Label-free Brillouin endo-microscopy for the quantitative 3D imaging of sub-micrometre biology.

This report presents an optical fibre-based endo-microscopic imaging tool that simultaneously measures the topographic profile and 3D viscoelastic properties of biological specimens through the phenomenon of time-resolved Brillouin scattering. This uses the intrinsic viscoelasticity of the specimen as a contrast mechanism without fluorescent tags or photoacoustic contrast mechanisms. We demonstrate 2 µm lateral resolution and 320 nm axial resolution for the 3D imaging of biological cells and Caenorhabditis elegans larvae. This has enabled the first ever 3D stiffness imaging and characterisation of the C. elegans larva cuticle in-situ. A label-free, subcellular resolution, and endoscopic compatible technique that reveals structural biologically-relevant material properties of tissue could pave the way toward in-vivo elasticity-based diagnostics down to the single cell level.

RT-SRTS: Angle-agnostic real-time simultaneous 3D reconstruction and tumor segmentation from single X-ray projection.

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiration limits its accuracy. Recently, 3D imaging from a single X-ray projection has received extensive attention as a promising approach to address this issue. However, current methods can only reconstruct 3D images without directly locating the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirements of motion control in radiotherapy. In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle. Furthermore, the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules have been proposed to aid feature extraction and improve segmentation accuracy. The proposed method was evaluated on fifteen patient cases and compared with three state-of-the-art methods. It not only delivers superior 3D reconstruction but also demonstrates commendable tumor segmentation results. Simultaneous reconstruction and segmentation can be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacies of both AEC and URE have also been validated in ablation studies. The code of work is available at https://github.com/ZywooSimple/RT-SRTS.

Improving ultrasound-based bone registration using the iterative closest point algorithm paired with a complex optimization solver.

The Iterative Close Point (ICP) algorithm is used for bone registrations based on ultrasound measurements. However, the ICP has been shown to suffer from local minima. The Complex optimization, as a more robust routine compared to the commonly used gradient-based algorithms, could be an alternative for solving the ICP problem. In this study, we investigated the effect of the initial estimate and the number of registration points on bone registrations achieved using the ICP and a Complex optimization routine and we compared it against using Quadratic Sequential Programming (SQP). Ultrasound measurements were performed with an A-mode probe on a bovine humerus and an ovine femur embedded into ballistic gel. Simultaneously, the bones and the probe were tracked in 3D space using retroreflective markers. Kinematic, ultrasound and geometrical data obtained from scans of the specimens and the probe served as input to a bone registrations routine. Registrations were performed using two ICP solvers for different initial estimates and number of registration points. On average, 68 % of the Complex optimization registrations had less than 1 mm translation error and less than 1° rotational error for perturbations of the initial estimate from the reference measurements compared to the 35 % of the SQP ones. Similar medians of registration errors were observed between the two methods for variations of the number of the employed registration points. Although the Complex optimization provided accurate bone registrations for all cases, the objective function could not always determine the registrations with the smallest registration error. Future research should explore methodologies to overcome this challenge.

Reliability and validity of handheld structured light scanners and a static stereophotogrammetry system in facial three-dimensional surface imaging.

Several new systems for three-dimensional (3D) surface imaging of the face have become available to assess changes following orthognathic or facial surgery. Before they can be implemented in practice, their reliability and validity must be established. Our aim, therefore, was to study the intra- and inter-system reliability and validity of 3dMD (stereophotogrammetry), Artec Eva and Artec Space Spider (both structured light scanners). Intra- and inter-system reliability, expressed in root mean square distance, was determined by scanning a mannequin's head and the faces of healthy volunteers multiple times. Validity was determined by comparing the linear measurements of the scans with the known distances of a 3D printed model. Post-processing errors were also calculated. Intra-system reliability after scanning the mannequin's head was best with the Artec Space Spider (0.04 mm Spider; 0.07 mm 3dMD; 0.08 mm Eva). The least difference in inter-system reliability after scanning the mannequin's head was between the Artec Space Spider and Artec Eva. The best intra-system reliability after scanning human subjects was with the Artec Space Spider (0.15 mm Spider; 0.20 mm Eva; 0.23 mm 3dMD). The least difference in inter-system reliability after scanning human subjects was between the Artec Eva and Artec Space Spider. The most accurate linear measurement validity occurred with the Artec Space Spider. The post-processing error was 0.01 mm for all the systems. The Artec Space Spider is the most reliable and valid scanning system.

Evaluating the precision and accuracy of digital dental models with a low-cost structured light device.

OBJECTIVE: This study evaluated the accuracy and precision of digital models acquisition using a home-built, low-cost scanning system based on the structured light method. METHODS: a plaster model (PM) was scanned using the experimental device (SL) and a dental desktop scanner (DS). The teeth dimensions of PM and SL models were measured in triplicate, with a caliper and digitally, respectively. The agreement of the measurements of each model was evaluated using the intraclass correlation coefficient, and the validity between the different measurement techniques was assessed using the Bland-Altman analysis. The accuracy and precision of the models were qualitatively investigated using the mesh superposition of the SL and DS models. RESULTS: A high intraclass correlation coefficient was observed in all models (PM=0.964; SL1=0.998; SL2=0.995; SL3=0.998), and there was no statistical difference between the measurements of the SL models (p>0.05). PM and SL model measurements were found to be in good agreement, with only 3.57% of the observed differences between the same measurement being located outside 95% limits of agreement according to Bland and Altman (0.43 and -0.40 mm). In the superimpositions of SL-SL and SL-DS models, areas of discrepancy greater than 0.5 mm were observed mainly in interproximal, occlusal, and cervical sites. CONCLUSION: These results indicate that the home-built SL scanning system did not possess sufficient accuracy and precision for many clinical applications. However, the consistency in preserving the dental proportions suggests that the equipment can be used for planning, storage, and simple clinical purposes.

Fully automatic tracking of native knee kinematics from stereo-radiography with digitally reconstructed radiographs.

Precise measurement of joint-level motion from stereo-radiography facilitates understanding of human movement. Conventional procedures for kinematic tracking require significant manual effort and are time intensive. The current work introduces a method for fully automatic tracking of native knee kinematics from stereo-radiography sequences. The framework consists of three computational steps. First, biplanar radiograph frames are annotated with segmentation maps and key points using a convolutional neural network. Next, initial bone pose estimates are acquired by solving a polynomial optimization problem constructed from annotated key points and anatomic landmarks from digitized models. A semidefinite relaxation is formulated to realize the global minimum of the non-convex problem. Pose estimates are then refined by registering computed tomography-based digitally reconstructed radiographs to masked radiographs. A novel rendering method is also introduced which enables generating digitally reconstructed radiographs from computed tomography scans with inconsistent slice widths. The automatic tracking framework was evaluated with stereo-radiography trials manually tracked with model-image registration, and with frames which capture a synthetic leg phantom. The tracking method produced pose estimates which were consistently similar to manually tracked values; and demonstrated pose errors below 1.0 degree or millimeter for all femur and tibia degrees of freedom in phantom trials. Results indicate the described framework may benefit orthopaedics and biomechanics applications through acceleration of kinematic tracking.

Preoperative three-dimensional modelling and virtual reality planning aids nephron sparing surgery in a child with bilateral Wilms tumour.

Bilateral Wilms tumour (BWT) is a surgically challenging condition. Virtual reality (VR) reconstruction aids surgeons to foresee the anatomy ahead of Nephron Sparing Surgery (NSS). Three-dimensional (3D) visualisation improves the anatomical orientation of surgeons performing NSS. We herewith report a case of BWT where VR planning and 3D printing were used to aid NSS. Conventional imaging is often found to be inadequate while assessing the tumour-organ-vascular anatomy. Advances like VR and 3D printing help surgeons plan better for complex surgeries like bilateral NSS. Next-generation extended reality tools will likely aid robotic-assisted precision NSS and improve patient outcomes.

Laparoscopy training of novices with complex curved instruments using 2D- and 3D-visualization.

PURPOSE: Beside many advantages, disadvantages such as reduced degrees of freedom and poorer depth perception are still apparent in laparoscopic surgery. 3D visualization and the development of complex instruments are intended to counteract the disadvantages. We want to find out whether the use of complex instruments and 3D visualization has an influence on the performance of novices. METHODS: 48 medical students with no experience in laparoscopic surgery or simulator-based laparoscopy training were included. They were randomized in four groups according to a stratification assessment. During a structured training period they completed the FLS-Tasks "PEG Transfer", "Pattern Cut" and "Intracorporeal Suture" and a transfer task based on these three. Two groups used conventional laparoscopic instruments with 3D or 2D visualization, two groups used complex curved instruments. The groups were compared in terms of their performance. RESULTS: In 2D laparoscopy there was a better performance with straight instruments vs. curved instruments in PEG Transfer and Intracorporeal Suture. In the transfer task, fewer errors were made with straight instruments. In 2D vs. 3D laparoscopy when using complex curved instruments there was an advantage in Intracorporeal Suture and PEG Transfer for 3D visualization. Regarding the transfer exercise, a better performance was observed and fewer errors were made in 3D group. CONCLUSION: We could show that learning laparoscopic techniques with complex curved instruments is more difficult with standard 2D visualization and can be overcome using 3D optics. The use of curved instruments under 3D vision seems to be advantageous when working on more difficult tasks.

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.

Significance: Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. Aim: We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. Approach: The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the x-y plane. Leveraging cell continuity in consecutive z-stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. Results: The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Conclusion: Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

Comparison of weighting algorithms to mitigate respiratory motion in free-breathing neonatal pulmonary radial UTE-MRI.

Background. Thoracoabdominal MRI is limited by respiratory motion, especially in populations who cannot perform breath-holds. One approach for reducing motion blurring in radially-acquired MRI is respiratory gating. Straightforward 'hard-gating' uses only data from a specified respiratory window and suffers from reduced SNR. Proposed 'soft-gating' reconstructions may improve scan efficiency but reduce motion correction by incorporating data with nonzero weight acquired outside the specified window. However, previous studies report conflicting benefits, and importantly the choice of soft-gated weighting algorithm and effect on image quality has not previously been explored. The purpose of this study is to map how variable soft-gated weighting functions and parameters affect signal and motion blurring in respiratory-gated reconstructions of radial lung MRI, using neonates as a model population.Methods. Ten neonatal inpatients with respiratory abnormalities were imaged using a 1.5 T neonatal-sized scanner and 3D radial ultrashort echo-time (UTE) sequence. Images were reconstructed using ungated, hard-gated, and several soft-gating weighting algorithms (exponential, sigmoid, inverse, and linear weighting decay outside the period of interest), with %Nprojrepresenting the relative amount of data included. The apparent SNR (aSNR) and motion blurring (measured by the maximum derivative of image intensity at the diaphragm, MDD) were compared between reconstructions.Results. Soft-gating functions produced higher aSNR and lower MDD than hard-gated images using equivalent %Nproj, as expected. aSNR was not identical between different gating schemes for given %Nproj. While aSNR was approximately linear with %Nprojfor each algorithm, MDD performance diverged between functions as %Nprojdecreased. Algorithm performance was relatively consistent between subjects, except in images with high noise.Conclusion. The algorithm selection for soft-gating has a notable effect on image quality of respiratory-gated MRI; the timing of included data across the respiratory phase, and not simply the amount of data, plays an important role in aSNR. The specific soft-gating function and parameters should be considered for a given imaging application's requirements of signal and sharpness.

[Spatial distribution pattern of local tumor progression analysis after microwave ablation of hepatocellular carcinoma based on three-dimensional magnetic resonance imaging].

Objective: To investigate the spatial distribution pattern of local tumor progression (LTP) for hepatocellular carcinoma (HCC) ≤5 cm after microwave ablation. Methods: A retrospective analysis was performed on 169 HCCs with matched MRI before and after ablation from December 2009 to December 2019. A tumor MRI was reconstructed using three-dimensional visualization technology. LTP was classified as contact or non-contact, early or late stage, according to whether LTP was in contact with the edge of the ablation zone and the occurrence time (24 months). The tumor-surrounded area was divided into eight quadrants by using the eight-quadrant map method. An analysis was conducted on the spatial correlation between the quadrant where the ablative margin (AM) safety boundary was located and the quadrant where different types of LTP occurred. The t-test, or rank-sum test, was used for the measurement data. 2-test for count data was used to compare the difference between the two groups. Results: The AM quadrant had a distribution of 54.4% LTP, 64.2% early LTP stage, and 69.1% contact LTP, suggesting this quadrant was much more concentrated than the other quadrants (P < 0.001). Additionally, the AM quadrant had only 15.2% of non-contact type LTP and 17.1% of late LTP, which was not significantly different from the average distribution probability of 12.5% (100/8%) among the eight quadrants (P = 0.667, 0.743). 46.6% of early contact type LTP was located at the ablation needle tip, 25.2% at the body, and 28.1% at the caudal, while the location distribution probabilities of non-early contact LTP were 34.8%, 31.8%, and 33.3%, respectively. Conclusion: LTP mostly occurs in areas where the ablation safety boundary is the shortest. However, non-contact LTP and late LTP stages exhibit the feature of uniform distribution. Thus, this type of LPT may result from an inadequate non-ablation safety boundary.

Vessel Wall Characterization Using Quantitative MR Imaging.

MR imaging's exceptional capabilities in vascular imaging stem from its ability to visualize and quantify vessel wall features, such as plaque burden, composition, and biomechanical properties. The application of advanced MR imaging techniques, including two-dimensional and three-dimensional black-blood MR imaging, T1 and T2 relaxometry, diffusion-weighted imaging, and dynamic contrast-enhanced MR imaging, wall shear stress, and arterial stiffness, empowers clinicians and researchers to explore the intricacies of vascular diseases. This array of techniques provides comprehensive insights into the development and progression of vascular pathologies, facilitating earlier diagnosis, targeted treatment, and improved patient outcomes in the management of vascular health.

Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.

BACKGROUND: Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images. METHODS: In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules. RESULTS: The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%. CONCLUSION: A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.

A modified 3D RPN for detecting lung nodules on CT images that exhibited greater sensitivity and CPM than did several previously reported CAD detection models was established.

[Educational Impact of Using Three-dimentional Models for Preoperative Simulation of Segmentectomy for Medical Students].

BACKGROUND: Lung segment anatomy is complex and difficult for medical students to comprehend, so education is a challenging issue. Three-dimensional (3D) models may be a more effective teaching tool for medical students. We evaluated the usefulness of 3D models in education for segmentectomy. METHODS: Sixty-six students who participated in clinical training for thoracic surgery from November 2020 to March 2022 were included in the study. Computed tomography (CT) images, 3D-CT images, and 3D models were used to investigate students' level of understanding for each lung segment. Levels of understanding were assessed using a questionnaire administered using the interview method. The results of the questionnaire were tabulated and analyzed using the Kruskal-Wallis test. RESULTS: The response rate was 100%. Comprehension scores for all questions were highest for 3D models, followed by 3D-CT and CT. These results suggest that use of a 3D entity model may have a high educational effect. CONCLUSION: Although it is unclear whether students' comprehension was directly related to their understanding of correct anatomy, our results suggest that 3D models may be an effective way for medical students to understand lung segmentectomy.

Efficient Model-Based Anthropometry under Clothing Using Low-Cost Depth Sensors.

Measuring human body dimensions is critical for many engineering and product design domains. Nonetheless, acquiring body dimension data for populations using typical anthropometric methods poses challenges due to the time-consuming nature of manual methods. The measurement process for three-dimensional (3D) whole-body scanning can be much faster, but 3D scanning typically requires subjects to change into tight-fitting clothing, which increases time and cost and introduces privacy concerns. To address these and other issues in current anthropometry techniques, a measurement system was developed based on portable, low-cost depth cameras. Point-cloud data from the sensors are fit using a model-based method, Inscribed Fitting, which finds the most likely body shape in the statistical body shape space and providing accurate estimates of body characteristics. To evaluate the system, 144 young adults were measured manually and with two levels of military ensembles using the system. The results showed that the prediction accuracy for the clothed scans remained at a similar level to the accuracy for the minimally clad scans. This approach will enable rapid measurement of clothed populations with reduced time compared to manual and typical scan-based methods.

Predictive value of placental real-time shear wave elastography combined with 3-dimensional power Doppler index for preeclampsia.

This study aimed to investigate the value of placental real-time shear wave elastography combined with three-dimensional power Doppler index (3D-PDI) in the prediction of preeclampsia. We conducted a retrospective study selecting 60 pregnant women diagnosed with preeclampsia as the experimental group and 60 normal pregnant women as the control group from January 2021 to December 2022. The elastic modulus values of different regions of the placenta and placental 3D-PDI were detected and compared between the two groups. The ROC curve was used to evaluate the diagnostic value of each parameter, alone or in combination, for preeclampsia. The study findings demonstrated that the elastic modulus values of different regions of the placenta and 3D-PDI of the two groups have statistical significance. The values of SWE, VI, FI, and VFI are different in prediction of preeclampsia, and the combination of various parameters can improve the prediction value. Overall, our study provides a valuable method for the prediction of preeclampsia with the advantages of non-invasiveness, efficiency, and simplicity.

Automatic multi-view pose estimation in focused cardiac ultrasound.

Focused cardiac ultrasound (FoCUS) is a valuable point-of-care method for evaluating cardiovascular structures and function, but its scope is limited by equipment and operator's experience, resulting in primarily qualitative 2D exams. This study presents a novel framework to automatically estimate the 3D spatial relationship between standard FoCUS views. The proposed framework uses a multi-view U-Net-like fully convolutional neural network to regress line-based heatmaps representing the most likely areas of intersection between input images. The lines that best fit the regressed heatmaps are then extracted, and a system of nonlinear equations based on the intersection between view triplets is created and solved to determine the relative 3D pose between all input images. The feasibility and accuracy of the proposed pipeline were validated using a novel realistic in silico FoCUS dataset, demonstrating promising results. Interestingly, as shown in preliminary experiments, the estimation of the 2D images' relative poses enables the application of 3D image analysis methods and paves the way for 3D quantitative assessments in FoCUS examinations.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR).

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Deformable registration of preoperative MR and intraoperative long-length tomosynthesis images for guidance of spine surgery via image synthesis.

PURPOSE: Improved integration and use of preoperative imaging during surgery hold significant potential for enhancing treatment planning and instrument guidance through surgical navigation. Despite its prevalent use in diagnostic settings, MR imaging is rarely used for navigation in spine surgery. This study aims to leverage MR imaging for intraoperative visualization of spine anatomy, particularly in cases where CT imaging is unavailable or when minimizing radiation exposure is essential, such as in pediatric surgery. METHODS: This work presents a method for deformable 3D-2D registration of preoperative MR images with a novel intraoperative long-length tomosynthesis imaging modality (viz., Long-Film [LF]). A conditional generative adversarial network is used to translate MR images to an intermediate bone image suitable for registration, followed by a model-based 3D-2D registration algorithm to deformably map the synthesized images to LF images. The algorithm's performance was evaluated on cadaveric specimens with implanted markers and controlled deformation, and in clinical images of patients undergoing spine surgery as part of a large-scale clinical study on LF imaging. RESULTS: The proposed method yielded a median 2D projection distance error of 2.0 mm (interquartile range [IQR]: 1.1-3.3 mm) and a 3D target registration error of 1.5 mm (IQR: 0.8-2.1 mm) in cadaver studies. Notably, the multi-scale approach exhibited significantly higher accuracy compared to rigid solutions and effectively managed the challenges posed by piecewise rigid spine deformation. The robustness and consistency of the method were evaluated on clinical images, yielding no outliers on vertebrae without surgical instrumentation and 3% outliers on vertebrae with instrumentation. CONCLUSIONS: This work constitutes the first reported approach for deformable MR to LF registration based on deep image synthesis. The proposed framework provides access to the preoperative annotations and planning information during surgery and enables surgical navigation within the context of MR images and/or dual-plane LF images.

Manifold-based denoising for Ferumoxytol-enhanced 3D cardiac cine MRI.

The two-dimensional (2D) cine cardiovascular magnetic resonance (CMR) technique is the reference standard for assessing cardiac function. However, one challenge with 2D cine is that the acquisition time for the whole cine stack is long and requires multiple breath holds, which may not be feasible for pediatric or ill patients. Though single breath-hold multi-slice cine may address the issue, it can only acquire low-resolution images, and hence, affect the accuracy of cardiac function assessment. To address these challenges, a Ferumoxytol-enhanced, free breathing, isotropic high-resolution 3D cine technique was developed. The method produces high-contrast cine images with short acquisition times by using compressed sensing together with a manifold-based method for image denoising. This study included fifteen patients (9.1 $ \pm $ 5.6 yrs.) who were referred for clinical cardiovascular magnetic resonance imaging (MRI) with Ferumoxytol contrast and were prescribed the 3D cine sequence. The data was acquired on a 1.5T scanner. Statistical analysis shows that the manifold-based denoised 3D cine can accurately measure ventricular function with no significant differences when compared to the conventional 2D breath-hold (BH) cine. The multiplanar reconstructed images of the proposed 3D cine method are visually comparable to the golden standard 2D BH cine method in terms of clarity, contrast, and anatomical precision. The proposed method eliminated the need for breath holds, reduced scan times, enabled multiplanar reconstruction within an isotropic data set, and has the potential to be used as an effective tool to access cardiovascular conditions.