An integrated visualization system for surgical planning and guidance using image fusion and an open MR

Prostate volume analysis in image registration for prostate cancer care: a verification study

Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urological prostate cancer care. Paired whole-pelvis MR and CT scan data were used (n = 20). A manual prostate CTV contour was performed independently on each patients MR and CT image. A semi-automated rigid-, automated rigid- and automated non-rigid registration technique was applied to align the MR and CT data. Dice Similarity Index (DSI), 95% Hausdorff distance (95%HD) and average surface distance (ASD) measures were used to assess the closeness of the manual and registered contours. The automated non-rigid approach had a significantly improved performance compared to the automated rigid- and semi-automated rigid-registration, having better average scores and decreased spread for the DSI, 95%HD and ASD (all p < 0.001). Additionally, the automated rigid approach had similar significantly improved performance compared to the semi-automated rigid registration across all accuracy metrics observed (all p < 0.001). Overall, all registration techniques studied here demonstrated sufficient accuracy for exploring their clinical use. While the fully automated non-rigid registration algorithm in the present study provided the most accurate registration, the semi-automated rigid registration is a quick, feasible, and accessible method to perform image registration for prostate cancer care by urologists and radiation oncologists now.

A simple and robust nanosystem for photoacoustic imaging of bladder cancer based on α5β1-targeted gold nanorods

BACKGROUND

Early detection and removal of bladder cancer in patients is crucial to prevent tumor recurrence and progression. Because current imaging techniques may fail to detect small lesions of in situ carcinomas, patients with bladder cancer often relapse after initial diagnosis, thereby requiring frequent follow-up and treatments.

RESULTS

In an attempt to obtain a sensitive and high-resolution imaging modality for bladder cancer, we have developed a photoacoustic imaging approach based on the use of PEGylated gold nanorods (GNRs) as a contrast agent, functionalized with the peptide cyclic [CphgisoDGRG] (Iso4), a selective ligand of α5β1 integrin expressed by bladder cancer cells. This product (called GNRs@PEG-Iso4) was produced by a simple two-step procedure based on GNRs activation with lipoic acid-polyethyleneglycol(PEG-5KDa)-maleimide and functionalization with peptide Iso4. Biochemical and biological studies showed that GNRs@PEG-Iso4 can efficiently recognize purified integrin α5β1 and α5β1-positive bladder cancer cells. GNRs@PEG-Iso4 was stable and did not aggregate in urine or in 5% sodium chloride, or after freeze/thaw cycles or prolonged exposure to 55 °C, and, even more importantly, do not settle after instillation into the bladder. Intravesical instillation of GNRs@PEG-Iso4 into mice bearing orthotopic MB49-Luc bladder tumors, followed by photoacoustic imaging, efficiently detected small cancer lesions. The binding to tumor lesions was competed by a neutralizing anti-α5β1 integrin antibody; furthermore, no binding was observed to healthy bladders (α5β1-negative), pointing to a specific targeting mechanism.

CONCLUSION

GNRs@PEG-Iso4 represents a simple and robust contrast agent for photoacoustic imaging and diagnosis of small bladder cancer lesions.

Vacuoles related to tissue neuron-astrocyte ratio and infiltration of macrophages/monocytes contribute to hyperintense brain signals on diffusion-weighted magnetic resonance imaging in sporadic Creutzfeldt-Jakob disease

BACKGROUND

Radiological features in patients with sporadic Creutzfeldt-Jakob disease (sCJD) are hyperintensity of the cerebral cortex and the basal ganglia displayed by diffusion-weighted magnetic resonance imaging (DW-MRI). We performed a quantitative study on neuropathological and radiological findings.

METHODS

Patient 1 received a definite diagnosis of MM1-type sCJD, while patient 2 received a definite diagnosis of MM1 + 2-type sCJD. Two DW-MRI scans were performed on each patient. DW-MRI was either taken the day before or on the day of the patient's death, and several hyperintense or isointense areas were marked as a region of interest (ROI). Mean signal intensity of the ROI was measured. Pathological quantitative assessments of the vacuoles, astrocytosis, infiltration of monocytes/macrophages, and proliferation of microglia was performed. Vacuole load (% area vacuole), glial fibrillary acidic protein (GFAP), CD68, and Iba-1 load were calculated. We defined spongiform change index (SCI) to indicate vacuoles related to a tissue neuron-astrocyte ratio. We assessed the correlation of intensity of the last DW-MRI and the pathological findings as well as association of changes of the signal intensity on the sequential images and the pathological findings.

RESULT

We observed a strong positive correlation between SCI and DW-MRI intensity. In the analysis using serial DW-MRI and pathological findings, we found that CD68 load was significantly larger in areas where signal intensity decreased, as compared to those areas where hyperintensity remained unchanged.

CONCLUSION

DW-MRI intensity in sCJD is associated with the ratio of neuron to astrocyte in the vacuoles and the infiltration of macrophages and/or monocytes.

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.

CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation

The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI) provides a valuable quantitative tool for the analysis of the morphology and course of individual CNs. Tractography-based approaches can describe and analyze the anatomical area of CNs by selecting the reference streamlines in combination with ROIs-based (regions-of-interests) or clustering-based. However, due to the slender structure of CNs and the complex anatomical environment, single-modality data based on dMRI cannot provide a complete and accurate description, resulting in low accuracy or even failure of current algorithms in performing individualized CNs segmentation. In this work, we propose a novel multimodal deep-learning-based multi-class network for automated cranial nerves tract segmentation without using tractography, ROI placement or clustering, called CNTSeg. Specifically, we introduced T1w images, fractional anisotropy (FA) images, and fiber orientation distribution function (fODF) peaks into the training data set, and design the back-end fusion module which uses the complementary information of the interphase feature fusion to improve the segmentation performance. CNTSeg has achieved the segmentation of 5 pairs of CNs (i.e. optic nerve CN II, oculomotor nerve CN III, trigeminal nerve CN V, and facial-vestibulocochlear nerve CN VII/VIII). Extensive comparisons and ablation experiments show promising results and are anatomically convincing even for difficult tracts. The code will be openly available at https://github.com/IPIS-XieLei/CNTSeg.

Reconstituting human somitogenesis in vitro

The segmented body plan of vertebrates is established during somitogenesis, a well-studied process in model organisms; however, the details of this process in humans remain largely unknown owing to ethical and technical limitations. Despite recent advances with pluripotent stem cell-based approaches^1-5, models that robustly recapitulate human somitogenesis in both space and time remain scarce. Here we introduce a pluripotent stem cell-derived mesoderm-based 3D model of human segmentation and somitogenesis-which we termed 'axioloid'-that captures accurately the oscillatory dynamics of the segmentation clock and the morphological and molecular characteristics of sequential somite formation in vitro. Axioloids show proper rostrocaudal patterning of forming segments and robust anterior-posterior FGF-WNT signalling gradients and retinoic acid signalling components. We identify an unexpected critical role of retinoic acid signalling in the stabilization of forming segments, indicating distinct, but also synergistic effects of retinoic acid and extracellular matrix on the formation and epithelialization of somites. Comparative analysis demonstrates marked similarities of axioloids to the human embryo, further validated by the presence of a Hox code in axioloids. Finally, we demonstrate the utility of axioloids for studying the pathogenesis of human congenital spine diseases using induced pluripotent stem cells with mutations in HES7 and MESP2. Our results indicate that axioloids represent a promising platform for the study of axial development and disease in humans.

Paroxysmal slow wave events are associated with cognitive impairment in patients with obstructive sleep apnea

BACKGROUND

Increasing evidence has supported a link between obstructive sleep apnea (OSA) and cognition, and blood-brain barrier (BBB) dysfunction which can be reflected by paroxysmal slow wave events (PSWEs) may be a potential mechanism. The purpose of our study was to investigate the correlation between the PSWEs and cognitive impairment in patients with OSA, with a focus on the possible mechanism.

METHODS

In total, 339 subjects with subjective snoring complaints from the Sleep Medicine Center underwent magnetic resonance imaging and whole-night polysomnography. OSA was defined as apnea-hypopnea index (AHI) ≥ 5 events/h. MCI was defined as the MoCA < 26 and met the criteria: (1) subjective cognitive impairment; (2) objective impairment in one or more cognitive domains; (3) slightly impaired complex instrumental daily abilities, but independent daily living abilities; and (4) no dementia. The PSWEs calculated by self-developed Python scripts were defined for EEG recordings as a median power frequency of < 6 Hz for more than five consecutive seconds. Serum cyclophilin A (CyPA) and matrix metalloproteinase-9 (MMP-9) levels and amyloid-β 42 levels in neuron-derived exosomes were determined. The participants who received continuous positive airway pressure (CPAP) were followed up and their PSWEs were recalculated after 1 year of treatment.

RESULTS

A total of 339 participants were divided into the OSA+MCI group (n = 157), OSA-MCI group (n = 118), and controls (normal cognitive state without OSA) (n = 64). The total PSWEs and the occurrence per minute of PSWEs at stage REM in the OSA+MCI group were higher than those in the OSA-MCI and control groups. The duration ratio of PSWEs at stage REM in the OSA+MCI group significantly increased. The total PSWEs and PSWEs at the F4-M1, O1-M2, and O2-M1 channels in stage REM were independently associated with cognitive impairment in OSA patients. There were positive correlations between the PSWEs and serum CyPA and MMP-9 levels in patients with OSA. The mediation analysis showed that the relationship between mean SaO₂ and percentage of sleep time spent with oxygen saturation <90% with MoCA scores was mediated by the total PSWEs (proportion of mediation 77.89% and 82.89%). The PSWEs were negatively correlated with global cognitive performance and cognitive subdomains. After 1 year of CPAP treatment, the total PSWEs, PSWEs in stage REM, and serum CyPA and MMP-9 levels decreased significantly, and MoCA scores were improved compared with baseline.

CONCLUSIONS

The PSWEs were implicated in cognitive impairment in patients with OSA, and the mechanisms of cognitive impairment due to hypoxia in OSA patients could be BBB dysfunction. The PSWEs can be used as a marker of cognitive impairment in patients with OSA.

TRIAL REGISTRATION

This trial is registered on the Chinese Clinical Trial Registry, number ChiCTR1900021544. The trial was registered on February 27, 2019.

Review of Generative Adversarial Networks in mono- and cross-modal biomedical image registration

Biomedical image registration refers to aligning corresponding anatomical structures among different images, which is critical to many tasks, such as brain atlas building, tumor growth monitoring, and image fusion-based medical diagnosis. However, high-throughput biomedical image registration remains challenging due to inherent variations in the intensity, texture, and anatomy resulting from different imaging modalities, different sample preparation methods, or different developmental stages of the imaged subject. Recently, Generative Adversarial Networks (GAN) have attracted increasing interest in both mono- and cross-modal biomedical image registrations due to their special ability to eliminate the modal variance and their adversarial training strategy. This paper provides a comprehensive survey of the GAN-based mono- and cross-modal biomedical image registration methods. According to the different implementation strategies, we organize the GAN-based mono- and cross-modal biomedical image registration methods into four categories: modality translation, symmetric learning, adversarial strategies, and joint training. The key concepts, the main contributions, and the advantages and disadvantages of the different strategies are summarized and discussed. Finally, we analyze the statistics of all the cited works from different points of view and reveal future trends for GAN-based biomedical image registration studies.

Associations of depressive symptoms with white matter abnormalities and regional cerebral blood flow in patients with amnestic mild cognitive impairment

AIM

Depressive symptoms are one of the most common neuropsychiatric symptoms in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), although the pathophysiologies of the depressive symptoms that occur in these diseases have not been elucidated to date. In this study, we therefore investigated the associations between depressive symptoms and cognitive performance, white matter abnormalities, and regional cerebral blood flow (rCBF) in amnestic MCI patients.

METHODS

Thirty-eight patients with amnestic MCI were analyzed. The volumes of periventricular hyperintensities (PVH) and deep white matter hyperintensities (DWMH) were measured on T2-fluid-attenuated inversion recovery magnetic resonance imaging using the imaging software 3D-slicer. Associations between the Geriatric Depression Scale (GDS) score and other neuropsychological test scores on the one hand and the PVH and DWMH volumes on the other were analyzed. Voxel-wise correlations of rCBF with GDS score, after controlling for the effects of age, were investigated using SPM8 software.

RESULTS

Significant correlations were identified between GDS score, Trail Making Test B and apathy scale scores on the one hand and PVH volume on the other. A significant negative association between GDS score and rCBF was identified in the right dominant bilateral dorsolateral prefrontal cortex (DLPFC).

CONCLUSIONS

Depressive symptoms are significantly associated with PVH volume in MCI patients. The rCBF of the DLPFC was significantly associated with depressive symptoms, suggesting that this area might be closely involved in the pathogenesis of the depressive symptoms observed in MCI patients. Geriatr Gerontol Int 2022; 22: 846-850.

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.

A Fast Method for Whole Liver- and Colorectal Liver Metastasis Segmentations from MRI Using 3D FCNN Networks

The liver is the most frequent organ for metastasis from colorectal cancer, one of the most common tumor types with a poor prognosis. Despite reducing surgical planning time and providing better spatial representation, current methods of 3D modeling of patient-specific liver anatomy are extremely time-consuming. The purpose of this study was to develop a deep learning model trained on an in-house dataset of 84 MRI volumes to rapidly provide fully automated whole liver and liver lesions segmentation from volumetric MRI series. A cascade approach was utilized to address the problem of class imbalance. The trained model achieved an average Dice score for whole liver segmentation of 0.944 ± 0.009 and 0.780 ± 0.119 for liver lesion segmentation. Furthermore, applying this method to a not-annotated dataset creates a complete 3D segmentation in less than 6 s per MRI volume, with a mean segmentation Dice score of 0.994 ± 0.003 for the liver and 0.709 ± 0.171 for tumors compared to manual corrections applied after the inference was achieved. Availability and integration of our method in clinical practice may improve diagnosis and treatment planning in patients with colorectal liver metastasis and open new possibilities for research into liver tumors.

DeepVolume: Brain Structure and Spatial Connection-Aware Network for Brain MRI Super-Resolution

Thin-section magnetic resonance imaging (MRI) can provide higher resolution anatomical structures and more precise clinical information than thick-section images. However, thin-section MRI is not always available due to the imaging cost issue. In multicenter retrospective studies, a large number of data are often in thick-section manner with different section thickness. The lack of thin-section data and the difference in section thickness bring considerable difficulties in the study based on the image big data. In this article, we introduce DeepVolume, a two-step deep learning architecture to address the challenge of accurate thin-section MR image reconstruction. The first stage is the brain structure-aware network, in which the thick-section MR images in axial and sagittal planes are fused by a multitask 3-D U-net with prior knowledge of brain volume segmentation, which encourages the reconstruction result to have correct brain structure. The second stage is the spatial connection-aware network, in which the preliminary reconstruction results are adjusted slice-by-slice by a recurrent convolutional network embedding convolutional long short-term memory (LSTM) block, which enhances the precision of the reconstruction by utilizing the previously unassessed sagittal information. We used 305 paired brain MRI samples with thickness of 1.0 mm and 6.5 mm in this article. Extensive experiments illustrate that DeepVolume can produce the state-of-the-art reconstruction results by embedding more anatomical knowledge. Furthermore, considering DeepVolume as an intermediate step, the practical and clinical value of our method is validated by applying the brain volume estimation and voxel-based morphometry. The results show that DeepVolume can provide much more reliable brain volume estimation in the normalized space based on the thick-section MR images compared with the traditional solutions.

Association of regional white matter hyperintensity volumes with cognitive dysfunction and vascular risk factors in patients with amnestic mild cognitive impairment

AIM

White matter hyperintensities (WMH) obtained by magnetic resonance imaging (MRI) have been reported to promote neurodegeneration and cognitive decline in patients with mild cognitive impairment (MCI). However, little is known about the association between regional WMH (rWMH) and cognitive dysfunction in MCI. We hence investigated the associations between rWMH volumes and cognitive dysfunction in MCI.

METHODS

Thirty-eight subjects with amnestic MCI were analysed. The volumes of periventricular hyperintensities (PVH) and deep WMH (DWMH) were measured on a T2-FLAIR MRI using a 3D-slicer, and regional PVH and DWMH (rPVH and rDWMH) volumes were calculated. The associations of rPVH and rDWMH volumes with cognition and blood levels of various molecules were investigated. Furthermore, rPVH and rDWMH volumes were compared between MCI with vascular risk factors, such as hypertension, diabetes mellitus (DM), and dyslipidemia, and those without these risk factors.

RESULTS

rPVH volume (bilateral cornu frontale, pars parietalis, and cornu occipitale) positively correlated with Trail Making Test-A/B scores and CysC level, whereas rDWMH volume did not correlate with any of the items. rPVH volumes (right cornu frontale, bilateral pars parietalis and cornu occipitale, and right pars temporalis) and rDWMH volumes (left frontal and parietal lobes) were significantly larger in MCI patients with DM than in those without.

CONCLUSIONS

PVH volumes (bilateral areas of cornu frontale, pars parietalis, and cornu occipitale) were closely associated with attention and executive dysfunction. Serum CysC level and DM were associated with WMH volume, suggesting that CysC level and DM might be important markers for determining treatment strategies for white matter abnormalities in MCI. Geriatr Gerontol Int 2021; 21: 644-650.

Comparing the inter-observer reliability of the Tada formula among neurosurgeons while estimating the intracerebral haematoma volume

OBJECTIVE

To compare the inter-observer reliability among neurosurgeons while estimating the intracerebral haematoma (ICH) volume by the Tada formula and assess its influence on predicting the severity and prognosis of various ICHs.

METHODS

We obtained clinical data from 262 consecutive patients with spontaneous ICH. The haematoma volume was independently calculated and compared by 3D Slicer and eight neurosurgeons. The inter-observer reliability was obtained by calculating the intraclass correlation coefficients (ICC) and Cohen's kappa score (kappa), within different shape and volume ICH subgroups. We conducted the receiver operating characteristic analysis to assess the predictive value of the ICH volume evaluated for clinical features, including the Glasgow Coma Scale at the onset of the disease, ICH-related surgical treatments, the length of stay in the intensive care unit, the length of hospitalisation, the modified Rankin Scale score at discharge, and in-hospital deaths.

RESULTS

The median haematoma volume was 17.4 ml (range, 7.3-34.7 ml). The estimated volumes were significantly different among neurosurgeons (p < 0.001). Six out of eight neurosurgeons demonstrated obvious deviations from the 3D Slicer software (p < 0.001). Round (ICC: 0.947) and tapered (ICC: 0.954) haematomas were more consistently evaluated between the neurosurgeons. We observed a substantial strength of agreement between neurosurgeons with kappa> 0.693 and ICC: 0.938 in the entire volume range, and slight to fair strength of agreement with kappa> 0.175 and ICC: 0.689 between 20 ml and 40 ml volume interval. All estimated volumes had a positive predictive value for clinical features, with the area under the curve > 0.5 (p < 0.05). However, the 3D Slicer software performed relatively better than most neurosurgeons.

CONCLUSIONS

There exists a significant inter-observer variability among neurosurgeons when utilizing the Tada formula, thus demonstrating significant implications for ICH-related clinical practices and researches.

Intraoperative MRI for Brain Tumors

INTRODUCTION

The use of intraoperative imaging has been a critical tool in the neurosurgeon's armamentarium and is of particular benefit during tumor surgery. This article summarizes the history of its development, implementation, clinical experience and future directions.

METHODS

We reviewed the literature focusing on the development and clinical experience with intraoperative MRI. Utilizing the authors' personal experience as well as evidence from the literature, we present an overview of the utility of MRI during neurosurgery.

RESULTS

In the 1990s, the first description of using a low field MRI in the operating room was published describing the additional benefit provided by improved resolution of MRI as compared to ultrasound. Since then, implementation has varied in magnetic field strength and in configuration from floor mounted to ceiling mounted units as well as those that are accessible to the operating room for use during surgery and via an outpatient entrance to use for diagnostic imaging. The experience shows utility of this technique for increasing extent of resection for low and high grade tumors as well as preventing injury to important structures while incorporating techniques such as intraoperative monitoring.

CONCLUSION

This article reviews the history of intraoperative MRI and presents a review of the literature revealing the successful implementation of this technology and benefits noted for the patient and the surgeon.

Association of White Matter Hyperintensity Progression with Cognitive Decline in Patients with Amnestic Mild Cognitive Impairment

Background:

White matter hyperintensities (WMH) on MRI have been reported to increase the risk of conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). However, effects of the progression of WMH on the cognition of patients with MCI remains unclear to date.

Objective:

To investigate the association between WMH progression and cognitive decline in amnestic MCI patients.

Methods:

Thirty-eight subjects with amnestic MCI were analyzed prospectively every year for 2 years. Fourteen MCI subjects dropped out on the final visit, and therefore 24 subjects with MCI were analyzed for the entire duration. The volumes of periventricular hyperintensities (PVH) and deep WMH (DWMH) were measured on T2 FLAIR using the 3D-slicer. The associations between PVH/DWMH progression and cognitive decline were investigated.

Results:

An increase in DWMH volume significantly correlated with changes in Mini-Mental State Examination and category verbal fluency scores, whereas an increase in PVH volume did not correlate with changes in any item.

Conclusion:

DWMH progression was closely associated with a decline in frontal lobe function and semantic memory, suggesting that WMH progression might affect some AD pathophysiologies in amnestic MCI patients.

A Platform Integrating Acquisition, Reconstruction, Visualization, and Manipulator Control Modules for MRI-Guided Interventions

This work presents a platform that integrates a customized MRI data acquisition scheme with reconstruction and three-dimensional (3D) visualization modules along with a module for controlling an MRI-compatible robotic device to facilitate the performance of robot-assisted, MRI-guided interventional procedures. Using dynamically-acquired MRI data, the computational framework of the platform generates and updates a 3D model representing the area of the procedure (AoP). To image structures of interest in the AoP that do not reside inside the same or parallel slices, the MRI acquisition scheme was modified to collect a multi-slice set of intraoblique to each other slices; which are termed composing slices. Moreover, this approach interleaves the collection of the composing slices so the same k-space segments of all slices are collected during similar time instances. This time matching of the k-space segments results in spatial matching of the imaged objects in the individual composing slices. The composing slices were used to generate and update the 3D model of the AoP. The MRI acquisition scheme was evaluated with computer simulations and experimental studies. Computer simulations demonstrated that k-space segmentation and time-matched interleaved acquisition of these segments provide spatial matching of the structures imaged with composing slices. Experimental studies used the platform to image the maneuvering of an MRI-compatible manipulator that carried tubing filled with MRI contrast agent. In vivo experimental studies to image the abdomen and contrast enhanced heart on free-breathing subjects without cardiac triggering demonstrated spatial matching of imaged anatomies in the composing planes. The described interventional MRI framework could assist in performing real-time MRI-guided interventions.

Can Surgeons Identify ACL Femoral Ridges Landmark and Optimal Tunnel Position? A 3D Model Study

Purpose

To examine the ability of surgeons to identify the osseous landmarks associated with the femoral anterior cruciate ligament (ACL) footprint and locate optimal tunnel placement on 3-dimensional (3D) printed models compared with intraoperative placement.

Methods

Twelve sports fellowship-trained orthopaedic surgeons were asked to identify a femoral landmark and an ACL footprint on 10 different 3D printed knees. The 3D models were made based on 20 real patients with different anatomical morphology who later received ACL reconstructive surgery using independent drilling. ImageJ software was used to quantify the measurements, which were then analyzed using descriptive statistics.

Results

Overall, none of the surgeons were able to consistently identify the junction of the bony ridges. The mean error per participant ranged from 2.81 to 7.34 mm in the proximal direction (P = 3.30e-05) and from 2.42 to 8.05 mm in the posterior direction (P =4.88e-12). None of the surgeons were able to appropriately identify the center of the femoral footprint on the anatomic 3D models. The difference between the center of the footprint surgeons identified on the 3D model and the tunnel graft location in surgery was significantly different (P = .0046). On average, the magnitude of the error when the surgeons performed the actual surgery was 3.72 ± 2.43 mm, whereas on the 3D models it was 5.82 ± 1.97 mm.

Conclusions

Experienced sports fellowship-trained orthopaedic surgeons were unable to correctly identify the junction of the intercondylar and bifurcate ridges and the native ACL footprint on 3D models. Operatively placed tunnels were more accurate implying that looking either through a scope or soft-tissue landmarks play a significant role in surgeons ACL footprint localization.

Clinical Relevance

The graft position for ACL reconstruction plays an important role on the kinematics of the knee. This paper shows that soft tissue landmarks are needed to provide reliable reference points for reconstruction.

Magnetic Resonance-Based Radiomic Analysis of Radiofrequency Lesion Predicts Outcomes After Percutaneous Cordotomy: A Feasibility Study

BACKGROUND

To date, there is limited data on evaluation of the cordotomy lesion and predicting clinical outcome.

OBJECTIVE

To evaluate the utility of magnetic resonance (MR)-based radiomic analysis to quantify microstructural changes created by the cordotomy lesion and predict outcome in patients undergoing percutaneous cordotomy for medically refractory cancer pain.

METHODS

This is a retrospective interpretation of prospectively acquired data in 10 patients (5 males, age range 43-76 yr) who underwent percutaneous computed tomography-guided high cervical cordotomy for medically refractory cancer pain between 2015 and 2016. All patients underwent magnetic resonance imaging (MRI) of the cordotomy lesion on postoperative day 1. After segmentation of T2-weighted images, 310 radiomic features were extracted. Pain outcomes were recorded on postoperative day 1 and day 7 using the visual analog scale. R software was used to build statistical models based on MRI radiomic features for prediction of pain outcomes.

RESULTS

A total of 20 relevant radiomic features were identified using the maximum relevance minimum redundanc method. Radiomics predicted postoperative day 1 pain scores with an accuracy of 90% (P = .046), 100% sensitivity, 75% specificity, 85.7% positive predictive value, and 100% negative predictive value. The radiomics model also predicted if the postoperative day 1 pain score was sustained on postoperative day 7 with an accuracy of 100% (P = .028), 100% sensitivity, 100% specificity, and 100% positive and negative predictive value.

CONCLUSION

MR-based radiomic analysis of the cordotomy lesion was predictive of pain outcomes at 1 wk after percutaneous cordotomy for intractable cancer pain.

Effect of Teaching Session on Resident Ability to Identify Anatomic Landmarks and Anterior Cruciate Ligament Footprint: A Study Using 3-Dimensional Modeling

Background:

Femoral tunnel positioning in anterior cruciate ligament reconstruction (ACLR) is an intricate procedure that requires highly specific surgical skills.

Purpose:

To report the ability of residents to identify femoral landmarks and the native ACL footprint before and after a structured formal teaching session as a reflection of overall surgical skill training for orthopaedic surgery residents in Canada.

Study Design:

Controlled laboratory study.

Methods:

A total of 13 senior orthopaedic residents were asked to identify a femoral landmark and an ACL footprint on ten 3-dimensional (3D)–printed knee models before and after a teaching session during the fall of 2018. The 3D models were made based on actual patients with different anatomic morphologic features. ImageJ software was used to quantify the measurements, which were then analyzed through use of descriptive statistics.

Results:

Before and after the teaching session, residents attempted to identify a specific anatomic location (bifurcate and intercondylar ridge intersection) with a mean error per participant ranging from 5.00 to 10.95 mm and 4.79 to 12.13 mm in magnitude, respectively. Furthermore, before and after the teaching session, residents identified the specific position to perform the surgical procedure (ACL femoral footprint), with a mean error per participant ranging from 4.58 to 8.80 mm and 3.87 to 11.07 mm in magnitude, respectively. The teaching session resulted in no significant improvement in identification of either the intersection of the bifurcate and intercondylar ridges (P = .9343 in the proximal-distal axis and P = .8133 in the anteroposterior axis) or the center of the femoral footprint (P = .7761 in the proximal-distal axis and P = .9742 in the anteroposterior axis).

Conclusion:

Although a formal teaching session was combined with a hands-on session that entailed real surgical instrumentation and fresh cadaveric specimens, the intervention seemed to have no direct impact on senior residents’ performance or their ability to demonstrate the material taught. This puts into question the format and efficacy of present teaching methods. Also, it is possible that the 3D spatial perception required to perform these skills is not something that can be taught effectively through a teaching session or at all. Further investigation is required regarding the effectiveness and application of surgical skill laboratories and simulations on the competencies of orthopaedic residents.

Segmentation of Magnetic Resonance Brain Images Using the Advanced Ant Colony Optimization Technique

MR Brain Image Segmentation is an important step in brain image analysis. It facilitates the automatic interpretation or diagnosis that helps in surgical planning, estimating the changes in the brain’s volume for various types of tissues, and recognizing different neural disorders. Many neurological disorders like epilepsy, Alzheimer’s, tumor, and cancer can be effectively quantified and analyzed by finding the volume of the brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluids (CSF). In manual segmentation of brain MRIs physicians manually determines the boundaries of different objects of interest and it is time-consuming and difficult. Thus, several accurate automatic brain MRI segmentation techniques with different levels of complexity have been proposed. This paper proposes an advanced thresholding technique for the segmentation of brain MRIs based on the biologically inspired Ant Colony Optimization (ACO) algorithm. Here the texture features are assumed as heuristic data. The experimental results for the T1-weighted brain MRIs have shown high accuracy than the conventional such as Fuzzy C-Means (FCM), Expectation-Maximization (EM), Improved Bacterial Foraging Algorithm (IBFA), and Improved Particle Swarm Optimization (IPSO).

Association of serum cystatin C with white matter abnormalities in patients with amnestic mild cognitive impairment

Aim

White matter hyperintensities (WMH) on MRI have been reported to be a risk factor for the conversion from mild cognitive impairment (MCI) to Alzheimer's disease, although the reason remains unclear. In the present study, we hence investigated the associations between WMH volumes and cognitive function, blood levels of various molecules, and the presence of lifestyle‐associated diseases in patients with amnestic MCI.

Methods

The initial data of 38 patients with amnestic MCI and 10 normal control individuals were analyzed. The volumes of periventricular hyperintensities (PVH) and deep WMH (DWMH) were measured on T2 fluid‐attenuated inversion recovery using the imaging software, 3D Slicer; and the association between PVH/DWMH volumes and cognitive function, blood levels of molecules (such as cystatin C [CysC], 25‐hydroxyvitamin D and homocysteine) and the presence of lifestyle‐associated diseases (such as hypertension, hyperlipidemia and diabetes mellitus) were analyzed.

Results

In the MCI group, the PVH volume : intracranial volume ratio significantly correlated with Trail Making Test‐A/B scores and CysC level by Pearson's analysis, and the PVH volume : intracranial volume ratio significantly correlated with only CysC levels, whereas the DWMH volume : intracranial volume ratio did not correlate with any items at all by linear multiple regression analysis.

Conclusions

PVH volume was closely associated with frontal lobe dysfunction, particularly with attention and executive dysfunction. Serum CysC level was associated with PVH volume, which suggests that CysC might be a useful marker for determining treatment strategies for white matter abnormalities in amnestic MCI. Geriatr Gerontol Int 2019; 19: 1036–1040.

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.

Volumes of brain structures in captive wild-type and laboratory rats: 7T magnetic resonance in vivo automatic atlas-based study

Selective breeding of laboratory rats resulted in changes of their behavior. Concomitantly, the albino strains developed vision related pathologies. These alterations certainly occurred on the background of modifications in brain morphology. The aim of the study was to assess and compare volumes of major structures in brains of wild-captive, laboratory albino and laboratory pigmented rats. High resolution T2-weighted images of brains of adult male Warsaw Wild Captive Pisula-Stryjek rats (WWCPS, a model of wild type), laboratory pigmented (Brown Norway strain, BN) and albino rats (Wistar strain, WI) were obtained with a 7T small animal-dedicated magnetic resonance tomograph. Volume quantification of whole brains and 50 brain structures within each brain were performed with the digital Schwarz rat brain atlas and a custom-made MATLAB/SPM8 scripts. Brain volumes were scaled to body mass, whereas volumes of brain structures were normalized to individual brain volumes. Normalized brain volume was similar in WWCPS and BN, but lower in WI. Normalized neocortex volume was smaller in both laboratory strains than in WWCPS and the visual cortex was smaller in albino WI rats than in WWCPS and BN. Relative volumes of phylogenetically older structures, such as hippocampus, amygdala, nucleus accumbens and olfactory nuclei, also displayed certain strain-related differences. The present data shows that selective breeding of laboratory rats markedly affected brain morphology, the neocortex being most significantly altered. In particular, albino rats display reduced volume of the visual cortex, possibly related to retinal degeneration and the development of blindness.

Evaluation of intraoperative MRI-assisted stereotactic brain tissue biopsy: a single-center experience in China

Background

This study aimed to investigate the value of high field-strength intraoperative magnetic resonance imaging (iMRI)-guided stereotactic biopsy in the surgery of intracranial space-occupying lesions.

Methods

A total of 87 patients who underwent stereotactic biopsy of intracranial lesions in the Peking University International Hospital from March 2016 to August 2018 were retrospectively surveyed; among these, 50 patients underwent MRI-guided stereotactic biopsy using the Leksell frame (iMRI group) and 37 cases received traditional stereotactic biopsy using the Leksell frame (control group). The accuracy rates and complications of the two groups were compared.

Results

A 100% positive diagnosis was observed in all cases (n = 50) in the iMRI group. In 4 cases, the biopsy site was clearly found to have deviated from the target point, and the biopsy was performed again. The control group had 33 cases (86.5%) with positive diagnosis. No severe complications like neural functional deficit were observed in the iMRI group, while two patients developed bleeding at the puncture site (1 case receiving surgery to remove the hematoma) in the control group. There were no deaths in either group.

Conclusion

iMRI-assisted stereotactic biopsy can confirm the target position and adjust the puncture path in real time. Compared to the traditional stereotactic biopsy technique, the iMRI method has a higher positive diagnostic rate, though surgical trauma and complications have no significant difference.

Optimization of soft X-ray phase-contrast tomography using a laser wakefield accelerator

X-ray phase-contrast imaging allows for non-invasive analysis in low-absorbing materials, such as soft tissue. Its application in medical or materials science has yet to be realized on a wider scale due to the requirements on the X-ray source, demanding high flux and small source size. Laser wakefield accelerators generate betatron X-rays fulfilling these criteria and can be suitable sources for phase-contrast imaging. In this work, we present the first phase-contrast images obtained by using ionization injection-based laser wakefield acceleration, which results in a higher photon yield and smoother X-ray beam profile compared to self-injection. A peak photon yield of 1.9 × 10¹¹ ph/sr and a source size of 3 μm were estimated. Furthermore, the current laser parameters produce an X-ray spectrum mainly in the soft X-ray range, in which laser-plasma based phase-contrast imaging had yet to be studied. The phase-contrast images of a Chrysopa lacewing resolve features on the order of 4 μm. These images are further used for a tomographic reconstruction and a volume rendering, showing details on the order of tens of μm.

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.

A lightweight rapid application development framework for biomedical image analysis

Biomedical imaging analysis typically comprises a variety of complex tasks requiring sophisticated algorithms and visualising high dimensional data. The successful integration and deployment of the enabling software to clinical (research) partners, for rigorous evaluation and testing, is a crucial step to facilitate adoption of research innovations within medical settings. In this paper, we introduce the Simple Medical Imaging Library Interface (SMILI), an object oriented open-source framework with a compact suite of objects geared for rapid biomedical imaging (cross-platform) application development and deployment. SMILI supports the development of both command-line (shell and Python scripting) and graphical applications utilising the same set of processing algorithms. It provides a substantial subset of features when compared to more complex packages, yet it is small enough to ship with clinical applications with limited overhead and has a license suitable for commercial use. After describing where SMILI fits within the existing biomedical imaging software ecosystem, by comparing it to other state-of-the-art offerings, we demonstrate its capabilities in creating a clinical application for manual measurement of cam-type lesions of the femoral head-neck region for the investigation of femoro-acetabular impingement (FAI) from three dimensional (3D) magnetic resonance (MR) images of the hip. This application for the investigation of FAI proved to be convenient for radiological analyses and resulted in high intra (ICC=0.97) and inter-observer (ICC=0.95) reliabilities for measurement of α-angles of the femoral head-neck region. We believe that SMILI is particularly well suited for prototyping biomedical imaging applications requiring user interaction and/or visualisation of 3D mesh, scalar, vector or tensor data.

A New Optimized Thresholding Method Using Ant Colony Algorithm for MR Brain Image Segmentation

Image segmentation is considered as one of the most fundamental tasks in image processing applications. Segmentation of magnetic resonance (MR) brain images is also an important pre-processing step, since many neural disorders are associated with brain's volume changes. As a result, brain image segmentation can be considered as an essential measure toward automated diagnosis or interpretation of regions of interest, which can help surgical planning, analyzing changes of brain's volume in different tissue types, and identifying neural disorders. In many neural disorders such as Alzheimer and epilepsy, determining the volume of different brain tissues (i.e., white matter, gray matter, and cerebrospinal fluids) has been proven to be effective in quantifying diseases. A traditional way for segmenting brain images involves the use of a medical expert's experience in manually determining the boundary of different regions of interest in brain images. It may seem that manual segmentation of MR brain images by an expert is the first and the best choice. However, this method is proved to be time-consuming and challenging. Hence, numerous MR brain image segmentation methods with different degrees of complexity and accuracy have been introduced recently. Our work proposes an optimized thresholding method for segmentation of MR brain images using biologically inspired ant colony algorithm. In this proposed algorithm, textural features are adopted as heuristic information. Besides, post-processing image enhancement based on homogeneity is also performed to achieve a better performance. The empirical results on axial T1-weighted MR brain images have demonstrated competitive accuracy to traditional meta-heuristic methods, K-means, and expectation maximization.

A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

PURPOSE

Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma.

EXPERIMENTAL DESIGN

Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients (n = 93) and orthotopic xenografts (OX; n = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin (POSTN) expression levels. RNA and protein levels confirmed RNAi-mediated POSTN knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict POSTN expression status in patient, mouse, and interspecies.

RESULTS

Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. POSTN expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted POSTN expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar POSTN expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; P = 02.021E-15).

CONCLUSIONS

We determined causality between radiomic texture features and POSTN expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human-mouse matched coclinical trials.

MRI Robots for Needle-Based Interventions: Systems and Technology

Magnetic resonance imaging (MRI) provides high-quality soft-tissue images of anatomical structures and radiation free imaging. The research community has focused on establishing new workflows, developing new technology, and creating robotic devices to change an MRI room from a solely diagnostic room to an interventional suite, where diagnosis and intervention can both be done in the same room. Closed bore MRI scanners provide limited access for interventional procedures using intraoperative imaging. MRI robots could improve access and procedure accuracy. Different research groups have focused on different technology aspects and anatomical structures. This paper presents the results of a systematic search of MRI robots for needle-based interventions. We report the most recent advances in the field, present relevant technologies, and discuss possible future advances. This survey shows that robotic-assisted MRI-guided prostate biopsy has received the most interest from the research community to date. Multiple successful clinical experiments have been reported in recent years that show great promise. However, in general the field of MRI robotic systems is still in the early stage. The continued development of these systems, along with partnerships with commercial vendors to bring this technology to market, is encouraged to create new and improved treatment opportunities for future patients.

Free water modeling of peritumoral edema using multi-fiber tractography: Application to tracking the arcuate fasciculus for neurosurgical planning

Purpose

Peritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema.

Materials and methods

We retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts.

Results

The AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p<0.0001) compared to UKFt, but not significantly larger (p = 0.0996) in hemispheres without edema. The AF size increase depended on the volume of edema: a significant correlation was found between AF volume affected by (intersecting) edema and AF volume change with the FW model (Pearson r = 0.806, p<0.0001). The mean FW fraction was significantly larger in tracts intersecting edema (p = 0.0271). Compared to the UKFt+rFA method, there was a significant increase of the volume of the AF tract that intersected the edema using the UKFt+FW method, while the whole AF volumes were similar. Expert judgment results, based on the five patients with the smallest AF volumes, indicated that the expert readers generally preferred the AF tract obtained by using the FW model, according to their anatomical knowledge and considering the potential influence of the final results on the surgical route.

Conclusion

Our results indicate that incorporating biophysical models of edema can increase the sensitivity of tractography in regions of peritumoral edema, allowing better tract visualization in patients with high grade gliomas and metastases.

Motion compensation for MRI-compatible patient-mounted needle guide device: estimation of targeting accuracy in MRI-guided kidney cryoablations

Patient-mounted needle guide devices for percutaneous ablation are vulnerable to patient motion. The objective of this study is to develop and evaluate a software system for an MRI-compatible patient-mounted needle guide device that can adaptively compensate for displacement of the device due to patient motion using a novel image-based automatic device-to-image registration technique. We have developed a software system for an MRI-compatible patient-mounted needle guide device for percutaneous ablation. It features fully-automated image-based device-to-image registration to track the device position, and a device controller to adjust the needle trajectory to compensate for the displacement of the device. We performed: (a) a phantom study using a clinical MR scanner to evaluate registration performance; (b) simulations using intraoperative time-series MR data acquired in 20 clinical cases of MRI-guided renal cryoablations to assess its impact on motion compensation; and (c) a pilot clinical study in three patients to test its feasibility during the clinical procedure. FRE, TRE, and success rate of device-to-image registration were 2.71 ± 2.29 mm, 1.74 ± 1.13 mm, and 98.3% for the phantom images. The simulation study showed that the motion compensation reduced the targeting error for needle placement from 8.2 mm to 5.4 mm (p  <  0.0005) in patients under general anesthesia (GA), and from 14.4 mm to 10.0 mm (p < 1.0 × 10(−5)) in patients under monitored anesthesia care (MAC). The pilot study showed that the software registered the device successfully in a clinical setting. Our simulation study demonstrated that the software system could significantly improve targeting accuracy in patients treated under both MAC and GA. Intraprocedural image-based device-to-image registration was feasible.

Survey of Non-Rigid Registration Tools in Medicine

We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using non-systematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support; (ii) 7 are built on software platforms, 5 were developed for brain image registration; (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016; (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools; and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively. Researchers in medical image analysis already have a large choice of registration tools freely available. However, the most recently published algorithms may not be included in the tools, yet.

A non-rigid registration method for the analysis of local deformations in the wood cell wall

This paper concerns the problem of wood cellular structure image registration. Given the large variability of wood geometry and the important changes in the cellular organization due to moisture sorption, an affine-based image registration technique is not exhaustive to describe the overall hygro-mechanical behaviour of wood at micrometre scales. Additionally, free tools currently available for non-rigid image registration are not suitable for quantifying the structural deformations of complex hierarchical materials such as wood, leading to errors due to misalignment. In this paper, we adapt an existing non-rigid registration model based on B-spline functions to our case study. The so-modified algorithm combines the concept of feature recognition within specific regions locally distributed in the material with an optimization problem. Results show that the method is able to quantify local deformations induced by moisture changes in tomographic images of wood cell wall with high accuracy. The local deformations provide new important insights in characterizing the swelling behaviour of wood at the cell wall level.

Three-Dimensional Bioprinting Strategies for Tissue Engineering

Over the past decades, many approaches have been developed to fabricate biomimetic extracellular matrices of desired properties for engineering functional tissues. However, the inability of these techniques to precisely control the spatial architecture has posed a significant challenge in producing complex tissues. 3D bioprinting technology has emerged as a potential solution by bringing unprecedented freedom and versatility in depositing biological materials and cells in a well-controlled manner in the 3D volumes, therefore achieving precision engineering of functional tissues. In this article, we review the application of 3D bioprinting to tissue engineering. We first discuss the general strategies for printing functional tissue constructs. We next describe different types of bioprinting with a focus on nozzle-based techniques and their respective advantages. Finally, we summarize the limitations of current technologies and propose challenges for future development of bioprinting.

Radiomics to predict immunotherapy-induced pneumonitis: proof of concept

We present the first reported work that explores the potential of radiomics to predict patients who are at risk for developing immunotherapy-induced pneumonitis. Despite promising results with immunotherapies, immune-related adverse events (irAEs) are challenging. Although less common, pneumonitis is a potentially fatal irAE. Thus, early detection is critical for improving treatment outcomes; an urgent need to identify biomarkers that predict patients at risk for pneumonitis exists. Radiomics, an emerging field, is the automated extraction of high fidelity, high-dimensional imaging features from standard medical images and allows for comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. In this pilot study, we sought to determine whether radiomics has the potential to predict development of pneumonitis. We performed radiomic analyses using baseline chest computed tomography images of patients who did (N = 2) and did not (N = 30) develop immunotherapy-induced pneumonitis. We extracted 1860 radiomic features in each patient. Maximum relevance and minimum redundancy feature selection method, anomaly detection algorithm, and leave-one-out cross-validation identified radiomic features that were significantly different and predicted subsequent immunotherapy-induced pneumonitis (accuracy, 100% [p = 0.0033]). This study suggests that radiomic features can classify and predict those patients at baseline who will subsequently develop immunotherapy-induced pneumonitis, further enabling risk-stratification that will ultimately lead to better treatment outcomes.

Tissue segmentation: a crucial tool for quantitative MRI and visualization of anatomical structures

Automatic or semi-automatic segmentation of tissue types or organs is well established for X-ray-based computed tomography, with its fixed grey-scale and tissue classes with well-established ranges of Hounsfield units. MRI is much more powerful with regard to soft tissue contrast and quantitative assessment of tissue properties (e.g., perfusion, diffusion, fat content), but the principle of signal generation and recording in MRI leads to inherent problems if simple threshold based segmentation procedures are applied. In this editorial in the special issue of MAGMA on tissue segmentation, a number of relevant methodical, scientific, and clinical aspects of reliable tissue segmentation using data recording by MRI are reported and discussed.

Automated white matter fiber tract identification in patients with brain tumors

We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography.

Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

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Spectral clustering machine learning approach for white matter tract identification

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Data-driven white matter parcellation learned from healthy subjects tractography

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White matter parcellation applied to 18 consecutive patients with brain tumors

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Arcuate fasciculus and corticospinal tracts identified in all patients

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All tracts within 3 mm of corresponding patient-specific functional activations

Volume of Brain Herniation in Patients with Ischemic Stroke After Decompressive Craniectomy

BACKGROUND

Decompressive craniectomy procedures are performed in patients with malignant intracranial hypertension. A bone flap is removed to relieve pressure. Later, a second operation is performed to reconstruct the skull after brain swelling has resolved. This surgical treatment would be improved if it were possible to perform a single operation that decompressed the brain acutely and eliminated the need for a second operation. To design a device and procedure that achieve this objective, it is essential to understand how the brain swells after a craniectomy procedure.

METHODS

We identified 20 patients with ischemic stroke who underwent a decompressive hemicraniectomy operation. Skull defect morphology and postoperative brain swelling were measured using computed tomography scan data. Additional intracranial volume created by placing a hypothetical cranial plate implant offset from the skull surface by 5 mm was measured for each patient.

RESULTS

The average craniectomy area and brain herniation volume was 9999 ± 1283 mm² and 30.48 ± 23.56 mL, respectively. In all patients, the additional volume created by this hypothetical implant exceeded the volume of brain herniation observed.

CONCLUSIONS

These findings show that a cranial plate with a 5-mm offset accommodates the brain swelling that occurs in this patient population.

Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma

BACKGROUND

Radiological assessments of biologically relevant regions in glioblastoma have been associated with genotypic characteristics, implying a potential role in personalized medicine. Here, we assess the reproducibility and association with survival of two volumetric segmentation platforms and explore how methodology could impact subsequent interpretation and analysis.

METHODS

Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients were segmented into five distinct compartments (necrosis, contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal tumor volumes) by two quantitative image segmentation platforms - 3D Slicer and a method based on Velocity AI and FSL. We investigated the internal consistency of each platform by correlation statistics, association with survival, and concordance with consensus neuroradiologist ratings using ordinal logistic regression.

RESULTS

We found high correlations between the two platforms for FLAIR, post contrast abnormal, and total abnormal tumor volumes (spearman's r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement was observed for necrosis and contrast-enhancement volumes (r(67) = 0.693 and 0.773 respectively), likely arising from differences in manual and automated segmentation methods of these regions by 3D Slicer and Velocity AI/FSL, respectively. Survival analysis based on AUC revealed significant predictive power of both platforms for the following volumes: contrast-enhancement, post contrast abnormal, and total abnormal tumor volumes. Finally, ordinal logistic regression demonstrated correspondence to manual ratings for several features.

CONCLUSION

Tumor volume measurements from both volumetric platforms produced highly concordant and reproducible estimates across platforms for general features. As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement differences between segmentation platforms for more detailed features could influence downstream survival or radio genomic analyses.

"Intrasellar Balloon Technique" in intraoperative MRI guided transsphenoidal endoscopic surgery for sellar region tumors. Usefulness on image interpretation and extent of resection evaluation. Technical note

BACKGROUND

Intraoperative magnetic resonance imaging (iMRI) is an effective and proven tool in transsphenoidal endoscopic surgery. However, image interpretation is not always easy and can be hindered by the presence of blood, tumor remains or the displacement of surrounding structures. In this article we present a novel technique based on using intrasellar ballons to reduce these difficulties and facilitate the surgeon's intraoperative assessment by iMRI.

METHODS

Eighteen patients with pituitary macroadenomas underwent transsphenoidal surgery during 2013-2014 under low-field iMRI control (PoleStar N20, 0.15 T). Intrasellar balloons were used in all of them to assess the presence of tumoral remnants. We compared the findings in iMRI and postoperative high-field MRI control scans and also analyzed the number of intermediate imaging controls needed during surgery using this technique.

RESULTS

In total, of the 18 patients, 14 underwent a complete resection. In the remaining four patients, a safe maximal resection was performed, leaving a remnant because of cavernous sinus invasion. In all cases, the balloons were a major help in distinguishing the anatomical structures from the tumoral remnants. Fewer imaging controls were required, and there were no false-positives or negative intraoperative findings. No complications related to the technique were registered.

CONCLUSION

The "intrasellar balloon technique" is a useful tool that facilitates surgeons' intraoperative decision making. It is an important contribution to overcome the limitations of low-field iMRI as it provides a precise delineation of the resection margins, reduces false-positives and -negatives, and decreases the number of intermediate imaging controls required.