Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction.

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical-pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

BCOR::CREBBP fusion in malignant neuroepithelial tumor of CNS expands the spectrum of methylation class CNS tumor with BCOR/BCOR(L1)-fusion.

Methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" was recently defined based on methylation profiling and tSNE analysis of a series of 21 neuroepithelial tumors with predominant presence of a BCOR fusion and/or characteristic CNV breakpoints at chromosome 22q12.31 and chromosome Xp11.4. Clear diagnostic criteria are still missing for this tumor type, specially that BCOR/BCOR(L1)-fusion is not a consistent finding in these tumors despite being frequent and that none of the Heidelberger classifier versions is able to clearly identify these cases, in particular tumors with alternative fusions other than those involving BCOR, BCORL1, EP300 and CREBBP. In this study, we introduce a BCOR::CREBBP fusion in an adult patient with a right temporomediobasal tumor, for the first time in association with methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" in addition to 35 cases of CNS neuroepithelial tumors with molecular and histopathological characteristics compatible with "CNS tumor with BCOR/BCOR(L1)-fusion" based on a comprehensive literature review and data mining in the repository of 23 published studies on neuroepithelial brain Tumors including 7207 samples of 6761 patients. Based on our index case and the 35 cases found in the literature, we suggest the archetypical histological and molecular features of "CNS tumor with BCOR/BCOR(L1)-fusion". We also present four adult diffuse glioma cases including GBM, IDH-Wildtype and Astrocytoma, IDH-Mutant with CREBBP fusions and describe the necessity of complementary molecular analysis in "CNS tumor with BCOR/BCOR(L1)-alterations for securing a final diagnosis.

Evolution of a Meningothelial Meningioma: From WHO Grade 1 to Anaplastic Grade 3 with Extracranial Metastasis Including Extensive Liver Metastasis.

A 61-year-old patient was diagnosed with a left-sided falx meningioma. Histopathological analysis following extirpation showed a meningothelial meningioma ZNS WHO grade 1 with sparse mitoses. Over the course of 12 years, the patient received irradiation (54.0 Gy), peptide radio-receptor therapy (177Lu-DOMITATE) and targeted therapy (mTOR inhibitor). Follow-up imaging revealed an increased size of the residual tumor. Due to increased liver function parameters, imaging of the liver was performed, showing widespread space-occupying lesions with atypical appearance. Biopsy revealed metastasis of the meningioma, now with 2.7 mitoses/mm2, necrosis and homozygous CDKN2A/B deletion, corresponding to an anaplastic CNS meningioma WHO grade 3. A second small meningioma on the left petroclival side has been consistent in size over 12 years. Metastatic meningiomas pose a pertinent clinical challenge due to poor prognosis. The lung, bone, liver and cervical lymph nodes are the most common sites of extracranial metastasis. According to the World Health Organization criteria, the most important predictive factor for recurrence and metastasis is the tumor grade.

Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality.

INTRODUCTION: Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2DLR) and T1 TSE (T1DLR) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence. MATERIAL AND METHODS: This retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale. RESULTS: Accelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging. CONCLUSION: In conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.

Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality.

BACKGROUND AND PURPOSE: This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging. METHODS: In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES ) and DL TSE sequences (TSEDL ) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert scale. RESULTS: TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated imaging. CONCLUSION: The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.

Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality.

RATIONALE AND OBJECTIVES: Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations. Therefore, the purpose of this study was to investigate the impact on image quality and diagnostic confidence of an innovative deep learning-based accelerated FLAIR (FLAIRDLR) sequence of the brain compared to conventional (standard) FLAIR (FLAIRS) imaging. MATERIALS AND METHODS: Seventy consecutive patients with staging cerebral MRIs were retrospectively enrolled in this single-center study. The FLAIRDLR was conducted using the same MRI acquisition parameters as the FLAIRS sequence, except for a higher acceleration factor for parallel imaging (from 2 to 4), which resulted in a shorter acquisition time of 1:39 minute instead of 2:40 minutes (-38%). Two specialized neuroradiologists evaluated the imaging datasets using a Likert scale that ranged from 1 to 4, with 4 indicating the best score for the following parameters: sharpness, lesion demarcation, artifacts, overall image quality, and diagnostic confidence. Additionally, the image preference of the readers and the interreader agreement were assessed. RESULTS: The average age of the patients was 63 ± 11years. FLAIRDLR exhibited significantly less image noise than FLAIRS, with P-values of< .001 and< .05, respectively. The sharpness of the images and the ability to detect lesions were rated higher in FLAIRDLR, with a median score of 4 compared to a median score of 3 in FLAIRS (P-values of<.001 for both readers). In terms of overall image quality, FLAIRDLR was rated superior to FLAIRS, with a median score of 4 vs 3 (P-values of<.001 for both readers). Both readers preferred FLAIRDLR in 68/70 cases. CONCLUSION: The feasibility of deep learning FLAIR brain imaging was shown with additional 38% reduction in examination time compared to standard FLAIR imaging. Furthermore, this technique has shown improvement in image quality, noise reduction, and lesion demarcation.

A novel fully automated method for measuring ASPECTS to improve stroke diagnosis: Comparison to traditional ASPECTS.

BACKGROUND AND PURPOSE: To compare the accuracy of subjective Alberta Stroke Program Early CT Score (sASPECTS) evaluation and that of an automated prototype software (aASPECTS) on nonenhanced CT (NECT) in patients with early anterior territory stroke and controls using side-to-side quantification of hypoattenuated brain areas. METHODS: We retrospectively analyzed the NECT scans of 42 consecutive patients with ischemic stroke before reperfusion and 42 controls using first sASPECTS and subsequently aASPECTS. We assessed the differences in Alberta Stroke Program Early CT Score (ASPECTS) and calculated the sensitivity and specificity of NECT with CT perfusion, whereas cerebral blood volume (CBV) served as the reference standard for brain infarction. RESULTS: The clot was located in the middle cerebral artery (MCA) in 47.6% of cases and the internal carotid artery (ICA) in 28.6% of cases. Ten cases presented combined ICA and MCA occlusions. The stroke was right sided in 52.4% of cases and left sided in 47.6%. Reader-based NECT analysis yielded a median sASPECTS of 10. The median CBV-based ASPECTS was 7. Compared to the area of decreased CBV, sASPECTS yielded a sensitivity of 12.5% and specificity of 86.8%. The software prototype (aASPECTS) yielded an overall sensitivity of 65.5% and a specificity of 92.2%. The interreader agreement for ASPECTS evaluation of admission NECT and follow-up CT was almost perfect (κ = .93). The interreader agreement of the CBV color map evaluation was substantial (κ = .77). CONCLUSIONS: aASPECTS of NECT can outperform sASPECTS for stroke detection.

Automated Intracranial Clot Detection: A Promising Tool for Vascular Occlusion Detection in Non-Enhanced CT.

(1) Background: to test the diagnostic performance of a fully convolutional neural network-based software prototype for clot detection in intracranial arteries using non-enhanced computed tomography (NECT) imaging data. (2) Methods: we retrospectively identified 85 patients with stroke imaging and one intracranial vessel occlusion. An automated clot detection prototype computed clot location, clot length, and clot volume in NECT scans. Clot detection rates were compared to the visual assessment of the hyperdense artery sign by two neuroradiologists. CT angiography (CTA) was used as the ground truth. Additionally, NIHSS, ASPECTS, type of therapy, and TOAST were registered to assess the relationship between clinical parameters, image results, and chosen therapy. (3) Results: the overall detection rate of the software was 66%, while the human readers had lower rates of 46% and 24%, respectively. Clot detection rates of the automated software were best in the proximal middle cerebral artery (MCA) and the intracranial carotid artery (ICA) with 88-92% followed by the more distal MCA and basilar artery with 67-69%. There was a high correlation between greater clot length and interventional thrombectomy and between smaller clot length and rather conservative treatment. (4) Conclusions: the automated clot detection prototype has the potential to detect intracranial arterial thromboembolism in NECT images, particularly in the ICA and MCA. Thus, it could support radiologists in emergency settings to speed up the diagnosis of acute ischemic stroke, especially in settings where CTA is not available.

Diagnostic Accuracy of 128-Slice Single-Source CT for the Detection of Dislocated Bucket Handle Meniscal Tears in the Setting of an Acute Knee Trauma-Correlation with MRI and Arthroscopy.

(1) Background: Meniscal tears are amongst the most common knee injuries. Dislocated bucket handle meniscal tears in particular should receive early intervention. The purpose of this study was to evaluate the diagnostic performance of CT in detecting dislocated bucket handle meniscal tears compared with the gold-standard MRI and arthroscopy. (2) Methods: Retrospectively, 96 consecutive patients underwent clinically indicated CT of the knee for suspected acute traumatic knee injuries (standard study protocol, 120 kV, 90 mAs). Inclusion criteria were the absence of an acute fracture on CT and a timely MRI (<6 months). Corresponding arthroscopy was assessed. Two experienced musculoskeletal radiologists analyzed the images for dislocated bucket handle meniscal tears, associated signs thereof (double posterior cruciate ligament sign, double delta sign, disproportional posterior horn sign), and subjective diagnostic confidence on a 5-point-Likert scale (1 = 'non-diagnostic image quality', 5 = 'very confident'). (3) Results: Dislocated bucket handle meniscal tears were detected on CT by standard three-plane bone kernel reconstructions with a sensitivity of 90.7% and a specificity of 99.3% by transferring the knowledge of established MRI signs. The additional use of soft-tissue kernel reconstructions in three planes increased the sensitivity by 4.0% to 94.7%, specificity to 100%, inter-rater agreement to 1.0, and the diagnostic confidence of both readers improved to a median 4/5 ('confident') in both readers. (4) Conclusions: Trauma CT scan of the knee with three-plane soft-tissue reconstructions delivers the potential for the detection of dislocated bucket handle meniscal tears with very high diagnostic accuracy.