CT MR Imaging Research Articles

Endovascular thrombectomy vs best medical management for late presentation acute ischaemic stroke with large vessel occlusion without CT perfusion or MR imaging selection: A systematic review and meta-analysis

BackgroundThe efficacy and safety of endovascular thrombectomy (EVT) beyond 6 hours from stroke onset for patients with large vessel occlusion (LVO) selected without CT perfusion(CTP) or MR imaging(MRI) is undetermined. We conducted a systematic review and meta-analysis of the current literature comparing outcomes for late presenting patients with LVO treated by best medical management (BMM) with those selected for EVT based only on non-contrast CT(NCCT)/CT angiography(CTA) (without CTP or MRI). MethodsPRISMA guidelines were employed. The primary outcome was functional independence (modified Rankin Scale 0-2) at 3 months. Secondary outcomes were symptomatic intracranial haemorrhage (sICH) and mortality at 3 months. Data were analysed using the random-effects model. ResultsSix studies of 2083 patients, including three randomised controlled trials, were included; 1271 patients were treated with EVT and 812 patients with BMM. Compared to BMM, patients treated with EVT demonstrated higher odds of achieving functional independence (39.0 % EVT vs 22.0 % BMM; OR = 2.55, 95 %CI 1.61-4.05,p < 0.0001, I2 = 74 %). The rates of sICH (OR = 2.09, 95 %CI 0.86-5.04,p = 0.10) and mortality (OR = 0.62, 95 %CI 0.35-1.10,p = 0.10) were not significantly different between each cohort. ConclusionCompared to BMM, late presenting stroke patients selected for EVT eligibility with NCCT/CTA only and treated with EVT achieved significantly higher rates of functional independence at 90 days, without increasing the incidence of sICH or mortality. Whilst these findings indicate that NCCT/CTA only may be used for EVT eligibility selection for patients who present beyond 6 hours from stroke onset, the results should be interpreted with caution due to the substantial heterogeneity between studies.

Synthetic lumbar MRI can aid in diagnosis and treatment strategies based on self-pix networks

CT and MR tools are commonly used to diagnose lumbar fractures (LF). However, numerous limitations have been found in practice. The aims of this study were to innovate and develop a spinal disease-specific neural network and to evaluate whether synthetic MRI of the LF affected clinical diagnosis and treatment strategies. A total of 675 LF patients who met the inclusion and exclusion criteria were included in the study. For each participant, two mid-sagittal CT and T2-weighted MR images were selected; 1350 pairs of LF images were also included. A new Self-pix based on Pix2pix and Self-Attention was constructed. A total of 1350 pairs of CT and MR images, which were randomly divided into a training group (1147 pairs) and a test group (203 pairs), were fed into Pix2pix and Self-pix. The quantitative evaluation included PSNR and SSIM (PSNR1 and SSIM1: real MR images and Pix2pix-generated MR images; PSNR2 and SSIM2: real MR images and Self-pix-generated MR images). The qualitative evaluation, including accurate diagnosis of acute fractures and accurate selection of treatment strategies based on Self-pix-generated MRI, was performed by three spine surgeons. In the LF group, PSNR1 and PSNR2 were 10.884 and 11.021 (p < 0.001), and SSIM1 and SSIM2 were 0.766 and 0.771 (p < 0.001), respectively. In the ROI group, PSNR1 and PSNR2 were 12.350 and 12.670 (p = 0.004), and SSIM1 and SSIM2 were 0.816 and 0.832 (p = 0.005), respectively. According to the qualitative evaluation, Self-pix-generated MRI showed no significant difference from real MRI in identifying acute fractures (p = 0.689), with a good sensitivity of 84.36% and specificity of 96.65%. No difference in treatment strategy was found between the Self-pix-generated MRI group and the real MRI group (p = 0.135). In this study, a disease-specific GAN named Self-pix was developed, which demonstrated better image generation performance compared to traditional GAN. The spine surgeon could accurately diagnose LF and select treatment strategies based on Self-pix-generated T2 MR images.

Bidirectional brain image translation using transfer learning from generic pre-trained models

Brain imaging plays a crucial role in the diagnosis and treatment of various neurological disorders, providing valuable insights into the structure and function of the brain. Techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) enable non-invasive visualization of the brain, aiding in the understanding of brain anatomy, abnormalities, and functional connectivity. However, cost and radiation dose may limit the acquisition of specific image modalities, so medical image synthesis can be used to generate required medical images without actual addition. CycleGAN and other GANs are valuable tools for generating synthetic images across various fields. In the medical domain, where obtaining labeled medical images is labor-intensive and expensive, addressing data scarcity is a major challenge. Recent studies propose using transfer learning to overcome this issue. This involves adapting pre-trained CycleGAN models, initially trained on non-medical data, to generate realistic medical images. In this work, transfer learning was applied to the task of MR-CT image translation and vice versa using 18 pre-trained non-medical models, and the models were fine-tuned to have the best result. The models’ performance was evaluated using four widely used image quality metrics: Peak-signal-to-noise-ratio, Structural Similarity Index, Universal Quality Index, and Visual Information Fidelity. Quantitative evaluation and qualitative perceptual analysis by radiologists demonstrate the potential of transfer learning in medical imaging and the effectiveness of the generic pre-trained model. The results provide compelling evidence of the model’s exceptional performance, which can be attributed to the high quality and similarity of the training images to actual human brain images. These results underscore the significance of carefully selecting appropriate and representative training images to optimize performance in brain image analysis tasks.

THE EFFECT OF HEALTHCARE TECHNOLOGY ON HEALTH EXPENDITURES

This study aimed to determine the effect of Magnetic Resonance Imaging and Computed Tomography imaging services, which are within the scope of health technologies, on Health Expenditures. Panel analyses was used for the analyses. In the study, health expenditures were determined as the dependent variable, and the number of MR imaging and CT imaging within the scope of health technology as the independent variable, and 16 countries with regular data for the years 2007-2018 were included in the analysis. These countries were Australia, Belgium, Canada, Chile, The Czech Republic, Denmark, France, Germany, Iceland, Israel, Korea, Latvia, Lithuania, Luxembourg, Slovak Republic, and Slovenia. As a result of the analyses, it was determined that a one-unit increase in the number of Computed Tomography imaging increased health expenditures by 3.23 units, and a one-unit increase in the number of Magnetic Resonance imaging increased health expenditures by 21.9 units. The results of the study revealed that there was a positive and long-term relationship between health expenditures and the number of Computed Tomography and Magnetic Resonance Imaging, and there was a causal relationship in different directions between the variables. In addition, it has been determined that the number of Magnetic Resonance Imaging has increased more than the number of Computed Tomography imaging over the years, and that the number of Magnetic Resonance Imaging has a higher impact on health expenditures than Computed Tomography. When these two results were evaluated together, it is predicted that evaluating Magnetic Resonance Imaging and developing remedial activities will reduce health expenditures.

Synthetic temporal bone CT generation from UTE-MRI using a cycleGAN-based deep learning model: advancing beyond CT-MR imaging fusion.

The aim of this study is to develop a deep-learning model to create synthetic temporal bone computed tomography (CT) images from ultrashort echo-time magnetic resonance imaging (MRI) scans, thereby addressing the intrinsic limitations of MRI in localizing anatomic landmarks in temporal bone CT. This retrospective study included patients who underwent temporal MRI and temporal bone CT within one month between April 2020 and March 2023. These patients were randomly divided into training and validation datasets. A CycleGAN model for generating synthetic temporal bone CT images was developed using temporal bone CT and pointwise encoding-time reduction with radial acquisition (PETRA). To assess the model's performance, the pixel count in mastoid air cells was measured. Two neuroradiologists evaluated the successful generation rates of 11 anatomical landmarks. A total of 102 patients were included in this study (training dataset, n = 54, mean age 58 ± 14, 34 females (63%); validation dataset, n = 48, mean age 61 ± 13, 29 females (60%)). In the pixel count of mastoid air cells, no difference was observed between synthetic and real images (679 ± 342 vs 738 ± 342, p = 0.13). For the six major anatomical sites, the positive generation rates were 97-100%, whereas those of the five major anatomical structures ranged from 24% to 83%. We developed a model to generate synthetic temporal bone CT images using PETRA MRI. This model can provide information regarding the major anatomic sites of the temporal bone using MRI. The proposed algorithm addresses the primary limitations of MRI in localizing anatomic sites within the temporal bone. CT is preferred for imaging the temporal bone, but has limitations in differentiating pathology there. The model achieved a high success rate in generating synthetic images of six anatomic sites. This can overcome the limitations of MRI in visualizing key anatomic sites in the temporal skull.

MR–CT image fusion method of intracranial tumors based on Res2Net

BackgroundInformation complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation.PurposeThe purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method.MethodsThis paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image.ResultsQualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics.ConclusionThe proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

From Detection to Decision: How STIR Sequence MRI Influences Treatment Strategies for Osteoporotic Vertebral Fractures.

Background/Objectives: Osteoporotic vertebral fractures (OVFs) significantly impair quality of life. This study evaluates the impact of STIR sequence MR imaging on clinical decision-making for treating OVFs, mainly focusing on how MRI findings influence treatment modifications compared to those based solely on CT scans. Methods: This retrospective analysis reviewed cases from the Manninger Jenő National Traumatology Institute over ten years, where patients with suspected OVFs underwent CT and STIR sequence MR imaging. The study examined changes in treatment plans initiated by MRI findings. The diagnostic effectiveness of MRI was compared against CT in terms of sensitivity, specificity, and the ability to influence clinical treatment paths. Results: MRI detected 1.65 times more fractures than CT scans. MRI influenced treatment adjustments in 67% of cases, leading to significant changes from conservative-conservative, conservative-surgery, and surgery-surgery based on fracture characterizations provided by MRI. Conclusions: This study demonstrates that integrating STIR sequence MR imaging into the diagnostic pathway for OVFs significantly enhances the accuracy of fracture detection and profoundly impacts treatment decisions. The ability of MRI to reveal specific fracture features that are not detectable by CT scans supports its importance in the clinical evaluation of OVFs, suggesting that MRI should be incorporated more into diagnostic protocols to improve patient management and outcomes. The findings advocate for further research to establish STIR MRI as a standard osteoporosis management tool and explore its long-term benefits in preventing secondary fractures.

Evaluation of The Impact of An Education Session on CT-MR Image Registration for Brain Stereotactic Radiosurgery

Evaluation of The Impact of An Education Session on CT-MR Image Registration for Brain Stereotactic Radiosurgery

Association between the Thickness or Area of the Temporal Muscle and Skeletal Muscle Mass in Bioimpedance Analysis

Introduction: Sarcopenia associated with stroke can significantly impact patient prognosis; however, the current standard diagnostic methods for sarcopenia are rarely used in stroke patients. Therefore, the aim of the current study was to investigate whether the temporal muscle thickness (TMT) or area (TMA) could serve as a surrogate marker for measuring skeletal muscle mass. Methods: This retrospective chart review study was conducted on 244 participants from March 2018 to February 2020. The TMT and TMA were measured at the supraorbital roof level using brain CT or T1-weighted MR imaging obtained from participants. The skeletal muscle mass and skeletal muscle index (SMI) and whole-body phase angle (WBPA) at 50 kHz were collected. Pearson correlation analysis was used to assess the relationship between the TMT or TMA and the results of the bioimpedance analysis. Results: The mean TMT showed significant positive correlations with skeletal muscle mass (male, r = 0.520; female, r = 0.706), SMI (male, r = 0.426; female, r = 0.582), and WBPA (male, r = 0.295; female, r = 0.232). The mean TMA showed significant positive correlations with skeletal muscle mass (male, r = 0.490; female, r = 0.657), SMI (male, r = 0.289; female, r = 0.473), and WBPA (male, r = 0.232; female, r = 0.243). Conclusion: We observed moderate to strong positive correlations between body composition analysis measured by BIA and TMT or TMA, suggesting that TMT or TMA could serve as a reliable surrogate marker for identifying low skeletal muscle mass in cerebrovascular disease.

Advances in chronic subdural hematoma and membrane imaging.

Chronic subdural hematoma (cSDH) is projected to become the most common cranial neurosurgical disease by 2030. Despite medical and surgical management, recurrence rates remain high. Recently, middle meningeal artery embolization (MMAE) has emerged as a promising treatment; however, determinants of disease recurrence are not well understood, and developing novel radiographic biomarkers to assess hematomas and cSDH membranes remains an active area of research. In this narrative review, we summarize the current state-of-the-art for subdural hematoma and membrane imaging and discuss the potential role of MR and dual-energy CT imaging in predicting cSDH recurrence, surgical planning, and selecting patients for embolization treatment.

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function.

Hyperpolarized 129Xe gas MRI is an emerging technique to evaluate and measure regional lung function including pulmonary gas distribution and gas exchange. Chest computed tomography (CT) still remains the clinical gold standard for imaging of the lungs, though, in part due to the rapid CT protocols that acquire high-resolution images in seconds and the widespread availability of CT scanners. Quantitative approaches have enabled the extraction of structural lung parenchymal, airway and vascular measurements from chest CT that have been evaluated in many clinical research studies. Together, CT and 129Xe MRI provide complementary information that can be used to evaluate regional lung structure and function, resulting in new insights into lung health and disease. 129Xe MR-CT image registration can be performed to measure regional lung structure-function to better understand lung disease pathophysiology, and to perform image-guided pulmonary interventions. Here, a method for 129Xe MRI-CT registration is outlined to support implementation in research or clinical settings. Registration methods and applications that have been employed to date in the literature are also summarized, and suggestions are provided for future directions that may further overcome technical challenges related to 129Xe MR-CT image registration and facilitate broader implementation of regional lung structure-function evaluation.

Imaging Outcomes of Emergency MR Imaging in Dizziness and Vertigo: A Retrospective Cohort Study.

Patients exhibiting acute dizziness or vertigo often represent a diagnostic challenge, and many undergo neuroimaging for stroke detection. We aimed to demonstrate the imaging outcomes of first-line emergency MR imaging among patients with acute dizziness or vertigo and to determine the clinical risk factors for stroke and other acute pathology. This retrospective study included consecutive patients with acute dizziness or vertigo referred for emergency MR imaging in a tertiary hospital during 5 years. We recorded and analyzed patient characteristics, relevant clinical information, and imaging outcomes. Risk score models were derived to predict which patients were more likely to present with positive MR imaging findings. A total of 1169 patients were included. Acute stroke was found in 17%; other clinically significant pathology, in 8% of patients. In 75% of the patients, emergency MR imaging showed no significant abnormalities. Risk factors for acute stroke included older age, male sex, and a prevalence of cardiovascular risk factors and neurologic signs. Isolated dizziness had no discriminative power on imaging outcomes, and 14% of these patients showed acute stroke. Risk scores had only moderate performance in predicting acute ischemic stroke (receiver operating characteristic area under curve = 0.75) or any significant pathology (receiver operating characteristic area under curve = 0.70). Acute dizziness and vertigo remain challenging even when emergency MR imaging is readily available. One in 4 patients had acute pathology on MR imaging. Predictors for acute pathology (older age, male sex, cardiovascular risk factors, and neurologic signs) may aid in patient selection for MR imaging, optimizing the yield and clinical impact of emergency neuroimaging. Low diagnostic yields of CT and internal acoustic canal MR imaging may offer an opportunity to reduce health care expenditures in the future.

Generation and applications of synthetic computed tomography images for neurosurgical planning.

CT and MRI are synergistic in the information provided for neurosurgical planning. While obtaining both types of images lends unique data from each, doing so adds to cost and exposes patients to additional ionizing radiation after MRI has been performed. Cross-modal synthesis of high-resolution CT images from MRI sequences offers an appealing solution. The authors therefore sought to develop a deep learning conditional generative adversarial network (cGAN) which performs this synthesis. Preoperative paired CT and contrast-enhanced MR images were collected for patients with meningioma, pituitary tumor, vestibular schwannoma, and cerebrovascular disease. CT and MR images were denoised, field corrected, and coregistered. MR images were fed to a cGAN that exported a "synthetic" CT scan. The accuracy of synthetic CT images was assessed objectively using the quantitative similarity metrics as well as by clinical features such as sella and internal auditory canal (IAC) dimensions and mastoid/clinoid/sphenoid aeration. A total of 92,981 paired CT/MR images obtained in 80 patients were used for training/testing, and 10,068 paired images from 10 patients were used for external validation. Synthetic CT images reconstructed the bony skull base and convexity with relatively high accuracy. Measurements of the sella and IAC showed a median relative error between synthetic CT scans and ground truth images of 6%, with greater variability in IAC reconstruction compared with the sella. Aerations in the mastoid, clinoid, and sphenoid regions were generally captured, although there was heterogeneity in finer air cell septations. Performance varied based on pathology studied, with the highest limitation observed in evaluating meningiomas with intratumoral calcifications or calvarial invasion. The generation of high-resolution CT scans from MR images through cGAN offers promise for a wide range of applications in cranial and spinal neurosurgery, especially as an adjunct for preoperative evaluation. Optimizing cGAN performance on specific anatomical regions may increase its clinical viability.

A Case of Forgotten Poisoning in a Patient Presenting with Speech Disorder

Anticholinergic poisoning is one of the most common causes of poisoning in the emergency department. Beautiful hawthorn (atropa belladonna), which is found in our country, is one of the plants that can cause anticholinergic syndrome. It is important in the differential diagnosis in cases progressing with general status disorder and loss of consciousness and psychotic findings. The onset of anticholinergic intoxication varies depending on the toxin and occurs within one to two hours following oral ingestion. The diagnosis of anticholinergic poisoning is based on clinical findings. Anticholinergic poisoning is easily diagnosed when a history of exposure to an anticholinergic substance is obtained and the patient shows altered mental status, delirium or hallucinations. However, in cases with no history and unknown exposure, the diagnosis of anticholinergic intoxication is considered when clinical signs and symptoms caused by mental status disorder and anticholinergic effects are detected on physical examination. A 75-year-old woman was admitted to the emergency department with complaints of slurred speech, altered consciousness and facial shifting that started about 2 hours ago. On physical examination, the patient was incoopere and disoriented, GCS:12 eyes were spontaneously open, obeying orders but making unintelligible sounds, nuchal rigidity was suspiciously positive, bilateral babinski reflex was positive. On admission vital signs were blood pressure 144/72 mmHg, pulse 98/min, temperature 37 °C, SO2 94%, fingerstick blood glucose 104 mg/dl and ECG was in normal sinus rhythm. There were no acute pathologic findings on both brain CT and brain MR imaging. In the control physical examination, both pupils were mydriatic, IR -/- and the patient had dry mouth. According to the anamnesis obtained from another relative of the patient, it was learned that they ate spinach in the evening and speech disorder started afterwards. The patient was diagnosed with anticholinergic intoxication considering that the spinach eaten by the patient with clinical findings might have been mixed with the beautiful hawthorn weed. In this case report, we aimed to emphasize the importance of detailing the anamnesis and the necessity of a complete systemic examination in patients presenting with confusion and speech disorder.

Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.

To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland-Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06mm) and MR models (1.36 ± 0.61mm) were significantly lower than that of the CT model (3.48 ± 5.0mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91-0.93). The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. Deep learning-based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.

MsgFusion: Medical Semantic Guided Two-Branch Network for Multimodal Brain Image Fusion

Multimodal image fusion plays an essential role in medical image analysis and application, where computed tomography (CT), magnetic resonance (MR), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) are commonly-used modalities, especially for brain disease diagnoses. Most existing fusion methods do not consider the characteristics of medical images, and they adopt similar strategies and assessment standards to natural image fusion. While distinctive medical semantic information (MS-Info) is hidden in different modalities, the ultimate clinical assessment of the fusion results is ignored. Our MsgFusion first builds a relationship between the key MS-Info of the MR/CT/PET/SPECT images and image features to guide the CNN feature extractions using two branches and the design of the image fusion framework. For MR images, we combine the spatial domain feature and frequency domain feature (SF) to develop one branch. For PET/SPECT/CT images, we integrate the gray color space feature and adapt the HSV color space feature (GV) to develop another branch. A classification-based hierarchical fusion strategy is also proposed to reconstruct the fusion images to persist and enhance the salient MS-Info reflecting anatomical structure and functional metabolism. Fusion experiments are carried out on many pairs of MR-PET/SPECT and MR-CT images. According to seven classical objective quality assessments and one new subjective clinical quality assessment from 30 clinical doctors, the fusion results of the proposed MsgFusion are superior to those of the existing representative methods.

Segmentation of abdominal CT and cardiac MR images with multi scale visual attention

Segmentation of abdominal CT and cardiac MR images with multi scale visual attention

Large intradiploic arachnoid cyst of the skull in child—a case report and new terminology proposition

We present a rare finding of the arachnoid matter invaginating into the base of middle cranial fossa and creating an abnormal space. Presented entity was incidentally found in head CT scan of 12-year-old male. Based on the radiological characteristics in CT scans and MR images, the diagnosis of intradiploic arachnoid cyst (AC) was suggested. After surgical intervention and histopathological analysis of the specimen, the diagnosis was confirmed. We assume this is the first description of large intrasphenoid AC without any traumatic or iatrogenic cause. The literature provides many different terms for the phenomenon. We are proposing the term intradiploic arachnoid diverticulum as the more accurate for capturing the essence of the phenomenon. It provides clear differentiation of the entities from classical arachnoid cysts since they are of different anatomical localization (intradural vs. extradural) and etiopathogenesis. Management with arachnoid diverticulum is not yet established, but observation with serial imaging studies should be recommended as primary management in case of asymptomatic cyst. When cyst is symptomatic, surgical treatment may be required.

Uncertainty-aware MR-based CT synthesis for robust proton therapy planning of brain tumour

Background and purposeDeep learning techniques excel in MR-based CT synthesis, but missing uncertainty prediction limits its clinical use in proton therapy. We developed an uncertainty-aware framework and evaluated its efficiency in robust proton planning. Materials and methodsA conditional generative-adversarial network was trained on 64 brain tumour patients with paired MR-CT images to generate synthetic CTs (sCT) from combined T1-T2 MRs of three orthogonal planes. A Bayesian neural network predicts Laplacian distributions for all voxels with parameters (μ, b). A robust proton plan was optimized using three sCTs of μ and μ±b. The dosimetric differences between the plan from sCT (sPlan) and the recalculated plan (rPlan) on planning CT (pCT) were quantified for each patient. The uncertainty-aware robust plan was compared to conventional robust (global ± 3 %) and non-robust plans. ResultsIn 8-fold cross-validation, sCT-pCT image differences (Mean-Absolute-Error) were 80.84 ± 9.84HU (body), 35.78 ± 6.07HU (soft tissues) and 221.88 ± 31.69HU (bones), with Dice scores of 90.33 ± 2.43 %, 95.13 ± 0.80 %, and 85.53 ± 4.16 %, respectively. The uncertainty distribution positively correlated with absolute prediction error (Correlation Coefficient: 0.62 ± 0.01). The uncertainty-conditioned robust optimisation improved the rPlan-sPlan agreement, e.g., D95 absolute difference (CTV) was 1.10 ± 1.24 % compared to conventional (1.64 ± 2.71 %) and non-robust (2.08 ± 2.96 %) optimisation. This trend was consistent across all target and organs-at-risk indexes. ConclusionThe enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients.

Sacral insufficiency fracture: a single-center experience of 185 patients with a minimum 5-year follow-up.

Sacral insufficiency fracture (SIF) is a commonly underdiagnosed etiology of back pain, especially in the geriatric and osteoporotic population. In this clinical study, we present our experience of 185 patients who were diagnosed with SIF and managed either with conservative or surgical treatment with a minimum 5-year follow-up. Patients who were diagnosed with SIF, managed either conservatively or surgically, and had a minimum 5-year follow-up medical record were included in this study. CT scans and MR imaging including coronal STIR sequence were obtained from all. Bone densitometry (DEXA) was performed to detect accompanying osteopenia or osteoporosis. Patients were treated either conservatively or surgically. VAS and ODI scores were evaluated prior to the treatment and 1st day, 10th day, 3rd month, and 1st year postoperatively. The mean age of 185 patients was 69.2 and the mean follow-up period was 7.23years (range: 5-11years). 46 (24.9%) patients had a previous spinal or spinopelvic surgery and spinal instrumentation was implemented in 22(11.89%) of them. The time interval between the fusion surgery and the diagnosis of SIF was approximately 9.48weeks. The fracture line could be detected with the MRI in 164 patients and with the CT in 177 patients. The fracture was bilateral in 120 (64.8%) patients. 102 patients were treated conservatively, and 83 received sacroplasty. VAS and ODI scores showed better improvement in pain and functionality in the surgical management group than in the conservative management group. SIF should be considered in the differential diagnosis of back and pelvic pain, especially in elderly and osteoporotic patients. MRI with coronal STIR imaging should be a standard protocol for patients with a risk of SIF. Our results show that sacroplasty provides better and faster pain relief and recovery than conservative management.