Series Of Images Research Articles

Diagnosis, Classifications and Treatment of Os Odontoideum: WFNS Spine Committee Recommendations.

A systematic literature review and consensus using Delphi method. The aim was to formulate consensus recommendations regarding the natural history, diagnosis, classification and optimal treatment of Os Odontoideum with global applicability. Os odontoideum (OO) is a rare anomaly of the cranio-vertebral junction (CVJ). Due to the paucity of literature, there is still considerable debate about the clinical management of OO. Using PubMed, the authors reviewed the literature on OO published from 2011 to 2022. Using the Delphi method, a panel expert spine surgeons and members of the WFNS Spine Committee analyzed the strength of the published literature, elaborated and voted statements concerning diagnosis and management. The diagnosis may be established incidentally. Symptoms may manifest as neck discomfort or encompass occipital-cervical pain, myelopathy, or vertebrobasilar ischemia. Diagnosis is usually made with plain radiographs and CT can. Dynamic x-rays identify C1-C2 instability whereas MRI assess spinal cord integrity and compression. Asymptomatic cases lacking radiologic instability are generally handled through regular observation and serial imaging, until predictors of neurological deterioration necessitate surgical intervention. In the event of atlantoaxial instability or neurological dysfunction, surgical intervention with instrumentation and fusion is required to maintain stability. In irreducible cases, C1-2 joint manipulation and distraction permits re-alignment and deformity correction avoiding decompression, either from anterior or posterior. The management guidelines for asymptomatic OO are still a grey zone as our understanding of the natural history is still vague. Therefore, we need more large-center studies to investigate this condition further. Whenever symptomatic, unstable or asymptomatic presenting with risk factors, OO is better managed with atlanto-axial fusion avoiding occipital inclusion in the construct. In irreducible OO, C1-2 joint manipulation and distraction is preferred to decompression.

(Semi-)automatic Extraction of Urban Planning Rules in French for Better Management of Land Artificialization

Land artificialization is a significant modern concern, as it is irreversible, diminishes agriculturally suitable land and causes environmental problems. Our project, Hérelles, aims to address this challenge by developing a framework for land artificialization management. In this framework, we associate urban planning rules in text form with clusters extracted from time series of satellite images. To achieve this, it is crucial to understand the planning rules with two key objectives: (1) to verify if the constraints derived from the rules are verifiable on satellite images and (2) to use these constraints to guide the labelling (or semantization) of clusters. The first step in this process involves the automatic extraction of rules from urban planning documents written in the French language. To solve this problem, we propose a method based on the multilabel classification of textual segments and their subsequent summarization. This method includes a special format for representing segments, in which each segment has a title and a subtitle. We then propose a cascade approach to address the hierarchy of class labels. Additionally, we develop several text augmentation techniques for French texts that can improve prediction results. Finally, we reformulate classified segments into concise text portions containing necessary elements for expert rule construction. We adapt an approach based on Meaning Representation (AMR) graphs to generate these portions in the French language and conduct a comparative analysis with ChatGPT. We experimentally demonstrate that the resulting framework correctly classifies each type of segment with more than 90% accuracy. Furthermore, our results indicate that ChatGPT outperforms the AMR-based approach, leading to a discussion of the advantages and limitations of both methods.

Automated Integration of AI Results into Radiology Reports Using Common Data Elements.

Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information. This article describes the application of CDEs as a standardized framework to embed AI-derived results into radiology reports. The authors defined a set of CDEs for measurements of the volume and attenuation of the liver and spleen. An AI system segmented the liver and spleen on non-contrast CT images of the abdomen and pelvis, and it recorded their measurements as CDEs using the Digital Imaging and Communications in Medicine Structured Reporting (DICOM-SR) framework to express the corresponding labels and values. The AI system successfully segmented the liver and spleen in non-contrast CT images and generated measurements of organ volume and attenuation. Automated systems extracted corresponding CDE labels and values from the AI-generated data, incorporated CDE values into the radiology report, and transmitted the generated image series to the Picture Archiving and Communication System (PACS) for storage and display. This study demonstrates the use of radiology CDEs in clinical practice to record and transfer AI-generated data. This approach can improve communication among radiologists and referring providers, harmonize data to enable large-scale research efforts, and enhance the performance of decision support systems. CDEs ensure consistency, interoperability, and clarity in reporting AI findings across diverse healthcare systems.

Effect of artificial intelligence-aided differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.

Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management. Five board-certified radiologists evaluated CT colonography images with colorectal polyps of all sizes and morphologies retrospectively and decided whether the depicted polyps required endoscopic resection. After a primary unassisted reading based on current guidelines, a second reading with access to the classification of a radiomics-based random-forest AI-model labelling each polyp as "non-adenomatous" or "adenomatous" was performed. Performance was evaluated using polyp histopathology as the reference standard. 77 polyps in 59 patients comprising 118 polyp image series (47% supine position, 53% prone position) were evaluated unassisted and AI-assisted by five independent board-certified radiologists, resulting in a total of 1180 readings (subsequent polypectomy: yes or no). AI-assisted readings had higher accuracy (76% +/- 1% vs. 84% +/- 1%), sensitivity (78% +/- 6% vs. 85% +/- 1%), and specificity (73% +/- 8% vs. 82% +/- 2%) in selecting polyps eligible for polypectomy (p < 0.001). Inter-reader agreement was improved in the AI-assisted readings (Fleiss' kappa 0.69 vs. 0.92). AI-based characterisation of colorectal polyps at CT colonography as a second reader might enable a more precise selection of polyps eligible for subsequent endoscopic resection. However, further studies are needed to confirm this finding and histopathologic polyp evaluation is still mandatory. Question This is the first study evaluating the impact of AI-based polyp classification in CT colonography on radiologists' therapy management. Findings Compared with unassisted reading, AI-assisted reading had higher accuracy, sensitivity, and specificity in selecting polyps eligible for polypectomy. Clinical relevance Integrating an AI tool for colorectal polyp classification in CT colonography could further improve radiologists' therapy recommendations.

Trochlear Morphological Changes in Skeletally Immature Patients Across Consecutive MRI Studies.

Trochlear dysplasia is a consistent risk factor for recurrent patellofemoral instability (PFI), but there is limited understanding of how the trochlea develops during growth. The aim of this study was to evaluate serial magnetic resonance imaging (MRI) studies performed in skeletally immature patients with and without PFI to characterize changes in trochlear anatomy over time. PFI leads to progressive worsening of trochlear dysplasia over time. Cohort study; Level of evidence, 3. A retrospective case-control study was conducted on pediatric patients (<18 years of age) with and without a diagnosis of PFI who had multiple ipsilateral MRI studies of the knee at least 6 months apart. Inclusion criteria were patients with open distal femoral physes at the initial MRI study and no intervening surgery between MRI studies. All patients with PFI were included, and 30 patients without PFI were identified for comparison. MRI scans were retrospectively reviewed to evaluate trochlear morphology using the Dejour and Oswestry-Bristol classifications and to measure the sulcus angle, trochlear depth index, medial condylar trochlear offset, and lateral trochlear inclination (LTI). Univariate and bivariate statistics were performed to evaluate differences in morphology between MRI studies and between groups. A total of 128 patients were identified (98 in the PFI group, 30 in the non-PFI group) with a mean age of 12.3 ± 2.4 years and mean time between MRI studies of 2.3 ± 1.5 years (range, 0.5-6.5 years). Among patients with PFI, rates of moderate to severe (Dejour grades B-D and Oswestry-Bristol classification flat or convex) trochlear dysplasia increased from the initial to most recent imaging study (67% vs 89%; P < .001), and statistically significantly more dysplastic LTI and sulcus angle were observed on follow-up (P < .05). Among the non-PFI group, the percentage of patients with normal trochlear morphology increased from 53% to 87% (P < .001), and less dysplastic measures of trochlear depth index, LTI, and sulcus angle were seen on follow-up imaging (P < .05). When comparing rates of change, trochlear metrics changed toward a more shallow and dysplastic direction in the PFI cohort and toward a deeper and less dysplastic direction in the non-PFI group. Skeletally immature patients with untreated PFI have trochlear dysplasia that progressively worsens over time. Conversely, those without PFI have trochlear characteristics that appear to normalize with growth.

Comparing the Palmar Radiocarpal Artery Vascularized Bone Graft with Alternatives for Unstable Scaphoid Nonunions: A Retrospective Analysis.

We compared the radiographic union and magnitude of humpback deformity correction when using different vascularized bone grafts (VBGs) and nonvascularized bone grafts (NVBGs) in the treatment of unstable scaphoid nonunions (USNUs). This was a retrospective radiographic review of 93 patients with an USNU treated between 2013 and 2022 at a single center by a single surgeon. Inclusion criteria included skeletally mature patients with radiographic evidence of an USNU resulting from failure of either nonsurgical or operative treatment. There were three treatment groups. Two groups were treated with either a palmar radiocarpal artery (PRCA) VBG or a 1,2 intercompartmental supraretinacular (1,2 IC-SRA) VBG. The third group comprised patients who were treated with a heterogeneous mix of NVBGs. Patients were followed radiographically at regular intervals until they were deemed healed or underwent a salvage procedure because of treatment failure. Measurements of carpal and scaphoid alignment were then repeated on the final imaging series and compared to preoperative measurements. Ninety patients were included in the final analysis. PRCA VBG had the highest radiographic union rate, followed by 1,2 IC-SRA VBG and finally NVBGs. PRCA VBG had a significantly higher rate of union than NVBGs. Scapholunate angle change and scaphoid length change were greatest with PRCA VBG. These differences were statistically significant for the former when compared with 1,2 IC-SRA VBG and NVBGs and for the latter when compared with the 1,2 IC-SRA VBG in both univariate and multivariable analyses. PRCA VBG had the highest radiographic union rate compared with alternative grafts as well as providing the greatest degree of humpback deformity correction in the treatment of USNUs. Therapeutic IV.

Evaluation of Tele-Education in Malawi for Detection of Macular Features Using Optical Coherence Tomography.

The study aimed to assess the reliability of tele-education in training a Malawian ophthalmology resident to interpret optical coherence tomography (OCT) images of patients with macular conditions. This was a retrospective analysis of 1000 macula-centered OCT image series from 1000 eyes of 1000 consecutive patients from Malawi, which involved initial interpretation by a German retina specialist (observer 1) (T0). Observer 1 then trained a Malawian resident (observer 2) via email, and observer 2 independently interpreted images at T1, followed by face-to-face training in Malawi and reinterpretation at T2 and T3 (3-month intervals). The observers had to recognize, on OCT imaging, the normal macular structure, vitreofoveal traction (VFT), absent fovea depression (FD), epiretinal membrane (ERM), lamellar macular hole (LMH), full-thickness macular hole (FTMH), foveoschisis, intraretinal pseudocyst (IPC), intraretinal hyperreflective foci (IHF), subretinal fluid (SRF), pigment epithelial detachment (PED), and drusen. Cohen's Kappa statistic measured inter-observer agreement. At T1 post-tele-education, almost perfect agreement (κ = 0.86; 99.6% agreement) was observed for FTMH. Agreement remained high at T2 post-face-to-face training (κ = 0.9; 99.7%) and decreased substantially at T3 (κ = 0.77; 99.4%). Following tele-education (T1), substantial agreement (κ range: 0.77 - 0.86) was found for IPC, PED, IHF, and FD, while ERM showed fair agreement (κ = 0.33; 81%). Face-to-face training notably improved agreement for SRF (T1 κ = 0.6, T2 κ = 0.63), LMH (T1 κ = 0.6, T2 κ = 0.67), and normal macular structure (T1 κ = 0.6, T2 κ = 0.62). There was good agreement in the detection of the majority of the OCT features seen in most of the macular pathologies following training through tele-education and the results did not change significantly following face-to-face teaching. Tele-education could reliably complement training in vitreoretinal diagnostic skills in resource-limited settings.

Classification of paravertebral muscle trophism and its correlation with thoraco-lumbar fascia thickening in patients with chronic low back pain.

The aim of this work is to demonstrate how the chronicity of low back pain can modify the trophism of the paraspinal muscles, by performing an ultrasound and MRI evaluation of the paraspinal muscles in the lumbar spine and correlating it to the time of onset of low back pain. An ultrasound evaluation was carried out in the lumbar area with a 5-17MHz linear probe of the paraspinal muscles of the lumbar region, compared with the MRI of the lumbar spine, in patients presented to our attention for chronic low back pain (> 6months), from January 2021 to January 2023. In each patient, two series of images were analyzed, in the coronal and sagittal planes. Between January 2021 and January 2023, a total of 79 patients were retrospectively evaluated by ultrasound for chronic low back pain. The patients, including 46 men and 33 women, had an average age of 51.6years (min 24-max 74). In the evaluation of the profile of the paravertebral muscles, 22 patients (27.8%) showed hypotrophy of the paravertebral muscles with a concave profile, while the measurement of the thickness of the fascia showed an average thickness of 2.19mm with a range between 1, 3 and 3.2mm and an interquartile range of 1.7-2.65mm. 77.2% of patients with a concave profile showed a thickness of the FTL > 2.5mm, and an average duration of symptoms of approximately 15.3months, to demonstrate a correlation between muscle hypotrophy, thickness of the FTL and duration of onset of symptoms. In the evaluation of chronic low back pain carried out with MRI and ultrasound, the paravertebral musculature profile and the thickness of the thoracolumbar fascia are two instrumental data which, associated with the proposed classification and correlated to the clinical picture, allow to determine the chronicity of the pathological picture.

P0260 Scoring indices grading radiologic disease activity severity on magnetic resonance imaging strongly reflect clinical healing of Crohn’s perianal fistulas

Abstract Background Magnetic resonance imaging (MRI) is the gold-standard for evaluating Crohn’s perianal fistulas. Multiple scoring indices to grade radiologic disease activity severity have been established, with limited comparative data and a lack of consensus regarding which index correlates most accurately with clinical findings. In a prospective MRI series, we explored the relationship between established radiologic indices and clinical healing. Methods Serial MRI scans from 60 adults with Crohn’s perianal fistulas enrolled in a prospective study were acquired. The prospective study evaluated the efficacy of a protocolised treatment strategy optimising care delivery at a single tertiary centre. Patients had ≥12 months follow-up with serial MRI performed annually. Each MRI scan had a paired physical examination, with clinical healing defined as the absence of externally draining perianal fistulas and no seton. MRI scans were reviewed by a specialist radiologist blinded to clinical findings. Radiologic disease activity severity was graded using the Van Assche Index (VAI), Magnetic Resonance Index for Assessing Fistulas in Patients with Crohn’s Disease (MAGNIFI-CD) and Fibrosis Score (FS). The primary endpoint was the relationship between radiologic indices and clinical healing. Secondary endpoints included the optimal index scores for clinical healing and the relationship between individual scoring criteria and clinical healing. Results A total of 135 MRI scans were acquired. All patients had baseline imaging, with 88% and 37% having serial imaging at 1 and 2 years, respectively. The median interval between baseline and final MRI was 16 months. Clinical healing significantly correlated with less severe radiologic disease activity, reflected by lower VAI (OR 0.70, P&amp;lt;0.001), lower MAGNIFI-CD (OR 0.76, P&amp;lt;0.001) and higher FS (OR 3.14, P&amp;lt;0.001). On ROC curve analysis, all radiologic indices exhibited a similarly high degree of correlation with clinical healing; AUROC of 0.91, 0.84 and 0.92, respectively (Figure 1). The optimal index scores for clinical healing were VAI ≤6 (sensitivity 83%, specificity 94%, AUROC 0.88), MAGNIFI-CD ≤8 (sensitivity 78%, specificity 73%, AUROC 0.75) and FS ≥5 (sensitivity 83%, specificity 94%, AUROC 0.88). On multivariable logistic regression analysis, increasing degree of fibrosis on FS (OR 2.68, P&amp;lt;0.001) and increasing fistula tract extension on VAI (OR 0.09, P&amp;lt;0.001) were independent positive and negative indicators of clinical healing, respectively. Conclusion Radiologic disease activity severity using established scoring indices have equally strong correlations with clinical healing of Crohn’s perianal fistulas. Further studies are needed to explore the predictive utility of individual radiologic parameters.

Research on Ship-Type Recognition Based on Fusion of Ship Trajectory Image and AIS Time Series Data

Achieving accurate and efficient ship-type recognition is crucial for the development and management of modern maritime traffic systems. To overcome the limitations of existing methods that rely solely on AIS time series data or navigation trajectory images as single-modal approaches, this study introduces TrackAISNet, a multimodal ship classification model that seamlessly integrates ship trajectory images with AIS time series data for improved performance. The model employs a parallel structure, utilizing a lightweight neural network to extract features from trajectory images, and a specially designed TCN-GA (Temporal Convolutional Network with Global Attention) to capture the temporal dependencies and long-range relationships in the AIS time series data. The extracted image features and temporal features are then fused, and the combined features are fed into a classification network for final classification. We conducted experiments on a self-constructed dataset of variable-length AIS time series data comprising four types of ships. The results show that the proposed model achieved an accuracy of 81.38%, recall of 81.11%, precision of 80.95%, and an F1 score of 81.38%, outperforming the benchmark single-modal algorithms. Additionally, on a publicly available dataset containing three types of fishing vessel operations, the model demonstrated improvements in accuracy, recall, and F1 scores by 5.5%, 4.88%, and 5.88%, respectively.

Tracking pollen tube and ovule development in vivo reveals rapid responses to pollination in Brassica napus

Abstract Background and Aims Pollination and subsequent fertilisation in most angiosperms are precursors of seed and fruit development. Thus, understanding the developmental processes can improve management of plant reproductive success and food security. Indeed, the window between ovule fertilisation and seed development is crucial for the accumulation of metabolites which determines ultimate seed quality and yield. Establishing detailed temporal maps of development to describe pollination to early seed development is therefore extremely valuable to provide context for molecular studies, plant breeding, and to refine crop management strategies for optimal seed quality. Methods Here, we characterise aspects of post-pollination responses in the globally important crop plant Brassica napus (oilseed rape, canola) with a high-resolution time series of microscope images of the floral organs during the first 48 hours post pollination. Key results We demonstrate the rapid response to pollination in B. napus (c.v. Westar), with pollen tubes germinating and traversing the style within just four hours. We also describe markers of early seed formation in response to fertilisation in the synchronous development of ovule area and stigma length. Conclusions Our results provide a series of temporal benchmarks for post-pollination floral morphology in B. napus, representing valuable reference points for studying and tracking pollination responses and early seed development.

Flow characteristics of sintered titanium-based porous transport layers using machine learning

Porous transport layers, sintered from sphere-based titanium material, can be used in electrolyzers. A sedimentation-based stochastic sphere model was used to create many versions of a sphere-based micro-structure. Aided by Lattice-Boltzmann simulations, the permeability was calculated based on the artificial micro-structures. These data were then used to train a convolutional neural network in order to characterize material on the basis of its micro-structure. Binary image series from the real material of porous transport layers were characterized using a deep learning model trained with artificial data. A reasonable degree of accuracy was achieved by the neural network in the prediction of the through-plane permeability of the real material, when the samples were manufactured from spherical particles.Graphic

Learning-based motion artifact correction in the Z-spectral domain for chemical exchange saturation transfer MRI.

To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI. A neural network was designed to correct motion artifacts directly from a Z-spectrum frequency (Ω) domain rather than an image spatial domain. Motion artifacts were simulated by modeling 3D rigid-body motion and readout-related motion during k-space sampling. A saturation-contrast-specific loss function was added to preserve amide proton transfer (APT) contrast, as well as enforce image alignment between motion-corrected and ground-truth images. The proposed neural network was evaluated on simulation data and demonstrated in healthy volunteers and brain tumor patients. The experimental results showed the effectiveness of motion artifact correction in the Z-spectrum frequency domain (MOCOΩ) compared to in the image spatial domain. In addition, a temporal convolution applied to a dynamic saturation image series was able to leverage motion artifacts to improve reconstruction results as a denoising process. The MOCOΩ outperformed existing techniques for motion correction in terms of image quality and computational efficiency. At 3 T, human experiments showed that the root mean squared error (RMSE) of APT images decreased from 4.7% to 2.1% at 1 μT and from 6.2% to 3.5% at 1.5 μT in case of "moderate" motion and from 8.7% to 2.8% at 1 μT and from 12.7% to 4.5% at 1.5 μT in case of "severe" motion, after motion artifact correction. The MOCOΩ could effectively correct motion artifacts in CEST MRI without compromising saturation transfer contrast.

Medial Open-Wedge High Tibial Osteotomy with Partial Meniscectomy and Without Cyst Excision for Popliteal Cysts: A Case Series.

Introduction: Popliteal cysts (PCs) are occasionally accompanied by knee osteoarthritis (OA) and varus malalignment. However, whether concomitant arthroscopic excision of PCs with medial open-wedge high tibial osteotomy (MOWHTO) improves the osteoarthritic environment remains unclear. Therefore, this study assessed serial changes in C-size, medial meniscus extrusion (MME), and cartilage status for up to 2 years following an MOWHTO. Methods: This study retrospectively used serial magnetic resonance imaging (MRI) evaluations to assess 26 consecutive patients who underwent MOWHTO. Of the 26 patients, six with preoperative PCs were included. Based on the arthroscopic findings at the time of the MOWHTO, concomitant meniscal and chondral lesions, and whether or not partial meniscectomy was performed, were evaluated. All patients underwent second-look arthroscopy with plate removal 2 years postoperatively. The PC size, MME, and cartilage sub-scores in the medial compartment of the whole-organ MRI score (WORMS) were assessed by serial MRI preoperatively and at 3, 6, 18, and 24 months postoperatively. The recurrence of PCs and clinical outcomes, including the Rauschning-Lindgren grade, were also evaluated when serial MRI was performed. Moreover, changes in cartilage status were assessed using two-stage arthroscopy. Results: All patients underwent concomitant partial meniscectomy for medial meniscal tears in the posterior horn. A significant decrease in the mean size of preoperative PCs (27.4 ± 22.3 mm) was noted from 3 months postoperatively (8.7 ± 7.6 mm, p = 0.018), and thereafter. The mean size of PCs further decreased with time until 2 years (1.5 ± 4.0 mm, p = 0.018) following an MOWHTO with partial meniscectomy. Moreover, significant improvements in the MME and WORMS values were noted from 3 to 24 months postoperatively. Meanwhile, no PC recurrence occurred during the follow-up period and the preoperative Rauschning-Lindgren grade improved significantly with time after MOWHTO (p = 0.026). Furthermore, the two-stage arthroscopic assessments showed significant improvements in ICRS grade in the medial femoral condyle (p = 0.038). Conclusions: After an MOWHTO with partial meniscectomy, PCs decreased with time up to 2 years postoperatively; no recurrence occurred during the follow-up period, although cyst excision was not concomitantly performed. Furthermore, the reduction in PCs corresponded with improvements in MME and chondral lesions in the knee joint following the MOWHTO.

An Aortic Root Abscess Mimic Identified by Multi-disciplinary Imaging Review: a Case Report

Abstract Background While echocardiography and cardiac Positron Emission Tomography (PET) can aid in the diagnosis of prosthetic valve endocarditis (PVE), post-operative changes can lead to false-positive imaging findings. We report a case of a patient with an aortic valve prosthesis with remnant BioGlue deposits mimicking a paravalvular abscess on imaging in the setting of suspected PVE. Case Summary A 67-year-old man presented with two days of fever, chills, and altered mentation. He had a history of two prior aortic root replacements—nineteen and four years prior to his presentation. He had blood cultures that were persistently positive for Pseudomonas aeruginosa. Initial transesophageal echocardiogram (TEE) was notable for a thickening of the posterior aortic root thought to be consistent with post-surgical changes. Cardiac PET showed significant uptake around the prosthetic aortic valve, concerning for a paravalvular abscess. However, given the patient’s high risk for re-do surgery, clearance of blood cultures and preserved valve function, our multidisciplinary endocarditis team (MET) recommended non-surgical management, and close follow-up. After six weeks of appropriate antibiotics, a TEE demonstrated concern for an evolving paravalvular abscess. The MET performed extensive review of his prior surgical interventions and cardiac imaging, revealing the previous use of surgical BioGlue and stability in the aortic root on imaging, consistent with non-infectious post-surgical changes and conservative management was recommended. Discussion Thorough review of prior interventions and serial imaging in patients with suspected PVE through a multidisciplinary team approach is essential in elucidating the complete, often complex, clinical picture and recommending the most appropriate management.

Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2

Abstract. The largest anthropogenic emissions of carbon dioxide (CO2) come from local sources, such as cities and power plants. The upcoming Copernicus CO2 Monitoring (CO2M) mission will provide satellite images of the CO2 and NO2 plumes associated with these sources at a resolution of 2 km × 2 km and with a swath of 250 km. These images could be exploited using atmospheric-plume inversion methods to estimate local CO2 emissions at the time of the satellite overpass and their corresponding uncertainties. To support the development of the operational processing of satellite imagery of the column-averaged CO2 dry-air mole fraction (XCO2) and tropospheric-column NO2, this study evaluates data-driven inversion methods, i.e., computationally light inversion methods that directly process information from satellite images, local winds, and meteorological data, without resorting to computationally expensive dynamical atmospheric transport models. We designed an objective benchmarking exercise to analyze and compare the performance of five different data-driven inversion methods: two implementations with different complexities for the cross-sectional flux approach (CSF and LCSF), as well as one implementation each for the integrated mass enhancement (IME), divergence (Div), and Gaussian plume (GP) model inversion approaches. This exercise is based on pseudo-data experiments with simulations of synthetic true emissions, meteorological and concentration fields, and CO2M observations across a domain of 750 km × 650 km, centered on eastern Germany, over 1 year. The performance of the methods is quantified in terms of the accuracy of single-image emission estimates (from individual images) or annual-average emission estimates (from the full series of images), as well as in terms of the number of instant estimates for the city of Berlin and 15 power plants within this domain. Several ensembles of estimations are conducted using different scenarios for the available synthetic datasets. These ensembles are used to analyze the sensitivity of performance to (1) data loss due to cloud cover, (2) uncertainty in the wind, or (3) the added value of simultaneous NO2 images. The GP and LCSF methods generate the most accurate estimates from individual images. The deviations between the emission estimates and the true emissions from these two methods have similar interquartile ranges (IQRs), ranging from ∼ 20 % to ∼ 60 % depending on the scenario. When taking cloud cover into account, these methods produce 274 and 318 instant estimates, respectively, from the ∼ 500 daily images, which cover significant portions of the plumes from the sources. Filtering the results based on the associated uncertainty estimates can improve the statistics of the IME and CSF methods but does so at the cost of a large decrease in the number of estimates. Due to a reliable estimation of uncertainty and, thus, a suitable selection of estimates, the CSF method achieves similar, if not better, accuracy statistics for instant estimates compared to the GP and LCSF methods after filtering. In general, the performance of retrieving single-image estimates improves when, in addition to XCO2 data, collocated NO2 data are used to characterize the structure of plumes. With respect to the estimates of annual emissions, the root mean square errors (RMSEs) for the most realistic benchmarking scenario are 20 % (GP), 27 % (CSF), 31 % (LCSF), 55 % (IME), and 79 % (Div). This study suggests that the Gaussian plume and/or cross-sectional approaches are currently the most efficient tools for providing estimates of CO2 emissions from satellite images, and their relatively light computational cost will enable the analysis of the massive amount of data to be provided by future satellite XCO2 imagery missions.

TRANSIENT ALTERATIONS IN THALAMO-CEREBELLAR FUNCTIONAL CONNECTIVITY IN PREMANIFEST HUNTINGTON'S DISEASE.

There are no disease modifying therapies for Huntington's disease (HD), a rare but fatal genetic neurodegenerative condition. To develop and test new management strategies, a better understanding of the mechanisms underlying HD progression is needed. Aberrant changes in thalamo-cortical and striato-cerebellar circuitry have been observed in asymptomatic HD, along with transient enlargement of the dentate nucleus. To evaluate the relationship between thalamo-cerebellar connectivity and HD progression. Prospective and retrospective. Patients with HD and healthy controls from a single-center dataset (n=34), and patients from the public TRACK-HD dataset (n=91). 3T and 7T. Thalamo-cerebellar connectivity was compared across patients and controls and related to motor scores and predicted years to symptom onset. Cross-sectional findings were validated within-patient by mapping changes in individual connectivity over time. HD effects on cognitive performance were also explored and related to connectivity. Kruskal-Wallis with post hoc Dunn's tests and Pearson correlations (p significant <0.05). In the 7T cohort, significant premanifest and control group differences in thalamo-dentate connectivity were observed (p Dunn <0.05, η 2 =.19-.22), with manifest HD connectivity approaching normative values. Thalamic connectivity with the dentate nucleus and anterior cerebellum also correlated with years to onset (p Den =0.06, r=0.42, p Ant <0.05, r=-0.45), together indicating potential transient functional alterations in premanifest HD. Similar patterns were observed between connectivity (thalamus to dentate nucleus and anterior lobe) and cognitive performance scores across all subjects (p<0.05, r Den =-0.17, r Ant =-0.18). In the premanifest TRACK-HD cohort, connectivity of multiple thalamo-cerebellar connections correlated with years to onset, revealing distinct patterns for patients with low versus high motor scores, again indicative of potential transient alterations. Exploratory non-parametric regression of serial imaging data further supported these findings. Transient changes in thalamo-cerebellar connectivity are seen in premanifest HD with increasing progression. More studies are needed to validate this potentially useful biomarker.

A framework for creating and analysing wordless picturebooks

Wordless picturebooks are texts that convey a narrative through a series of images with little or no written information and have gained popularity among academics, readers, and illustrators. These books have evolved from purely educational tools to complex and experimental works, attracting prominent artists and addressing challenging themes. Described as "crossover", wordless picturebooks frequently defy traditional age, addressing and attracting readers of all ages. While research on wordless picturebooks is growing, literature generally focuses on wordless picturebooks as educational tools for a younger readership and the aesthetic qualities of the finished picturebook - rarely examining the complex process of creating a wordless, visual narrative. In this article we reflect on the creation of a wordless crossover picturebook to gain insight into this process from Maria van Os's perspective as a practising illustrator and illustration lecturer. The practice-based approach sheds light on the wordless crossover picturebook as an artefact and the process undertaken by an author-illustrator when creating a wordless narrative. A framework for creating wordless narratives was conceptualised by distilling the findings from the practice and framing them within narrative theory. The framework is intended to aid students, lecturers, and practitioners in creating and analysing wordless picturebooks.

Characterization of adrenal glands on computed tomography with a 3D V-Net-based model

ObjectivesTo evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal.MethodsA total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. The other cohort, consisting of 959 patients with pathologically confirmed adrenal lesions (external validation dataset 1), was included for validation of the classification performance of this model. Then, another consecutive cohort of patients with a history of malignancy (N = 479) was used for validation in the screening population (external validation dataset 2). Parameters of sensitivity, accuracy, etc., were used, and the performance of the model was compared to the radiology report in these validation scenes.ResultsThe DSC of the test set of the segmentation model was 0.900 (0.810–0.965) (median (interquartile range)). The model showed sensitivities and accuracies of 99.7%, 98.3% and 87.2%, 62.2% in external validation datasets 1 and 2, respectively. It showed no significant difference comparing to radiology reports in external validation datasets 1 and lesion-containing groups of external validation datasets 2 (p = 1.000 and p > 0.05, respectively).ConclusionThe 3D V-Net-based segmentation model of adrenal lesions can be used for the binary classification of adrenal glands.Critical relevance statementA 3D V-Net-based segmentation model of adrenal lesions can be used for the detection of abnormalities of adrenal glands, with a high accuracy in the pre-surgical scene as well as a high sensitivity in the screening scene.Key PointsAdrenal lesions may be prone to inter-observer variability in routine diagnostic workflow.The study developed a 3D V-Net-based segmentation model of adrenal lesions with DSC 0.900 in the test set.The model showed high sensitivity and accuracy of abnormalities detection in different scenes.Graphical

Multinodular and Vacuolating Neuronal Tumors of the Cerebrum: A Systematic Review of the Literature.

Multinodular and vacuolating neuronal tumors (MVNTs) of the cerebrum are rare, seizure-related, low-grade tumors of the central nervous system that usually affect young adults. First described by Huse etal. in 2013, these neoplasms are usually located within the deep cortical ribbon and the superficial white matter and have a characteristic cytoarchitecture of cells with neuronal and glial differentiation that form multiple nodules with conspicuous vacuolation. Because of their benign nature and indolent clinical course, radiologically based differentiation from other entities is of paramount importance to avoid unnecessary surgical intervention. To the best of our knowledge, our study represents the first systematic review in the literature aiming to delineate the characteristics of MVNTs regarding epidemiology, clinical manifestation, histopathology, imaging, and management. PubMed/MEDLINE and SCOPUS databases were systematically investigated for MVNT cases until November 2023. The search yielded 29 case reports comprising 41 patients with a mean age of 32.6 years and 7 case series with 164 patients. MVNTs were most commonly located in the supratentorial compartment, affecting the temporal, frontal, or parietal lobes. Their most frequent initial clinical manifestation was either seizures or headaches. On conventional magnetic resonance imaging techniques, they usually appear hypointense in T1-weighted images and hyperintense in T2-weighted and fluid-attenuated inversion recovery images and lack perilesional edema or postcontrast enhancement. MVNTs do not seem to change size or recur, even after partial resection of the tumor, indicating their indolent course, and, thus, surveillance with serial magnetic resonance imaging is the most appropriate management technique for these lesions.