DICOM Format Research Articles

CTNI-67. AI BASED PREDICTION OF CLINICAL TTFIELDS RESPONSE IN GLIOBLASTOMA PATIENTS (RETROSPECTIVE ANALYSIS)

Abstract RESEARCH QUESTION Accurate and individualized prediction of response to therapies is central to precision medicine and a major goal in neuro-oncology. An extension of overall survival (OS) and progression-free survival (PFS) was observed in patients treated with tumor treating fields (TTFields) plus maintenance temozolomide compared to those treated with maintenance temozolomide alone and TTFields have become an integral part of the treatment of glioblastoma. To identify patients who benefit most from TTFields, we aim to implement a state-of-the-art machine learning approach to create a model capable of identifying responding patients early in treatment. METHODOLOGY In this retrospective analysis, patients with IDH-wildtype glioblastoma treated with TTFields, in addition to RT and temozolomide, as first-line therapy are included. Clinical data and MRI raw data (in DICOM format) are collected. The preprocessing of the MRI data includes reorientation, bias field correction, registration, segmentation, and intensity normalization. This is followed by the extraction of a Radiomics signature. The latter, along with the clinical data, is trained using various machine learning models. The model validation occurs on an independent test cohort. Data collection from various neuro-oncological centers across Europe is ongoing. RESULTS So far, data from 134 TTFields patients and 131 control patients have been collected. Initial results, providing detailed insights into the model architecture and performance, will be presented at the time of the SNO meeting. CONCLUSION This study has the potential to optimize the treatment of glioblastoma patients by using machine learning to allow the early identification of patients that respond to first line therapy.

Lumbar Spinal Stenosis Grading in Multiple Level Magnetic Resonance Imaging Using Deep Convolutional Neural Networks.

Retrospective magnetic resonance imaging grading with comparison between experts and deep convolutional neural networks (CNNs). The application of deep learning to clinical diagnosis has gained popularity. This approach can accelerate image interpretation and serve as a screening tool to help doctors. A comparison was conducted between retrospective magnetic resonance imaging (MRI) grading performed by experts and grading obtained using CNN classifiers. Data were collected from the lumbar axial dataset in the DICOM format. Two experts labeled the sampled images using the same diagnostic tools: localization of patches near the spinal canal, rootlet leveling, and stenosis grading. Comprehensive comparisons were presented for both rootlet cord classification and stenosis grading. Rootlet-cord classification for the two analyzers was 90.3% and the F1 score was 86.6%. The agreement of Analyzers-Classifiers was 92.7% and 96.8% for data with 90.6% and 95.6% F1 scores, respectively. For stenosis grading, there was an agreement of 89.2% between the two analyzers, resulting in an F1 score of 76.5%. The grades of the Analyzers-Classifiers agreed on 91.5/89.4% of the data, with an F1 score of 78.4/75.7%. Analyzer1 and Analyzer2 classified >74% as grade A (78.8% and 74.4%, respectively), 15.4% and 18.6% as grade B, 4.2% and 6.0% as grade C, and 1.6% and 2.0% as grade D, respectively. The fully automated deep learning model showed competitive results in stenosis grade diagnosis and rootlet cord classification under similar anatomical conditions. However, abrupt anatomical changes can lead to a puzzle diagnosis based only on images.

Application of 3D-Slicer Software in the Treatment of Gliomas.

To explore the application of 3D-Slicer multimodal image fusion technology in the surgical treatment of gliomas. A retrospective analysis of clinical data from 48 cases of intracranial gliomas treated with 3D-Slicer multimodal image fusion technology from September 2021 to February 2023. Preoperative cranial CT, MRI, TOF-MRA, DTI, and MRV sequence examinations were completed. DICOM format medical imaging data were processed using the 3D-Slicer software. Multimodal fusion and 3-dimensional reconstruction of the tumor and surrounding structures were conducted to clarify the relationship between the tumor and surrounding structures. The surgery path was simulated through 3D virtual visualization images, enabling precise preoperative planning and assisting in surgical treatment. Out of 48 patients, complete tumor resection was achieved in 45 cases. One case encountered severe postoperative cerebral edema, no infections were reported, 1 patient had slightly reduced muscle strength after surgery compared with before, and no cases of severe neurological function damage or surgical death were recorded. 3D-Slicer multimodal image fusion technology can assist in detailed preoperative planning for the surgical treatment of intracranial glioma, reducing surgical complications, improving the rate of total tumor resection, and reducing postoperative complications.

Distraction Osteotomy Combined With Orthodontics: An Effective Way to Decrease Short-term Recurrence in Treating Child Craniofacial Microsomia.

This study aims to investigate strategies for reducing early relapse following mandibular distraction osteotomy (MDO) in craniofacial microsomia (CFM) patients. Forty CFM patients were divided into 2 groups: 20 underwent MDO alone, while 20 received MDO combined with orthodontic treatment (MDO-O). Preoperative, prefixation, and postfixation CT data in DICOM format were collected and reconstructed using Proplan software. Spatial geometric analysis was conducted to assess the inclination angle of the occlusal plane (OP), and OP deviation was directly measured using the software. Patients in the MDO-O group demonstrated a statistically significant improvement in OP inclination at the end of the fixation period compared with those who underwent MDO alone (P=0.003). Combining MDO with a bite plate and bite glue sticks in CFM patients during the mixed dentition period enhances OP outcomes, effectively reducing short-term recurrence.

Влияние нарушений качества рентгенографических исследований органов грудной клетки на работу врачей-рентгенологов и диагностического искусственного интеллекта.

When performing radiographic studies, quality violations and errors periodically occur, making it difficult for an image to be interpreted and described by both a radiologist and software based on artificial intelligence technology. Incorrect filling of meta-information in DICOM format headers may be a separate problem for diagnostic artificial intelligence. Purpose: determination of quality violations of the chest x-ray examinations, which most strongly impede the work of radiologists and diagnostic software based on artificial intelligence in the conditions of the Moscow city health care system. Material and methods. To study the impact of the quality of x-ray examinations on the work of a radiologist, an online survey was conducted among radiologists, employees of the Moscow reference center for radiation diagnostics. To study the impact of the quality of x-ray examinations on the work of AI-based software, an analysis of the results of processing diagnostic studies according to the modalities chest x-ray and lung fluorography for the 4th quarter of 2023 by artificial intelligence services was carried out as part of an “Experiment on the use of innovative computer vision technologies for medical image analysis and subsequent applicability in the healthcare system of Moscow”. Results. The survey involved 172 radiologists. The most common quality violations by doctors are incompleteness of chest coverage and asymmetric position of the patient, as well as violations of image clarity / contrast. At the same time, the incompleteness of the chest coverage causes the greatest difficulties in interpreting the study for doctors with up to 5 years of experience. For more experienced doctors, the greatest difficulties are caused by violations of the clarity and contrast of images. The main problems hindering the description of studies by AI-services are non-filling or incorrect filling of meta-information about the study stored in DICOM format, as well as violations of the patient’s laying and positioning. Conclusion. The problems we have identified indicate the need for more careful adherence to the methodology of conducting diagnostic studies, especially with regard to filling out meta-information about the study. AI-based software developers need to evaluate the performance of their solutions based on the developed testing and monitoring methodology.

Prenatal (Second Trimester and Third Trimester) and Postnatal (Third and Fourth Week After Birth) Developmental Radiological Investigation of Sheep Skulls.

The skull is a complex, vital structure made up of many bones. It has been observed that studies on skulls help to determine species and sex. In recent years, craniometric studies have been frequently used to determine morphometric features in animals. In our study, 150 skulls were studied, 50 from the second trimester (25 males and 25 females), 50 from the third trimester (25 males and 25 females), and 50 from the third and fourth week after birth (25 males and 25 females). The skulls were sectioned by computerised tomography and stored in DICOM format. From the images, nine different measurements and five index calculations were made. The similarities and differences between species were determined by ignoring sex differences. Correlation analyses were performed to compare measurement parameters between animal species. In addition, the results of statistical analyses between sexes were evaluated without species distinction. SL, CW and ICI parameters were highly statistically significant between measurements in the second trimester, third trimester and postnatal group (p < 0.01). In the third trimester, the EHC measurement parameter was found to be highly significant between males and female (p < 0.01). In Table5, the CL parameter was positively correlated with CW, IHC, EHC, ILC, ELC, FMW and FMH. There are very few studies on foetal development. It is predicted that the data obtained can be used in zoo-archaeology, anatomy, surgery and forensic medicine. In addition, radiographic determination of craniometric features will help in the treatment and prognosis of diseases.

Comparison of clinical efficacy between autologous partially demineralized dentin matrix and deproteinized bovine bone mineral for bone augmentation in orthodontic patients with alveolar bone deficiency: a randomized controlled clinical trial

BackgroundIt is common to see patients who need orthodontic treatment but with insufficient alveolar bone volume. However, safe and effective tooth movement requires sufficient alveolar bone width and height. The aim of this study is to compare the bone augmentation efficacy of Autologous Partially Demineralized Dentin Matrix (APDDM) and Deproteinized Bovine Bone Mineral (DBBM) in orthodontic patients with insufficient bone by using a randomized controlled clinical trial approach.Materials and methodsTwenty-seven orthodontic patients involving 40 posterior teeth alveolar sites (n = 40) with insufficient alveolar bone volume were randomly divided into a control group (n = 20) and an experimental group (n = 20). The patients in the experimental group were treated with APDDM, and those in the control group were treated with DBBM. After surgery, the adjacent teeth are moved toward the bone grafting sites according to the orthodontic treatment plan. Patients completed a postoperative response questionnaire by the Visual Analogue Scale (VAS) score to indicate pain and swelling in the bone grafted area at the time of suture removal; and CBCT scans were conducted before surgery, 6 months and 2 years after surgery to assess changes in buccal and central alveolar heights, as well as widths at the alveolar ridge apex and 3 mm, 5 mm below the apex, respectively. The CBCT image sequences were imported into Mimics 21.0 software in DICOM format. The data of the patients in both groups were collected and analyzed by SPSS 25.0.ResultsThe VAS scores were significantly lower in the APDDM group than in the DBBM group (p < 0.05). Significant increases were observed in alveolar bone height and width at 6 months and 2 years postoperative (p < 0.05); At 2 years, the APDDM group exhibited a reduction in buccal crest height and in 3 mm, 5 mm width below alveolar ridge apex, relative to 6 months (p < 0.05), while the DBBM group showed a decrease only in the central height of the alveolar bone (p < 0.05). There was a significant bone augmentation increase found only 3 mm below the alveolar ridge apex in the APDDM group compared with the DBBM group among all 6 months group comparison (p < 0.05). At 2 years, the augmentation effects were similar across both groups (p > 0.05).ConclusionRadiomics analysis indicates that APDDM serves as a viable bone augmentation material for orthodontic patients with insufficient alveolar bone volume, achieving comparable clinical efficacy to DBBM. Additionally, APDDM is associated with a milder postoperative response than DBBM.The registration number (TRN)ChiCTR2400084607.

To evaluate the cortical bone thickness for ideal placement of infrazygomatic crest (IZC) and mandibular buccal shelf (MCS) bone screws in patients with different facial pattern using cone-beam computed tomography (CBCT)

This study aimed to measure the cortical bone thickness at different anatomical sites of InfraZygomatic Crest and Mandibular Buccal Shelf region for the ideal placement of IZC and MBS screws respectively in patients with different facial patterns using CBCT.Total participants were divided into 3 groups- GROUP A: horizontal growth pattern (FMA &amp;#60;22°); GROUP B: average growth pattern (FMA 22°-28°); GROUP C: vertical growth pattern(FMA &amp;#62;28°). Sixty CBCT images were taken into consideration. They were converted into standard DICOM format. Bone thickness were measured in the InfraZygomatic Crest (IZC) of the maxilla and in the Mandibular Buccal Shelf (MBS) region of the mandible using the Kodak CareStream CS 9300 Software.The results indicate that there was a highly statistical significant difference seen in the overall cortical bone thickness of IZC and MBS at various anatomical sites. The highest mean thickness was found to be at the MB70◦ maxillary 1st molar and distal root of the mandibular 1st molar. There was no difference in the bone thickness at the IZC region and at the MBS region seen between the horizontal and vertical facial patterns whereas the average facial patterns show the highest mean thickness but was not statistically significantThe study conducted on the Indian population yielded results demonstrating the possibly appropriate position for the IZC bonescrews and MBS bonescrews suggested that placing the IZC bonescrews at the mesiobuccal root of the maxillary 1st molar at an angulation of 70◦ and the MBS bonescrews at the distal root of the mandibular 1st molar at 11mm from the cementoenamel junction irrespective of the facial patterns could provide positive results.

Determining orbital volumes of sheep, goats and gazelles via computed tomography.

Orbital volume refers to the sum of bulbus oculi volume and orbital cavity volume. Scientists use orbital volume as a guide to diagnose, treat and prognose various eye diseases such as thyroid eye disease and enophthalmos. This study aims to examine the orbital volumes of sheep, goats and gazelles to demonstrate homotypic variations and interspecific differences between them. Crania of six male tuj sheep, five male hair goats and five male gazelle were used in this study. The crania were scanned using a 64-detector multi-detector computed tomography with a slice thickness of 0.625mm at 80kV, 200 MA and 639 mGy. The cross-sectional images were stored in DICOM format and then transferred to MIMICS 20.1 software. Afterwards, models were made by establishing the orbital borders on the cross-sections. Volume values were calculated based on the models. No homotypic variation was found between the right and left volumes within the species (p > 0.05). A statistically significant difference was observed in terms of interspecific right and left orbital volumes between sheep and gazelles (p < 0.05). It was concluded that the orbital parameters of the sheep, goats and gazelles in the same family help reveal their differences in the visual anatomy of these species and provide reference data for clinical diagnosis, treatment and the prognosis follow-up of ocular diseases in animals.

Architecture Optimization for Hybrid Deep Residual Networks in Liver Tumor Segmentation Using a GA

Nowadays, liver tumor segmentation has been widely applied for vital medical objectives such as illness diagnosis, treatment, and evaluation of liver function. In this work, we aim to improve the performance of hybrid convolution neural network (CNN) models for liver tumor segmentation. A new automatic design method is introduced to build a hybrid CNN model with an optimal architecture using a combination of the original U_Net and the Res-UNet. In the proposed design method, the Res-UNet model is divided into different blocks, and then many different candidate architectures for the hybrid CNN model can be created using gradually these blocks to replace their corresponding blocks of the original U-Net. The replacement is executed if the candidate architecture using a residual block gives better performance than the corresponding one that uses the original U-Net block. We use a genetic algorithm (GA) to get the optimal architecture among the candidate architectures that gives the best performance for the hybrid CNN model. During the optimization process, in addition to the optimal architecture, other crucial configurations such as the learning algorithm, learning rate, and batch size are also determined. We refer to the hybrid CNN model that has the best architecture as GA_Res_UNet. Comparison has been made with other CNN models that are commonly used in image segmentation of liver tumors. We use the 3D-IRCADb01 liver tumor dataset that contains 2800 magnetic resonance (MR) images of 20 patients. Each image has a liver tumor with its corresponding mask. All MR images are resized to 256 × 256 in DICOM format. The findings indicate that GA_Res_UNet outperformed the other compared models over the considered dataset. It achieves 98.5% of dice coefficient and 99.8% of accuracy. Moreover, two other liver tumor segmentation datasets (LiTS17 and CHAOS) are utilized in another comparison to show the superior performance of our proposed model. The reported results show that the proposed model outperformed the other compared state-of-the-art models.

Clinical observation of three-dimensional reconstruction in thoracoscopic segmental pneumonectomy.

Accurately identifying the branches of pulmonary segmental vessels and bronchi, as well as adjacent structures, and determining the spatial location of lesions within pulmonary segments, are major challenges for thoracic surgeons. The application of three-dimensional reconstruction technology holds promise in addressing this issue. To evaluate the clinical value of three-dimensional reconstruction in thoracoscopic segmental surgery. Seventy-seven patients who underwent thoracoscopic segmental surgery combined with three-dimensional reconstruction at our hospital from January 1, 2020, to August 31, 2023, were retrospectively analyzed. Preoperative chest enhanced CT scans were conducted, and MIMICS software aided in reconstructing DICOM format original data for patients with pulmonary nodules to facilitate intraoperative nodule localization. Accurate segmental pneumonectomy was performed by comparing preoperative anatomical identification of target segmental arteries, veins, and bronchi, with surgical details and postoperative outcomes recorded, including intraoperative pulmonary resection distribution, operation time, blood loss, chest tube drainage, extubation time, hospital stay, and complications. Following preoperative three-dimensional reconstruction, successful segmental lung surgeries were performed, predominantly with single segmental resection (92.2%), and a minority with combined segmentectomy (7.8%). Median operation time was 130225 minutes, with intraoperative blood loss at 70100 mL and postoperative chest tube drainage at 347 mL (159690mL). Median extubation time and hospital stay were 4 days and 7 days, respectively. Complications within the 3-month follow-up affected 11.7% of cases, including persistent pulmonary leakage (7.1%), pulmonary infection (4.3%), atelectasis (4.3%), and pleural effusion (1.4%), with no fatalities. Preoperative 3D reconstruction can help the operator to perform safe, efficient and accurate thoracoscopic segmental pneumonectomy, which is worth popularizing in clinic.

From medical imaging to 3D printed anatomical models: a low-cost, affordable 3D printing approach.

To share our experience in creating precise anatomical models using available open-source software. An affordable method is presented, where from a DICOM format of a computed tomography, a segmentation of the region of interest is achieved. The image is then processed for surface improvement and the DICOM format is converted to STL. Error correction is achieved and the model is optimized to be printed by stereolithography with a desktop 3D printer. Precise measurements of the dimensions of the DICOM file (CT), the STL file, and the printed model (3D) were carried out. For the C6 vertebra, the dimensions of the horizontal axis were 55.3 mm (CT), 55.337 mm (STL), and 55.3183 mm (3D). The dimensions of the vertebral body were 14.2 mm (CT), 14.551 mm (STL), and 14.8159 mm (3D). The length of the spinous process was 18.2 mm (CT), 18.283 mm (STL), and 18.2266 mm (3D), while its width was 8.5 mm (CT), 8.3644 mm (STL), and 8.3226 mm (3D). For the C7 vertebra, the dimensions of the horizontal axis were 58.6 mm (CT), 58.739 mm (STL), and 58.7144 mm (3D). The dimensions of the vertebral body were 14 mm (CT), 14.0255 mm (STL), and 14.2312 mm (3D). The length of the spinous process was 18.7 mm (CT), 18.79 mm (STL), and 18.6458 mm (3D), and its width was 8.9 mm (CT), 8.988 mm (STL), and 8.9760 mm (3D). The printing of a 3D model of bone tissue using this algorithm is a viable, useful option with high precision.

A cross-sectional CBCT assessment of the relative position of one-piece titanium-zirconium mini-implants placed for mandibular overdentures using non-guided surgery.

To assess the relative position of mini-implants to retain a mandibular overdenture, according to the surgical protocol, technical and anatomical factors. Mandibular cone-beam computed tomography (CBCT) scans were analyzed for 73 patients who received four one-piece titanium-zirconium mini-implants. Drilling was performed using a 1.6 mm needle drill and a 2.2 mm Pilot Drill, according to the bone density with a surgical stent. Post-insertion CBCT images in DICOM format were analyzed using the E-Vol-DX software with BAR filters. Divergence angle between implants and between implants and the overdenture path of insertion was measured using CliniView 10.2.6 software. Divergence between implants ranged from 0° to 22.3° (mean = 4.2; SD = 3.7) in the lateral and from 0° to 26.2° (mean = 5.3; SD = 4.1) in the frontal projections (p < .001). Only 1 (0.2%) and 3 (0.7%) of the measurements were higher than 20° in the lateral and frontal views, respectively. The mean angulations between the implant and the path of insertion for the overdenture were 9.3° (SD = 7.5) and 4.0° (SD = 2.9) for the lateral and frontal views, respectively (p < .001). Regression analyses showed a significant association between the divergence of implants and the frontal view projection (p < .001), greater distance between the paired implants (p = .017), the flapped surgical protocol (p = .002), higher final insertion torque (p = .011), and deeper preparation with the needle drill (p < .001). The mini-implants were placed with low divergence angles and satisfactory parallelism. Factors including shorter distances between the implants, higher density bone, and a flapless surgical approach all contributed positively to improved parallelism of the mini-implants.

O-220 Ovarify: a deep learning model for automatic calculation of ovarian morphological features in pelvic ultrasonography

Abstract Study question Can a deep learning model accurately characterize ovarian features on pelvic ultrasounds? Summary answer We developed a deep learning model, Ovarify, that can accurately classify and segment ovarian features on pelvic ultrasounds. What is known already Ovarian morphology, as detected using pelvic ultrasonography, is used widely in the assessment of gynecological health. Morphological metrics such as ovarian volume and antral follicle count have known diagnostic and prognostic value for a variety of gynecological outcomes, ranging from risk for ovulatory disorders to likelihood of response to assisted reproductive technologies. However, a lack of standardization in the interpretation of ovarian features, and the time-intensive nature of validated image analysis techniques (such as 2D grid analysis), limits their utility in clinical practice. Deep learning provides an opportunity to overcome these limitations. Study design, size, duration The training dataset for the model comprised 426 3D pelvic ultrasounds obtained using the GE Voluson ultrasound platform, yielding 56,538 individual frames with potential ovarian content. In addition, a similarly collected validation dataset consisted of 95 ultrasounds, yielding 11,304 frames. The held-out test set comprised 107 ultrasounds with 14,767 frames. All frames were manually labeled with the associated 2D ovarian contour, and 163 of the ultrasounds had an associated ovarian volume label. Participants/materials, setting, methods Ovarify consists of two stages: 1) a lightweight classifier that labels ultrasound frames as either positive or negative (having or not having ovarian content) and 2) a subsequent U-Net-inspired segmentation module with an Xception backbone and attention-based decoder that segments the ovary and antral follicles in all frames previously classified as positive. Classification performance was evaluated using F1-score and area under the receiver operating characteristic curve (AUROC). Segmentation performance was evaluated using the Jaccard index. Main results and the role of chance For the classification stage, Ovarify achieved an AUROC of 0.980±0.002 and 0.976±0.004 on the training and validation sets, respectively. On the held-out test set, the model achieved an AUROC of 0.971±0.005. In addition, the model achieved an F1-score of 0.825±0.006, 0.810±0.013, and 0.784±0.014 on the training, validation, and held-out testing sets, respectively. The high AUROC and F1-scores across both seen and unseen datasets support a high-performing classifier. In the segmentation stage, Ovarify reached a median Jaccard index of 0.815±0.004 on the unseen test set when tasked with segmenting the ovary, and a median Jaccard index of 0.657±0.018 on the unseen test set when segmenting antral follicles. High agreement between the ground truth ovary and follicle labels and the predicted segmented regions increases confidence in the downstream morphological analysis. For example, the Pearson correlation coefficient between the Ovarify-calculated ovarian volume and manually-validated ovarian volume was 0.813, indicating that an automatic ovarian volume calculation highly aligns with the ground truth. Ovarify was able to provide predictions in under one minute for the entirety of the held-out test set. Limitations, reasons for caution Ovarify was trained on images generated by the GE Voluson platform and has not been evaluated for generalizability across ultrasound images generated using machines whose output does not match the DICOM format standard. Wider implications of the findings The model developed is relevant for automatically calculating ovarian morphological features from pelvic ultrasounds. This approach has implications in removing user-specific bias and error when calculating biomarkers associated with female reproductive health, including but not limited to ovulatory disorders such as polycystic ovary syndrome (PCOS). Trial registration number not applicable

Characterizing pediatric head patient size in Moroccan population: Establishing age-dependent relationships for accurate CT dose estimation

Accurate dose estimation in computed tomography (CT) scans is crucial and relies on precise normalization of output dose, typically measured by the volume CT dose index (CTDIvol). Key metrics, including effective diameter (Deff) and water-equivalent diameter (Dw), play pivotal roles in characterizing patient size. However, a notable gap exists in delineating the specific relationships between age and head patient size (Deff and Dw) for pediatric patients in Morocco. The primary objective of this study was to establish these critical associations between patient age and head patient size (Deff and Dw), providing a foundation for calculating size-specific dose estimates (SSDE) in pediatric head CT examinations. A retrospective analysis of data from 134 pediatric patients, aged 0–13 yr, comprising 71 males and 63 females who underwent head CT scans, was conducted. Utilizing the Radiant DICOM Viewer, patient sizes were measured in terms of both lateral and anterior-posterior dimensions for Deff and Dw calculations based on CT images in DICOM format. Our analysis revealed robust correlations between patient size (Deff and Dw) and the patient’s age, with R2 values ranging from 0.65 to 0.86. Notably, larger Dw values were consistently observed compared to Deff. For male patients, Deff measurements ranged from 9.02 to 18.77 cm, with Dw values spanning 9.83 to 20.16 cm. Female patients exhibited Deff values ranging from 8.77 to 17.41 cm and Dw values ranging from 8.92 to 18.37 cm. These findings shed light on the crucial relationship between age and patient size, facilitating more precise dose calculations.

Development of a novel three-dimensional injection guide for trigeminal ganglia

BackgroundTrigeminal ganglion (TG) plays an important role in the process of orthodontic pain. It’s necessary to design an accurate, precise and minimally invasive trigeminal ganglion injection guide plate to study TG. MethodsMicro-CT was used to obtain the Dicom format data, and three-dimensional (3D) software (mimics and magics23.03) was used to reconstruct 3D head models. Design and modifications of the TG injection guide plate were performed in Magic 23.03 software, and the guide plate was produced by a 3D stereolithography printer. X-ray, micro-CT, Evans blue, and virus transduction were used to demonstrate the accuracy of the guide-assisted injection. Pain levels were evaluated after using the injection guide by a bite force test and Von Frey test. ResultsX-ray and micro-CT tests confirmed that the injection needle reached the bilateral trigeminal ganglia fossa. The Evans blue test and virus transduction proved that the injected drug could be accurately injected into the bilateral trigeminal ganglion and the lentivirus could be successfully transfected. The percentage of accurate injection was 10/10 (bilateral trigeminal ganglia). Orofacial pain induced by the trigeminal ganglion injection was mild and returned to baseline within seven days. ConclusionThe injection guide described in this study is viable and reliable for the delivery of drugs and virus transduction into the trigeminal ganglia.

Implementing ABCD studyⓇ MRI sequences for multi-site cohort studies: Practical guide to necessary steps, preprocessing methods, and challenges

Large multi-site studies that combine magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience research. However, scanner or site variability and non-standardised image acquisition protocols, data processing and analysis pipelines can adversely affect the reliability and repeatability of MRI derived brain measures. We implemented a standardised MRI protocol based on that used in the Adolescent Brain Cognition Development (ABCD)Ⓡ study in two sites, and across four MRI scanners.Twice repeated measurements of a single healthy volunteer were obtained in two sites and in four 3T MRI scanners (vendors: Siemens, Philips, and GE). Imaging data included anatomical scans (T1 weighted, T2 weighted), diffusion weighted imaging (DWI) and resting state functional MRI (rs-fMRI). Standardised containerized pipelines were utilised to pre-process the data and different image quality metrics and test-retest variability of different brain metrics were evaluated.The implementation of the MRI protocols was possible with minor adjustments in acquisition (e.g. repetition time (TR), higher b-values) and exporting (DICOM formats) of images due to different technical performance of the scanners. This study provides practical insights into the implementation of standardised sequences and data processing for multisite studies, showcase the benefits of containerised preprocessing tools, and highlights the need for careful optimisation of multisite image acquisition.

Monte Carlo multiscale modelling of photon and proton distribution in heterogenous tissue

Enhancements in radiotherapy treatment outcomes can be realised by minimising uncertainties in dose distribution. Current treatment planning struggles to accurately calculate dose distribution in complex heterogeneous areas, thereby increasing the uncertainty associated with dose distribution. This research focuses on studying microscopic dose distribution in the temporal bone and cochlea. This study utilised an open-access DICOM format dataset for resected temporal bone and cochlea tissue, employing the FLUKA MC code to simulate potential high-dose scenarios in volume-modulated arc therapy using the FLOOD option. Simulations were conducted at 23 photon and proton energy levels, ranging from 0.055 to 5.5 MeV for photons and 37.59–124.83 MeV for protons, to calculate dose distributions. The largest proportion of the dose (48.8%) was deposited in high-density bone at photon energies between 0.055 and 0.09 MeV. Above 0.125 MeV, a notable shift in dose distribution to low-density tissues occurred, reaching a deposition of 53%. In intermediate-density soft bone, dose distribution was 26.4% at 0.07 MeV and decreased to 19.7% at 2.5 MeV, reflecting a 29% difference in dose distribution across the energy spectrum. In proton simulations, dose distribution at low energy (37.59 MeV) revealed no significant changes across low (54.86%), intermediate (19.75%), and high-density (25.39%) areas. Similar patterns were observed at high energy (124.83 MeV), with dose distributions of 54.21% in low-, 19.79% in intermediate-, and 26% in high-density areas. The simulations demonstrated that proton dose distribution was not significantly influenced by tissue heterogeneity in micro-CT data. The photoelectric effect at low energy levels contributed minimally to the dose in soft bone, favouring higher deposition in high-density bone, despite a lower weighting factor at low energies compared with high energies.

PyDesigner v1.0: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Magnetic Resonance Imaging.

PyDesigner is a Python-based software package based on the original Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline (Dv1) for dMRI preprocessing and tensor estimation. This software is openly provided for non-commercial research and may not be used for clinical care. PyDesigner combines tools from FSL and MRtrix3 to perform denoising, Gibbs ringing correction, eddy current motion correction, brain masking, image smoothing, and Rician bias correction to optimize the estimation of multiple diffusion measures. It can be used across platforms on Windows, Mac, and Linux to accurately derive commonly used metrics from DKI, DTI, WMTI, FBI, and FBWM datasets as well as tractography ODFs and .fib files. It is also file-format agnostic, accepting inputs in the form of .nii, .nii.gz, .mif, and dicom format. User-friendly and easy to install, this software also outputs quality control metrics illustrating signal-to-noise ratio graphs, outlier voxels, and head motion to evaluate data integrity. Additionally, this dMRI processing pipeline supports multiple echo-time dataset processing and features pipeline customization, allowing the user to specify which processes are employed and which outputs are produced to meet a variety of user needs.

A randomized controlled clinical trial of concentrated growth factor combined with sodium hyaluronate in the treatment of temporomandibular joint osteoarthritis

ObjectiveTo investigate the effect of concentrated growth factor (CGF) combined with sodium hyaluronate (SH) on temporomandibular joint osteoarthritis (TMJOA).MethodsSixty patients with TMJOA who were diagnosed by cone-beam computed tomography (CBCT) between March 2020 and March 2023 at the Stomatological Hospital of Xi’an Jiaotong University were randomly divided into a control group (n = 30) and an experimental group (n = 30). The patients in the experimental group were treated with CGF + SH, and those in the control group were treated with SH only. The visual analogue scale (VAS) score indicating pain in the temporomandibular joint (TMJ) area; the Helkimo Clinical Dysfunction Index (Di); and changes in condylar CBCT at the first visit and 2 weeks, 3 months and 6 months after treatment were recorded. The CBCT data of the patients in the experimental and control groups were collected, and the three-dimensional CBCT image sequences were imported into Mimics Medical 19.0 software in DICOM format for condylar reconstruction.ResultsThe VAS scores at 2 weeks, 3 months and 6 months after treatment were significantly lower in the experimental group than in the control group (P < 0.05), and the pain in the experimental group was significantly relieved. The Di was significantly lower in the experimental group than in the control group (P < 0.05), and the clinical function of the TMJ improved. After treatment, the CBCT score was significantly lower in the experimental group than in the control group (P < 0.05), and the condylar bone cortex was obviously repaired. Observation of the condylar bone cortex by three-dimensional reconstruction showed the same results as those obtained by CBCT.ConclusionCGF combined with SH is effective in the treatment of TMJOA and can improve muscle pain, TMJ pain, Impaired TMJ function, Impaired range of movement, Pain on movement of the mandible and promote bone repair.The registration number (TRN)ChiCTR2400082712.The date of registrationApril 5, 2024.