Deep Learning Accelerated Brain Diffusion-Weighted MRI with Super Resolution Processing.

OBJECTIVES: To investigate the clinical feasibility and image quality of accelerated brain diffusion-weighted imaging (DWI) with deep learning image reconstruction and super resolution. METHODS: 85 consecutive patients with clinically indicated MRI at a 3 T scanner were prospectively included. Conventional diffusion-weighted data (c-DWI) with four averages were obtained. Reconstructions of one and two averages, as well as deep learning diffusion-weighted imaging (DL-DWI), were accomplished. Three experienced readers evaluated the acquired data using a 5-point Likert scale regarding overall image quality, overall contrast, diagnostic confidence, occurrence of artefacts and evaluation of the central region, basal ganglia, brainstem, and cerebellum. To assess interrater agreement, Fleiss' kappa (Ï°) was determined. Signal intensity (SI) levels for basal ganglia and the central region were estimated via automated segmentation, and SI values of detected pathologies were measured. RESULTS: Intracranial pathologies were identified in 35 patients. DL-DWI was significantly superior for all defined parameters, independently from applied averages (p-value <0.001). Optimum image quality was achieved with DL-DWI by utilizing a single average (p-value <0.001), demonstrating very good (80.9%) to excellent image quality (14.5%) in nearly all cases, compared to 12.5% with very good and 0% with excellent image quality for c-MRI (p-value <0.001). Comparable results could be shown for diagnostic confidence. Inter-rater Fleiss' Kappa demonstrated moderate to substantial agreement for virtually all defined parameters, with good accordance, particularly for the assessment of pathologies (p = 0.74). Regarding SI values, no significant difference was found. CONCLUSION: Ultra-fast diffusion-weighted imaging with super resolution is feasible, resulting in highly accelerated brain imaging while increasing diagnostic image quality.

Sex-specific Impact of the first COVID-19 Lockdown on Age Structure and Case Acuity at Admission in a Patient Population in southwestern Germany: a retrospective comparative Study in Neuroradiology.

OBJECTIVES: A hard lockdown was presumed to lead to delayed diagnosis and treatment of serious diseases, resulting in higher acuity at admission. This should be elaborated based on the estimated acuity of the cases, changes in findings during hospitalisation, age structure and biological sex. DESIGN: Retrospective monocentric cross-sectional study. SETTING: German Neuroradiology Department at a . PARTICIPANTS: In 2019, n=1158 patients were admitted in contrast to n=884 during the first hard lockdown in 2020 (11th-13th week). MAIN OUTCOME MEASURES: Three radiologists evaluated the initial case acuity, classified them into three groups (not acute, subacute and acute), and evaluated if there was a relevant clinical deterioration. The data analysis was conducted using non-parametric methods and multivariate regression analysis. RESULTS: A 24% decrease in the number of examinations from 2019 to 2020 (p=0.025) was revealed. In women, the case acuity increased by 21% during the lockdown period (p=0.002). A 30% decrease in acute cases in men was observable (in women 5% decrease). Not acute cases decreased in both women and men (47%; 24%), while the subacute cases remained stable in men (0%) and decreased in women (28%). Regression analysis revealed the higher the age, the higher the acuity (p<0.001 in both sexes), particularly among women admitted during the lockdown period (p=0.006). CONCLUSION: The lockdown led to a decrease in neuroradiological consultations, with delays in seeking medical care. In women, the number of most severe cases remained stable, whereas the mean case acuity and age increased. This could be due to greater pandemic-related anxiety among women, however, with severe symptoms they were seeking for medical help. In contrast in men, the absolute number of most severe cases decreased, whereas the mean acuity and age remained nearly unaffected. This could be attributable to a reduced willingness to seek for medical consultation.

Ultrafast Brain MRI with Deep Learning Reconstruction for Suspected Acute Ischemic Stroke.

Background Deep learning (DL)-accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic performance of DL-accelerated MRI reconstructions in acute suspected stroke are lacking. Purpose To investigate the interchangeability of DL-accelerated MRI with conventional MRI in patients with suspected acute ischemic stroke at 1.5 T. Materials and Methods In this prospective study, 211 participants with suspected acute stroke underwent clinically indicated MRI at 1.5 T between June 2022 and March 2023. For each participant, conventional MRI (including T1-weighted, T2-weighted, T2*-weighted, T2 fluid-attenuated inversion-recovery, and diffusion-weighted imaging; 14 minutes 18 seconds) and DL-accelerated MRI (same sequences; 3 minutes 4 seconds) were performed. The primary end point was the interchangeability between conventional and DL-accelerated MRI for acute ischemic infarction detection. Secondary end points were interchangeability regarding the affected vascular territory and clinically relevant secondary findings (eg, microbleeds, neoplasm). Three readers evaluated the overall occurrence of acute ischemic stroke, affected vascular territory, clinically relevant secondary findings, overall image quality, and diagnostic confidence. For acute ischemic lesions, size and signal intensities were assessed. The margin for interchangeability was chosen as 5%. For interrater agreement analysis and interrater reliability analysis, multirater Fleiss κ and the intraclass correlation coefficient, respectively, was determined. Results The study sample consisted of 211 participants (mean age, 65 years ± 16 [SD]); 123 male and 88 female). Acute ischemic stroke was confirmed in 79 participants. Interchangeability was demonstrated for all primary and secondary end points. No individual equivalence indexes (IEIs) exceeded the interchangeability margin of 5% (IEI, -0.002 [90% CI: -0.007, 0.004]). Almost perfect interrater agreement was observed (P > .91). DL-accelerated MRI provided higher overall image quality (P < .001) and diagnostic confidence (P < .001). The signal properties of acute ischemic infarctions were similar in both techniques and demonstrated good to excellent interrater reliability (intraclass correlation coefficient, ≥0.8). Conclusion Despite being four times faster, DL-accelerated brain MRI was interchangeable with conventional MRI for acute ischemic lesion detection. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Haller in this issue.

Reduction of Distortion Artifacts in Brain MRI Using a Field Map-based Correction Technique in Diffusion-weighted Imaging : A Prospective Study.

PURPOSE: The aim of this study was to evaluate the image quality and feasibility of a field map-based technique to correct for susceptibility-induced geometric distortions which are typical for diffusion EPI brain imaging. METHODS: We prospectively included 52 patients during clinical routine in this single-center study. All scans were performed on a 3T MRI. Patients' indications for MRI mainly consisted of suspected stroke due to the clinical presentation. For the morphological comparison of the corrected and uncorrected EPI diffusion, three experienced radiologists assessed the image quality of the sequences in a blinded and randomized fashion using a Likert scale (1 being poor; 5 being excellent). To ensure comparability of the two methods, an additional quantitative analysis of the apparent diffusion coefficient (ADC) was performed. RESULTS: Corrected EPI diffusion was rated significantly superior in all the selected categories: overall level of artifacts (p < 0.001), degree of distortion at the frontal, temporal, occipital and brainstem levels (p < 0.001), conspicuousness of ischemic lesions (p < 0.001), image quality (p < 0.001), naturality (p < 0.001), contrast (p < 0.001), and diagnostic confidence (p < 0.001). CONCLUSION: Corrected EPI diffusion offers a significant reduction of geometric distortion in all evaluated brain regions and an improved conspicuousness of ischemic lesions. Image quality, overall artifacts, naturality, contrast and diagnostic confidence were also rated superior in comparison to uncorrected EPI diffusion.

Measurement of Retrobulbar Blood Flow and Vascular Reactivity-Relevance for Ocular and Cardiovascular Diseases.

Abnormal retrobulbar hemodynamics have been linked to the development of various ocular diseases, including glaucoma, age-related macular degeneration, and diabetic retinopathy. Additionally, altered retrobulbar blood flow has been observed in patients with severe cardiovascular diseases, including carotid artery occlusion, stroke, heart failure, and acute coronary syndrome. Due to the complex and intricate anatomy of retrobulbar blood vessels and their location behind the eyeball, measurement of retrobulbar blood flow and vascular reactivity, as well as the interpretation of the findings, are challenging. Various methods, such as color Doppler imaging, computed tomography angiography or magnetic resonance imaging, have been employed to assess retrobulbar blood flow velocities in vivo. Color Doppler imaging represents a fast and non-invasive method to measure retrobulbar blood flow velocities in vivo. While no information about vessel diameter can be gained performing this method, computed tomography angiography and magnetic resonance imaging provide information about vessel diameter and detailed information on the anatomical course. Additionally, ex vivo studies, such as myography, utilizing genetically modified animal models may provide high optical resolution for functional vascular investigations in these small vessels. To our best knowledge, this is the first review, presenting a detailed overview of methods aiming to evaluate retrobulbar blood flow and vascular reactivity in both humans and laboratory animals. Furthermore, we will summarize the disturbances observed in retrobulbar blood flow in retinal, optic nerve, and cardiovascular diseases.

DSA-Based 2D Perfusion Measurements in Delayed Cerebral Ischemia to Estimate the Clinical Outcome in Patients with Aneurysmal Subarachnoid Hemorrhage: A Technical Feasibility Study.

(1) Background: To predict clinical outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) and delayed cerebral ischemia (DCI) by assessment of the cerebral perfusion using a 2D perfusion angiography (2DPA) time-contrast agent (CA) concentration model. (2) Methods: Digital subtraction angiography (DSA) data sets of n = 26 subjects were acquired and post-processed focusing on changes in contrast density using a time-concentration model at three time points: (i) initial presentation with SAH (T0); (ii) vasospasm-associated acute clinical impairment (T1); and (iii) directly after endovascular treatment (T2) of SAH-associated large vessel vasospasm (LVV), which resulted in n = 78 data sets. Maximum slope (MS in SI/ms), time-to-peak (TTP in ms), and maximum amplitude of a CA bolus (dSI) were measured in brain parenchyma using regions of interest (ROIs). First, acquired parameters were standardized to the arterial input function (AIF) and then statistically analyzed as mean values. Additionally, data were clustered into two subsets consisting of patients with regredient or with stable/progredient symptoms (or Doppler signals) after endovascular treatment (n = 10 vs. n = 16). (3) Results: Perfusion parameters (MS, TTP, and dSI) differed significantly between T0 and T1 (p = 0.003 each). Significant changes between T1 and T2 were only detectable for MS (0.041 ± 0.016 vs. 0.059 ± 0.026; p = 0.011) in patients with regredient symptoms at T2 (0.04 ± 0.012 vs. 0.066 ± 0.031; p = 0.004). For dSI, there were significant differences between T0 and T2 (5095.8 ± 2541.9 vs. 3012.3 ± 968.3; p = 0.001), especially for those with stable symptoms at T2 (5685.4 ± 2967.2 vs. 3102.8 ± 1033.2; p = 0.02). Multiple linear regression analysis revealed that a) the difference in MS between T1 and T2 and b) patient's age (R = 0.6; R2 = 0.34; p = 0.009) strongly predict the modified Rankin Scale (mRS) at discharge. (4) Conclusions: 2DPA allows the direct measurement of treatment effects in SAH associated DCI and may be used to predict outcomes in these critically ill patients.

Clinical feasibility and validation of the accelerated T2 mapping sequence GRAPPATINI in brain imaging.

Rationale and objectives: To prospectively evaluate feasibility and robustness of an accelerated T2 mapping sequence (GRAPPATINI) in brain imaging and to assess its synthetic T2-weighted images (sT2w) in comparison with a standard T2-weighted sequence (T2 TSE). Material and methods: Volunteers were included to evaluate the robustness and consecutive patients for morphological evaluation. They were scanned on a 3 T MR-scanner. Healthy volunteers underwent GRAPPATINI of the brain three times (day 1: scan/rescan; day 2: follow-up). Patients between the ages of 18 and 85 years who were able to provide written informed consent and who had no MRI contraindications were included. For morphological comparison two radiologists with 5 and 7 years of experience in brain MRI evaluated image quality using a Likert scale (1 being poor, 4 being excellent) in a blinded and randomized fashion. Results: Images were successfully acquired in ten volunteers with a mean age of 25 years (ranging from 22 to 31 years) and 52 patients (23 men/29 women) with a mean age of 55 years (range of 22-83 years). Most brain regions showed repeatable and reproducible T2 values (rescan: CoV 0.75%-2.06%, ICC 69%-92.3%; follow-up: CoV 0.41%-1.59%, ICC 79.4%-95.8%), except for the caudate nucleus (rescan: CoV 7.25%, ICC 66.3%; follow-up: CoV 4.78%, ICC 80.9%). Image quality of sT2w was rated inferior to T2 TSE (median for T2 TSE: 3; sT2w: 1-2), but measurements revealed good interrater reliability of sT2w (lesion counting: ICC 0.85; diameter measure: ICC 0.68 and 0.67). Conclusion: GRAPPATINI is a feasible and robust T2 mapping sequence of the brain on intra- and intersubject level. The resulting sT2w depict brain lesions comparable to T2 TSE despite its inferior image quality.

Deep learning for accurately recognizing common causes of shoulder pain on radiographs.

OBJECTIVE: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. MATERIALS AND METHODS: We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity. RESULTS: The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification. CONCLUSION: CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload.

CT diagnostics of pulmonary embolism: Does iodine delivery rate still affect image quality in iterative reconstruction?

BACKGROUND: Computed tomographic (CT) imaging in suspected pulmonary artery embolism represents the standard procedure. Studies without iterative reconstruction proved beneficial using increased iodine delivery rate (IDR). This study compares image quality in pulmonary arteries on iteratively reconstructed CT images of patients with suspected pulmonary embolism using different IDR. MATERIAL AND METHODS: 1065 patients were included in the study. Patients in group A (n = 493) received an iodine concentration of 40 g/100 ml (IDR 1.6 g/s) and patients in group B (n = 572) an iodine concentration of 35 g/100 ml (IDR 1.4 g/s) at a flow rate of 4 ml/s. A 80-detector spiral CT scanner with iterative reconstruction was used. We measured mean density values in truncus pulmonalis, both pulmonary arteries and segmental pulmonary arteries. Subjectively, the contrast of apical and basal pulmonary arteries was determined on a 4-point Likert scale. RESULTS: Radiodensity was significantly higher in all measured pulmonary arteries using the increased IDR (p < 0.001). TP: 483.0 HU vs. 393.4 HU; APD: 452.1 HU vs. 372.1 HU; APS: 448.2 HU vs. 374.4 HU; ASP: 443.9 vs. 374.4 HU. Subjectively assessed contrast enhancement in apical (p = 0.077) and basal (p = 0.429) lung sections showed no significant differences. CONCLUSION: Higher IDR improves objective image quality in all patients with significantly higher radiodensities by iterative reconstruction. Subjective contrast of apical and basal lung sections did not differ. The number of non-sufficient scans decreased with high IDR.