Using Augmented Reality Technology to Optimize Transfacet Lumbar Interbody Fusion: A Case Report.

The transfacet minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is a novel approach available for the management of lumbar spondylolisthesis. It avoids the need to manipulate either of the exiting or traversing nerve roots, both protected by the bony boundaries of the approach. With the advancement in operative technologies such as navigation, mapping, segmentation, and augmented reality (AR), surgeons are prompted to utilize these technologies to enhance their surgical outcomes. A 36-year-old male patient was complaining of chronic progressive lower back pain. He was found to have grade 2 L4/5 spondylolisthesis. We studied the feasibility of a trans-Kambin or a transfacet MIS-TLIF, and decided to proceed with the latter given the wider corridor it provides. Preoperative trajectory planning and level segmentation in addition to intraoperative navigation and image merging were all utilized to provide an AR model to guide us through the surgery. The use of AR can build on the safety and learning of novel surgical approaches to spine pathologies. However, larger high-quality studies are needed to further objectively analyze its impact on surgical outcomes and to expand on its application.

Incidence and imaging characteristics of difficult to detect retrospectively identified brain metastases in patients receiving repeat courses of stereotactic radiosurgery.

PURPOSE: During stereotactic radiosurgery (SRS) planning for brain metastases (BM), brain MRIs are reviewed to select appropriate targets based on radiographic characteristics. Some BM are difficult to detect and/or definitively identify and may go untreated initially, only to become apparent on future imaging. We hypothesized that in patients receiving multiple courses of SRS, reviewing the initial planning MRI would reveal early evidence of lesions that developed into metastases requiring SRS. METHODS: Patients undergoing two or more courses of SRS to BM within 6 months between 2016 and 2018 were included in this single-institution, retrospective study. Brain MRIs from the initial course were reviewed for lesions at the same location as subsequently treated metastases; if present, this lesion was classified as a "retrospectively identified metastasis" or RIM. RIMs were subcategorized as meeting or not meeting diagnostic imaging criteria for BM (+ DC or -DC, respectively). RESULTS: Among 683 patients undergoing 923 SRS courses, 98 patients met inclusion criteria. There were 115 repeat courses of SRS, with 345 treated metastases in the subsequent course, 128 of which were associated with RIMs found in a prior MRI. 58% of RIMs were + DC. 17 (15%) of subsequent courses consisted solely of metastases associated with + DC RIMs. CONCLUSION: Radiographic evidence of brain metastases requiring future treatment was occasionally present on brain MRIs from prior SRS treatments. Most RIMs were + DC, and some subsequent SRS courses treated only + DC RIMs. These findings suggest enhanced BM detection might enable earlier treatment and reduce the need for additional SRS.

Severe CNS involvement in a subset of long-term treated children with infantile-onset Pompe disease.

INTRODUCTION: The standard of care for patients with infantile-onset Pompe disease (IOPD) is enzyme replacement therapy (ERT), which does not cross the blood brain barrier. While neuromuscular manifestations of IOPD are well-described, central nervous system (CNS) manifestations of this disorder are far less characterized. Here we describe severe CNS-related neurological manifestations including seizures and encephalopathy in six individuals with IOPD. METHOD: We identified six children with IOPD who developed CNS manifestations such as seizures and/or encephalopathy. We studied their brain magnetic resonance imaging scans (MRIs) and graded the severity of white matter hyperintensities (WMHI) using the Fazekas scale scoring system as previously published. Longitudinal cognitive measures were available from 4/6 children. RESULTS: All six IOPD patients (4 males/2 females) had been treated with ERT for 12-15 years. Seizures and/or encephalopathy were noted at a median age at onset of 11.9 years (range 9-15 years). All were noted to have extensive WMHI in the brain MRIs and very high Fazekas scores which preceded the onset of neurological symptoms. Longitudinal IQ scores from four of these children suggested developmental plateauing. DISCUSSION: Among a subset of IOPD patients on long-term ERT, CNS manifestations including hyperreflexia, encephalopathy and seizures may become prominent, and there is likely an association between these symptoms and significant WMHI on MRI. Further study is needed to identify risk factors for CNS deterioration among children with IOPD and develop interventions to prevent neurological decline.

Using Novel Segmentation Technology to Define Safe Corridors for Minimally Invasive Posterior Lumbar Interbody Fusion.

BACKGROUND AND OBJECTIVES: There has been a rise in minimally invasive methods to access the intervertebral disk space posteriorly given their decreased tissue destruction, lower blood loss, and earlier return to work. Two such options include the percutaneous lumbar interbody fusion through the Kambin triangle and the endoscopic transfacet approach. However, without accurate preoperative visualization, these approaches carry risks of damaging surrounding structures, especially the nerve roots. Using novel segmentation technology, our goal was to analyze the anatomic borders and relative sizes of the safe triangle, trans-Kambin, and the transfacet corridors to assist surgeons in planning a safe approach and determining cannula diameters. METHODS: The areas of the safe triangle, Kambin, and transfacet corridors were measured using commercially available software (BrainLab, Munich, Germany). For each approach, the exiting nerve root, traversing nerve roots, theca, disk, and vertebrae were manually segmented on 3-dimensional T2-SPACE magnetic resonance imaging using a region-growing algorithm. The triangles' borders were delineated ensuring no overlap between the area and the nerves. RESULTS: A total of 11 patients (65.4 ± 12.5 years, 33.3% female) were retrospectively reviewed. The Kambin, safe, and transfacet corridors were measured bilaterally at the operative level. The mean area (124.1 ± 19.7 mm2 vs 83.0 ± 11.7 mm2 vs 49.5 ± 11.4 mm2) and maximum permissible cannula diameter (9.9 ± 0.7 mm vs 6.8 ± 0.5 mm vs 6.05 ± 0.7 mm) for the transfacet triangles were significantly larger than Kambin and the traditional safe triangles, respectively (P < .001). CONCLUSION: We identified, in 3-dimensional, the borders for the transfacet corridor: the traversing nerve root extending inferiorly until the caudal pedicle, the theca medially, and the exiting nerve root superiorly. These results illustrate the utility of preoperatively segmenting anatomic landmarks, specifically the nerve roots, to help guide decision-making when selecting the optimal operative approach.

Duke Liver Dataset: A Publicly Available Liver MRI Dataset with Liver Segmentation Masks and Series Labels.

The Duke Liver Dataset contains 2146 abdominal MRI series from 105 patients, including a majority with cirrhotic features, and 310 image series with corresponding manually segmented liver masks.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Two-Year Clinical and Radiographic Outcomes for Percutaneous Lumbar Interbody Fusion With an Expandable Titanium Cage Through Kambin's Triangle Without Facetectomy.

BACKGROUND: There has been heightened interest in performing percutaneous lumbar interbody fusions (percLIFs) through Kambin's triangle, an anatomic corridor allowing entrance into the disc space. However, due to its novelty, there are limited data regarding the long-term benefits of this procedure. Our objective was to determine the long-term efficacy and durability of the percutaneous insertion of an expandable titanium cage through Kambin's triangle without facetectomy. METHODS: A retrospective review of patients undergoing percLIF via Kambin's triangle using an expandable titanium cage was performed. Demographics, visual analog scale (VAS) scores, Oswestry Disability Index (ODI), radiographic measurements, perioperative variables, and complications were recorded. VAS, ODI, and radiographic measurements were compared with baseline using the generalized estimating equations assuming normally distributed data. Fusion was assessed with computed tomography (CT) at 1 and 2 years after the procedure. RESULTS: A total of 49 patients were included. Spondylolisthesis, lumbar lordosis (LL), sacral slope, pelvic tilt, and anterior/posterior disc space height were all significantly improved postoperatively at each time point of 3, 6, 12, and 24 months (P < 0.001). Pelvic incidence-LL mismatch decreased significantly at each follow-up (P < 0.001) with a mean reduction of 4° by 24 months. VAS back scores reduced by >2 points at the 6, 12, and 24 month follow-ups. ODI scores reduced by >15 points at the 12- and 24-month follow-ups. Of the patients who had 1- and 2-year CT images, fusion rates at those time points were 94.4% (17/18) and 87.5% (7/8), respectively. The mean annual rate of surgically significant adjacent segment disease was 2.74% through an average follow-up of 2.74 years. CONCLUSION: These results highlight that percLIF, a procedure done without an endoscope or facetectomy, can be performed using an expandable titanium cage through Kambin's triangle with excellent radiographic and clinical results. CLINICAL RELEVANCE: percLIF via Kambin's triangle is a safe and succesful procedure with long-term improvements in both clinical and radiographic outcomes.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa).

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.

Systematic Analysis of Common Factors Impacting Deep Learning Model Generalizability in Liver Segmentation.

Purpose: To investigate the effect of training data type on generalizability of deep learning liver segmentation models. Materials and Methods: This Health Insurance Portability and Accountability Act-compliant retrospective study included 860 MRI and CT abdominal scans obtained between February 2013 and March 2018 and 210 volumes from public datasets. Five single-source models were trained on 100 scans each of T1-weighted fat-suppressed portal venous (dynportal), T1-weighted fat-suppressed precontrast (dynpre), proton density opposed-phase (opposed), single-shot fast spin-echo (ssfse), and T1-weighted non-fat-suppressed (t1nfs) sequence types. A sixth multisource (DeepAll) model was trained on 100 scans consisting of 20 randomly selected scans from each of the five source domains. All models were tested against 18 target domains from unseen vendors, MRI types, and modality (CT). The Dice-Sørensen coefficient (DSC) was used to quantify similarity between manual and model segmentations. Results: Single-source model performance did not degrade significantly against unseen vendor data. Models trained on T1-weighted dynamic data generally performed well on other T1-weighted dynamic data (DSC = 0.848 ± 0.183 [SD]). The opposed model generalized moderately well to all unseen MRI types (DSC = 0.703 ± 0.229). The ssfse model failed to generalize well to any other MRI type (DSC = 0.089 ± 0.153). Dynamic and opposed models generalized moderately well to CT data (DSC = 0.744 ± 0.206), whereas other single-source models performed poorly (DSC = 0.181 ± 0.192). The DeepAll model generalized well across vendor, modality, and MRI type and against externally sourced data. Conclusion: Domain shift in liver segmentation appears to be tied to variations in soft-tissue contrast and can be effectively bridged with diversification of soft-tissue representation in training data.Keywords: Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Supervised Learning, CT, MRI, Liver Segmentation Supplemental material is available for this article. © RSNA, 2023.

Novel Approach to Percutaneous Lumbar Surgeries via Kambin's Triangle-Radiographic and Surgical Planning Analysis with Nerve Segmentation Technology.

OBJECTIVE: While Kambin's Triangle has become an ever more important anatomic window given its proximity to the exiting nerve root, there have been limited studies examining the effect of disease on the corridor. Our goal was to better understand how pathology can affect Kambin's Triangle, thereby altering the laterality of approach for percutaneous lumbar interbody fusion (percLIF). METHODS: The authors performed a single-center retrospective review of patients evaluated for percLIF. The areas of Kambin's Triangle were measured without and with nerve segmentation. For the latter, the lumbosacral nerve roots on 3-dimensional T2 magnetic resonance imaging were manually segmented. Next, the borders of Kambin's Triangle were delineated, ensuring no overlap between the area and nerve above. RESULTS: Fifteen patients (67.5 ± 9.7 years, 46.7% female) were retrospectively reviewed. We measured 150 Kambin's Triangles. The mean areas from L1-S1 were 50.0 ± 12.3 mm2, 73.8 ± 12.5 mm2, 83.8 ± 12.2 mm2, 88.5 ± 19.0 mm2, and 116 ± 29.3 mm2, respectively. When pathology was present, the areas significantly decreased at L4-L5 (P = 0.046) and L5-S1 (P = 0.049). Higher spondylolisthesis and smaller posterior disk heights were linked with decreased areas via linear regression analysis (P < 0.05). When nerve segmentation was used, the areas were significantly smaller from L1-L5 (P < 0.05). Among 11 patients who underwent surgery, none suffered from postoperative neuropathies. CONCLUSIONS: These results illustrate the feasibility of preoperatively segmenting lumbosacral nerves and measuring Kambin's Triangle to help guide surgical planning and determine the ideal laterality of approach for percLIF.

Head CT deep learning model is highly accurate for early infarct estimation.

Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r2 > 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-positive mixed territory infarcts), model sensitivity was 97%, specificity 99%, for detection of infarcts larger than the 70 mL volume threshold used for patient selection in several major randomized controlled trials of thrombectomy treatment.

How dimensions can guide surgical planning and training: a systematic review of Kambin's triangle.

OBJECTIVE: The authors sought to analyze the current literature to determine dimensional trends across the lumbar levels of Kambin's triangle, clarify the role of imaging techniques for preoperative planning, and understand the effect of inclusion of the superior articular process (SAP). This compiled knowledge of the triangle is needed to perform successful procedures, reduce nerve root injuries, and help guide surgeons in training. METHODS: The authors performed a search of multiple databases using combinations of keywords: Kambin's triangle, size, measurement, safe triangle, and bony triangle. Articles were included if their main findings included measurement of Kambin's triangle. The PubMed, Scopus, Ovid, Cochrane, Embase, and Medline databases were systematically searched for English-language articles with no time frame restrictions through July 2022. RESULTS: Eight studies comprising 132 patients or cadavers were included in the study. The mean ± SD age was 66.69 ± 9.6 years, and 53% of patients were male. Overall, the size of Kambin's triangle increased in area moving down vertebral levels, with L5-S1 being the largest (133.59 ± 4.36 mm2). This trend followed a linear regression model when SAP was kept (p = 0.008) and removed (p = 0.003). There was also a considerable increase in the size of Kambin's triangle if the SAP was removed. CONCLUSIONS: Here, the authors have provided the first reported systematic review of the literature of Kambin's triangle, its measurements at each lumbar level, and key areas of debate related to the definition of the working safe zone. These findings indicate that CT is heavily utilized for imaging of the safe zone, the area of Kambin's triangle tends to increase caudally, and variation exists between patients. Future studies should focus on using advanced imaging techniques for preoperative planning and establishing guidelines for surgeons.

Novel Merging of CT and MRI to Allow for Safe Navigation into Kambin's Triangle for Percutaneous Lumbar Interbody Fusion-Initial Case Series Investigating Safety and Efficacy.

BACKGROUND: For percutaneous lumbar fusion (percLIF), magnetic resonance imaging and computed tomography are critical to defining surgical corridors. Currently, these scans are performed separately, and surgeons then use fluoroscopy or neuromonitoring to guide instruments through Kambin's triangle. However, anatomic variations and intraoperative positional changes are possible, meaning that safely accessing Kambin's triangle remains a challenge because nerveroot visualization without endoscopes has not been thoroughly described. OBJECTIVE: To overcome the known challenges of percLIF and reduce the likelihood of iatrogenic injuries by showing real-time locations of neural and bony anatomy. METHODS: The authors demonstrate an intraoperative navigational platform that applies nerve root segmentation and image fusion to assist with percLIF. Five patients from a single institution were included. RESULTS: Of the 5 patients, the mean age was 71 ± 8 years and 3 patients (60%) were female. One patient had general anesthesia while the remaining 4 patients underwent awake surgery with spinal anesthesia. The mean area for the L4-L5 Kambin's triangle was 76.1 ± 14.5 mm 2 . A case example is shown where the side of approach was based on the fact that Kambin's triangle was larger on one side compared with the other. The mean operative time was 170 ± 17 minutes, the mean blood loss was 32 ± 16 mL, and the mean hospital length of stay was 19.6 ± 8.3 hours. No patients developed postoperative complications. CONCLUSION: This case series demonstrates the successful and safe application of nerve segmentation using magnetic resonance imaging/computed tomography fusion to perform percLIF and provide positive patient outcomes.

A Deep Learning-Based Computer Aided Detection (CAD) System for Difficult-to-Detect Brain Metastases.

PURPOSE: We sought to develop a computer-aided detection (CAD) system that optimally augments human performance, excelling especially at identifying small inconspicuous brain metastases (BMs), by training a convolutional neural network on a unique magnetic resonance imaging (MRI) data set containing subtle BMs that were not detected prospectively during routine clinical care. METHODS AND MATERIALS: Patients receiving stereotactic radiosurgery (SRS) for BMs at our institution from 2016 to 2018 without prior brain-directed therapy or small cell histology were eligible. For patients who underwent 2 consecutive courses of SRS, treatment planning MRIs from their initial course were reviewed for radiographic evidence of an emerging metastasis at the same location as metastases treated in their second SRS course. If present, these previously unidentified lesions were contoured and categorized as retrospectively identified metastases (RIMs). RIMs were further subcategorized according to whether they did (+DC) or did not (-DC) meet diagnostic imaging-based criteria to definitively classify them as metastases based upon their appearance in the initial MRI alone. Prospectively identified metastases (PIMs) from these patients, and from patients who only underwent a single course of SRS, were also included. An open-source convolutional neural network architecture was adapted and trained to detect both RIMs and PIMs on thin-slice, contrast-enhanced, spoiled gradient echo MRIs. Patients were randomized into 5 groups: 4 for training/cross-validation and 1 for testing. RESULTS: One hundred thirty-five patients with 563 metastases, including 72 RIMS, met criteria. For the test group, CAD sensitivity was 94% for PIMs, 80% for +DC RIMs, and 79% for PIMs and +DC RIMs with diameter <3 mm, with a median of 2 false positives per patient and a Dice coefficient of 0.79. CONCLUSIONS: Our CAD model, trained on a novel data set and using a single common MR sequence, demonstrated high sensitivity and specificity overall, outperforming published CAD results for small metastases and RIMs - the lesion types most in need of human performance augmentation.

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

Imaging AI in Practice: A Demonstration of Future Workflow Using Integration Standards.

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.