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The community that relies on 3D Slicer is large and active: (numbers below updated on January 24th, 2023)

  • 1,768+ papers on PubMed citing the Slicer platform paper
    • Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J-C., Pujol S., Bauer C., Jennings D., Fennessy F.M., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magnetic Resonance Imaging. 2012 Nov;30(9):1323-41. PMID: 22770690. PMCID: PMC3466397.


The following is a sample of the research performed using 3D Slicer outside of the group that develops it. in 2023

We monitor PubMed and related databases to update these lists, but if you know of other research related to the Slicer community that should be included here please email: marianna (at) bwh.harvard.edu.

Contents

2023

Henri IV of France's Larynx 3D Reconstitution

Publication: Eur Arch Otorhinolaryngol. 2023 Feb;280(2):919-24. PMID: 36149490

Authors: Baudouin R, Amelot A, Laprie Y, Crevier-Buchman L, Maeda S, Huynh-Charlier I, Hans S, Charlier P.

Institution: Department of Otolaryngology-Head & Neck Surgery, Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Université Paris Saclay), Montigny-le-Bretonneux, France.

Abstract: Objectives: King Henri IV of France (reign from 1589 to 1610) was one of the most important kings of France. Embalmed and buried in Saint-Denis, his remains were beheaded in 1793. His head (including his larynx) survived in successive private collections until its definitive identification in 2010. The purpose of the study was to provide a morphologic study of the larynx with a 3D reconstitution.

Methods: A flexible endoscopy was performed via the mouth and via the trachea. Measures of the larynx (vocal folds lengths, thickness, width, larynx height) were collected from the CT-scan by a panel of experts blind each other. The segmentation of the laryngeal anatomical components (vocal folds, cartilages) was performed using 3D Slicer. Mesh smoothing and 3D reconstitution were performed using Fusion 360®. Reconstitution was discussed between the experts. Decision was made by consensus after discussion.

Results: Cricoid, thyroid, arytenoid cartilages, vocal folds and hyoid bone were identified and a computed 3D reconstitution of the larynx was made. The laryngeal 3D model appeared morphologically similar to a living subject. Measures were similar but smaller than those of a modern subject.

Conclusions: The 3D reconstitution of the larynx of Henri IV of France was conducted from the CT-scan of his mummified head. This work constitutes a first valuable morphologic analysis of a larynx from an embalmed individual. This anatomical work is the first step towards the reconstruction of the voice of this historical character, which we hope to concretize with computer modeling tools in a second step.

A Three-Dimensionally Printed Otological Model for Cholesteatoma Mastoidectomy Training

Publication: Eur Arch Otorhinolaryngol. 2023 Feb;280(2):671-80. PMID: 35789285

Authors: de Souza MA, Bento RF, Lopes PT.

Institution: Otolaryngology Department, University of São Paulo School of Medicine, São Paulo, Brazil.

Abstract: Purpose: To relate the creation and expert validation (face and content validity) of an affordable three-dimensional (3D) printed model of temporal bones with chronic otitis media with cholesteatoma (COMC) as a simulator for mastoidectomy.

Methods: We performed computed tomography (CT) of the temporal bones of a patient with COMC followed at the University of São Paulo (USP) Hospital with 3D Slicer to create a 3-D model of the affected bone using light-curing resin and silicone (cholesteatoma). The final 3-D printed images were scored by 10 otologists using a customized version of the Michigan Standard Simulation Scale Experience (MiSSES). Internal consistency and inter-rater reliability were assessed using Cronbach's α and intraclass correlations.

Results: Otologists consistently scored the model positively for fidelity, educational value, reactions, and the overall model quality. Nine otologists agreed that the model was a good educational device for surgical training of COMC. All experts deemed the model ready-or nearly ready-for use. The final cost of the model, including raw materials and manufacturing, was 120 USD.

Conclusions: Using 3D printing technology, we created the first anatomically accurate, low-cost, disease-reproducing 3D model of temporal bones for mastoidectomy training for cholesteatoma.


Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma

Publication: Cancers (Basel). 2023 Jan 5;15(2):365. PMID: 36672315 | PDF

Authors: Tao YY, Shi Y, Gong XQ, Li L, Li ZM, Yang L, Zhang XM.

Institution: Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.

Abstract: Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour and the third leading cause of cancer death in the world. The emerging field of radiomics involves extracting many clinical image features that cannot be recognized by the human eye to provide information for precise treatment decision making. Radiomics has shown its importance in HCC identification, histological grading, microvascular invasion (MVI) status, treatment response, and prognosis, but there is no report on the preoperative prediction of programmed death ligand-2 (PD-L2) expression in HCC. The purpose of this study was to investigate the value of MRI radiomic features for the non-invasive prediction of immunotherapy target PD-L2 expression in hepatocellular carcinoma (HCC). A total of 108 patients with HCC confirmed by pathology were retrospectively analysed. Immunohistochemical analysis was used to evaluate the expression level of PD-L2. 3D Slicer software was used to manually delineate volumes of interest (VOIs) and extract radiomic features on preoperative T2-weighted, arterial-phase, and portal venous-phase MR images. Least absolute shrinkage and selection operator (LASSO) was performed to find the best radiomic features. Multivariable logistic regression models were constructed and validated using fivefold cross-validation. The area under the receiver characteristic curve (AUC) was used to evaluate the predictive performance of each model. The results show that among the 108 cases of HCC, 50 cases had high PD-L2 expression, and 58 cases had low PD-L2 expression. Radiomic features correlated with PD-L2 expression. The T2-weighted, arterial-phase, and portal venous-phase and combined MRI radiomics models showed AUCs of 0.789 (95% CI: 0.702-0.875), 0.727 (95% CI: 0.632-0.823), 0.770 (95% CI: 0.682-0.875), and 0.871 (95% CI: 0.803-0.939), respectively. The combined model showed the best performance. The results of this study suggest that prediction based on the radiomic characteristics of MRI could noninvasively predict the expression of PD-L2 in HCC before surgery and provide a reference for the selection of immune checkpoint blockade therapy.

Evaluation of the Prognosis of Acute Subdural Hematoma According to the Density Differences Between Gray and White Matter

Publication: Front Neurol. 2023 Jan 6;13:1024018. PMID: 36686517 | PDF

Authors: Li Z, Feng Y, Wang P, Han S, Zhang K, Zhang C, Lu S, Lv C, Zhu F, Bie L.

Institution: Department of Neurosurgery of the First Clinical Hospital, Jilin University, Changchun, China.

Abstract: Objective: Acute subdural hematoma (ASDH) is a common neurological emergency, and its appearance on head-computed tomographic (CT) imaging helps guide clinical treatment. To provide a basis for clinical decision-making, we analyzed that the density difference between the gray and white matter of the CT image is associated with the prognosis of patients with ASDH.

Methods: We analyzed the data of 194 patients who had ASDH as a result of closed traumatic brain injury (TBI) between 2018 and 2021. The patients were subdivided into surgical and non-surgical groups, and the non-surgical group was further subdivided into "diffused [hematoma]" and "non-diffused" groups. The control group's CT scans were normal. The 3D Slicer software was used to quantitatively analyze the density of gray and white matter depicted in the CT images.

Results: Imaging evaluation showed that the median difference in density between the gray and white matter on the injured side was 4.12 HU (IQR, 3.91-4.22 HU; p < 0.001) and on the non-injured side was 4.07 HU (IQR, 3.90-4.19 HU; p < 0.001), and the hematoma needs to be surgically removed. The median density difference value of the gray and white matter on the injured side was 3.74 HU (IQR, 3.53-4.01 HU; p < 0.001) and on the non-injured side was 3.71 HU (IQR, 3.69-3.73 HU; p < 0.001), and the hematoma could diffuse in a short time.

Conclusion: Quantitative analysis of the density differences in the gray and white matter of the CT images can be used to evaluate the clinical prognosis of patients with ASDH.


The Knosp Criteria Revisited: 3-Dimensional Volumetric Analysis as a Predictive Tool for Extent of Resection in Complex Endoscopic Pituitary Surgery

Publication: Front Oncol. Neurosurgery. 2023 Jan 1;92(1):179-85. PMID: 36170168

Authors: DiRisio AC, Feng R, Shuman WH, Platt S, Price G, Dullea JT, Gilja S, D'Andrea MR, Delman BN, Bederson JB, Shrivastava RK.

Institution: Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Abstract: Background: The Knosp criteria have been the historical standard for predicting cavernous sinus invasion, and therefore extent of surgical resection, of pituitary macroadenomas. Few studies have sought to reappraise the utility of this tool after recent advances in visualization and modeling of tumors in complex endoscopic surgery.

Objective: To evaluate our proposed alternative method, using 3-dimensional (3D) volumetric imaging, and whether it can better predict extent of resection in nonfunctional pituitary adenomas.

Methods: Patients who underwent endoscopic transsphenoidal resection of pituitary macroadenomas at our institution were reviewed. Information was collected on neurological, endocrine, and visual function. Volumetric segmentation was performed using 3D Slicer software. Relationship of tumor volume, clinical features, and Knosp grade on extent of resection was examined.

Results: One hundred forty patients were identified who had transsphenoidal resection of nonfunctional pituitary adenomas. Macroadenomas had a median volume of 6 cm3 (IQR 3.4-8.7), and 17% had a unilateral Knosp grade of at least 3B. On multiple logistic regression, only smaller log-transformed preoperative tumor volume was independently associated with increased odds of gross total resection (GTR; odds ratio: 0.27, 95% CI: 0.07-0.89, P < .05) when controlling for tumor proliferative status, age, and sex (area under the curve 0.67). The Knosp criteria did not independently predict GTR in this cohort (P > .05, area under the curve 0.46).

Conclusion: Increasing use of volumetric 3D imaging may better anticipate extent of resection compared with the Knosp grade metric and may have a greater positive predictive value for GTR. More research is needed to validate these findings and implement them using automated methods.


Differentiation of Lung Metastases Originated From Different Primary Tumors Using Radiomics Features Based on CT Imaging

Publication: Acad Radiol. 2023 Jan;30(1):40-6. PMID: 35577699

Authors: Shang H, Li J, Jiao T, Fang C, Li K, Yin D, Zeng Q.

Institution: Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China.

Abstract: Rationale and objectives: To explore the feasibility of differentiating three predominant metastatic tumor types using lung computed tomography (CT) radiomics features based on supervised machine learning.

Materials and methods: This retrospective analysis included 252 lung metastases (LM) (from 78 patients), which were divided into the training (n = 176) and test (n = 76) cohort randomly. The metastases originated from colorectal cancer (n = 97), breast cancer (n = 87), and renal carcinoma (n = 68). An additional 77 LM (from 35 patients) were used for external validation. All radiomics features were extracted from lung CT using an open-source software, 3D Slicer3D Slicer. The least absolute shrinkage and selection operator (LASSO) method selected the optimal radiomics features to build the model. Random forest and support vector machine (SVM) were selected to build three-class and two-class models. The performance of the classification model was evaluated with the area under the receiver operating characteristic curve (AUC) by two strategies: one-versus-rest and one-versus-one.

Results: Eight hundred and fifty-one quantitative radiomics features were extracted from lung CT. By LASSO, 23 optimal features were extracted in three-class, and 25, 29, and 35 features in two-class for differentiating every two of three LM (colorectal cancer vs. renal carcinoma, colorectal cancer vs. breast cancer, and breast cancer vs. renal carcinoma, respectively). The AUCs of the three-class model were 0.83 for colorectal cancer, 0.79 for breast cancer, and 0.91 for renal carcinoma in the test cohort. In the external validation cohort, the AUCs were 0.77, 0.83, and 0.81, respectively. Swarmplot shows the distribution of radiomics features among three different LM types. In the two-class model, high accuracy and AUC were obtained by SVM. The AUC of discriminating colorectal cancer LM from renal carcinoma LM was 0.84, and breast cancer LM from colorectal cancer LM and renal carcinoma LM were 0.80 and 0.94, respectively. The AUCs were 0.77, 0.78, and 0.84 in the external validation cohort.

Conclusion: Quantitative radiomics features based on Lung CT exhibited good discriminative performance in LM of primary colorectal cancer, breast cancer, and renal carcinoma.

Multimodal Measurements of Levator Bowl Volume in Nulligravid Asymptomatic Women: Endovaginal Ultrasound Versus MRI

Publication: Int Urogynecol J. 2023 Jan 19. PMID: 36656345

Authors: Chill HH, Martin LC, Abramowitch SD, Rostaminia G.

Institution: Female Pelvic Medicine and Reconstructive Surgery (FPMRS), Division of Urogynecology, University of Chicago, Northshore University HealthSystem, Skokie, IL, USA.

Abstract: Introduction and hypothesis: Measurements of levator bowl volume using advanced imaging, may be predictive of pelvic floor muscle function. The aim of this study was to compare the volume of the levator bowl using both magnetic resonance imaging (MRI) and endovaginal ultrasound (EVU) of healthy asymptomatic women.

Methods: All participants underwent a comprehensive interview including completion of the Pelvic Floor Distress Inventory Questionnaire-20 questionnaire, pelvic examination with a pelvic organ prolapse quantification evaluation, MRI, and EVU. The pelvic floor was segmented using 3D Slicer and the MRI segmentations were trimmed using two methods: soft-tissue landmarks and the field of view (FOV) of the ultrasound volume. The levator bowl volume of the 3D segmented shapes was measured using Blender's 3D printing toolkit. Normality was tested using the Shapiro-Wilks test and comparisons were made using self-paired t tests.

Results: The final analysis included 19 patients. Levator bowl volume measured via MRI was larger than that measured in EVU (46.1 ± 7.9 cm3 vs 27.4 ± 5.9 cm3, p<0.001). Reducing the FOV of the MRI to that of EVU caused the MRI volume to be much closer to the EVU volume (35.5 ± 3.3 cm3 vs 27.4 ± 5.9 cm3, p<0.001); however, it remained significantly larger.

Conclusion: Levator bowl volume measured using MRI was larger than that measured using EVU no matter the method of delineation of the levator muscles. Although EVU is safe, cheap, and easy to perform, it captures a smaller volume of levator bowel than MRI.


A Magnetic Resonance Imaging Based Radiomics Model to Predict Mitosis Cycles in Intracranial Meningioma

Publication: Sci Rep. 2023 Jan 18;13(1):969. PMID: 36653482 | PDF

Authors:Krähling H, Musigmann M, Akkurt BH, Sartoretti T, Sartoretti E, Henssen DJHA, Stummer W, Heindel W, Brokinkel B, Mannil M.

Institution: University Clinic for Radiology, University Hospital Muenster, Westfälische Wilhelms-University Muenster, Muenster, Germany.

Abstract: The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen's Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.


Advantages of a Training Course for Surgical Planning in Virtual Reality for Oral and Maxillofacial Surgery: Crossover Study

Publication: JMIR Serious Games. 2023 Jan 19;11:e40541. PMID: 36656632

Authors: Ulbrich M, Van den Bosch V, Bönsch A, Gruber LJ, Ooms M, Melchior C, Motmaen I, Wilpert C, Rashad A, Kuhlen TW, Hölzle F, Puladi B.

Institution: Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany.

Abstract: Background: As an integral part of computer-assisted surgery, virtual surgical planning (VSP) leads to significantly better surgery results, such as for oral and maxillofacial reconstruction with microvascular grafts of the fibula or iliac crest. It is performed on a 2D computer desktop screen (DS) based on preoperative medical imaging. However, in this environment, VSP is associated with shortcomings, such as a time-consuming planning process and the requirement of a learning process. Therefore, a virtual reality (VR)-based VSP application has great potential to reduce or even overcome these shortcomings due to the benefits of visuospatial vision, bimanual interaction, and full immersion. However, the efficacy of such a VR environment has not yet been investigated.

Objective: This study aimed to demonstrate the possible advantages of a VR environment through a substep of VSP, specifically the segmentation of the fibula (calf bone) and os coxae (hip bone), by conducting a training course in both DS and VR environments and comparing the results.

Methods: During the training course, 6 novices were taught how to use a software application in a DS environment, 3D Slicer) and in a VR environment (Elucis) for the segmentation of the fibula and os coxae, and they were asked to carry out the maneuvers as accurately and quickly as possible. Overall, 13 fibula and 13 os coxae were segmented for each participant in both methods (VR and DS), resulting in 156 different models (78 fibula and 78 os coxae) per method (VR and DS) and 312 models in total. The individual learning processes in both environments were compared using objective criteria (time and segmentation performance) and self-reported questionnaires. The models resulting from the segmentation were compared mathematically (Hausdorff distance and Dice coefficient) and evaluated by 2 experienced radiologists in a blinded manner.

Results: A much faster learning curve was observed for the VR environment than the DS environment (β=.86 vs β=.25). This nearly doubled the segmentation speed (cm3/min) by the end of training, leading to a shorter time (P<.001) to reach a qualitative result. However, there was no qualitative difference between the models for VR and DS (P=.99). The VR environment was perceived by participants as more intuitive and less exhausting, and was favored over the DS environment.

Conclusions: The more rapid learning process and the ability to work faster in the VR environment could save time and reduce the VSP workload, providing certain advantages over the DS environment.

Lesion Size and Long-Term Cognitive Outcome After Pediatric Stroke: A Comparison Between Two Techniques to Assess Lesion Size

Publication: Eur J Paediatr Neurol. 2023 Jan 7;42:126-132. PMID: 36641854

Authors: Everts R, Bertato S, Steinlin M, Slavova N, Grunt S, Steiner L.

Institution: Division of Neuropediatrics, Development and Rehabilitation, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Abstract: Background: There is little consensus on how lesion size impacts long-term cognitive outcome after pediatric arterial ischemic stroke (AIS). This study, therefore, compared two techniques to assessed lesion size in the chronic phase after AIS and determined their measurement agreement in relation to cognitive functions in patients after pediatric stroke.

Methods: Twenty-five patients after pediatric AIS were examined in the chronic phase (>2 years after stroke) in respect to intelligence, memory, executive functions, visuo-motor functions, motor abilities, and disease-specific outcome. Lesion size was measured using the ABC/2 formula and segmentation technique (3D Slicer). Correlation analysis determined the association between volumetry techniques and outcome measures in respect to long-term cognitive outcome.

Results: The measurements from the ABC/2 and segmentation technique were strongly correlated (r = 0.878, p < .001) and displayed agreement in particular for small lesions. Lesion size from both techniques was significantly correlated with disease-specific outcome (p < .001) and processing speed (p < .005) after controlling for age at stroke and multiple comparison.

Conclusion: The two techniques showed convergent validity and were both significantly correlated with long-term outcome after pediatric AIS. Compared to the time-consuming segmentation technique, ABC/2 facilitates clinical and research work as it requires relatively little time and is easy to apply.

Association Between Emphysema and Other Pulmonary Computed Tomography Patterns in COVID-19 Pneumonia

Publication: J Med Virol. 2023 Jan;95(1):e28293. PMID: 36358023

Authors: Han K, Wang J, Zou Y, Zhang Y, Zhou L, Yin Y.

Institution: Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Boston, USA.

Abstract: To evaluate the chest computed tomography (CT) findings of patients with Corona Virus Disease 2019 (COVID-19) on admission to hospital. And then correlate CT pulmonary infiltrates involvement with the findings of emphysema. We analyzed the different infiltrates of COVID-19 pneumonia using emphysema as the grade of pneumonia. We applied open-source assisted software, 3D Slicer to model the lungs and lesions of 66 patients with COVID-19, which were retrospectively included. we divided the 66 COVID-19 patients into the following two groups: (A) 12 patients with less than 10% emphysema in the low-attenuation area less than -950 Hounsfield units (%LAA-950), (B) 54 patients with greater than or equal to 10% emphysema in %LAA-950. Imaging findings were assessed retrospectively by two authors and then pulmonary infiltrates and emphysema volumes were measured on CT using 3D Slicer software. Differences between pulmonary infiltrates, emphysema, Collapsed, affected of patients with CT findings were assessed by Kruskal-Wallis and Wilcoxon test, respectively. Statistical significance was set at p < 0.05. The left lung (A) affected left lung 20.00/affected right lung 18.50, (B) affected left lung 13.00/affected right lung 11.50 was most frequently involved region in COVID-19. In addition, collapsed left lung, (A) collapsed left lung 4.95/collapsed right lung 4.65, (B) collapsed left lung 3.65/collapsed right lung 3.15 was also more severe than the right one. There were significant differences between the Group A and Group B in terms of the percentage of CT involvement in each lung region (p < 0.05), except for the inflated affected total lung (p = 0.152). The median percentage of collapsed left lung in the Group A was 20.00 (14.00-30.00), right lung was 18.50 (13.00-30.25) and the total was 19.00 (13.00-30.00), while the median percentage of collapsed left lung in the Group B was 13.00 (10.00-14.75), right lung was 11.50 (10.00-15.00) and the total was 12.50 (10.00-15.00). The percentage of affected left lung is an independent predictor of emphysema in COVID-19 patients. We need to focus on the left lung of the patient as it is more affected. The people with lower levels of emphysema may have more collapsed segments. The more collapsed segments may lead to more serious clinical feature.


A High-Resolution Pediatric Female Whole-Body Numerical Model With Comparison to a Male Model

Publication: Phys Med Biol. 2023 Jan 13;68(2). PMID: 36595234

Authors: Ntolkeras G, Jeong H, Zöllei L, Dmytriw AA, Purvaziri A, Lev MH, Grant PE, Bonmassar G.

Institution: Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Boston, USA.

Abstract: Objective: Numerical models are central in designing and testing novel medical devices and in studying how different anatomical changes may affect physiology. Despite the numerous adult models available, there are only a few whole-body pediatric numerical models with significant limitations. In addition, there is a limited representation of both male and female biological sexes in the available pediatric models despite the fact that sex significantly affects body development, especially in a highly dynamic population. As a result, we developed Athena, a realistic female whole-body pediatric numerical model with high-resolution and anatomical detail. Approach: We segmented different body tissues through Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images of a healthy 3.5 year-old female child using 3D Slicer. We validated the high anatomical accuracy segmentation through two experienced sub-specialty-certified neuro-radiologists and the inter and intra-operator variability of the segmentation results comparing sex differences in organ metrics with physiologic values. Finally, we compared Athena with Martin, a similar male model, showing differences in anatomy, organ metrics, and MRI dosimetric exposure. Main results: We segmented 267 tissue compartments, which included 50 brain tissue labels. The tissue metrics of Athena displayed no deviation from the literature value of healthy children. We show the variability of brain metrics in the male and female models. Finally, we offer an example of computing Specific Absorption Rate and Joule heating in a toddler/preschooler at 7 T MRI. Significance: This study introduces a female realistic high-resolution numerical model using MRI and CT scans of a 3.5 year-old female child, the use of which includes but is not limited to radiofrequency safety studies for medical devices (e.g. an implantable medical device safety in MRI), neurostimulation studies, and radiation dosimetry studies. This model will be open source and available on the Athinoula A. Martinos Center for Biomedical Imaging website.



Anatomical Morphology and Related Angles of Foramen Ovale: A Three-dimensional Computed Tomography Reconstruction

Publication: J Coll Physicians Surg Pak. 2023 Jan;33(1):109-111. PMID: 36597246

Authors: Cheng Z, Hu YL, Sun YX, Liang LZ, Pan DD, Wang DW.

Institution: Department of Neurosurgery, The Second Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China.

Abstract: This study aimed to report the three-dimensional reconstruction of the foramen ovale (FO) based on computed tomography angiography and describe its shape and related angles. A retrospective analysis of 199 adult patients who were hospitalised at the Department of Neurosurgery, The Second Affiliated Hospital of Bengbu Medical College, Bengbu, China, from January to December 2020 was conducted. The original DICOM files of patients' computed tomography scans were processed by 3D Slicer software to reconstruct the three-dimensional skull. The morphological characteristics of the FO on both sides were analysed. Their size, related angles and volumes, and the differences between the two sides and gender were compared. A total of 398 FO from 199 patients were studied. The most frequent shape of the FO was oval, accounting for 54.27%. The mean lengths of the right and the left sides were 5.40±1.51 and 5.10±1.18mm, respectively. The mean width on the right and left sides was 3.23±1.16 and 3.33±1.19 mm, respectively. The FO is most commonly oval in shape. Clinicians may use the anatomical characteristics regarding the size and shape of the FO for diagnosis and treatment. Key Words: Foramen ovale, Computed tomographic angiography, 3-Dimensional anatomy.


MRI Radiomic Features of Peritumoral Edema May Predict the Recurrence Sites of Glioblastoma Multiforme

Publication: Front Oncol. 2023 Jan 4;12:1042498. PMID: 36686829

Authors: Long H, Zhang P, Bi Y, Yang C, Wu M, He D, Huang S, Yang K, Qi S, Wang J.

Institution: Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Abstract: Background and purpose: As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management.

Materials and methods: The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D Slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value.

Results: Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM.

Conclusions: Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.