Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience

Primacy of vision shapes behavioral strategies and neural substrates of spatial navigation in marmoset hippocampus

The role of the hippocampus in spatial navigation has been primarily studied in nocturnal mammals, such as rats, that lack many adaptations for daylight vision. Here we demonstrate that during 3D navigation, the common marmoset, a new world primate adapted to daylight, predominantly uses rapid head-gaze shifts for visual exploration while remaining stationary. During active locomotion marmosets stabilize the head, in contrast to rats that use low-velocity head movements to scan the environment as they locomote. Pyramidal neurons in the marmoset hippocampus CA3/CA1 regions predominantly show mixed selectivity for 3D spatial view, head direction, and place. Exclusive place selectivity is scarce. Inhibitory interneurons are predominantly mixed selective for angular head velocity and translation speed. Finally, we found theta phase resetting of local field potential oscillations triggered by head-gaze shifts. Our findings indicate that marmosets adapted to their daylight ecological niche by modifying exploration/navigation strategies and their corresponding hippocampal specializations.

CT reconstruction based 3D model of the digital cushion's blood supply in the hind foot of an African savanna elephant (Loxodonta africana)

Introduction

Foot health is crucial for elephants, as pathological lesions of the feet are a leading cause of euthanasia in captive elephants, which are endangered species. Proper treatment of the feet, particularly in conditions affecting the digits and the digital cushion, requires a thorough understanding of the underlying anatomy. However, only limited literature exists due to the small population and the epidemiological foot diseases which often precludes many deceased elephants from scientific study. The aim of this study was to provide a detailed anatomical description of the blood supply to the African elephant's hindfoot.

Methods

The healthy right hindlimb of a 19-year-old deceased female African savanna elephant was examined using computed tomography. Following a native sequence, 48 mL of barium-based contrast agent was injected into the caudal and cranial tibial arteries, and a subsequent scan was performed. The images were processed with 3D Slicer software.

Results

The medial and lateral plantar arteries run in a symmetrical pattern. They each have a dorsal and a plantar branch, which reach the plantar skin before turning toward the axial plane of the sole to reach the digital cushion from the proximal direction. An accurate 3D model of the arteries and the bones of the foot, a set of labeled images and an animation of the blood supply have been created for ease of understanding.

Discussion

In contrast to domestic ungulates, the digital cushion of the hindlimb is supplied differently from that of the forelimb. The lack of large vessels in its deeper layers indicates a slow regeneration time. This novel anatomical information may be useful in the planning of surgical interventions and in emergency medical procedures.

CT based 3D reconstruction of the forefoot's blood supply in a white rhinoceros

BACKGROUND

The white rhinoceros (Ceratotherium simum) is close to extinction, listed as "Near Threatened", with a decreasing population on the Red List of Threatened Species of the International Union for Conservation of Nature. In at least 50% of the specimens in captivity, podiatric diseases, such as osteitis, osteomyelitis, chip fractures, enthesophytes, fractures and osteoarthritis were found during necropsy. These osteal deformations cause further pathogenic alterations in the soft tissues, particularly in the digital cushion. The literature provides good description of the skeleton of the rhino's limbs, but similar for the vascular system is non-existent. In order to recognize the symptoms in an early state and for a successful surgical treatment, precise knowledge of the vascular anatomy is essential. The purpose of our study was to provide detailed anatomical description of the blood supply of the digits and that of the digital cushion.

RESULTS

The blood supply of the distal foot, digits and digital cushions were perfectly visible on the reconstructed and coloured 3D models. The deep palmar arch provided not only the blood supply to the digits but had a palmaro-distal running branch which developed a trifurcation proximal to the proximal sesamoid bones of the third digit. Two of its branches participated in the blood supply of the digits' proximal palmar surface, while the major branch supplied the digital cushion from proximal direction.

CONCLUSIONS

Our findings show a unique blood supply: the main vessels of the digital cushion stem both directly from the deep palmar arch and from the digits' own arteries. The detailed description of vessels may be useful in planning surgery of the region and also in cases where the veins of the ear are not accessible.

The impact of the size and angle of the cochlear basal turn on translocation of a pre-curved mid-scala cochlear implant electrode

Scalar translocation is a severe form of intra-cochlear trauma during cochlear implant (CI) electrode insertion. This study explored the hypothesis that the dimensions of the cochlear basal turn and orientation of its inferior segment relative to surgically relevant anatomical structures influence the scalar translocation rates of a pre-curved CI electrode. In a cohort of 40 patients implanted with the Advanced Bionics Mid-Scala electrode array, the scalar translocation group (40%) had a significantly smaller mean distance A of the cochlear basal turn (p < 0.001) and wider horizontal angle between the inferior segment of the cochlear basal turn and the mastoid facial nerve (p = 0.040). A logistic regression model incorporating distance A (p = 0.003) and horizontal facial nerve angle (p = 0.017) explained 44.0-59.9% of the variance in scalar translocation and correctly classified 82.5% of cases. Every 1mm decrease in distance A was associated with a 99.2% increase in odds of translocation [95% confidence interval 80.3%, 100%], whilst every 1-degree increase in the horizontal facial nerve angle was associated with an 18.1% increase in odds of translocation [95% CI 3.0%, 35.5%]. The study findings provide an evidence-based argument for the development of a navigation system for optimal angulation of electrode insertion during CI surgery to reduce intra-cochlear trauma.

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, has become a valuable tool in medical imaging education due to its integration of various AI applications. Through its open-source architecture, students can gain practical experience with diverse medical images and the latest AI technology, reinforcing their understanding of anatomy and imaging technology while fostering independent learning and clinical reasoning skills. The implementation of this platform improves instruction quality and nurtures skilled professionals who can meet the demands of clinical practice, research institutions, and technology innovation enterprises. AI algorithms' application in medical image processing have facilitated their translation from the lab to practical clinical applications and education.

WarpDrive: Improving spatial normalization using manual refinements

Spatial normalization-the process of mapping subject brain images to an average template brain-has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.

[18F]FDG PET-MR characterization of aortitis in the IL1rn-/- mouse model of giant-cell arteritis

BACKGROUND

Metabolic imaging is routinely used to demonstrate aortitis in patients with giant-cell arteritis. We aimed to investigate the preclinical model of aortitis in BALB/c IL1rn-/- mice using [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography-magnetic resonance (PET-MR), gamma counting and immunostaining. We used 15 first-generation specific and opportunistic pathogen-free (SOPF) 9-week-old IL1rn-/- mice, 15 wild-type BALB/cAnN mice and 5 s-generation specific pathogen-free (SPF) 9-week-old IL1rn-/-. Aortic [18F]FDG uptake was assessed as the target-to-background ratio (TBR) using time-of-flight MR angiography as vascular landmarks.

RESULTS

[18F]FDG uptake measured by PET or gamma counting was similar in the first-generation SOPF IL1rn-/- mice and the wild-type group (p > 0.05). However, the first-generation IL1rn-/- mice exhibited more interleukin-1β (p = 0.021)- and interleukin-6 (p = 0.019)-positive cells within the abdominal aorta than the wild-type mice. In addition, the second-generation SPF group exhibited significantly higher TBR (p = 0.0068) than the wild-type mice on the descending thoracic aorta, unlike the first-generation SOPF IL1rn-/- mice.

CONCLUSIONS

In addition to the involvement of interleukin-1β and -6 in IL1rn-/- mouse aortitis, this study seems to validate [18F]FDG PET-MR as a useful tool for noninvasive monitoring of aortitis in this preclinical model.

Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework

Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen ([Formula: see text]) and clinical practice ([Formula: see text]). The model robustness was further evaluated on three independent open-source datasets ([Formula: see text] scans) consisting of cadaveric specimen scans. For the in-house datasets, Dice scores of [Formula: see text], intersection-over-union scores of [Formula: see text] and average Hausdorff distances of [Formula: see text] and [Formula: see text] voxel units were achieved. The landmark localization task was performed automatically with an average localization error of [Formula: see text] voxel units. A robust, albeit reduced performance could be attained for the catalogue of three open-source datasets. Results of the ablation studies with 43 mono-parametric variations of the basal architecture and training protocol provided task-optimal parameters for both categories. Ablation studies against single-task variants of the basal architecture showed a clear performance benefit of coupling landmark localization with segmentation and a dataset-dependent performance impact on segmentation ability.

3D Reconstruction of the Blood Supply in an Elephant's Forefoot Using Fused CT and MRI Sequences

Being the largest still-living terrestrial mammal on earth, an elephant's feet play an important role in its health status. The musculoskeletal structures in the forefoot are well described in the literature, but information about vascularization is limited. The novel aim of this work is to provide anatomical guidance to structures found in the forefoot, focusing on the arterial system. Initially, native CT and MRI sequences were taken of the left forefoot of a deceased 6-year-old female Asian elephant; the foot was then filled with an iodine-containing contrast medium through the a. mediana and the CT scans were repeated in the same position. The images obtained were processed with 3D Slicer software for the 3D reconstruction of the bones and arteries. The results clearly showed the palmar blood supply of the forefoot. A so far undescribed vessel was revealed, stemming from the a. metacarpea, supplying the first digit and the digital cushion. The course of the deep palmar arch's terminal section was also established. This paper provides the first description of the exact disposition of the arteries in the palmar aspect of an elephant's forefoot and may be used in planning surgeries in clinically affected animals.

Concordance between US and MRI Two-Dimensional Measurement and Volumetric Segmentation in Fetal Ventriculomegaly

We provide a study comparison between two-dimensional measurement and volumetric (3D) segmentation of the lateral ventricles and brain structures in fetuses with isolated and non-isolated ventriculomegaly with 3D virtual organ computer-aided analysis (VOCAL) ultrasonography vs. magnetic resonance imaging (MRI) analyzed with 3D-Slicer software. In this cross-sectional study, 40 fetuses between 20 and 38 gestational weeks with various degrees of ventriculomegaly were included. A total of 71 ventricles were measured with ultrasound (US) and with MRI. A total of 64 sonographic ventricular volumes, 80 ventricular and 40 fetal brain MR volumes were segmented and analyzed using both imaging modalities by three observers. Sizes and volumes of the ventricles and brain parenchyma were independently analyzed by two radiologists, and interobserver correlation of the results with 3D fetal ultrasound data was performed. The semiautomated rotational multiplanar 3D VOCAL technique was performed for ultrasound volumetric measurements. Results were compared to manually extracted ventricular and total brain volumes in 3D-Slicer. Segmentation of fetal brain structures (cerebral and cerebellar hemispheres, brainstem, ventricles) performed independently by two radiologists showed high interobserver agreement. An excellent agreement between VOCAL and MRI volumetric and two-dimensional measurements was established, taking into account the intraclass correlation coefficients (ICC), and a Bland-Altman plot was established. US and MRI are valuable tools for performing fetal brain and ventricular volumetry for clinical prognosis and patient counseling. Our datasets could provide the backbone for further construction of quantitative normative trajectories of fetal intracranial structures and support earlier detection of abnormal brain development and ventriculomegaly, its timing and progression during gestation.

Open-Source Ultrasound Trainer for Healthcare Professionals: A Pilot Randomized Control Trial

INTRODUCTION

This technical report describes the development of a high-fidelity, open-source ultrasound trainer and showcases its abilities through a proof-of-concept, pilot randomized control trial. The open-source ultrasound trainer (OSUT) aims to enhance anatomical visualization during ultrasound education. The OSUT can attach to any ultrasound transducer, uses minimal hardware, and is able to be used during live patient ultrasound examinations.

METHODS

After viewing a standardized training video lecture, 24 incoming first-year medical students with no prior ultrasound experience were randomized into a control group given an ultrasound system or an intervention group given the OSUT in addition to an ultrasound system. Both groups were tasked with localizing the thyroid, abdominal aorta, and right kidney on a patient. Performance outcomes were structure localization time, ultrasound image accuracy, and preactivity and postactivity participant confidence.

RESULTS

The OSUT decreased right kidney localization time (Kruskal-Wallis, P < 0.001), increased sonographer right kidney accuracy ratings (Mann-Whitney U , U = 10.5, P < 0.05), and increased confidence in structure identification (Mann-Whitney U , U = 37, P = 0.045) and overall ultrasound ability (Wilcoxon signed-rank test, P = 0.007). There was no significant change in localization time, accuracy ratings, or participant confidence for locating the thyroid and abdominal aorta.

CONCLUSIONS

A high-fidelity, open-source ultrasound trainer was developed to aid healthcare professionals in learning diagnostic ultrasound. The study demonstrated the potential beneficial effects of the OSUT in localizing the right kidney, showcasing its adaptability and accessibility for ultrasound education for certain anatomical structures.

Introduction of 3D Printing in a German Municipal Hospital-Practice Guide for CMF Surgery

Study Design

This study aimed to introduce 3D printing in a municipal hospital to improve the treatment of craniomaxillofacial patients and optimize costs and operating time. Thus we describe the implementation of low-cost in-house 3D printing to facilitate orbital- and mandible reconstruction in CMF surgery. Moreover, we address legal requirements, safety at work, fire- and data protection. Finally, we want to share our experiences using 3D printing and point out its advantages in providing better patient care.

Methods

We outline the setup of in-house 3D printing and focus on obeying German health care regulations. We based our approach on a fused deposition modeling 3D printer and free software. As proof of concept, we treated 4 cases of severe orbital trauma and 1 case of mandibular reconstruction. We printed a 3D patient-specific model for each case and adapted a titanium mesh implant, respectively, a titanium reconstruction plate before performing the surgery.

Results

Our approach reduced costs, duration of anesthesia, operating time, recovery time, and postoperative swelling and increased the revenue. Functional outcome in orbital reconstruction like eye movement and double vision, was improved compared to the conventional technique. No severe complications like loss-of-vision or surgical revision occurred. Likewise, mandibular reconstruction showed no plate loosening or plate fracture.

Conclusion

The implementation of cost-efficient 3D printing resulted in successful patient treatment with excellent outcomes. Our practice guide offers a 3D printing workflow and could be adapted to fit the needs of other specialties like neurosurgery, orthopedic surgery as well.

The Schöttle Point Is Consistently Located Distal to the Medial Femoral Physis in Pediatric Patients: A Digitally Reconstructed Radiographic Study

BACKGROUND

Significant controversy surrounds ideal tunnel position for medial patellofemoral ligament (MPFL) reconstruction (MPFLR) in the pediatric setting. The start point for femoral tunnel positioning (the Schöttle point) relative to the distal medial femoral physis is not well defined. Previous studies provide conflicting data regarding position of the MPFL origin and the Schöttle point relative to the distal femoral physis.

HYPOTHESIS

The Schöttle point would be consistently distal to the distal medial femoral physis.

STUDY DESIGN

Descriptive laboratory study.

METHODS

The institutional picture archiving and communication system was queried for computed tomography (CT) imaging studies of pediatric knees. Data were imported to an open-source image computing platform. True lateral digitally reconstructed radiographs and 3-dimensional (3D) renderings were generated, and the Schöttle point was registered in 3D space. Then, 3D distance measurements were obtained from the Schöttle point to the distal medial femoral physis.

RESULTS

A total of 49 pediatric knee CT scans were included. Mean age was 13.0 ± 2.3 years. Mean minimum distance from the medial physis to the Schöttle point was 9.9 ± 3.0 mm (range, 3.4-16.1 mm). In 49 of 49 cases (100%), the Schöttle point was distal to the physis. Using a 6-mm reaming diameter would result in 3 of 49 (6%) femurs having violation of the distal medial femoral physis. Moving the start point 3 mm distally would result in 0 of 49 (0%) sustaining physeal injury.

CONCLUSION/CLINICAL RELEVANCE

The Schöttle point is consistently distal to the distal medial femoral physis. The mean minimum distance from the Schöttle point to the physis on the medial cortex is 9.9 mm. The Schöttle point provides a safe and reliable radiographic landmark for pediatric MPFLR, although reaming diameter should be considered.

Three-dimensional printing and 3D slicer powerful tools in understanding and treating neurosurgical diseases

With the development of the 3D printing industry, clinicians can research 3D printing in preoperative planning, individualized implantable materials manufacturing, and biomedical tissue modeling. Although the increased applications of 3D printing in many surgical disciplines, numerous doctors do not have the specialized range of abilities to utilize this exciting and valuable innovation. Additionally, as the applications of 3D printing technology have increased within the medical field, so have the number of printable materials and 3D printers. Therefore, clinicians need to stay up-to-date on this emerging technology for benefit. However, 3D printing technology relies heavily on 3D design. 3D Slicer can transform medical images into digital models to prepare for 3D printing. Due to most doctors lacking the technical skills to use 3D design and modeling software, we introduced the 3D Slicer to solve this problem. Our goal is to review the history of 3D printing and medical applications in this review. In addition, we summarized 3D Slicer technologies in neurosurgery. We hope this article will enable many clinicians to leverage the power of 3D printing and 3D Slicer.

Longitudinal Changes in MRI Muscle Morphometry and Composition in People With Inclusion Body Myositis

BACKGROUND AND OBJECTIVES

Limited data suggest that quantitative MRI (qMRI) measures have potential to be used as trial outcome measures in sporadic inclusion body myositis (sIBM) and as a noninvasive assessment tool to study sIBM muscle pathologic processes. Our aim was to evaluate changes in muscle structure and composition using a comprehensive multiparameter set of qMRI measures and to assess construct validity and responsiveness of qMRI measures in people with sIBM.

METHODS

This was a prospective observational cohort study with assessments at baseline (n = 30) and 1 year (n = 26). qMRI assessments include thigh muscle volume (TMV), inter/intramuscular adipose tissue (IMAT), muscle fat fraction (FF), muscle inflammation (T2 relaxation time), IMAT from T2* relaxation (T2*-IMAT), intermuscular connective tissue from T2* relaxation (T2*-IMCT), and muscle macromolecular structure from the magnetization transfer ratio (MTR). Physical performance assessments include sIBM Physical Functioning Assessment (sIFA), 6-minute walk distance, and quantitative muscle testing of the quadriceps. Correlations were assessed using the Spearman correlation coefficient. Responsiveness was assessed using the standardized response mean (SRM).

RESULTS

After 1 year, we observed a reduction in TMV (6.8%, p < 0.001) and muscle T2 (6.7%, p = 0.035), an increase in IMAT (9.7%, p < 0.001), FF (11.2%, p = 0.030), connective tissue (22%, p = 0.995), and T2*-IMAT (24%, p < 0.001), and alteration in muscle macromolecular structure (ΔMTR = -26%, p = 0.002). A decrease in muscle T2 correlated with an increase in T2*-IMAT (r = -0.47, p = 0.008). Deposition of connective tissue and IMAT correlated with deterioration in sIFA (r = 0.38, p = 0.032; r = 0.34, p = 0.048; respectively), whereas a decrease in TMV correlated with a decrease in quantitative muscle testing (r = 0.36, p = 0.035). The most responsive qMRI measures were T2*-IMAT (SRM = 1.50), TMV (SRM = -1.23), IMAT (SRM = 1.20), MTR (SRM = -0.83), and T2 relaxation time (SRM = -0.65).

DISCUSSION

Progressive deterioration in muscle quality measured by qMRI is associated with a decline in physical performance. Inflammation may play a role in triggering fat infiltration into muscle. qMRI provides valid and responsive measures that might prove valuable in sIBM experimental trials and assessment of muscle pathologic processes.

CLASSIFICATION OF EVIDENCE

This study provides Class I evidence that qMRI outcome measures are associated with physical performance measures in patients with sIBM.

Machine learning based analysis of stroke lesions on mouse tissue sections

An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named "StrokeAnalyst", which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2-2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models.

Practical methods for segmentation and calculation of brain volume and intracranial volume: a guide and comparison

BACKGROUND

Accurate segmentation and calculation of total brain volume (BV) and intracranial volume (ICV) (further-volumetry) may serve various clinical tasks and research studies in neuroscience. Manual segmentation is extremely time consuming. There is a relative lack of published broad recommendations and comparisons of tools for automated volumetry, especially for users without expertise in computer science, for settings with limited resources, and when neuroimaging quality is suboptimal due to clinical circumstances. Our objective is to decrease the barrier to entry for research and clinical groups to perform volumetric cranial imaging analysis using free and reliable software tools.

METHODS

Automated volumetry from computed tomography (CT)/magnetic resonance imaging (MRI) scans was accomplished using 3D Slicer (v. 4.11.0), FreeSurfer (v. 7.1.1), and volBrain (v. 1.0) in a cohort of 39 patients with ischemic middle cerebral artery territory brain infarcts in the acute stage. Visual inspection for accuracy was also performed. Statistical analysis included coefficient of determination (R2) and Bland-Altman (B-A) plots. A multifaceted comparison between 3D Slicer, FreeSurfer, and volBrain from practical user perspective was performed to compile a list of distinguishing features.

RESULTS

BV: FreeSurfer, 3D Slicer, and volBrain provide similar estimations when high quality T1-MRI scans with 1 mm slices (3D scans) are available, whereas 3 mm and thicker slices (2D scans) introduce a dispersion in results. ICV: the most accurate volumetry is provided by 3D Slicer using CT scans. volBrain uses T1-MRIs and also provides good results which agree with 3D Slicer. Both of these methods may be more trustworthy than T1 MRI-derived FreeSurfer calculations.

CONCLUSIONS

All three studied tools of automated intracranial and brain volumetry-3D Slicer, FreeSurfer, and volBrain-are free, reliable, require no complex programming, but still have certain limitations and significant differences. Based on our investigation findings, the readers should be able to select the right volumetry tool and neuroimaging study, and then follow provided step-by-step instructions to accomplish specific volumetry tasks.

Lead-OR: a multimodal platform for deep brain stimulation surgery

Background:

Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced.

Methods:

Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform.

Results:

We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool.

Conclusions:

This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages.

Funding:

Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), and Foundation for OCD Research (FFOR).

Deep brain stimulation is an established therapy for patients with Parkinson’s disease and an emerging option for other neurological conditions. Electrodes are implanted deep in the brain to stimulate precise brain regions and control abnormal brain activity in those areas. The most common target for Parkinson’s disease, for instance, is a structure called the subthalamic nucleus, which sits at the base of the brain, just above the brain stem.

To ensure electrodes are placed correctly, surgeons use various sources of information to characterize the patient’s brain anatomy and decide on an implant site. These data include brain scans taken before surgery and recordings of brain activity taken during surgery to confirm the intended implant site. Sometimes, the brain activity signals from this last confirmation step may slightly alter surgical plans. It represents one of many challenges for clinical teams: to analyse, assimilate, and communicate data as it is collected during the procedure.

Oxenford et al. developed a software pipeline to aggregate the data surgeons use to implant electrodes. The open-source platform, dubbed Lead-OR, visualises imaging data and brain activity recordings (termed electrophysiology data) in real time. The current set-up integrates with commercial tools and existing software for surgical planning.

Oxenford et al. tested Lead-OR on data gathered retrospectively from 32 patients with Parkinson’s who had electrodes implanted in their subthalamic nucleus. The platform showed good agreement between imaging and electrophysiology data, although there were some unavoidable discrepancies, arising from limitations in the imaging pipeline and from the surgical procedure. Lead-OR was also able to correct for brain shift, which is where the brain moves ever so slightly in the skull.

With further validation, this proof-of-concept software could serve as a useful decision-making tool for surgical teams implanting electrodes for deep brain stimulation. In time, if implemented, its use could improve the accuracy of electrode placement, translating into better surgical outcomes for patients. It also has the potential to integrate forthcoming ultra-high-resolution data from current brain mapping projects, and other commercial surgical planning tools.

NousNav: A low-cost neuronavigation system for deployment in lower-resource settings

PURPOSE

NousNav is a complete low-cost neuronavigation system that aims to democratize access to higher-quality healthcare in lower-resource settings. NousNav's goal is to provide a model for local actors to be able to reproduce, build and operate a fully functional neuronavigation system at an affordable cost.

METHODS

NousNav is entirely open source and relies on low-cost off-the-shelf components, which makes it easy to reproduce and deploy in any region. NousNav's software is also specifically devised with the low-resource setting in mind.

RESULTS

It offers means for intuitive intraoperative control. The designed interface is also clean and simple. This allows for easy intraoperative use by either the practicing clinician or a nurse. It thus alleviates the need for a dedicated technician for operation.

CONCLUSION

A prototype implementation of the design was built. Hardware and algorithms were designed for robustness, ruggedness, modularity, to be standalone and data-agnostic. The built prototype demonstrates feasibility of the objectives.

FreeSurfer and 3D Slicer-Assisted SEEG Implantation for Drug-Resistant Epilepsy

Objective

Our study aimed to develop an approach to improve the speed and resolution of cerebral-hemisphere and lesion modeling and evaluate the advantages and disadvantages of robot-assisted surgical planning software.

Methods

We applied both conventional robot planning software (method 1) and open-source auxiliary software (FreeSurfer and 3D Slicer; method 2) to model the brain and lesions in 19 patients with drug-resistant epilepsy. The patients' mean age at implantation was 21.4 years (range, 6–52 years). Each patient received an average of 12 electrodes (range, 9–16) between May and November 2021. The electrode-implantation plan was designed based on the models established using the two methods. We statistically analyzed and compared the duration of designing the models and planning the implantation using these two methods and performed the surgeries with the implantation plan designed using the auxiliary software.

Results

A significantly longer time was needed to reconstruct a cerebral-hemisphere model using method 1 (mean, 206 s) than using method 2 (mean, 20 s) (p < 0.05). Both methods identified a mean of 1.4 lesions (range, 1–5) in each patient. Overall, using method 1 required longer (mean, 130 s; range, 48–436) than using method 2 (mean, 68.1 s; range, 50–104; p < 0.05). In addition, the clarity of the model based on method 1 was lower than that based on method 2. To devise an electrode-implantation plan, it took 9.1–25.5 min (mean, 16) and 6.6–14.8 min (mean, 10.2) based on methods 1 and 2, respectively (p < 0.05). The average target point error of 231 electrodes amounted to 1.90 mm ± 0.37 mm (range, 0.33–3.61 mm). The average entry point error was 0.89 ± 0.26 mm (range, 0.17–1.67 mm). None of the patients presented with intracranial hemorrhage or infection, and no other serious complications were observed.

Conclusions

FreeSurfer and 3D Slicer-assisted SEEG implantation is an excellent approach to enhance modeling speed and resolution, shorten the electrode-implantation planning time, and boost the efficiency of clinical work. These well-known, trusted open-source programs do not have explicitly restricted licenses. These tools, therefore, seem well suited for clinical-research applications under the premise of approval by an ethics committee, informed consent of the patient, and clinical judgment of the surgeon.

Experimental Demonstration of Trans-Skull Volumetric Passive Acoustic Mapping With the Heterogeneous Angular Spectrum Approach

Real-time, 3-D, passive acoustic mapping (PAM) of microbubble dynamics during transcranial focused ultrasound (FUS) is essential for optimal treatment outcomes. The angular spectrum approach (ASA) potentially offers a very efficient method to perform PAM, as it can reconstruct specific frequency bands pertinent to microbubble dynamics and may be extended to correct aberrations caused by the skull. Here, we experimentally assess the abilities of heterogeneous ASA (HASA) to perform trans-skull PAM. Our experimental investigations demonstrate that the 3-D PAMs of a known 1-MHz source, constructed with HASA through an ex vivo human skull segment, reduced both the localization error (from 4.7 ± 2.3 to 2.3 ± 1.6 mm) and the number, size, and energy of spurious lobes caused by aberration, with the modest additional computational expense. While further improvements in the localization errors are expected with arrays with denser elements and larger aperture, our analysis revealed that experimental constraints associated with the array pitch and aperture (here, 1.8 mm and 2.5 cm, respectively) can be ameliorated by interpolation and peak finding techniques. Beyond the array characteristics, our analysis also indicated that errors in the registration (translation and rotation of ±5 mm and ±5°, respectively) of the skull segment to the array can lead to peak localization errors of the order of a few wavelengths. Interestingly, errors in the spatially dependent speed of sound in the skull (±20%) caused only subwavelength errors in the reconstructions, suggesting that registration is the most important determinant of point source localization accuracy. Collectively, our findings show that HASA can address source localization problems through the skull efficiently and accurately under realistic conditions, thereby creating unique opportunities for imaging and controlling the microbubble dynamics in the brain.

A machine-learning-based method for automatizing lattice-Boltzmann simulations of respiratory flows

Many simulation workflows require to prepare the data for the simulation manually. This is time consuming and leads to a massive bottleneck when a large number of numerical simulations is requested. This bottleneck can be overcome by an automated data processing pipeline. Such a novel pipeline is developed for a medical use case from rhinology, where computer tomography recordings are used as input and flow simulation data define the results. Convolutional neural networks are applied to segment the upper airways and to detect and prepare the in- and outflow regions for accurate boundary condition prescription in the simulation. The automated process is tested on three cases which have not been used to train the networks. The accuracy of the pipeline is evaluated by comparing the network-generated output surfaces to those obtained from a semi-automated procedure performed by a medical professional. Except for minor deviations at interfaces between ethmoidal sinuses, the network-generated surface is sufficiently accurate. To further analyze the accuracy of the automated pipeline, flow simulations are conducted with a thermal lattice-Boltzmann method for both cases on a high-performace computing system. The comparison of the results of the respiratory flow simulations yield averaged errors of less than 1% for the pressure loss between the in- and outlets, and for the outlet temperature. Thus, the pipeline is shown to work accurately and the geometrical deviations at the ethmoidal sinuses to be negligible.

A community-based approach to image analysis of cells, tissues and tumors

Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. As such, our Image Analysis Working Group (IAWG), composed of researchers in the Cancer Systems Biology Consortium (CSBC) and the Physical Sciences - Oncology Network (PS-ON), convened a workshop on "Computational Challenges Shared by Diverse Imaging Platforms" to characterize these common issues and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).

Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients Undergoing Autologous Transplantation: A Feasibility Study with CT Data

Multiple myeloma is a plasma cell dyscrasia characterized by focal and non-focal bone lesions. Radiomic techniques extract morphological information from computerized tomography images and exploit them for stratification and risk prediction purposes. However, few papers so far have applied radiomics to multiple myeloma. A retrospective study approved by the institutional review board: n = 51 transplanted patients and n = 33 (64%) with focal lesion analyzed via an open-source toolbox that extracted 109 radiomics features. We also applied a dedicated tool for computing 24 features describing the whole skeleton asset. The redundancy reduction was realized via correlation and principal component analysis. Fuzzy clustering (FC) and Hough transform filtering (HTF) allowed for patient stratification, with effectiveness assessed by four skill scores. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. These scores were higher than the ones associated with a standard cytogenetic classification. The Mann–Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This AI-based interpretation of radiomics features stratified relapsed and non-relapsed MM patients, showing some potentiality for the determination of the prognostic image-based biomarkers in disease follow-up.

Choroidal macrovessel: Systematic review and analysis of anatomic origin

There are various hypotheses for the anatomic origin of a choroidal macrovessel. We assess whether a choroidal macrovessel is a dilated posterior ciliary artery. A systematic review of published literature on choroidal macrovessels was performed with two additional cases from our institution. We compared the visible entry and vascular course of the macrovessel in the published literature. We performed a comparative analysis using indocyanine green angiography, swept source optical computed tomography, and 3D reconstruction of two choroidal macrovessels using 3D Slicer (Harvard, Boston, USA, https://www.slicer.org/). From the 14 studies found, 18 cases met inclusion criteria. The reported literature and our two cases showed a radiating course along a sectoral distribution pattern of either short or long posterior ciliary arteries. Our review of literature and 3D reconstruction analysis support the hypothesis that choroidal macrovessels are dilated posterior ciliary arteries.

Demonstration of treatment planning software for hyperthermic intraperitoneal chemotherapy in a rat model

BACKGROUND

Hyperthermic intraperitoneal chemotherapy (HIPEC) is administered to treat residual microscopic disease after cytoreductive surgery (CRS). During HIPEC, fluid (41-43 °C) is administered and drained through a limited number of catheters, risking thermal and drug heterogeneities within the abdominal cavity that might reduce effectiveness. Treatment planning software provides a unique tool for optimizing treatment delivery. This study aimed to investigate the influence of treatment-specific parameters on the thermal and drug homogeneity in the peritoneal cavity in a computed tomography based rat model.

METHOD

We developed computational fluid dynamics (CFD) software simulating the dynamic flow, temperature and drug distribution during oxaliplatin based HIPEC. The influence of location and number of catheters, flow alternations and flow rates on peritoneal temperature and drug distribution were determined. The software was validated using data from experimental rat HIPEC studies.

RESULTS

The predicted core temperature and systemic oxaliplatin concentration were comparable to the values found in literature. Adequate placement of catheters, additional inflow catheters and higher flow rates reduced intraperitoneal temperature spatial variation by -1.4 °C, -2.3 °C and -1.2 °C, respectively. Flow alternations resulted in higher temperatures (up to +1.5 °C) over the peritoneal surface. Higher flow rates also reduced the spatial variation of chemotherapy concentration over the peritoneal surface resulting in a more homogeneous effective treatment dose.

CONCLUSION

The presented treatment planning software provides unique insights in the dynamics during HIPEC, which enables optimization of treatment-specific parameters and provides an excellent basis for HIPEC treatment planning in human applications.

Interdisciplinary Online Hackathons as an Approach to Combat the COVID-19 Pandemic: Case Study

Background

The COVID-19 outbreak has affected the lives of millions of people by causing a dramatic impact on many health care systems and the global economy. This devastating pandemic has brought together communities across the globe to work on this issue in an unprecedented manner.

Objective

This case study describes the steps and methods employed in the conduction of a remote online health hackathon centered on challenges posed by the COVID-19 pandemic. It aims to deliver a clear implementation road map for other organizations to follow.

Methods

This 4-day hackathon was conducted in April 2020, based on six COVID-19–related challenges defined by frontline clinicians and researchers from various disciplines. An online survey was structured to assess: (1) individual experience satisfaction, (2) level of interprofessional skills exchange, (3) maturity of the projects realized, and (4) overall quality of the event. At the end of the event, participants were invited to take part in an online survey with 17 (+5 optional) items, including multiple-choice and open-ended questions that assessed their experience regarding the remote nature of the event and their individual project, interprofessional skills exchange, and their confidence in working on a digital health project before and after the hackathon. Mentors, who guided the participants through the event, also provided feedback to the organizers through an online survey.

Results

A total of 48 participants and 52 mentors based in 8 different countries participated and developed 14 projects. A total of 75 mentorship video sessions were held. Participants reported increased confidence in starting a digital health venture or a research project after successfully participating in the hackathon, and stated that they were likely to continue working on their projects. Of the participants who provided feedback, 60% (n=18) would not have started their project without this particular hackathon and indicated that the hackathon encouraged and enabled them to progress faster, for example, by building interdisciplinary teams, gaining new insights and feedback provided by their mentors, and creating a functional prototype.

Conclusions

This study provides insights into how online hackathons can contribute to solving the challenges and effects of a pandemic in several regions of the world. The online format fosters team diversity, increases cross-regional collaboration, and can be executed much faster and at lower costs compared to in-person events. Results on preparation, organization, and evaluation of this online hackathon are useful for other institutions and initiatives that are willing to introduce similar event formats in the fight against COVID-19.

SlicerArduino: A Bridge between Medical Imaging Platform and Microcontroller

Interaction between medical image platform and external environment is a desirable feature in several clinical, research, and educational scenarios. In this work, the integration between 3D Slicer package and Arduino board is introduced, enabling a simple and useful communication between the two software/hardware platforms. The open source extension, programmed in Python language, manages the connection process and offers a communication layer accessible from any point of the medical image suite infrastructure. Deep integration with 3D Slicer code environment is provided and a basic input–output mechanism accessible via GUI is also made available. To test the proposed extension, two exemplary use cases were implemented: (1) INPUT data to 3D Slicer, to navigate on basis of data detected by a distance sensor connected to the board, and (2) OUTPUT data from 3D Slicer, to control a servomotor on the basis of data computed through image process procedures. Both goals were achieved and quasi-real-time control was obtained without any lag or freeze, thus boosting the integration between 3D Slicer and Arduino. This integration can be easily obtained through the execution of few lines of Python code. In conclusion, SlicerArduino proved to be suitable for fast prototyping, basic input–output interaction, and educational purposes. The extension is not intended for mission-critical clinical tasks.

The Effect of Registration on Voxel-Wise Tofts Model Parameters and Uncertainties from DCE-MRI of Early-Stage Breast Cancer Patients Using 3DSlicer

We quantitatively investigate the influence of image registration, using open-source software (3DSlicer), on kinetic analysis (Tofts model) of dynamic contrast enhanced MRI of early-stage breast cancer patients. We also show that registration computation time can be reduced by reducing the percent sampling (PS) of voxels used for estimation of the cost function. DCE-MRI breast images were acquired on a 3T-PET/MRI system in 13 patients with early-stage breast cancer who were scanned in a prone radiotherapy position. Images were registered using a BSpline transformation with a 2 cm isotropic grid at 100, 20, 5, 1, and 0.5PS (BRAINSFit in 3DSlicer). Signal enhancement curves were analyzed voxel-by-voxel using the Tofts kinetic model. Comparing unregistered with registered groups, we found a significant change in the 90th percentile of the voxel-wise distribution of K^trans. We also found a significant reduction in the following: (1) in the standard error (uncertainty) of the parameter value estimation, (2) the number of voxel fits providing unphysical values for the extracellular-extravascular volume fraction (v_e > 1), and (3) goodness of fit. We found no significant differences in the median of parameter value distributions (K^trans, v_e) between unregistered and registered images. Differences between parameters and uncertainties obtained using 100PS versus 20PS were small and statistically insignificant. As such, computation time can be reduced by a factor of 2, on average, by using 20PS while not affecting the kinetic fit. The methods outlined here are important for studies including a large number of post-contrast images or number of patient images.

SciKit-Surgery: compact libraries for surgical navigation

Purpose

This paper introduces the SciKit-Surgery libraries, designed to enable rapid development of clinical applications for image-guided interventions. SciKit-Surgery implements a family of compact, orthogonal, libraries accompanied by robust testing, documentation, and quality control. SciKit-Surgery libraries can be rapidly assembled into testable clinical applications and subsequently translated to production software without the need for software reimplementation. The aim is to support translation from single surgeon trials to multicentre trials in under 2 years.

Methods

At the time of publication, there were 13 SciKit-Surgery libraries provide functionality for visualisation and augmented reality in surgery, together with hardware interfaces for video, tracking, and ultrasound sources. The libraries are stand-alone, open source, and provide Python interfaces. This design approach enables fast development of robust applications and subsequent translation. The paper compares the libraries with existing platforms and uses two example applications to show how SciKit-Surgery libraries can be used in practice.

Results

Using the number of lines of code and the occurrence of cross-dependencies as proxy measurements of code complexity, two example applications using SciKit-Surgery libraries are analysed. The SciKit-Surgery libraries demonstrate ability to support rapid development of testable clinical applications. By maintaining stricter orthogonality between libraries, the number, and complexity of dependencies can be reduced. The SciKit-Surgery libraries also demonstrate the potential to support wider dissemination of novel research.

Conclusion

The SciKit-Surgery libraries utilise the modularity of the Python language and the standard data types of the NumPy package to provide an easy-to-use, well-tested, and extensible set of tools for the development of applications for image-guided interventions. The example application built on SciKit-Surgery has a simpler dependency structure than the same application built using a monolithic platform, making ongoing clinical translation more feasible.

Application of holographic augmented reality for external approaches to the frontal sinus

BACKGROUND

External approaches to the frontal sinus such as osteoplastic flaps are challenging because they require blind entry into the sinus, posing risks of injury to the brain or orbit. Intraoperative computed tomography (CT)-based navigation is the current standard for planning the approach, but still necessitates blind entry into the sinus. The aim of this work was to describe a novel technique for external approaches to the frontal sinus using a holographic augmented reality (AR) application.

METHODS

Our team developed an AR system to create a 3-dimensional (3D) hologram of key anatomical structures, based on CT scans images. Using Magic Leap AR goggles for visualization, the frontal sinus hologram was aligned to the surface anatomy in 6 fresh cadaveric heads' anatomic boundaries, and the boundaries of the frontal sinus were demarcated based on the margins of the fused image. Trephinations and osteoplastic flap approaches were performed. The specimens were re-scanned to assess the accuracy of the osteotomy with respect to the actual frontal sinus perimeter.

RESULTS

Registration and surgery were completed successfully in all specimens. Registration required an average of 2 minutes. The postprocedure CT showed a mean difference of 1.4 ± 4.1 mm between the contour of the osteotomy and the contour of the frontal sinus. One surgical complication (posterior table perforation) occurred (16%).

CONCLUSION

We describe proof of concept of a novel technique utilizing AR to enhance external approaches to the frontal sinus. Holographic AR-enhanced surgical navigation holds promise for enhanced visualization of target structures during surgical approaches to the sinuses.

SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality

Virtual reality (VR) provides immersive visualization that has proved to be useful in a variety of medical applications. Currently, however, no free open-source software platform exists that would provide comprehensive support for translational clinical researchers in prototyping experimental VR scenarios in training, planning or guiding medical interventions. By integrating VR functions in 3D Slicer, an established medical image analysis and visualization platform, SlicerVR enables virtual reality experience by a single click. It provides functions to navigate and manipulate the virtual scene, as well as various settings to abate the feeling of motion sickness. SlicerVR allows for shared collaborative VR experience both locally and remotely. We present illustrative scenarios created with SlicerVR in a wide spectrum of applications, including echocardiography, neurosurgery, spine surgery, brachytherapy, intervention training and personalized patient education. SlicerVR is freely available under BSD type license as an extension to 3D Slicer and it has been downloaded over 7,800 times at the time of writing this article.

SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization

PURPOSE

We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors.

PATIENTS AND METHODS

In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI.

RESULTS

We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each.

CONCLUSION

SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Preliminary Results of High-Precision Computed Diffusion Weighted Imaging for the Diagnosis of Hepatocellular Carcinoma at 3 Tesla

Objective

To compare the utility of high-precision computed diffusion-weighted imaging (hc-DWI) and conventional computed DWI (cc-DWI) for the diagnosis of hepatocellular carcinoma (HCC) at 3 T.

Methods

We subjected 75 HCC patients to DWI (b-value 150 and 600 s/mm²). To generate hc-DWI we applied non-rigid image registration to avoid the mis-registration of images obtained with different b-values. We defined c-DWI with a b-value of 1500 s/mm² using DWI with b-value 150 and 600 s/mm² as cc-DWI, and c-DWI with b-value 1500 s/mm² using registered DWI with b-value 150 and 600 s/mm² as hc-DWI. A radiologist recorded the contrast ratio (CR) between HCC and the surrounding hepatic parenchyma.

Results

The CR for HCC was significantly higher on hc- than cc-DWIs (median 2.0 vs. 1.8, P < 0.01).

Conclusion

The CR of HCC can be improved with image registration, indicating that hc-DWI is more useful than cc-DWI for the diagnosis of HCC.

An automated dose tracking system for adaptive radiation therapy

BACKGROUND AND OBJECTIVE

The implementation of adaptive radiation therapy (ART) into routine clinical practice is technically challenging and requires significant resources to perform and validate each process step. The objective of this report is to identify the key components of ART, to illustrate how a specific automated procedure improves efficiency, and to facilitate the routine clinical application of ART.

METHODS

Data was used from patient images, exported from a clinical database and converted to an intermediate format for point-wise dose tracking and accumulation. The process was automated using in-house developed software containing three modularized components: an ART engine, user interactive tools, and integration tools. The ART engine conducts computing tasks using the following modules: data importing, image pre-processing, dose mapping, dose accumulation, and reporting. In addition, custom graphical user interfaces (GUIs) were developed to allow user interaction with select processes such as deformable image registration (DIR). A commercial scripting application programming interface was used to incorporate automated dose calculation for application in routine treatment planning. Each module was considered an independent program, written in C++or C#, running in a distributed Windows environment, scheduled and monitored by integration tools.

RESULTS

The automated tracking system was retrospectively evaluated for 20 patients with prostate cancer and 96 patients with head and neck cancer, under institutional review board (IRB) approval. In addition, the system was evaluated prospectively using 4 patients with head and neck cancer. Altogether 780 prostate dose fractions and 2586 head and neck cancer dose fractions went processed, including DIR and dose mapping. On average, daily cumulative dose was computed in 3 h and the manual work was limited to 13 min per case with approximately 10% of cases requiring an additional 10 min for image registration refinement.

CONCLUSIONS

An efficient and convenient dose tracking system for ART in the clinical setting is presented. The software and automated processes were rigorously evaluated and validated using patient image datasets. Automation of the various procedures has improved efficiency significantly, allowing for the routine clinical application of ART for improving radiation therapy effectiveness.

Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images

The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49  mm; 51 of the outliers deviated more than two catheter widths (3.4  mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44  mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.

Anatomical Region Segmentation for Objective Surgical Skill Assessment with Operating Room Motion Data

Background  Most existing objective surgical motion analysis schemes are limited to structured surgical tasks or recognition of motion patterns for certain categories of surgeries. Analyzing instrument motion data with respect to anatomical structures can break the limit, and an anatomical region segmentation algorithm is required for the analysis. Methods  An atlas was generated by manually segmenting the skull base into nine regions, including left/right anterior/posterior ethmoid sinuses, frontal sinus, left and right maxillary sinuses, nasal airway, and sphenoid sinus. These regions were selected based on anatomical and surgical significance in skull base and sinus surgery. Six features, including left and right eye center, nasofrontal beak, anterior tip of nasal spine, posterior edge of hard palate at midline, and clival body at foramen magnum, were used for alignment. The B-spline deformable registration was adapted to fine tune the registration, and bony boundaries were automatically extracted for final precision improvement. The resultant deformation field was applied to the atlas, and the motion data were clustered according to the deformed atlas. Results  Eight maxillofacial computed tomography scans were used in experiments. One was manually segmented as the atlas. The others were segmented by the proposed method. Motion data were clustered into nine groups for every dataset and outliers were filtered. Conclusions  The proposed algorithm improved the efficiency of motion data clustering and requires limited human interaction in the process. The anatomical region segmentations effectively filtered out the portion of motion data that are out of surgery sites and grouped them according to anatomical similarities.