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PURPOSE: To assess the image quality and impact on acquisition time of a novel deep learning based T2 Dixon sequence (T2DL) of the spine. METHODS: This prospective, single center study included n = 44 consecutive patients with a clinical indication for lumbar MRI at our university radiology department between September 2022 and March 2023. MRI examinations were performed on 1.5-T and 3-T scanners (MAGNETOM Aera and Vida; Siemens Healthineers, Erlangen, Germany) using dedicated spine coils. The MR study protocol consisted of our standard clinical protocol, including a T2 weighted standard Dixon sequence (T2std) and an additional T2DL acquisition. The latter used a conventional sampling pattern with a higher parallel acceleration factor. The individual contrasts acquired for Dixon water-fat separation were then reconstructed using a dedicated research application. After reconstruction of the contrast images from k-space data, a conventional water-fat separation was performed to provide derived water images. Two readers with 6 and 4 years of experience in interpreting MSK imaging, respectively, analyzed the images in a randomized fashion. Regarding overall image quality, banding artifacts, artifacts, sharpness, noise, and diagnostic confidence were analyzed using a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent image quality). Statistical analyses included the Wilcoxon signed-rank test and weighted Cohen's kappa statistics. RESULTS: Forty-four patients (mean age 53 years (±18), male sex: 39 %) were prospectively included. Thirty-one examinations were performed on 1.5 T and 13 examinations on 3 T scanners. A sequence was successfully acquired in all patients. The total acquisition time of T2DL was 93 s at 1.5-T and 86 s at 3-T, compared to 235 s, and 257 s, respectively for T2std (reduction of acquisition time: 60.4 % at 1.5-T, and 66.5 % at 3-T; p < 0.01). Overall image quality was rated equal for both sequences (median T2DL: 5[3 -5], and median T2std: 5 [2 -5]; p = 0.57). T2DL showed significantly reduced noise levels compared to T2std (5 [4 -5] versus 4 [3 -4]; p < 0.001). In addition, sharpness was rated to be significantly higher in T2DL (5 [4 -5] versus 4 [3 -5]; p < 0.001). Although T2DL displayed significantly more banding artifacts (5 [2 -5] versus 5 [4 -5]; p < 0.001), no significant impact on readers diagnostic confidence between sequences was noted (T2std: 5 [2 -5], and T2DL: 5 [3 -5]; p = 0.61). Substantial inter-reader and intrareader agreement was observed for T2DL overall image quality (κ: 0.77, and κ: 0.8, respectively). CONCLUSION: T2DL is feasible, yields an image quality comparable to the reference standard while substantially reducing the acquisition time.
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OBJECTIVES: Clinical experience regarding the use of dedicated photon-counting breast CT (PC-BCT) for diagnosis of breast microcalcifications is scarce. This study systematically compares the detection and classification of breast microcalcifications using a dedicated breast photon-counting CT, especially designed for examining the breast, in comparison with digital breast tomosynthesis (DBT). MATERIALS AND METHODS: This is a prospective intraindividual study on women with DBT screening-detected BI-RADS-4/-5 microcalcifications who underwent PC-BCT before biopsy. PC-BCT images were reconstructed with a noninterpolated spatial resolution of 0.15 × 0.15 × 0.15 mm (reconstruction mode 1 [RM-1]) and with 0.3 × 0.3 × 0.3 mm (reconstruction mode 2 [RM-2]), plus thin-slab maximum intensity projection (MIP) reconstructions. Two radiologists independently rated the detection of microcalcifications in direct comparison with DBT on a 5-point scale. The distribution and morphology of microcalcifications were then rated according to BI-RADS. The size of the smallest discernible microcalcification particle was measured. For PC-BCT, the average glandular dose was determined by Monte Carlo simulations; for DBT, the information provided by the DBT system was used. RESULTS: Between September 2022 and July 2023, 22 participants (mean age, 61; range, 42-85 years) with microcalcifications (16 malignant; 6 benign) were included. In 2/22 with microcalcifications in the posterior region, microcalcifications were not detectable on PC-BCT, likely because they were not included in the PC-BCT volume. In the remaining 20 participants, microcalcifications were detectable. With high between-reader agreement (κ > 0.8), conspicuity of microcalcifications was rated similar for DBT and MIPs of RM-1 (mean, 4.83 ± 0.38 vs 4.86 ± 0.35) (P = 0.66), but was significantly lower (P < 0.05) for the remaining PC-BCT reconstructions: 2.11 ± 0.92 (RM-2), 2.64 ± 0.80 (MIPs of RM-2), and 3.50 ± 1.23 (RM-1). Identical distribution qualifiers were assigned for PC-BCT and DBT in 18/20 participants, with excellent agreement (κ = 0.91), whereas identical morphologic qualifiers were assigned in only 5/20, with poor agreement (κ = 0.44). The median size of smallest discernible microcalcification particle was 0.2 versus 0.6 versus 1.1 mm in DBT versus RM-1 versus RM-2 (P < 0.001), likely due to blooming effects. Average glandular dose was 7.04 mGy (PC-BCT) versus 6.88 mGy (DBT) (P = 0.67). CONCLUSIONS: PC-BCT allows reliable detection of in-breast microcalcifications as long as they are not located in the posterior part of the breast and allows assessment of their distribution, but not of their individual morphology.
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Axillary lymphadenopathy (LA) after COVID-19 vaccination is now known to be a common side effect. In these cases, malignancy cannot always be excluded on the basis of morphological imaging criteria.Narrative review for decision-making regarding control and follow-up intervals for axillary LA according to currently published research. This article provides a practical overview of the management of vaccine-associated LA using image examples and a flowchart and provides recommendations for follow-up intervals. A particular focus is on patients presenting for diagnostic breast imaging. The diagnostic criteria for pathological lymph nodes (LN) are explained.Axillary LA is a common adverse effect after COVID-19 vaccination (0.3-53%). The average duration of LA is more than 100 days. LA is also known to occur after other vaccinations, such as the seasonal influenza vaccine. Systematic studies on this topic are missing. Other causes of LA after vaccination (infections, autoimmune diseases, malignancies) should be considered for the differential diagnosis. If the LA persists for more than 3 months after COVID-19 vaccination, a primarily sonographic follow-up examination is recommended after another 3 months. A minimally invasive biopsy of the LA is recommended if a clinically suspicious LN persists or progresses. In the case of histologically confirmed breast cancer, a core biopsy without a follow-up interval is recommended regardless of the vaccination, as treatment appropriate to the stage should not be influenced by follow-up intervals. For follow-up after breast cancer, the procedure depends on the duration of the LA and the woman's individual risk of recurrence.Vaccination history should be well documented and taken into account when evaluating suspicious LN. Biopsy of abnormal, persistent, or progressive LNs is recommended. Preoperative staging of breast cancer should not be delayed by follow-up. The risk of false-positive findings is accepted, and the suspicious LNs are histologically examined in a minimally invasive procedure. · The vaccination history must be documented (vaccine, date, place of application).. · If axillary LA persists for more than 3 months after vaccination, a sonographic follow-up examination is recommended after 3 months.. · Enlarged LNs that are persistent, progressive in size, or are suspicious on control sonography should be biopsied.. · Suspicious LNs should be clarified before starting oncological therapy, irrespective of the vaccination status, according to the guidelines and without delaying therapy.. · Wilpert C, Wenkel E, Baltzer PA et al. Vaccine-associated axillary lymphadenopathy with a focus on COVID-19 vaccines. Fortschr Röntgenstr 2024; DOI 10.1055/a-2328-7536.
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BACKGROUND: Accurate assessment of breast implants is important for appropriate clinical management. We evaluated silicone properties and diagnostic accuracy for characterizing silicone implants and detecting degenerative changes including rupture in photon-counting computed tomography (PCCT). METHODS: Over 16 months, we prospectively included patients with silicone implants and available breast magnetic resonance imaging (MRI) who received thoracic PCCT performed in prone position. Consensus reading of all available imaging studies including MRI served as reference standard. Two readers evaluated all implants in PCCT reconstructions for degenerative changes. In a subgroup of implants, mean density of silicone, adjacent muscle, and fat were measured on PCCT reconstructions. Contrast-to-noise ratios (CNRs) were calculated for implant-to-muscle and implant-to-fat. RESULTS: Among 21 subjects, aged 60 ± 13.1 years (mean ± standard deviation) with 29 implants PCCT showed the following: high accuracy for linguine sign, intraimplant fluid (all > 0.99), peri-implant silicone (0.95), keyhole sign (0.90), and folds of the membrane (0.81); high specificity for linguine sign, intraimplant fluid, keyhole sign, folds of the membrane (all > 0.99), and peri-implant silicone (0.98); and high sensitivity for linguine sign and intraimplant fluid (all > 0.99). In a subgroup of 12 implants, the highest CNR for implant-to-muscle was observed on virtual unenhanced reconstructions (20.9) and iodine maps (22.9), for implant-to-fat on iodine maps (27.7) and monoenergetic reconstructions (31.8). CONCLUSIONS: Our findings demonstrate that silicone breast implants exhibit distinct contrast properties at PCCT, which may provide incremental information for detection of degenerative changes and rupture of implants. RELEVANCE STATEMENT: Thoracic photon-counting computed tomography is a promising modality for the diagnostic assessment of silicone breast implants. KEY POINTS: ⢠Thoracic photon-counting computed tomography demonstrates unique contrast properties of silicone breast implants. ⢠Iodine map reconstructions reveal strong contrast-to-noise ratios for implant-to-muscle and implant-to-fat. ⢠Thoracic photon-counting computed tomography shows high diagnostic accuracy in detecting implant degeneration and rupture. TRIAL REGISTRATION: German Clinical Trials Register number DRKS00028997, date of registration 2022-08-08, retrospectively registered.
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Implantes de Mama , Iodo , Humanos , Implantes de Mama/efeitos adversos , Projetos Piloto , Silicones , Tomografia Computadorizada por Raios X , Pessoa de Meia-Idade , Idoso , FemininoRESUMO
· Breast MRI is an essential part of breast imaging. · The recommendations for performing breast MRI have been updated. · A table provides a compact and quick overview. More detailed comments supplement the table.. · The "classic" breast MRI can be performed based on the recommendations. Tips for special clinical questions, such as implant rupture, mammary duct pathology or local lymph node status, are included.. ZITIERWEISE: · Wenkel E, Wunderlich P, Fallenberg E etâal. Aktualisierung der Empfehlungen der AG Mammadiagnostik der Deutschen Röntgengesellschaft zur Durchführung der Mamma-MRT. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2216-0782.
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OBJECTIVE: Accurate locoregional staging is crucial for effective breast cancer treatment. Photon-counting computed tomography (PC-CT) is an emerging technology with high spatial resolution and the ability to depict uptake of contrast agents in tissues, making it a promising tool for breast cancer imaging. The aim of this study was to establish the feasibility of locoregional staging of breast cancer through contrast-enhanced thoracic PC-CT, assess its diagnostic performance, and compare it with that of digital mammography (DM). MATERIALS AND METHODS: Patients with newly diagnosed breast cancer, DM, and indication of thoracic CT staging were prospectively enrolled in this clinical cohort study over a period of 6 months. Participants underwent contrast-enhanced thoracic PC-CT and breast magnetic resonance imaging in prone position. After blinding to patient data, 2 radiologists independently rated PC-CT and DM regarding the following 6 characteristics: (1) diameter of the largest mass lesion, (2) infiltration of cutis/pectoral muscle/thoracic wall, (3) number of mass lesions, (4) presence/absence of adjacent ductal carcinoma in situ (DCIS), (5) tumor conspicuity, and (6) diagnostic confidence. Reference standard was generated from consensus reading of magnetic resonance imaging combined with all histopathological/clinical data by an independent adjudication committee applying TNM eighth edition. RESULTS: Among 32 enrolled female subjects (mean ± SD age, 59 ± 13.0 years), diagnostic accuracy for T-classification was higher for PC-CT compared with DM (0.94 vs 0.50, P < 0.01). Moreover, the correlation of the number of detected tumor masses with the reference standard was stronger for PC-CT than for DM (0.72 vs 0.50, P < 0.01). We observed that PC-CT significantly (P < 0.04) outperformed DM regarding not only sensitivity (0.83 and 0.25, respectively) but also specificity (0.99 and 0.80, respectively) for adjacent DCIS. The κ values for interreader reliability were higher for PC-CT compared with DM (mean 0.88 vs 0.54, respectively; P = 0.01). CONCLUSIONS: Photon-counting computed tomography outperformed DM in T-classification and provided higher diagnostic accuracy for the detection of adjacent DCIS. Therefore, opportunistic locoregional staging of breast cancer in contrast-enhanced thoracic PC-CT is feasible and could overcome limitations of DM with the potential to improve patient management.
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OBJECTIVES: Diffusion-weighted imaging (DWI) enhances specificity in multiparametric breast MRI but is associated with longer acquisition time. Deep learning (DL) reconstruction may significantly shorten acquisition time and improve spatial resolution. In this prospective study, we evaluated acquisition time and image quality of a DL-accelerated DWI sequence with superresolution processing (DWI DL ) in comparison to standard imaging including analysis of lesion conspicuity and contrast of invasive breast cancers (IBCs), benign lesions (BEs), and cysts. MATERIALS AND METHODS: This institutional review board-approved prospective monocentric study enrolled participants who underwent 3 T breast MRI between August and December 2022. Standard DWI (DWI STD ; single-shot echo-planar DWI combined with reduced field-of-view excitation; b-values: 50 and 800 s/mm 2 ) was followed by DWI DL with similar acquisition parameters and reduced averages. Quantitative image quality was analyzed for region of interest-based signal-to-noise ratio (SNR) on breast tissue. Apparent diffusion coefficient (ADC), SNR, contrast-to-noise ratio, and contrast (C) values were calculated for biopsy-proven IBCs, BEs, and for cysts. Two radiologists independently assessed image quality, artifacts, and lesion conspicuity in a blinded independent manner. Univariate analysis was performed to test differences and interrater reliability. RESULTS: Among 65 participants (54 ± 13 years, 64 women) enrolled in the study, the prevalence of breast cancer was 23%. Average acquisition time was 5:02 minutes for DWI STD and 2:44 minutes for DWI DL ( P < 0.001). Signal-to-noise ratio measured in breast tissue was higher for DWI STD ( P < 0.001). The mean ADC values for IBC were 0.77 × 10 -3 ± 0.13 mm 2 /s in DWI STD and 0.75 × 10 -3 ± 0.12 mm 2 /s in DWI DL without significant difference when sequences were compared ( P = 0.32). Benign lesions presented with mean ADC values of 1.32 × 10 -3 ± 0.48 mm 2 /s in DWI STD and 1.39 × 10 -3 ± 0.54 mm 2 /s in DWI DL ( P = 0.12), and cysts presented with 2.18 × 10 -3 ± 0.49 mm 2 /s in DWI STD and 2.31 × 10 -3 ± 0.43 mm 2 /s in DWI DL . All lesions presented with significantly higher contrast in the DWI DL ( P < 0.001), whereas SNR and contrast-to-noise ratio did not differ significantly between DWI STD and DWI DL regardless of lesion type. Both sequences demonstrated a high subjective image quality (29/65 for DWI STD vs 20/65 for DWI DL ; P < 0.001). The highest lesion conspicuity score was observed more often for DWI DL ( P < 0.001) for all lesion types. Artifacts were scored higher for DWI DL ( P < 0.001). In general, no additional artifacts were noted in DWI DL . Interrater reliability was substantial to excellent (k = 0.68 to 1.0). CONCLUSIONS: DWI DL in breast MRI significantly reduced scan time by nearly one half while improving lesion conspicuity and maintaining overall image quality in a prospective clinical cohort.
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Neoplasias da Mama , Feminino , Humanos , Neoplasias da Mama/diagnóstico por imagem , Cistos , Aprendizado Profundo , Imagem de Difusão por Ressonância Magnética/métodos , Imagem Ecoplanar/métodos , Imageamento por Ressonância Magnética , Estudos Prospectivos , Reprodutibilidade dos Testes , Masculino , Adulto , Pessoa de Meia-Idade , Idoso , Mama/diagnóstico por imagemRESUMO
Background Radiological imaging guidelines are crucial for accurate diagnosis and optimal patient care as they result in standardized decisions and thus reduce inappropriate imaging studies. Purpose In the present study, we investigated the potential to support clinical decision-making using an interactive chatbot designed to provide personalized imaging recommendations from American College of Radiology (ACR) appropriateness criteria documents using semantic similarity processing. Methods We utilized 209 ACR appropriateness criteria documents as specialized knowledge base and employed LlamaIndex, a framework that allows to connect large language models with external data, and the ChatGPT 3.5-Turbo to create an appropriateness criteria contexted chatbot (accGPT). Fifty clinical case files were used to compare the accGPT's performance against general radiologists at varying experience levels and to generic ChatGPT 3.5 and 4.0. Results All chatbots reached at least human performance level. For the 50 case files, the accGPT performed best in providing correct recommendations that were "usually appropriate" according to the ACR criteria and also did provide the highest proportion of consistently correct answers in comparison with generic chatbots and radiologists. Further, the chatbots provided substantial time and cost savings, with an average decision time of 5 minutes and a cost of 0.19 for all cases, compared to 50 minutes and 29.99 for radiologists (both p < 0.01). Conclusion ChatGPT-based algorithms have the potential to substantially improve the decision-making for clinical imaging studies in accordance with ACR guidelines. Specifically, a context-based algorithm performed superior to its generic counterpart, demonstrating the value of tailoring AI solutions to specific healthcare applications.
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Algoritmos , Software , Humanos , Tomada de Decisão Clínica , Redução de Custos , RadiologistasRESUMO
BACKGROUND: As an integral part of computer-assisted surgery, virtual surgical planning (VSP) leads to significantly better surgery results, such as for oral and maxillofacial reconstruction with microvascular grafts of the fibula or iliac crest. It is performed on a 2D computer desktop screen (DS) based on preoperative medical imaging. However, in this environment, VSP is associated with shortcomings, such as a time-consuming planning process and the requirement of a learning process. Therefore, a virtual reality (VR)-based VSP application has great potential to reduce or even overcome these shortcomings due to the benefits of visuospatial vision, bimanual interaction, and full immersion. However, the efficacy of such a VR environment has not yet been investigated. OBJECTIVE: This study aimed to demonstrate the possible advantages of a VR environment through a substep of VSP, specifically the segmentation of the fibula (calf bone) and os coxae (hip bone), by conducting a training course in both DS and VR environments and comparing the results. METHODS: During the training course, 6 novices were taught how to use a software application in a DS environment (3D Slicer) and in a VR environment (Elucis) for the segmentation of the fibula and os coxae, and they were asked to carry out the maneuvers as accurately and quickly as possible. Overall, 13 fibula and 13 os coxae were segmented for each participant in both methods (VR and DS), resulting in 156 different models (78 fibula and 78 os coxae) per method (VR and DS) and 312 models in total. The individual learning processes in both environments were compared using objective criteria (time and segmentation performance) and self-reported questionnaires. The models resulting from the segmentation were compared mathematically (Hausdorff distance and Dice coefficient) and evaluated by 2 experienced radiologists in a blinded manner. RESULTS: A much faster learning curve was observed for the VR environment than the DS environment (ß=.86 vs ß=.25). This nearly doubled the segmentation speed (cm3/min) by the end of training, leading to a shorter time (P<.001) to reach a qualitative result. However, there was no qualitative difference between the models for VR and DS (P=.99). The VR environment was perceived by participants as more intuitive and less exhausting, and was favored over the DS environment. CONCLUSIONS: The more rapid learning process and the ability to work faster in the VR environment could save time and reduce the VSP workload, providing certain advantages over the DS environment.
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OBJECTIVES: Levonorgestrel-releasing intrauterine contraceptive devices (LNG-IUDs) are designed to exhibit only local hormonal effects. There is an ongoing debate on whether LNG-IUDs can have side effects similar to systemic hormonal medication. Benign background parenchymal enhancement (BPE) in dynamic contrast-enhanced (DCE) MRI has been established as a sensitive marker of hormonal stimulation of the breast. We investigated the association between LNG-IUD use and BPE in breast MRI to further explore possible systemic effects of LNG-IUDs. METHODS: Our hospital database was searched to identify premenopausal women without personal history of breast cancer, oophorectomy, and hormone replacement or antihormone therapy, who had undergone standardized DCE breast MRI at least twice, once with and without an LNG-IUD in place. To avoid confounding aging-related effects on BPE, half of included women had their first MRI without, the other half with, LNG-IUD in place. Degree of BPE was analyzed according to the ACR categories. Wilcoxon-matched-pairs signed-rank test was used to compare the distribution of ACR categories with vs. without LNG-IUD. RESULTS: Forty-eight women (mean age, 46 years) were included. In 24/48 women (50% [95% CI: 35.9-64.1%]), ACR categories did not change with vs. without LNG-IUDs. In 23/48 women (48% [33.9-62.1%]), the ACR category was higher with vs. without LNG-IUDs; in 1/48 (2% [0-6%]), the ACR category was lower with vs. without LNG-IUDs. The change of ACR category depending on the presence or absence of an LNG-IUD proved highly significant (p < 0.001). CONCLUSION: The use of an LNG-IUD can be associated with increased BPE in breast MRI, providing further evidence that LNG-IUDs do have systemic effects. KEY POINTS: ⢠The use of levonorgestrel-releasing intrauterine contraceptive devices is associated with increased background parenchymal enhancement in breast MRI. ⢠This suggests that hormonal effects of these devices are not only confined to the uterine cavity, but may be systemic. ⢠Potential systemic effects of levonorgestrel-releasing intrauterine contraceptive devices should therefore be considered.