Tomography Datasets Research Articles

NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution

Medical images often require rescaling to various spatial resolutions to ensure interpretations at different levels. Conventional deep learning-based image super-resolution (SR) enhances the fixed-scale resolution. Implicit neural representation (INR) is a promising way of achieving arbitrary-scale image SR. However, existing INR-based methods require the repeated execution of the neural network (NN), which is slow and inefficient. In this paper, we present Neural Explicit Representation (NExpR) for fast arbitrary-scale medical image SR. Our algorithm represents an image with an explicit analytical function, whose input is the low-resolution image and output is the parameterization of the analytical function. After obtaining the analytical representation through a single NN inference, SR images of arbitrary scales can be derived by evaluating the explicit functions at desired coordinates. Because of the analytical explicit representation, NExpR is significantly faster than INR-based methods. In addition to speed, our method achieves on-par or better image quality than other strong competitors. Extensive experiments on Magnetic Resonance Imaging (MRI) datasets, including ProstateX, fastMRI, and our in-house clinical prostate dataset, as well as the Computerized Tomography (CT) dataset, specifically the Medical Segmentation Decathlon (MSD) liver dataset, demonstrate the superiority of our method. Our method reduces the rescaling time from the order of 1 ms to the order of 0.01 ms, achieving an over 100× speedup without losing the image quality. Code is available at https://github.com/Calvin-Pang/NExpR.

Exploiting activation radiation from neutron tomography reveals the hidden elemental composition of 3D art objects for free

Neutron tomography is gaining popularity particularly in cultural heritage research, for non-destructively analysing the inner structure of bulk metal artefacts, such as bronzes, but the induced temporary decay radiation is often considered as a drawback. However, this delayed gamma-emission can be put to good use: by performing gamma spectroscopy after neutron tomography, the interior elemental composition of artefacts can be obtained “for free”. Inspired by this, we propose a ray-tracing approach to non-invasively quantify both interior geometry and elemental composition using only a single neutron tomography experiment. This strategy aligns well with both the aim for efficient use of neutron beam time and the expectation from curators and conservators for minimal neutron irradiation. Here, we outline the core principle of this method, demonstrate the extent of its quantification capability on bulk objects of known composition by fusing neutron tomography and delayed-gamma spectroscopy data sets. We also showcase its practical application on an ancient solid-cast Indonesian bronze statuette, by which we gain insights into how the pristine inner bronze segregated into a different composition than the surrounding shell. Similarly, the method allows us to quantify the composition of a hidden offering in the statuette that consecrates the bronze for worship purposes.

Detecting Porosity in Additively Manufactured Parts with Deep Learning and Artificial Computed Tomography Data

ABSTRACT Deep learning has recently gained significant attention in nondestructive testing tasks due to its ability to effectively analyze complex data and identify patterns. This paper proposes a methodology for defect detection on an X-ray computed tomography dataset utilizing the Mask R-CNN algorithm. The proposed approach generates artificial data from the original dataset and introduces a metric for classifying the intensity of the defects. The Mask R-CNN is trained for defect localization and classification, and the results show that the proposed approach performs competitively with, and in many cases better than, reported papers on the same dataset. This paper provides a detailed analysis of the proposed methodology, including experimental results and discussions, demonstrating its effectiveness and potential for future research.

Challenging underground geophysical, geological and topographical surveys in the Borna Maggiore di Pugnetto karst collapse cave to delineate its genesis and actual structure

AbstractElectric resistivity surveys in karst environments are commonly employed to establish parameters that can help in the evaluation of collapse risk related to sinkhole or cave formation. However, these surveys are often executed from the surface with consequent limits in resolution and identification potential as a function of coverage thicknesses. Application of these methodologies directly inside known caves, for a better understanding of their formation mechanisms, is uncommon due to accessibility problems, the nontrivial referencing issues that arise when operating in an underground environment and the challenging 2D/3D interpretation issues emerging from the presence of the cavity itself. This paper reports on the application of electric resistivity tomography along with specific geological and topographic mapping, inside the Borna Maggiore di Pugnetto karst collapse cave. Comprehensive knowledge of this cave, developed in mica‐rich and carbonate‐rich calcschists, is problematic with traditional investigations, due to the cave breakdown that masks its structure. In this study, the 3D geometry of the cave is reconstructed using a topographical survey. This reconstruction is then utilised to perform a 3D inversion of the electrical resistivity tomography (ERT) dataset. The results of both 2D and 3D inversions are compared and discussed, focusing on the survey's ability to identify resistivity anomalies within the 3D volume surrounding the cave. Additionally, an open‐source script is provided to facilitate the replication of this 3D modelling and inversion in similar underground contexts. Results of the paper show the effectiveness of the proposed surveys in the delineation of genesis and actual structure of the cave. The paper also proposes a methodological approach that can be adopted in similar contexts to enhance the understanding of speleogenesis.

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation of target volumes is critical. To alleviate the data demand of deep learning and promote the establishment and promotion of auto-segmentation models in small and medium-sized oncology departments and single centres, we proposed an auto-segmentation algorithm to determine the cervical cancer target volume in small samples based on multi-decoder and semi-supervised learning (MDSSL), and we evaluated the accuracy via an independent test cohort. In this study, we retrospectively collected computed tomography (CT) datasets from 71 pelvic cervical cancer patients, and a 3:4 ratio was used for the training and testing sets. The clinical target volumes (CTVs) of the primary tumour area (CTV1) and pelvic lymph drainage area (CTV2) were delineated. For definitive radiotherapy (dRT), the primary gross target volume (GTVp) was simultaneously delineated. According to the data characteristics for small samples, the MDSSL network structure based on 3D U-Net was established to train the model by combining clinical anatomical information, which was compared with other segmentation methods, including supervised learning (SL) and transfer learning (TL). The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) were used to evaluate the segmentation performance. The ability of the segmentation algorithm to improve the efficiency of online adaptive radiation therapy (ART) was assessed via geometric indicators and a subjective evaluation of radiation oncologists (ROs) in prospective clinical applications. Compared with the SL model and TL model, the proposed MDSSL model displayed the best DSC, HD95 and ASD overall, especially for the GTVp of dRT. We calculated the above geometric indicators in the range of the ground truth (head-foot direction). In the test set, the DSC, HD95 and ASD of the MDSSL model were 0.80/5.85 mm/0.95 mm for CTV1 of post-operative radiotherapy (pRT), 0.84/ 4.88 mm/0.73 mm for CTV2 of pRT, 0.84/6.58 mm/0.89 mm for GTVp of dRT, 0.85/5.36 mm/1.35 mm for CTV1 of dRT, and 0.84/4.09 mm/0.73 mm for CTV2 of dRT, respectively. In a prospective clinical study of online ART, the target volume modification time (MTime) was 3–5 min for dRT and 2–4 min for pRT, and the main duration of CTV1 modification was approximately 2 min. The introduction of the MDSSL method successfully improved the accuracy of auto-segmentation for the cervical cancer target volume in small samples, showed good consistency with RO delineation and satisfied clinical requirements. In this prospective online ART study, the application of the segmentation model was demonstrated to be useful for reducing the target volume delineation time and improving the efficiency of the online ART workflow, which can contribute to the development and promotion of cervical cancer online ART.

Auto-contouring of cardiac substructures for Stereotactic arrhythmia radioablation (STAR): A STOPSTORM.eu consortium study

Background/PurposeHigh doses to healthy cardiac substructures (CS) in stereotactic arrhythmia radioablation (STAR) raise concerns regarding potential treatment-induced cardio-toxicity. However, CS contours are not routinely created, hindering the understanding of the CS dose–effect relationships. To address this issue, the alignment of CS contouring was initiated within the STOPSTORM consortium. In this study, we developed and evaluated auto-contouring models trained to delineate CS and major vessels in ventricular tachycardia (VT) patients. MethodsEight centres provided standard treatment planning computed tomography (CT) and/or contrast-enhanced CT datasets of 55 VT patients, each including 16 CS. Auto-contouring models were trained to contour either large structures or small structures. Dice Similarity Coefficient (DSC), 95 % Hausdorff distance (HD95) and volume ratio (VR) were used to evaluate model performance versus inter-observer variation (IOV) on seven VT patient test cases. Significant differences were tested using the Mann-Whitney U test. ResultsThe performance on the four chambers and the major vessels (median DSC: 0.88; HD95: 5.8–19.4 mm; VR: 1.09) was similar to the IOV (median DSC: 0.89; HD95: 4.8–14.0 mm; VR: 1.20).For the valves, model performance (median DSC: 0.37; HD95: 11.6 mm; VR: 1.63) was similar to the IOV (median DSC: 0.41; HD95: 12.4 mm; VR: 3.42), but slightly worse for the coronary arteries (median DSC: 0.33 vs 0.42; HD95: 24.4 mm vs 16.9 mm; VR: 1.93 vs 3.30). The IOV for these small structures remains large despite using contouring guidelines. ConclusionCS auto-contouring models trained on VT patient data perform similarly to IOV. This allows for time-efficient evaluation of CS as possible organs-at-risk.

Detecting damage in composites using volume decomposition analysis of tomographic data

Detection of damage in a single tow ceramic matrix composite specimen has been achieved using orthogonal decomposition of volumetric tomographic datasets collected at four tensile loads. This decomposition approach has been applied at two different length scales: (i) individual fibres and (ii) bulk volumes containing fibres and matrix material. Volumes were first decomposed to feature vectors, orders of magnitude smaller than the original volume they describe, and then comparisons between datasets at different load levels were made in feature vector space. The results show quantitative measurements of damage location, damage morphology and the relative growth of this damage with increased load when compared with a dataset with less or no damage. No prior knowledge of the dataset or training of algorithms is required for damage to be detected, it is only necessary that at least two datasets are available for comparison, e.g. from in situ or repeated scanning measurements. Results are generated on significantly shorter timescales when compared with previous automated approaches to tomography data processing. This approach has the potential to be applied to damage detection in a range of materials through comparisons of volumetric datasets from a range of measurement or computational techniques.

MISNeR: Medical Implicit Shape Neural Representation for Image Volume Visualisation

AbstractThree‐dimensional visualisation of mesh reconstruction of the medical images is commonly used for various clinical applications including pre / post‐surgical planning. Such meshes are conventionally generated by extracting the surface from volumetric segmentation masks. Therefore, they have inherent limitations of staircase artefacts due to their anisotropic voxel dimensions. The time‐consuming process for manual refinement to remove artefacts and/or the isolated regions further adds to these limitations. Methods for directly generating meshes from volumetric data by template deformation are often limited to simple topological structures, and methods that use implicit functions for continuous surfaces, do not achieve the level of mesh reconstruction accuracy when compared to segmentation‐based methods. In this study, we address these limitations by combining the implicit function representation with a multi‐level deep learning architecture. We introduce a novel multi‐level local feature sampling component which leverages the spatial features for the implicit function regression to enhance the segmentation result. We further introduce a shape boundary estimator that accelerates the explicit mesh reconstruction by minimising the number of the signed distance queries during model inference. The result is a multi‐level deep learning network that directly regresses the implicit function from medical image volumes to a continuous surface model, which can be used for mesh reconstruction from arbitrary high volume resolution to minimise staircase artefacts. We evaluated our method using pelvic computed tomography (CT) dataset from two public sources with varying z‐axis resolutions. We show that our method minimised the staircase artefacts while achieving comparable results in surface accuracy when compared to the state‐of‐the‐art segmentation algorithms. Furthermore, our method was 9 times faster in volume reconstruction than comparable implicit shape representation networks.

Automatic Method of Macular Diseases Detection Using Deep CNN-GRU Network in OCT Images

Abstract The increasing development of Deep Learning mechanism allowed ones to create semi-fully or fully automated diagnosis software solutions for medical imaging diagnosis. The convolutional neural networks are widely applied for central retinal diseases classification based on OCT images. The main aim of this study is to propose a new network, Deep CNN-GRU for classification of early-stage and end-stages macular diseases as age-related macular degeneration and diabetic macular edema (DME). Three types of disorders have been taken into consideration: drusen, choroidal neovascularization (CNV), DME, alongside with normal cases. The created automatic tool was verified on the well-known Labelled Optical Coherence Tomography (OCT) dataset. For the classifier evaluation the following measures were calculated: accuracy, precision, recall, and F1 score. Based on these values, it can be stated that the use of a GRU layer directly connected to a convolutional network plays a pivotal role in improving previously achieved results. Additionally, the proposed tool was compared with the state-of-the-art of deep learning studies performed on the Labelled OCT dataset. The Deep CNN-GRU network achieved high performance, reaching up to 98.90% accuracy. The obtained results of classification performance place the tool as one of the top solutions for diagnosing retinal diseases, both early and late stage.

Endosomal membrane budding patterns in plants

Multivesicular endosomes (MVEs) sequester membrane proteins destined for degradation within intralumenal vesicles (ILVs), a process mediated by the membrane-remodeling action of Endosomal Sorting Complex Required for Transport (ESCRT) proteins. In Arabidopsis, endosomal membrane constriction and scission are uncoupled, resulting in the formation of extensive concatenated ILV networks and enhancing cargo sequestration efficiency. Here, we used a combination of electron tomography, computer simulations, and mathematical modeling to address the questions of when concatenated ILV networks evolved in plants and what drives their formation. Through morphometric analyses of tomographic reconstructions of endosomes across yeast, algae, and various land plants, we have found that ILV concatenation is widespread within plant species, but only prevalent in seed plants, especially in flowering plants. Multiple budding sites that require the formation of pores in the limiting membrane were only identified in hornworts and seed plants, suggesting that this mechanism has evolved independently in both plant lineages. To identify the conditions under which these multiple budding sites can arise, we used particle-based molecular dynamics simulations and found that changes in ESCRT filament properties, such as filament curvature and membrane binding energy, can generate the membrane shapes observed in multiple budding sites. To understand the relationship between membrane budding activity and ILV network topology, we performed computational simulations and identified a set of membrane remodeling parameters that can recapitulate our tomographic datasets.

Hyperparameter selection for dataset-constrained semantic segmentation: Practical machine learning optimization.

This paper provides a pedagogical example for systematic machine learning optimization in small dataset image segmentation, emphasizing hyperparameter selections. A simple process is presented for medical physicists to examine hyperparameter optimization. This is also applied to a case-study, demonstrating the benefit of the method. An unrestricted public Computed Tomography (CT) dataset, with binary organ segmentation, was used to develop a multiclass segmentation model. To start the optimization process, a preliminary manual search of hyperparameters was conducted and from there a grid search identified the most influential result metrics. A total of 658 different models were trained in 2100h, using 13160 effective patients. The quantity of results was analyzed using random forest regression, identifying relative hyperparameter impact. Metric implied segmentation quality (accuracy 96.8%, precision 95.1%) and visual inspection were found to be mismatched. In this work batch normalization was most important, but performance varied with hyperparameters and metrics selected. Targeted grid-search optimization and random forest analysis of relative hyperparameter importance, was an easily implementable sensitivity analysis approach. The proposed optimization method gives a systematic and quantitative approach to something intuitively understood, that hyperparameters change model performance. Even just grid search optimization with random forest analysis presented here can be informative within hardware and data quality/availability limitations, adding confidence to model validity and minimize decision-making risks. By providing a guided methodology, this work helps medical physicists to improve their model optimization, irrespective of specific challenges posed by datasets and model design.

A high-fidelity personalised 3d printed simulator for the left atrial appendage occlusion procedure

Purpose This study aims to develop a realistic 3D printing-based simulator for the training and planning of the left atrial appendage occlusion (LAAO) to be used in the cath lab. Design/methodology/approach Starting from a pre-operative computed tomography dataset of a patient already treated with LAAO, the model was obtained, consisting in the right and left heart, opportunely assembled to replicate the position and orientation of the patient's anatomy while lying on the cath lab table. Different 3D printing techniques and materials were used to mimic the interaction between the cardiac tissue and the clinical instrumentation. The simulator was tested in the cath lab under proper image guidance by three LAAO expert operators. The clinicians were required to assign a score in terms of realism to each material used to fabricate the fossa ovalis and the LAA. Findings The simulated interventions were successfully performed by the operators, who were able to navigate in the system and release the occluder device as during a real procedure. The intraoperative images acquired during the simulations were highly comparable with data from a real intervention, with a mean percentage difference below 10%. Originality/value This study demonstrated the feasibility of the proposed simulator to faithfully replicate the LAAO procedure and its potentiality to be used for multiple purposes, including the training of the young clinicians, the evaluation of the most complicated cases and the design of novel occluder devices, in a fully realistic setting.

Cross-Professional Evaluation of 3D Visualization of Liver Malignancies in the Decade of AI and Automatic Segmentation: A Benefit for Multidisciplinary Teams and Tumor Board Decisions?

Purpose To investigate whether automatic 3D visualization of computed tomography (CT) data sets with singular liver tumor compared to 2D images could foster a broader understanding of tumor localization and resectability in the liver within a multidisciplinary team and might therefore be a useful tool in multidisciplinary decision-making. Material and methods The study was configured as a web-based questionnaire. Physicians of all levels of medical training from surgery, radiology, and gastroenterology departments were recruited. A total of seven cases with singular liver tumor CT images with adequate quality were selected. Automatic 3D segmentation was performed using Universal Atlas (Release 5.0) as part of the Brainlab of Elements software suite (Brainlab AG, Munich, Germany). All cases were randomly presented in a 2D and 3D manner. After every case-presentation, multiple choice (single answer) questions concerning tumor extent and resectability were asked. The questions as well as the answers defined to be correct, were evaluated by two senior consultants from the radiology and surgery department.The primary outcome parameters were the correctness of answers stratified for medical specialty and for the level of medical training. The secondary outcome was the time needed for the evaluation of seven liver cases using 2D versus 3D images. Sixadditional questions were tailored to evaluate the subjective value of the 3D visualization. Results A total of 92 participants participated in the study, 31.5% of them were abdominal surgeons, 34.8% gastroenterologists, and 33.7% radiologists. Based on the level of medical training, 66 were residents (71.7%) and 26 consultants (28.3%). Only radiologists answered more questions correctly using 2D imaging compared to the 3D method (p = 0.006). There was no statistically significant difference between correctly answered questions when using 2D vs. 3D visualization in the gastroenterologist and surgeon groups (p > 0.05). The resident subgroup showed no statistically significant difference when using the 2D vs. 3D images (p > 0.05), the consultant subgroup answered more questions correctly using 2D imaging (p = 0.009). Physicians with elementary experience of liver pathology also showed no difference in 2D vs. 3D (p = 0.332), physicians with proficient experience of liver pathology answered more questions correctly using 2D imaging (p = 0.010). The median time taken for the evaluation of the seven liver cases was only significantly faster for the gastroenterologist group (p = 0.006) using the 3D analysis (median: 9.1 minutes) than the 2D analysis (median: 10.7 minutes). Over 80% of the participants found the 3D presentation to be a helpful additional tool for the clinical routine according to the subjective questionnaire. Conclusion In this study 3D visualization of liver tumors was evaluated as helpful within a multidisciplinary team of radiologists, surgeons, and gastroenterologists. However, significantly superior results in the understanding of liver anatomy could not be demonstrated by means of 3D visualization. It may be that more immersive technologies such as augmented reality or virtual reality will lead to a superior understanding compared to conventional presentation of information in 2D cross-sectional images.

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer. Computed tomography (CT) data sets of 197 prostate cancer patients were collected. Two auto-delineation models were built for radical radiotherapy and postoperative radiotherapy of prostate cancer respectively, and each model included CTVn for pelvic lymph nodes and CTVp for prostate tumors or prostate tumor beds. In the radical radiotherapy model, the volumetric dice (VD) coefficient of CTVn calculated by AI, was higher than that of the one delineated by the junior physicians (0.85vs. 0.82, p=0.018); In the postoperative radiotherapy model, the quantitative parameter of CTVn and CTVp, counted by AI, was better than that of the junior physicians. The median delineation time for AI was 0.23min in the postoperative model and 0.26min in the radical model, which were significantly shorter than those of the physicians (50.40 and 45.43min, respectively, p<0.001). The correction time of the senior physician for AI was much shorter compared with that for the junior physicians in both models (p<0.001). Using deep learning and attention mechanism, a highly consistent and time-saving contouring model was built for CTVs of pelvic lymph nodes and prostate tumors or prostate tumor beds for prostate cancer, which also might be a good approach to train junior radiation oncologists.

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer.

To generate and investigate a supervised deep learning algorithm for creating synthetic computed tomography (sCT) images from kilovoltage cone-beam computed tomography (kV-CBCT) images for adaptive radiation therapy (ART) in head and neck cancer (HNC). This study generated the supervised U-Net deep learning model using 3,491 image pairs from planning computed tomography (pCT) and kV-CBCT datasets obtained from 40 HNC patients. The dataset was split into 80% for training and 20% for testing. The evaluation of the sCT images compared to pCT images focused on three aspects: Hounsfield units accuracy, assessed using mean absolute error (MAE) and root mean square error (RMSE); image quality, evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) between sCT and pCT images; and dosimetric accuracy, encompassing 3D gamma passing rates for dose distribution and percentage dose difference. MAE, RMSE, PSNR, and SSIM showed improvements from their initial values of 53.15 ± 40.09, 153.99 ± 79.78, 47.91 ± 4.98 dB, and 0.97 ± 0.02 to 41.47 ± 30.59, 130.39 ± 78.06, 49.93 ± 6.00 dB, and 0.98 ± 0.02, respectively. Regarding dose evaluation, 3D gamma passing rates for dose distribution within sCT images under 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria, yielded passing rates of 92.1% ± 3.8%, 93.8% ± 3.0%, and 96.9% ± 2.0%, respectively. The sCT images exhibited minor variations in the percentage dose distribution of the investigated target and structure volumes. However, it is worth noting that the sCT images exhibited anatomical variations when compared to the pCT images. These findings highlight the potential of the supervised U-Net deep learningmodel in generating kV-CBCT-based sCT images for ART in patients with HNC.

Freeze-casting uniformity and domains

Freeze-casting is a technique for making centimeter long microchannel structures. Freeze-cast samples are often characterized from 2D cross-sectional images, resulting in weak determination of porosity and surface area as function of height of the sample. Here we analyze a freeze-cast 40 mm long sample of ▪ (LCSM) for which we have a full X-ray tomography dataset with a voxel size of 23.34×23.34×23.34μm3, as well as a high resolution cutout with a voxel size of 2.77×2.77×2.77μm3. We analyze the porosity and tortuosity and show that the latter is 1.17 and 1.27 for the solid and pore phase respective in the direction along the microchannels, but 5.12 and 8.83 in the perpendicular direction. We also quantify the domain structure of the freeze-casted pores and show that this is not uniform for the sample, but that the angular orientation and number of domains is preserved throughout the sample.

Evaluation of Inverse Planned and Forward Planned Intensity Modulated Radiotherapy Techniques in Breast Cancer

Introduction: The objective of this study is to compare the dosimetric parameters of radiation to the whole breast between the two intensity-modulated radiotherapy (IP IMRT) techniques, i.e. Inverse planned IMRT (IP IMRT) and Forward Planned IMRT (FP IMRT) with regard to target coverage (PTV) and irradiation of organs at risk (OAR). Material and Methods: Plain and Contrast enhanced computed tomography (CECT) datasets were created for 41 patients treated with whole breast radiation therapy. CT simulation and treatment is performed using deep inspiratory breath hold technique (DIBH). Radiotherapy treatment Planning is done using Eclipse Treatment Planning System (version 13.7) with a prescription dose of 40 Gy in 15#. The developed treatment plans were subjected to objective comparison of PTV and OARs using dose volume histograms (DVH). Results: IP IMRT plans provided better coverage (99.5% vs 97.6%), comparable though higher maximum dose (Dmax 45.0 VS 44.1 Gy), higher hot spot (PTV105% 49.2 vs 33), lower volumes receiving 20, 25, 30 Gy (V20, V25, V30) for heart, more homogeneous (homogeneity index 0.10 vs. 0.14) and conformal dose distribution (conformity Index 1.0 vs 0.98) compared to FP IMRT. Regarding OAR dosimetry it is observed that FP IMRT showed reduced mean dose to Coronary artery (LADCA), Contralateral Lung (CL), Contralateral breast (CB) along with reduction in low dose region (V5) to all OARs under study. It was also observed that Monitor units used and planning time were lower for FP IMRT. Conclusion: On weighing different dosimetric factors, both the techniques have displayed their own advantages and disadvantages. Choosing a planning technique needs to be customized taking into consideration various factors such as breast topography, size and volumes of breast, availability of expertise planning skills and resources.

Development of the humeral head offset index for control of humeral torsion

BackgroundControl of humeral torsion can present a challenge, especially intraoperatively during closed reduction and fixation of humeral shaft fractures or 2-part surgical neck fractures of the proximal humerus. The objective of this study is to develop and validate an indirect method for the assessment of humeral torsion using an index that is linearly correlated with rotational arm position and can be derived from only a single plain radiographic image of the proximal humerus.MethodsThe Humeral Head Offset Index (HHOI) is calculated as the ratio of the medial and lateral offset of the humeral head measured from the outer cortices of the shaft on a plain radiographic or fluoroscopic image. The relationship of HHOI with humeral torsion was first verified on a sawbone model with radiopaque characteristics under fluoroscopic control. Different degrees of retroversion were simulated through manual rotation of the humerus with a digital protractor in 5° increments until 40° internally rotated and then in 5° increments until 40° externally rotated from the neutral position. The same procedure was subsequently performed digitally on Digitally Reconstructed Radiographs (DRRs) from computed tomography (CT) dataset of the sawbone. Next, the HHOI index was applied to eight randomly selected patients with total humerus CT using the same method. Spearman’s rho was calculated for the bivariate analysis of correlation between the simulated degree of retroversion and the HHOI. Strength of correlation was classified according to Koo and Li. Interrater and intrarater reliability of three blinded observers with repetition of measurement after three months were analyzed by assessing the intraclass correlation coefficient (ICC).ResultsBoth in the sawbone model and in DRRs, we demonstrated a high to very high significant linear correlation between simulated retroversion and the HHOI. ICC values demonstrated excellent interrater reliability and excellent intrarater reliability for measurement of the HHOI.ConclusionsThe HHOI is a new, simple, reliable index that has a linear relationship to the rotation of the humerus and can therefore allow an indirect control of humeral torsion in comparison to the contralateral side.

MiLoPYP: self-supervised molecular pattern mining and particle localization in situ

Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proteins. Here, we present MiLoPYP, a two-step dataset-specific contrastive learning-based framework that enables fast molecular pattern mining followed by accurate protein localization. MiLoPYP’s ability to effectively detect and localize a wide range of targets including globular and tubular complexes as well as large membrane proteins, will contribute to streamline and broaden the applicability of high-resolution workflows for in situ structure determination.

Medial prefrontal neuroplasticity during extended-release naltrexone treatment of opioid use disorder – a longitudinal structural magnetic resonance imaging study

Opioid use disorder (OUD) has been linked to macroscopic structural alterations in the brain. The monthly injectable, extended-release formulation of μ-opioid antagonist naltrexone (XR-NTX) is highly effective in reducing opioid craving and preventing opioid relapse. Here, we investigated the neuroanatomical effects of XR-NTX by examining changes in cortical thickness during treatment for OUD. Forty-seven OUD patients underwent structural magnetic resonance imaging and subjectively rated their opioid craving ≤1 day before (pre-treatment) and 11 ± 3 days after (on-treatment) the first XR-NTX injection. A sample of fifty-six non-OUD individuals completed a single imaging session and served as the comparison group. A publicly available [¹¹C]carfentanil positron emission tomography dataset was used to assess the relationship between changes in cortical thickness and μ-opioid receptor (MOR) binding potential across brain regions. We found that the thickness of the medial prefrontal and anterior cingulate cortices (mPFC/aCC; regions with high MOR binding potential) was comparable between the non-OUD individuals and the OUD patients at pre-treatment. However, among the OUD patients, mPFC/aCC thickness significantly decreased from pre-treatment to on-treatment. A greater reduction in mPFC/aCC thickness was associated with a greater reduction in opioid craving. Taken together, our study suggests XR-NTX-induced cortical thickness reduction in the mPFC/aCC regions in OUD patients. The reduction in thickness does not appear to indicate a restoration to the non-OUD level but rather reflects XR-NTX’s distinct therapeutic impact on an MOR-rich brain structure. Our findings highlight the neuroplastic effects of XR-NTX that may inform the development of novel OUD interventions.