla were achieved., Conclusion: TSE DL of the hip is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared with TSE S , reducing the acquisition time significantly.

Objectives: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI)., Materials and Methods: This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively., Results: A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated ( P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets., Conclusions: The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: This study aimed to assess the technical feasibility, the impact on image quality, and the acquisition time (TA) of a new deep-learning-based reconstruction algorithm in diffusion weighted imaging (DWI) of breast magnetic resonance imaging (MRI)., Methods: Retrospective analysis of 55 female patients who underwent breast DWI at 1.5 T. Raw data were reconstructed using a deep-learning (DL) reconstruction algorithm on a subset of the acquired averages, therefore a reduction of TA. Clinically used standard DWI sequence (DWI Std ) and the DL-reconstructed images (DWI DL ) were compared. Two radiologists rated the image quality of b800 and ADC images, using a Likert-scale from 1 to 5 with 5 being considered perfect image quality. Signal intensities were measured by placing a region of interest (ROI) at the same position in both sequences., Results: TA was reduced by 40 % in DWI DL , compared to DWI Std , DWI DL improved noise and sharpness while maintaining contrast, the level of artifacts, and diagnostic confidence. There were no differences regarding the signal intensity values of the apparent diffusion coefficient (ADC), (p = 0.955), b50-values (p = 0.070) and b800-values (p = 0.415) comparing standard and DL-imaging. Lesion assessment showed no differences regarding the number of lesions in ADC and DWI (both p = 1.000) and regarding the lesion diameter in DWI (p = 0.961;0.972) and ADC (p = 0.961;0.972)., Conclusions: The novel deep-learning-based reconstruction algorithm significantly reduces TA in breast DWI, while improving sharpness, reducing noise, and maintaining a comparable level of image quality, artifacts, contrast, and diagnostic confidence. DWI DL does not influence the quantifiable parameters., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Background: Patient-centered radiology and employee-centered radiology are being increasingly discussed as an extension of the established structure- and process-oriented management perspective. Concerning potential conflicts, it is unclear if and how these approaches should best be implemented in a radiology department., Objective: The aim of this narrative review is to identify and critically correlate underlying characteristics of patient-centered and employee-centered approaches including their similarities, conflicts, and synergies as applicable to the radiological work environment., Materials and Methods: Based on a literature search using PubMed, Scopus, Web of Science, and Google Scholar, the current body of knowledge regarding patient- and employee-centered radiology is presented., Results: Patient- and employee-centered radiology focus on the individual needs of patients and employees, respectively, and promise to improve patient satisfaction, healthcare outcomes, and organizational performance. Conflicts result from an increased organizational complexity and the concurrent utilization of limited resources, such as time, money, and staff. Overall, however, synergies outweigh the potential conflicts., Conclusions: Successful implementation of patient- and employee-centered approaches in radiology requires a human-centered leadership approach and an overarching strategy with the execution of specific interventions in the processes. We provide specific recommendations to this effect., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht.

Objective: The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy., Materials and Methods: Between October 2020 and June 2021, seventeen participants (eight patients, nine healthy subjects; mean age: 43 ± 16 (20-70) years, eight men) were prospectively included in this study. Each patient underwent two examinations: standard MRI, including TSE sequences reconstructed with a generalized autocalibrating partial parallel acquisition reconstruction (TSE STD ), and prospectively undersampled TSE sequences reconstructed with a DL reconstruction (TSE DL ). Two radiologists evaluated the images concerning image quality, noise, edge sharpness, artifacts, diagnostic confidence, and delineation of anatomical structures using a 5-point Likert scale, and rated the images concerning the detection of common pathologies., Results: Image quality was significantly improved in TSE DL (mean 4.35, IQR 4-5) compared to TSE STD (mean 3.76, IQR 3-4, p = 0.008). Moreover, TSE DL showed decreased noise (mean 4.29, IQR 3.5-5) compared to TSE STD (mean 3.35, IQR 3-4, p = 0.004). Ratings for delineation of anatomical structures, artifacts, edge sharpness, and diagnostic confidence did not differ significantly between TSE DL and TSE STD ( p > 0.05). Inter-reader agreement was substantial to almost perfect (κ = 0.628-0.904). No difference was found concerning the detection of pathologies between the readers and between TSE DL and TSE STD . Using DL, the acquisition time could be reduced by more than 35% compared to TSE STD ., Conclusion: TSE DL provided improved image quality and decreased noise while receiving equal ratings for edge sharpness, artifacts, delineation of anatomical structures, diagnostic confidence, and detection of pathologies compared to TSE STD . Providing more than a 35% reduction of acquisition time, TSE DL may be clinically relevant for elbow imaging due to increased patient comfort and higher patient throughput.

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., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity in the diagnosis of the most common solid cancer in men. However, the capacity of MRI systems is limited. Here we investigate the ability of deep learning image reconstruction to accelerate time consuming diffusion-weighted imaging (DWI) acquisition while maintaining diagnostic image quality., Method: In this retrospective study, raw data of DWI sequences of consecutive patients undergoing MRI of the prostate at a tertiary care hospital in Germany were reconstructed using standard and deep learning reconstruction. To simulate a shortening of acquisition times by 39 %, one instead of two and six instead of ten averages were used in the reconstruction of b = 0 and 1000 s/mm 2 images, respectively. Image quality was assessed by three radiologists and objective image quality metrics., Results: After the application of exclusion criteria, 35 out of 147 patients examined between September 2022 and January 2023 were included in this study. The radiologists perceived less image noise on deep learning reconstructed images at b = 0 s/mm 2 images and ADC maps with good inter-reader agreement. Signal-to-noise ratios were similar overall with discretely reduced values in the transitional zone after deep learning reconstruction., Conclusions: An acquisition time reduction of 39 % without loss in image quality is feasible in DWI of the prostate when using deep learning image reconstruction., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: EW and TB are employed by Siemens Healthcare GmbH. The other authors have nothing to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objectives: To evaluate work expectations of radiologists at different career levels, their fulfillment, prevalence of exhaustion, and exhaustion-associated factors., Methods: A standardized digital questionnaire was distributed internationally to radiologists of all career levels in the hospital and in ambulatory care via radiological societies and sent manually to 4500 radiologists of the largest German hospitals between December 2020 and April 2021. Statistics were based on age- and gender-adjusted regression analyses of respondents working in Germany (510 out of 594 total respondents)., Results: The most frequent expectations were "joy at work" (97%) and a "good working atmosphere" (97%), which were considered fulfilled by at least 78%. The expectation of a "structured residency within the regular time interval" (79%) was more frequently judged fulfilled by senior physicians (83%, odds ratio (OR) 4.31 [95% confidence interval (95% CI) 1.95-9.52]), chief physicians (85%, 6.81 [95% CI 1.91-24.29]), and radiologists outside the hospital (88%, 7.59 [95% CI 2.40-24.03]) than by residents (68%). Exhaustion was most common among residents (physical exhaustion: 38%; emotional exhaustion: 36%), in-hospital specialists (29%; 38%), and senior physicians (30%; 29%). In contrast to paid extra hours, unpaid extra hours were associated with physical exhaustion (5-10 extra hours: OR 2.54 [95% CI 1.54-4.19]). Fewer opportunities to shape the work environment were related to a higher probability of physical (2.03 [95% CI 1.32-3.13]) and emotional (2.15 [95% CI 1.39-3.33]) exhaustion., Conclusions: While most radiologists enjoy their work, residents wish for more training structure. Ensuring payment of extra hours and employee empowerment may help preventing burnout in high-risk groups., Key Points: • Most important work expectations of radiologists who work in Germany are "joy at work," a "good working atmosphere," "support for further qualification," and a "structured residency within the regular time interval," with the latter containing potential for improvement according to residents. • Physical and emotional exhaustion are common at all career levels except for chief physicians and for radiologists who work outside the hospital in ambulatory care. • Exhaustion as a major burnout criterion is associated with unpaid extra hours and reduced opportunities to shape the work environment., (© 2023. The Author(s).)

Rationale and Objectives: To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examination (VIBE SR ) at 3 Tesla. The standard T1-weighted images were used as the reference standard (VIBE SD )., Materials and Methods: Patients with diverse abdominal pathologies, who underwent a clinically indicated contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and June 2021 were retrospectively included. Following the acquisition of the standard VIBE SD sequences, additional images for the non-contrast, dynamic contrast-enhanced and post-contrast T1-weighted VIBE acquisition were retrospectively reconstructed using the same raw data and employing a prototypical deep learning-based super-resolution reconstruction algorithm. The algorithm was designed to enhance edge sharpness by avoiding conventional k-space filtering and to perform a partial Fourier reconstruction in the slice phase-encoding direction for a predefined asymmetric sampling ratio. In the retrospective reconstruction, the asymmetric sampling was realized by omitting acquired samples at the end of the acquisition and therefore corresponding to a shorter acquisition. Four radiologists independently analyzed the image datasets (VIBE SR and VIBE SD ) in a blinded manner. Outcome measures were: sharpness of abdominal organs, sharpness of vessels, image contrast, noise, hepatic lesion conspicuity and size, overall image quality and diagnostic confidence. These parameters were statistically compared and interrater reliability was computed using Fleiss' Kappa and intraclass correlation coefficient (ICC). Finally, the rate of detection of hepatic lesions was documented and was statistically compared using the paired Wilcoxon test., Results: A total of 32 patients aged 59 ± 16 years (23 men (72%), 9 women (28%)) were included. For VIBE SR , breath-hold time was significantly reduced by approximately 13.6% (VIBE SR 11.9 ± 1.2 seconds vs. VIBE SD : 13.9 ± 1.4 seconds, p < 0.001). All readers rated sharpness of abdominal organs, sharpness of vessels to be superior in images with VIBE SR (p values ranged between p = 0.005 and p < 0.001). Despite reduction of acquisition time, image contrast, noise, overall image quality and diagnostic confidence were not compromised, as there was no evidence of a difference between VIBE SR and VIBE SD (p > 0.05). The inter-reader agreement was substantial with a Fleiss' Kappa of >0.7 in all contrast phases. A total of 13 hepatic lesions were analyzed. The four readers observed a superior lesion conspicuity in VIBE SR than in VIBE SD (p values ranged between p = 0.046 and p < 0.001). In terms of lesion size, there was no significant difference between VIBE SD and VIBE SR for all readers. Finally, there was an excellent inter-reader agreement regarding lesion size (ICC > 0.9). For all readers, no statistically significant difference was observed regarding detection of hepatic lesions between VIBE SD and VIBE SR ., Conclusion: The deep learning-based super-resolution reconstruction with partial Fourier in the slice phase-encoding direction enabled a reduction of breath-hold time and improved image sharpness and lesion conspicuity in T1-weighted gradient echo sequences in abdominal magnetic resonance imaging at 3 Tesla. Faster acquisition time without compromising image quality or diagnostic confidence was possible by using this deep learning-based reconstruction technique., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objectives: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone., Materials and Methods: In this retrospective multicentric study, 180 MRIs from 54 patients were annotated (semi)manually and used to train an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. The quality of the automatic segmentation was evaluated on 15 manually segmented whole-body MRIs from 3 centers using the dice score. In 3 independent test sets from 3 centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation and manual ADC measurements from 2 independent radiologists was evaluated. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed MM who had undergone bone marrow biopsy, ADC measurements were correlated with biopsy results using Spearman correlation., Results: The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, whereas the interrater experiment gave mean dice scores of 0.86, 0.86, and 0.77, respectively. The agreement between radiologists' manual ADC measurements and automatic ADC measurements was as follows: the bias between the first reader and the automatic approach was 49 × 10 -6 mm 2 /s, 7 × 10 -6 mm 2 /s, and -58 × 10 -6 mm 2 /s, and the bias between the second reader and the automatic approach was 12 × 10 -6 mm 2 /s, 2 × 10 -6 mm 2 /s, and -66 × 10 -6 mm 2 /s for the right pelvis, the left pelvis, and the sacral bone, respectively. The bias between reader 1 and reader 2 was 40 × 10 -6 mm 2 /s, 8 × 10 -6 mm 2 /s, and 7 × 10 -6 mm 2 /s, and the mean absolute difference between manual readers was 84 × 10 -6 mm 2 /s, 65 × 10 -6 mm 2 /s, and 75 × 10 -6 mm 2 /s. Automatically extracted ADC values significantly correlated with bone marrow plasma cell infiltration ( R = 0.36, P = 0.007)., Conclusions: In this study, a nnU-Net was trained that can automatically segment pelvic bone marrow from whole-body ADC maps in multicentric data sets with a quality comparable to manual segmentations. This approach allows automatic, objective bone marrow ADC measurements, which agree well with manual ADC measurements and can help to overcome interrater variability or nonrepresentative measurements. Automatically extracted ADC values significantly correlate with bone marrow plasma cell infiltration and might be of value for automatic staging, risk stratification, or therapy response assessment., Competing Interests: Conflicts of interest and sources of funding: This study did not receive specific funding. The following relationships are disclosed: M.W., P.N., N.S., K.-C. K., J.K., V.W., T.H., M.G., J.C., K.S.Z., F.B., T.N., M.D., L.T.R., A.J., K.S., E.K.M., N.W., S.A., M.H., T.F.W., S.D., F.T.K., and K.M.-H. have nothing to declare. S.S. reported receiving travel grants or honoraria for presentations from Celgene, BMS, Janssen, Takeda, and Amgen. H.G. reported receiving grants and/or provision of investigational medicinal product from Amgen, Array Biopharma/Pfizer, BMS, Celgene, Chugai, Dietmar-Hopp-Foundation, Janssen, Johns Hopkins University, Mundipharma GmbH, and Sanofi; receiving research support from Amgen, BMS, Celgene, GlycoMimetics Inc., GSK, Heidelberg Pharma, Hoffmann-La Roche, Karyopharm, Janssen, Incyte, Millenium Pharmaceuticals Inc., Molecular Partners, Merck Sharp and Dohme, MorphoSys AG, Pfizer, Sanofi, Takeda, and Novartis; being on the advisory boards of Adaptive Biotechnology, Amgen, BMS, Janssen, Sanofi, and receiving honoraria from Amgen, BMS, Chugai, GlaxoSmithKline, Janssen, Novartis, Sanofi and Pfizer. H.-P.S. declares receiving consulting fee or honorarium from Siemens, Curagita, Profound, and Bayer; declares receiving travel support from Siemens, Curagita, Profound, and Bayer; is a board member of Curagita; provides consultancy for Curagita and Bayer; declares receiving grants/grants pending from BMBF, Deutsche Krebshilfe, Dietmar-Hopp-Stiftung, and Roland-Ernst-Stiftung; and declares receiving payment for lectures from Siemens, Curagita, Profound, and Bayer., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: The purpose of this study was to investigate the impact of deep learning accelerated diffusion-weighted imaging (DWI DL ) in 1.5-T liver MRI on image quality, sharpness, and diagnostic confidence., Materials and Methods: One-hundred patients who underwent liver MRI at 1.5-T including DWI with two different b-values (50 and 800 s/mm²) between February and April 2022 were retrospectively included. There were 54 men and 46 women, with a mean age of 59 ± 14 (SD) years (range: 21-88 years). The single average raw data were retrospectively processed using a deep learning (DL) image reconstruction algorithm leading to a simulated acquisition time of 1 min 28 s for DWI DL as compared to 2 min 31 s for standard DWI (DWI Std ) via reduction of signal averages. All DWI datasets were reviewed by four radiologists using a Likert scale ranging from 1-4 using the following criteria: noise level, extent of artifacts, sharpness, overall image quality, and diagnostic confidence. Furthermore, quantitative assessment of noise and signal-to-noise ratio (SNR) was performed via regions of interest., Results: No significant differences were found regarding artifacts and overall image quality (P > 0.05). Noise measurements for the spleen, liver, and erector spinae muscles revealed significantly lower noise for DWI DL versus DWI Std (P < 0.001). SNR measurements in the above-mentioned tissues also showed significantly superior results for DWI DL versus DWI Std for b = 50 s/mm² and ADC maps (all P < 0.001). For b = 800 s/mm², significantly superior results were found for the spleen, right hemiliver, and erector spinae muscles., Conclusions: DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality., Competing Interests: Declaration of Competing Interest Thomas Benkert and Elisabeth Weiland are employees of Siemens Healthcare GmbH., (Copyright © 2022 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Purpose: Magnetic resonance imaging (MRI) scan time remains a limited and valuable resource. This study evaluates the diagnostic performance of a deep learning (DL)-based accelerated TSE study protocol compared to a standard TSE study protocol in ankle MRI., Material and Methods: Between October 2020 and July 2021 forty-seven patients were enrolled in this study for an intraindividual comparison of a standard TSE study protocol and a DL TSE study protocol either on a 1.5 T or a 3 T scanner. Two radiologists evaluated the examinations regarding structural pathologies and image quality categories (5-point-Likert-scale; 1 = "non diagnostic", 5 = "excellent")., Results: Both readers showed almost perfect/perfect agreement of DL TSE with standard TSE in all analyzed structural pathologies (0.81-1.00) with a median "good" or "excellent" rating (4-5/5) in all image quality categories in both 1.5 T and 3 T MRI. The reduction of total acquisition time of DL TSE compared to standard TSE was 49% in 1.5 T and 48% in 3 T MRI to a total acquisition time of 5 min 41 s and 5 min 46 s., Conclusion: In ankle MRI the new DL-based accelerated TSE study protocol delivers high agreement with standard TSE and high image quality, while reducing the acquisition time by 48%., (© 2023. The Author(s).)

Background Deep learning (DL)-based MRI reconstructions can reduce examination times for turbo spin-echo (TSE) acquisitions. Studies that prospectively employ DL-based reconstructions of rapidly acquired, undersampled spine MRI are needed. Purpose To investigate the diagnostic interchangeability of an unrolled DL-reconstructed TSE (hereafter, TSE DL ) T1- and T2-weighted acquisition method with standard TSE and to test their impact on acquisition time, image quality, and diagnostic confidence. Materials and Methods This prospective single-center study included participants with various spinal abnormalities who gave written consent from November 2020 to July 2021. Each participant underwent two MRI examinations: standard fully sampled T1- and T2-weighted TSE acquisitions (reference standard) and prospectively undersampled TSE DL acquisitions with threefold and fourfold acceleration. Image evaluation was performed by five readers. Interchangeability analysis and an image quality-based analysis were used to compare the TSE and TSE DL images. Acquisition time and diagnostic confidence were also compared. Interchangeability was tested using the individual equivalence index regarding various degenerative and nondegenerative entities, which were analyzed on each vertebra and defined as discordant clinical judgments of less than 5%. Interreader and intrareader agreement and concordance (κ and Kendall τ and W statistics) were computed and Wilcoxon and McNemar tests were used. Results Overall, 50 participants were evaluated (mean age, 46 years ± 18 [SD]; 26 men). The TSE DL method enabled up to a 70% reduction in total acquisition time (100 seconds for TSE DL vs 328 seconds for TSE, P < .001). All individual equivalence indexes were less than 4%. TSE DL acquisition was rated as having superior image noise by all readers ( P < .001). No evidence of a difference was found between standard TSE and TSE DL regarding frequency of major findings, overall image quality, or diagnostic confidence. Conclusion The deep learning (DL)-reconstructed turbo spin-echo (TSE) method was found to be interchangeable with standard TSE for detecting various abnormalities of the spine at MRI. DL-reconstructed TSE acquisition provided excellent image quality, with a 70% reduction in examination time. German Clinical Trials Register no. DRKS00023278 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hallinan in this issue.

Purpose: The purpose of this study was to evaluate the impact of a deep learning-based super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric interpolated breath-hold examination; VIBE) on the assessment of pancreatic MRI at 1.5 T compared to standard VIBE imaging (VIBE STD )., Materials and Methods: This retrospective single-center study was conducted between April 2021 and October 2021. Fifty patients with a total of 50 detectable pancreatic lesion entities were included in this study. There were 27 men and 23 women, with a mean age of 69 ± 13 (standard deviation [SD]) years (age range: 33-89 years). VIBE STD (precontrast, dynamic, postcontrast) was retrospectively processed with a deep learning-based super-resolution algorithm including a more aggressive partial Fourier setting leading to a simulated acquisition time reduction (VIBE SR ). Image analysis was performed by two radiologists regarding lesion detectability, noise levels, sharpness and contrast of pancreatic edges, as well as regarding diagnostic confidence using a 5-point Likert-scale with 5 being the best., Results: VIBE SR was rated better than VIBE STD by both readers regarding lesion detectability (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5], for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5]) for reader 2; both P <0.001), noise levels (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), sharpness and contrast of pancreatic edges (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), as well as regarding diagnostic confidence (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001). There were no significant differences between lesion sizes as measured by the two readers on VIBE SR and VIBE STD images (P > 0.05). The mean acquisition time for VIBE STD (15 ± 1 [SD] s; range: 11-16 s) was longer than that for VIBE SR (13 ± 1 [SD] s; range: 11-14 s) (P < 0.001)., Conclusion: Our results indicate that the newly developed deep learning-based super-resolution algorithm adapted to partial Fourier acquisitions has a positive influence not only on shortening the examination time but also on improvement of image quality in pancreatic MRI., (Copyright © 2022 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Objectives: A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters., Materials and Methods: Fifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBE S ), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBE DL ). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured., Results: The sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBE DL (all P < 0.001). There was no significant difference in the diameter or the localization of the largest lung lesion in VIBE DL compared to VIBE S . Lesion sharpness as well as detectability was rated significantly better by both readers with VIBE DL (both P < 0.001)., Conclusion: The application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time., (© 2023. The Author(s).)

Objectives: Thin-slice prostate MRI might be beneficial for prostate cancer diagnostics. However, prolongation of acquisition time is a major drawback of thin-slice imaging. Therefore, the purpose of this study was to investigate the impact of a thin-slice deep learning accelerated T2-weighted (w) TSE imaging sequence (T2 DLR ) of the prostate as compared to conventional T2w TSE imaging (T2 S )., Materials and Methods: Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2 S (3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2 DLR (2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2 S was 4:12 min compared to 4:37 min for T2 DLR . Imaging datasets were evaluated by two radiologists using a Likert-scale ranging from 1-4, with 4 being the best regarding the following parameters: sharpness, lesion detectability, artifacts, overall image quality, and diagnostic confidence. Furthermore, preference of T2 S versus T2 DLR was evaluated., Results: The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2 DLR with a median of 4 versus a median of 3 in T2 S ( p < 0.001 for both readers). Image noise was evaluated to be significantly worse in T2 DLR as compared to T2 S ( p < 0.001 and p = 0.021, respectively). Overall image quality was also evaluated to be superior in T2 DLR versus T2 S with a median of 4 versus 3 ( p < 0.001 for both readers). Both readers chose T2 DLR in 29 cases as their preference., Conclusions: Thin-slice T2 DLR of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.

To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE DL )-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard T2-weighted fat-suppressed multi-shot turbo spin echo-sequence. A total of 320 patients who underwent a clinically indicated liver MRI at 1.5 T and 3 T between August 2020 and February 2021 were enrolled in this single-center, retrospective study. HASTE DL and standard sequences were assessed regarding overall and organ-based image quality, noise, contrast, sharpness, artifacts, diagnostic confidence, as well as lesion detectability using a Likert scale ranging from 1 to 4 (4 = best). The number of visible lesions of each organ was counted and the largest diameter of the major lesion was measured. HASTE DL showed excellent image quality (median 4, interquartile range 3-4), although BLADE (median 4, interquartile range 4-4) was rated significantly higher for overall and organ-based image quality of the adrenal gland (P < .001), contrast (P < 0.001), sharpness (P < 0.001), artifacts (P < 0.001), as well as diagnostic confidence (P < .001). No significant differences were found concerning noise (P = 0.886), organ-based image quality of the liver, pancreas, spleen, and kidneys (P = 0.120-0.366), number and measured diameter of the detected lesions (ICC = 0.972-1.0). Reduction of the aquisition time (TA) was at least 89% for 1.5 T images and 86% for 3 T images. HASTE DL provided excellent image quality, good diagnostic confidence and lesion detection compared to a standard T2-sequences, allowing an eminent reduction of the acquisition time., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

With the increasing need for minimally invasive procedures based on lower complication rates, higher patient acceptance, and technical developments, there is a growing focus on the sound interventional training of young radiologists. This survey aimed to analyze the current situation in interventional radiology (IR) training in Germany to detect shortcomings and identify areas for improvement.From November 1-30, 2020, an online questionnaire was distributed to representative radiological associations and societies with the request to forward it to radiology residents and radiologists < 40 years. The 44 questions covered six distinct areas from personal working conditions to the characterization of the IR department, training conditions, role of women in IR, and attendance at congresses/external training.A total of 330 participants completed the questionnaire. 77 % of participants expressed a high interest in IR, and 47 % could even imagine subspecializing in interventional radiology. Most institutions provided the necessary learning conditions and infrastructure. The rate of overall satisfaction with IR training conditions was 45 % (vs. a dissatisfaction rate of 39 %). However, females showed a lower satisfaction rate with their training environment than male participants (28 % vs. 51 %; P = 0.06). Positive correlations with work satisfaction were found for the presence and duration of the IR rotation, the number of partly independently/mentored performed interventions, and structured feedback. Moreover, the need for a structured training curriculum was expressed by 67 % of participants.Radiological residents and young radiologists expressed a high interest in interventional radiology, and they rate the infrastructure of German hospitals regarding IR as sufficient. However, they expressed the need for consistent IR rotations and better-structured resident and postgraduate education (curricula & interviews).Interest in interventional radiology among radiological residents and young radiologists in Germany is high, but satisfaction with interventional radiology training leaves room for improvement. The most frequently mentioned aspects that can improve IR training were · organized rotations of at least 6 months. · structured curriculums with face-to-face feedback. · structured guidance by senior interventionists during procedures. CITATION FORMAT: · Sieren M, Katoh M, Mahnken AH et al. Work and Training Conditions of German Residents and Young Radiologists in Interventional Radiology - A Nationwide Survey. Fortschr Röntgenstr 2022; 194: 1346 - 1357., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background and Purpose: Radiographic shunt series are still the imaging technique of choice for radiologic evaluation of VP-shunt complications. Radiographic shunt series are associated with high radiation exposure and have a low diagnostic performance. Our aim was to investigate the diagnostic performance of whole-body ultra-low-dose CT for detecting mechanical ventriculoperitoneal shunt complications., Materials and Methods: This retrospective study included 186 patients (mean age, 54.8 years) who underwent whole-body ultra-low-dose CT (100 kV[peak]; reference, 10 mAs). Two radiologists reviewed the images for the presence of ventriculoperitoneal shunt complications, image quality, and diagnostic confidence. On a 5-point Likert scale, readers scored image quality and diagnostic confidence (1 = very low, 5 = very high). Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Radiation dose estimation of whole-body ultra-low-dose CT was calculated and compared with the radiation dose of a radiographic shunt series., Results: 34 patients positive for VP-shunt complications were correctly identified on whole-body ultra-low-dose CT by both readers. No false-positive or -negative cases were recorded by any of the readers, yielding a sensitivity of 100% (95% CI, 87.3%-100%), a specificity of 100% (95% CI, 96.9%-100%), and perfect agreement (κ = 1). Positive and negative predictive values were high at 100%. Shunt-specific image quality and diagnostic confidence were very high (median score, 5; range, 5-5). Interobserver agreement was substantial for image quality (κ = 0.73) and diagnostic confidence (κ = 0.78). The mean radiation dose of whole-body ultra-low-dose CT was significantly lower than the radiation dose of a conventional radiographic shunt series (0.67 [SD, 0.4] mSv versus 1.57 [SD, 0.6] mSv; 95% CI, 0.79-1.0 mSv; P  < .001)., Conclusions: Whole-body ultra-low-dose CT allows detection of ventriculoperitoneal shunt complications with excellent diagnostic accuracy and diagnostic confidence. With concomitant radiation dose reduction on contemporary CT scanners, whole-body ultra-low-dose CT should be considered an alternative to the radiographic shunt series., (© 2022 by American Journal of Neuroradiology.)

Rationale and Objectives: To investigate image quality and rate of lesion detection in a novel three-dimensional T2-weighted turbo-spin-echo sequence with inner-volume excitation (zoomed imaging) and iterative denoising processing in pelvic MRI at 1.5T. Two-dimensional T2-weighted turbo-spin-echo sequences were used as the clinical reference standard (2D-T2-TSE)., Materials and Methods: This is a prospective study of patients with various pelvic pathologies. Each patient underwent standard 2D-T2-TSE in three planes with two-fold acceleration as well as a single three-dimensional T2-TSE in the sagittal plane with four-fold acceleration known as Sampling-Perfection-with-Application-optimized-Contrast-using-different-flip-angle-Evolutions (3D-T2-SPACE). The 3D-T2-SPACE images were reconstructed in three orthogonal planes at a slice thickness of 2 mm (vs. 2D-T2-TSE at 4 mm). Two radiologists conducted a qualitative image analysis on standard 2D-T2-TSE and multiplanar reconstructed 3D-T2-SPACE images. These parameters were compared and inter-reader agreement was computed. Furthermore, each reader documented the observed lesions of various pelvic organs. The rate of lesion detection was compared between readers and sequences. Inter-reader and inter-sequence agreement were computed., Results: Forty patients (25 females) were included. Mean patient age was 58 ± 13 years. 3D-T2-SPACE enabled an approximate 22% reduction of acquisition time and 50% of reconstructed slice thickness. 3D-T2-SPACE showed fewer artifacts than 2D-T2-TSE (p < 0.001). However, 2D-T2-TSE was rated to have significantly higher signal intensity than 3D-T2-SPACE (p < 0.001). There were no significant differences between the two sequences regarding all other parameters. Inter-reader agreement regarding image quality parameters was substantial (Kappa = 0.772). For all analyzed pelvic anatomic structures, inter-reader and inter-sequence agreement for lesion detection was excellent (Kappa > 0.80)., Conclusion: 3D-T2-SPACE with the inner-volume excitation and iterative denoising is clinically feasible at 1.5 T, enabling faster imaging, thinner slices, and significant reduction of artifacts. Despite that signal intensity was inferior in the SPACE images, overall image quality, diagnostic confidence and lesion detection were not compromised. This prospective study sets the stage for further clinical implementation and future investigations tailored to specific indications in pelvis MRI., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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.

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) (100 kV/130 mAs), whereas another 60 studies were included using a low-dose protocol (LD) applying spectral shaping with tin filtration of high tube voltages (Sn150 kV/80 mAs). 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 1.5 mm in LD and 1.0 mm in SP (ground truth 1.5 mm). CTDIvol was 3.36 mGy in LD (DLP: 119.3 mGycm) and 8.27 mGy in SP (DLP: 293.6 mGycm). The mean patient age in SP was 47 ± 17 years and in LD 49 ± 13 years. Ureterolithiasis was found in 33 cases in SP and 32 cases in LD. The median concretion size was 3 mm in SP and 4 mm in LD. The median ED in LD was 1.3 mSv (interquartile range (IQR) 0.3 mSv) compared to 2.3 mSv (IQR 0.9 mSv) in SP (p < 0.001). The SSDEmean of LD was also significantly lower compared to SP with 2.4 mGy (IQR 0.4 mGy) vs. 4.8 mGy (IQR 2.3 mGy) (p < 0.001). The SNR was significantly lower in LD compared to SP (p < 0.001). 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 (p = 0.648)., 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 et al. Low-Dose CT for Renal Calculi Detection Using Spectral Shaping of High Tube Voltage. Fortschr Röntgenstr 2022; 194: 1012 - 1019., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Objectives: The aim of this study was to evaluate the image quality and diagnostic performance of a deep-learning (DL)-accelerated two-dimensional (2D) turbo spin echo (TSE) MRI of the knee at 1.5 and 3 T in clinical routine in comparison to standard MRI., Material and Methods: Sixty participants, who underwent knee MRI at 1.5 and 3 T between October/2020 and March/2021 with a protocol using standard 2D-TSE (TSE S ) and DL-accelerated 2D-TSE sequences (TSE DL ), were enrolled in this prospective institutional review board-approved study. Three radiologists assessed the sequences regarding structural abnormalities and evaluated the images concerning overall image quality, artifacts, noise, sharpness, subjective signal-to-noise ratio, and diagnostic confidence using a Likert scale (1-5, 5 = best)., Results: Overall image quality for TSE DL was rated to be excellent (median 5, IQR 4-5), significantly higher compared to TSE S (median 5, IQR 4 - 5, p < 0.05), showing significantly lower extents of noise and improved sharpness (p < 0.001). Inter- and intra-reader agreement was almost perfect (κ = 0.92-1.00) for the detection of internal derangement and substantial to almost perfect (κ = 0.58-0.98) for the assessment of cartilage defects. No difference was found concerning the detection of bone marrow edema and fractures. The diagnostic confidence of TSE DL was rated to be comparable to that of TSE S (median 5, IQR 5-5, p > 0.05). Time of acquisition could be reduced to 6:11 min using TSE DL compared to 11:56 min for a protocol using TSE S ., Conclusion: TSE DL of the knee is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared to TSE S , reducing the acquisition time about 50%., Key Points: • Deep-learning reconstructed TSE imaging is able to almost halve the acquisition time of a three-plane knee MRI with proton density and T1-weighted images, from 11:56 min to 6:11 min at 3 T. • Deep-learning reconstructed TSE imaging of the knee provided significant improvement of noise levels (p < 0.001), providing higher image quality (p < 0.05) compared to conventional TSE imaging. • Deep-learning reconstructed TSE imaging of the knee had similar diagnostic performance for internal derangement of the knee compared to standard TSE., (© 2022. The Author(s).)

Background: The aim of this study was to assess the technical feasibility and the impact on image quality and acquisition time of a deep learning-accelerated fat-saturated T2-weighted turbo spin echo sequence in musculoskeletal imaging of the extremities. Methods: Twenty-three patients who underwent MRI of the extremities were prospectively included. Standard T2w turbo inversion recovery magnitude (TIRMStd) imaging was compared to a deep learning-accelerated T2w TSE (TSEDL) sequence. Image analysis of 23 patients with a mean age of 60 years (range 30−86) was performed regarding image quality, noise, sharpness, contrast, artifacts, lesion detectability and diagnostic confidence. Pathological findings were documented measuring the maximum diameter. Results: The analysis showed a significant improvement for the T2 TSEDL with regard to image quality, noise, contrast, sharpness, lesion detectability, and diagnostic confidence, as compared to T2 TIRMStd (each p < 0.001). There were no differences in the number of detected lesions. The time of acquisition (TA) could be reduced by 52−59%. Interrater agreement was almost perfect (κ = 0.886). Conclusion: Accelerated T2 TSEDL was technically feasible and superior to conventionally applied T2 TIRMStd. Concurrently, TA could be reduced by 52−59%. Therefore, deep learning-accelerated MR imaging is a promising and applicable method in musculoskeletal imaging.

(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.

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.

Purpose: During the SARS-CoV-2 pandemic, higher education worldwide had to switch to digital formats. The purpose of this study was to evaluate CoRad-19, a digital teaching tool created by the German Radiological Society for medical students during the COVID-19 pandemic., Materials and Methods: A total of 13 German-speaking universities implemented CoRad-19 in their curriculum and partially or completely replaced their classes with the online courses. Previous experience and contact with radiology and the participants' opinions regarding the medium of e-learning were surveyed using a custom questionnaire. The subjective level of knowledge regarding the individual modules was also surveyed before and after participation to measure learning effects. The data of 994 medical students from the participating sites were analyzed and compared intraindividually using the Friedman test., Results: From 4/1/2020-10/1/2020, 451 complete data sets from a total of 994 surveys were included. E-learning was rated "very useful" both before and after course participation (4 [IQR 3-4], p = 0.527, r = 0.16). E-learning as a method was also rated as a "very good" medium both before and after participation (4 [IQR 3-4], p = 0.414, r = 0.17). After participation, participants rated radiology as particularly suitable for digital teaching (before: 3 [IQR 3-4] vs. after 4 [IQR 3-4], p = 0.005, r = 0.6). Significant learning gains were measurable in all course modules (p ≤ 0.009). Post-hoc analysis showed interest in radiology to increase significantly after course participation (p = 0.02)., Conclusion: In the representative survey, significant learning effects were observed in all course modules. In addition, it should be particularly emphasized that the students' interest in radiology was increased by course participation. Thus, the German Radiological Society provided significant support to German-speaking medical faculties with respect to maintaining excellent education using CoRad-19., Key Point: · Co-Rad-19 course participation results in measurable subjective learning effects and increases student interest in radiology.., Citation Format: · Brendlin AS, Molwitz I, Oechtering TH et al. CoRad-19 - Modular Digital Teaching during the SARS-CoV-2 Pandemic. Fortschr Röntgenstr 2022; 194: 644 - 651., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

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., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

(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.

Purpose: Comparison of puncture deviation and puncture duration between computed tomography (CT)- and C-arm CT (CACT)-guided puncture performed by residents in training (RiT)., Methods: In a cohort of 25 RiTs enrolled in a research training program either CT- or CACT-guided puncture was performed on a phantom. Prior to the experiments, the RiT's level of training, experience playing a musical instrument, video games, and ball sports, and self-assessed manual skills and spatial skills were recorded. Each RiT performed two punctures. The first puncture was performed with a transaxial or single angulated needle path and the second with a single or double angulated needle path. Puncture deviation and puncture duration were compared between the procedures and were correlated with the self-assessments., Results: RiTs in both the CT guidance and CACT guidance groups did not differ with respect to radiologic experience (p = 1), angiographic experience (p = 0.415), and number of ultrasound-guided puncture procedures (p = 0.483), CT-guided puncture procedures (p = 0.934), and CACT-guided puncture procedures (p = 0.466). The puncture duration was significantly longer with CT guidance (without navigation tool) than with CACT guidance with navigation software (p < 0.001). There was no significant difference in the puncture duration between the first and second puncture using CT guidance (p = 0.719). However, in the case of CACT, the second puncture was significantly faster (p = 0.006). Puncture deviations were not different between CT-guided and CACT-guided puncture (p = 0.337) and between the first and second puncture of CT-guided and CACT-guided puncture (CT: p = 0.130; CACT: p = 0.391). The self-assessment of manual skills did not correlate with puncture deviation (p = 0.059) and puncture duration (p = 0.158). The self-assessed spatial skills correlated positively with puncture deviation (p = 0.011) but not with puncture duration (p = 0.541)., Conclusion: The RiTs achieved a puncture deviation that was clinically adequate with respect to their level of training and did not differ between CT-guided and CACT-guided puncture. The puncture duration was shorter when using CACT. CACT guidance with navigation software support has a potentially steeper learning curve. Spatial skills might accelerate the learning of image-guided puncture., Key Points: · The CT-guided and CACT-guided puncture experience of the RiTs selected as part of the program "Researchers for the Future" of the German Roentgen Society was adequate with respect to the level of training.. · Despite the lower collective experience of the RiTs with CACT-guided puncture with navigation software assistance, the learning curve regarding CACT-guided puncture may be faster compared to the CT-guided puncture technique.. · If the needle path is complex, CACT guidance with navigation software assistance might have an advantage over CT guidance.., Citation Format: · Meine TC, Hinrichs JB, Werncke T et al. Phantom study for comparison between computed tomography- and C-Arm computed tomography-guided puncture applied by residents in radiology. Fortschr Röntgenstr 2022; 194: 272 - 280., Competing Interests: Lorenz Biggemann: L. B. declares travel grant from Siemens Healthineers and speakers honorarium from Bristol Myer-Squibb unrelated to this project.Jan –Peter Grunz: J.-P. G. declares employment as “Research Consultant” at Siemens Healthineers unrelated to this project.Markus Kopp: M. B. declares participation at the Siemens Healthineers speakers’ bureau unrelated to this project.Simon Lennartz: S. L. declares institutional research support from Philips unrelated to this project.Timo C. Meine: T.C.M. declares passive participation at the BTG TheraSphere™ DACH Summit 2018 unrelated to this to this project.Bernhard C. Meyer: B.C.M. declares relationships with Siemens Healthcare and ProMedicus (outside the submitted work).Frank K. Wacker: F. K. W. declares relationships with Siemens Healthcare and ProMedicus (outside the submitted work).David Zopfs: D. Z. declares institutional research support from Philips Healthcare unrelated to this project., (Thieme. All rights reserved.)

Objectives: The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradient echo imaging of the abdomen., Materials and Methods: Fifty consecutive patients who underwent a 1.5 T contrast-enhanced magnetic resonance imaging examination of the abdomen between April and May 2021 were included in this retrospective study. After acquisition of a conventional T1-weighted volumetric interpolated breath-hold examination using Dixon for water-fat separation (VIBEStd), the acquired data were reprocessed including a superresolution algorithm that was optimized for partial Fourier acquisitions (VIBESR). To accelerate theoretically the acquisition process, a more aggressive partial Fourier setting was applied in VIBESR reconstructions practically corresponding to a shorter acquisition for the data included in the retrospective reconstruction. Precontrast, dynamic contrast-enhanced, and postcontrast data sets were processed. Image analysis was performed by 2 radiologists independently in a blinded random order without access to clinical data regarding the following criteria using a Likert scale ranging from 1 to 4 with 4 being the best: noise levels, sharpness and contrast of vessels, sharpness and contrast of organs and lymph nodes, overall image quality, diagnostic confidence, and lesion conspicuity.Wilcoxon signed rank test for paired data was applied to test for significance., Results: Mean patient age was 61 ± 14 years. Mean acquisition time for the conventional VIBEStd sequence was 15 ± 1 seconds versus theoretical 13 ± 1 seconds of acquired data used for the VIBESR reconstruction. Noise levels were evaluated to be better in VIBESR with a median of 4 (4-4) versus a median of 3 (3-3) in VIBEStd by both readers (P < 0.001). Sharpness and contrast of vessels as well as organs and lymph nodes were also evaluated to be superior in VIBESR compared with VIBEStd with a median of 4 (4-4) versus a median of 3 (3-3) (P < 0.001). Diagnostic confidence was also rated superior in VIBESR with a median of 4 (4-4) versus a median of 3.5 (3-4) in VIBEStd by reader 1 and with a median of 4 (4-4) for VIBESR and a median of 4 (4-4) for VIBEStd by reader 2 (both P < 0.001)., Conclusions: Image enhancement using deep learning-based superresolution tailored to partial Fourier acquisitions of T1-weighted gradient echo imaging of the abdomen provides improved image quality and diagnostic confidence in combination with more aggressive partial Fourier settings leading to shorter scan time., Competing Interests: Conflicts of interest and sources of funding: D.N. and S.A. are employees of Siemens Healthineers. The other authors have no conflicts of interest to declare., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

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 (iMAR spine , iMAR hip , iMAR extremity ). 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 iMAR spine , 82,295 ± 41,983 for iMAR hip , and 81,956 ± 41,890 for iMAR extremity . 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 (iMAR spine and iMAR hip ), and 4 (iMAR extremity ); for the hybrid fixator, 1 (noMAR), 2 (iMAR spine ), and 3 (iMAR hip and iMAR extremity ); and for the monotube fixator, 2 (noMAR), 3 (iMAR spine and iMAR hip ), and 4 (iMAR extremity ). Metal artifact burden was lower and overall image quality was higher ( p < .05) for iMAR hip and iMAR extremity than noMAR and iMAR spine for all fixators (aside from image quality of iMAR hip and iMAR extremity vs iMAR spine for regular fixators) but were not different (all, p > .05) between iMAR hip and iMAR extremity . Median diagnostic confidence was 4 for all fixators and reconstructions. Independent predictors of overall quality relative to noMAR were iMAR spine (odds ratio [OR] = 1.92-5.51), iMAR hip (OR = 5.56-31.10), and iMAR extremity (OR = 7.07-38.21). All iMAR presets introduced new reconstruction artifacts for all examinations for both readers. CONCLUSION . For the three fixator types, iMAR hip and iMAR extremity 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.

(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.

Objectives: To find out the opinion of radiological inpatient and outpatient medical staff regarding the measures taken in relation to the COVID-19 pandemic during the first and second waves and to identify the measures that are still perceived as needing improvement., Materials and Methods: We conducted an anonymous online survey among more than 10 000 radiologists/technicians in Germany from January 5 to January 31, 2021. A total of 862 responses (head physicians, n = 225 [inpatient doctors, n = 138; outpatient doctors, n = 84; N/A, n = 3]; radiologic personnel, n = 637 [inpatient doctor, n = 303; outpatient doctor, n = 50; inpatient technician, n = 217; outpatient technician, n = 26; N/A, n = 41]) were received. Questions of approximation, yes/no questions, and Likert scales were used., Results: During the first/second wave, 70 % (86/123)/43 % (45/104) of inpatient and 26 % (17/66)/10 % (5/52) of outpatient head physicians agreed that they received financial support from the authorities but the majority rated the financial support as insufficient. During the first and second wave, 33 % (8/24) and 80 % (16/20) of outpatient technicians agreed that they were adequately provided with personal protective equipment. The perceived lack of personal protective equipment improved for all participants during the second wave. Inpatient [outpatient] technicians perceived an increased workload in the first and second wave: 72 % (142/198) [79 % (19/24)] and 84 % (146/174) [80 % (16/20)]., Conclusion: Technicians seem increasingly negatively affected by the COVID-19 pandemic in Germany. Financial support by the competent authorities seems to be in need of improvement., Key Points: · The accessibility of personal protective equipment resources improved in the second wave.. · In particular, radiology technicians seem increasingly negatively affected by the COVID-19 pandemic.. · Financial and consulting support from the government could be improved.., Citation Format: · Bernatz S, Afat S, Othman AE et al. Impact of the COVID-19 Pandemic on Radiology in Inpatient and Outpatient Care in Germany: A Nationwide Survey Regarding the First and Second Wave. Fortschr Röntgenstr 2022; 194: 70 - 82., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

(1) To explore the potential impact of an AI dual-energy CT (DECT) prototype on decision making and workflows by investigating its capabilities to differentiate COVID-19 from immunotherapy-related pneumonitis. (2) Methods: From 3 April 2020 to 12 February 2021, DECT from biometrically matching patients with COVID-19, pneumonitis, and inconspicuous findings were selected from our clinical routine. Three blinded readers independently scored each pulmonary lobe analogous to CO-RADS. Inter-rater agreement was determined with an intraclass correlation coefficient (ICC). Averaged perfusion metrics per lobe (iodine uptake in mg, volume without vessels in ml, iodine concentration in mg/mL) were extracted using manual segmentation and an AI DECT prototype. A generalized linear mixed model was used to investigate metric validity and potential distinctions at equal CO-RADS scores. Multinomial regression measured the contribution "Reader", "CO-RADS score", and "perfusion metrics" to diagnosis. The time to diagnosis was measured for manual vs. AI segmentation. (3) Results: We included 105 patients (62 ± 13 years, mean BMI 27 ± 2). There were no significant differences between manually and AI-extracted perfusion metrics ( p = 0.999). Regardless of the CO-RADS score, iodine uptake and concentration per lobe were significantly higher in COVID-19 than in pneumonitis ( p < 0.001). In regression, iodine uptake had a greater contribution to diagnosis than CO-RADS scoring (Odds Ratio (OR) = 1.82 [95%CI 1.10-2.99] vs. OR = 0.20 [95%CI 0.14-0.29]). The AI prototype extracted the relevant perfusion metrics significantly faster than radiologists (10 ± 1 vs. 15 ± 2 min, p < 0.001). (4) Conclusions: The investigated AI prototype positively impacts decision making and workflows by extracting perfusion metrics that differentiate COVID-19 from visually similar pneumonitis significantly faster than radiologists.

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.

Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting might change daily clinical practice. The aim of this review was to present current deep learning technologies, with a focus on magnetic resonance image reconstruction. The first part of this manuscript concentrates on the basic technical principles that are necessary for deep learning image reconstruction. The second part highlights the translation of these techniques into clinical practice. The third part outlines the different aspects of image reconstruction techniques, and presents a review of the current literature regarding image reconstruction and image post-processing in MRI. The promising results of the most recent studies indicate that deep learning will be a major player in radiology in the upcoming years. Apart from decision and diagnosis support, the major advantages of deep learning magnetic resonance imaging reconstruction techniques are related to acquisition time reduction and the improvement of image quality. The implementation of these techniques may be the solution for the alleviation of limited scanner availability via workflow acceleration. It can be assumed that this disruptive technology will change daily routines and workflows permanently.

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., Competing Interests: Competing interests: All authors declare no conflict of interest for this study. SF and AFC are employees of Siemens Healthcare and had no control over the data., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations at 3 T and to develop a DL-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE) sequence for 2-dimesional T2-weighted fat-suppressed magnetic resonance imaging of the abdomen at 3 T using a variable flip angle (FA) evolution for the refocusing radiofrequency pulses, as well as to evaluate its feasibility and image quality in comparison to state-of-the-art T2-weighted fat-suppressed imaging technique (BLADE)., Materials and Methods: First, a suitable FA evolution with low cardiac motion-related signal loss (CRSL) and low SAR was determined through a prospective volunteer study with 11 participants. Image quality and diagnostic confidence with 5 different FA evolutions of a HASTEDL were assessed to identify the most suitable FA evolution. Second, the identified FA evolution was implemented clinically and evaluated in 51 patients undergoing a clinically indicated liver magnetic resonance imaging at 3 T. Two radiologists assessed the HASTEDL and standard sequences regarding overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence using a Likert scale ranging from 1 to 4, with 4 being the best. Comparative analyses were conducted to assess the differences between HASTEDL (acquisition time, 21 seconds; single breath-hold) and the routinely used T2-weighted BLADE sequence (acquisition time, 4 minutes; respiratory triggering)., Results: From the volunteer study, the FA evolution characterized by the control points 130-90-110-130 degrees (HASTEDL) was identified as optimal among the 5 evolutions evaluated and was implemented in our clinical protocol. In all 51 patients, HASTEDL was successfully acquired at 3 T and showed excellent image quality (median, 4; interquartile range, 3-4). Although BLADE was rated significantly higher for overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence than HASTEDL, no differences were found concerning the number (n = 102) and measured diameter of the detected hepatic lesions between the 2 sequences BLADE and HASTEDL., Conclusions: The proposed single-breath-hold abdominal HASTEDL with variable refocusing FAs is feasible at 3 T within SAR limits and yields high image quality and diagnostic confidence as compared with a standard T2-weighted acquisition technique, at a 10th of the acquisition time., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Hepatobiliary interventions are regarded as difficult minimally-invasive procedures that require experience and skills of physicians. To facilitate the surgical training, we develop a soft, high-fidelity and durable liver phantom with detailed morphology. The phantom is anatomically accurate and feasible for the multi-modality medical imaging, including computer tomography (CT), ultrasound, and endoscopy. The CT results show that the phantom resembles the detailed anatomy of real livers including the biliary ducts, with a spatial root mean square error (RMSE) of 1.7 ± 0.7 mm and 0.9 ± 0.2 mm for the biliary duct and the liver outer shape, respectively. The sonographic signals and the endoscopic appearance highly mimic those of the real organ. An electric sensing system was developed for the real-time quantitative tracking of the transhepatic puncturing needle. The fabrication method herein is accurate and reproducible, and the needle tracking system offers a robust and general approach to evaluate the centesis outcome., (© 2021. The Author(s).)

Objectives: The aim of this study was to investigate the impact of a deep learning-based superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold examination (VIBESR) on image quality in comparison with standard VIBE images (VIBESD)., Methods: Between May and August 2020, a total of 46 patients with various abdominal pathologies underwent contrast-enhanced upper abdominal VIBE magnetic resonance imaging (MRI) at 1.5 T. After data acquisition, the precontrast and postcontrast T1-weighted VIBE raw data were processed by a deep learning-based prototype algorithm for deblurring and denoising the images as well as for enhancing their sharpness (VIBESR). In a randomized and blinded manner, 2 radiologists independently analyzed the image data sets using the unprocessed images VIBESD as a standard reference. Outcome measures were as follows: overall image quality, anatomic clarity of organ borders, sharpness of vessels, artifacts, noise, and diagnostic confidence. All ratings were performed on an ordinal 4-point Likert scale. If the MRI examination encompassed a hepatic lesion, the maximum diameter of the largest hepatic lesion was quantified, and lesion sharpness and conspicuity were evaluated on an ordinal 4-point Likert scale. In addition, a post hoc regression analysis for lesion evaluation was computed. Finally, interrater/intrarater agreement was analyzed., Results: The overall image quality, anatomic clarity of organ borders, and sharpness of vessels in both precontrast and postcontrast images were rated significantly higher in VIBESR than in VIBESD (P < 0.001). Similarly, diagnostic confidence was higher in VIBESR than in VIBESD (P < 0.001). Furthermore, VIBESR images were rated to have significantly less noise and fewer artifacts in comparison with VIBESD (P < 0.001). The interreader agreement was substantial with a Cohen κ of 0.72 for the precontrast analysis and a κ of 0.74 for the postcontrast analysis. A total of 28 hepatic lesions were analyzed. For both readers, lesion sharpness and conspicuity were rated significantly better in VIBESR than in VIBESD in both the precontrast and postcontrast data sets (P < 0.01), which was consistent with the post hoc regression analysis (for every 1-point increase in sharpness/conspicuity, the odds ratio revealed a positive relation with VIBESR of 13-fold to 17-fold in comparison with VIBESD; P < 0.001). In terms of lesion size, there was no significant difference between the precontrast VIBESD and VIBESR or between the postcontrast VIBESD and VIBESR for both readers. Similarly, there was an excellent interreader agreement regarding lesion size (intraclass correlation coefficient, >0.9)., Conclusions: The data-driven superresolution reconstruction (VIBESR) is clinically feasible for precontrast and postcontrast upper abdominal VIBE MRI, providing improved image quality, diagnostic confidence, and lesion conspicuity compared with standard VIBESD images., Competing Interests: Conflicts of interest and sources of funding: D.N. and S.K. are employees of Siemens Healthcare. No funding was obtained., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2 S ), the novel T2 TSE sequence with DLR (T2 DLR ) was applied in three planes. Overall, the acquisition time for T2 S resulted in 10:21 min versus 3:50 min for T2 DLR . The image evaluation was performed by two radiologists independently using a Likert scale ranging from 1-4 (4 best) applying the following criteria: noise levels, artifacts, overall image quality, diagnostic confidence, and lesion conspicuity. Additionally, T2 and PI-RADS scoring were performed. The mean patient age was 69 ± 9 years (range, 49-85 years). The noise levels and the extent of the artifacts were evaluated to be significantly improved in T2 DLR versus T2 S by both readers ( p < 0.05). Overall image quality was also evaluated to be superior in T2 DLR versus T2 S in all three acquisition planes ( p = 0.005-<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2 DLR with a median of 4 versus a median of 3 in T2 S ( p = 0.001 and <0.001, respectively). T2-weighted TSE imaging of the prostate in three planes with an acquisition time reduction of more than 60% including DLR is feasible with a significant improvement of image quality.

(1) Background: To evaluate the diagnostic performance of a simulated ultra-low-dose (ULD), high-pitch computed tomography pulmonary angiography (CTPA) protocol with low tube current (mAs) and reduced scan range for detection of pulmonary embolisms (PE). (2) Methods: We retrospectively included 130 consecutive patients (64 ± 16 years, 69 female) who underwent clinically indicated high-pitch CTPA examination for suspected acute PE on a 3rd generation dual-source CT scanner (SOMATOM FORCE, Siemens Healthineers, Forchheim, Germany). ULD datasets with a realistic simulation of 25% mAs, reduced scan range (aortic arch-basal pericardium), and Advanced Modeled Iterative Reconstruction (ADMIRE®, Siemens Healthineers, Forchheim, Germany) strength 5 were created. The effective radiation dose (ED) of both datasets (standard and ULD) was estimated using a dedicated dosimetry software solution. Subjective image quality and diagnostic confidence were evaluated independently by three reviewers using a 5-point Likert scale. Objective image quality was compared using noise measurements. For assessment of diagnostic accuracy, patients and pulmonary vessels were reviewed binarily for affection by PE, using standard CTPA protocol datasets as the reference standard. Percentual affection of pulmonary vessels by PE was computed for disease severity (modified Qanadli score). (3) Results: Mean ED in ULD protocol was 0.7 ± 0.3 mSv (16% of standard protocol: 4.3 ± 1.7 mSv, p < 0.001, r > 0.5). Comparing ULD to standard protocol, subjective image quality and diagnostic confidence were comparably good ( p = 0.486, r > 0.5) and image noise was significantly lower in ULD ( p < 0.001, r > 0.5). A total of 42 patients (32.2%) were affected by PE. ULD protocol had a segment-based false-negative rate of only 0.1%. Sensitivity for detection of any PE was 98.9% (95% CI, 97.2-99.7%), specificity was 100% (95% CI, 99.8-100%), and overall accuracy was 99.9% (95% CI, 98.6-100%). Diagnoses correlated strongly between ULD and standard protocol (Chi-square (1) = 42, p < 0.001) with a decrease in disease severity of only 0.48% (T = 1.667, p = 0.103). (4) Conclusions: Compared to a standard CTPA protocol, the proposed ULD protocol proved reliable in detecting and ruling out acute PE with good levels of image quality and diagnostic confidence, as well as significantly lower image noise, at 0.7 ± 0.3 mSv (84% dose reduction).

This study analyzed the radiation exposure of a new ultra-low dose (ULD) protocol compared to a high-quality (HQ) protocol for CT-torsion measurement of the lower limb. The analyzed patients ( n = 60) were examined in the period March to October 2019. In total, 30 consecutive patients were examined with the HQ and 30 consecutive patients with the new ULD protocol comprising automatic tube voltage selection, automatic exposure control, and iterative image reconstruction algorithms. Radiation dose parameters as well as the contrast-to-noise ratio (CNR) and diagnostic confidence (DC; rated by two radiologists) were analyzed and potential predictor variables, such as body mass index and body volume, were assessed. The new ULD protocol resulted in significantly lower radiation dose parameters, with a reduction of the median total dose equivalent to 0.17 mSv in the ULD protocol compared to 4.37 mSv in the HQ protocol ( p < 0.001). Both groups showed no significant differences in regard to other parameters ( p = 0.344-0.923). CNR was 12.2% lower using the new ULD protocol ( p = 0.033). DC was rated best by both readers in every HQ CT and in every ULD CT. The new ULD protocol for CT-torsion measurement of the lower limb resulted in a 96% decrease of radiation exposure down to the level of a single pelvic radiograph while maintaining good image quality.

Objectives: The aim of this study was to investigate the impact of a novel edge enhancement and iterative denoising algorithm in 1.5-T T1-weighted dynamic contrast-enhanced (DCE) gradient echo (GRE) magnetic resonance imaging of the abdomen on image quality, noise levels, diagnostic confidence, and lesion detectability., Materials and Methods: Fifty patients who underwent a clinically indicated magnetic resonance imaging with DCE imaging of the abdomen between June and August 2020 were included in this retrospective, monocentric, institutional review board-approved study. For DCE imaging, a series of 3 volume interpolated breath-hold examinations (VIBEs) was performed. The raw data of all DCE imaging studies were processed twice, once using standard reconstruction (DCES) and again using an edge enhancement and iterative denoising approach (DCEDE). All imaging studies were randomly reviewed by 2 radiologists independently regarding noise levels, arterial contrast, sharpness of vessels, overall image quality, and diagnostic confidence using a Likert scale ranging from 1 to 4, with 4 being the best. Furthermore, lesion detectability was evaluated using the same ranking system., Results: All 50 imaging studies were successfully reconstructed with both methods. Interreader agreement (Cohen κ) was substantial to perfect for both readers. Arterial contrast and sharpness of vessels were rated superior by both readers with a median of 4 in DCEDE versus a median of 3 in DCES (P < 0.001). Furthermore, noise levels as well as overall image quality were rated higher with a median of 4 in DCEDE compared with a median of 3 in DCES (P < 0.001). Lesion detectability was evaluated to be superior in DCEDE with a median of 4 versus DCES with a median of 3 (P < 0.001). Consequently, diagnostic confidence was also rated to be superior in DCEDE with a median of 4 versus DCES with a median of 3 (P < 0.001)., Conclusions: Iterative denoising and edge enhancement are feasible in DCE imaging of the abdomen providing superior arterial contrast, noise levels, and overall image quality. Furthermore, lesion detectability and diagnostic confidence were significantly improved using this novel reconstruction method. Further reduction of acquisition time might be possible via reduction of increased noise levels using this presented method., Competing Interests: Conflicts of interest and sources of funding: none declared. D.N. and S.K. are employees of Siemens Healthcare GmbH., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)