Publications by Year: 2021

2021
Zhou H, Jayender J. EMDQ-SLAM: Real-time High-resolution Reconstruction of Soft Tissue Surface from Stereo Laparoscopy Videos. Med Image Comput Comput Assist Interv. 2021;12904 :331-340.Abstract
We propose a novel stereo laparoscopy video-based non-rigid SLAM method called EMDQ-SLAM, which can incrementally reconstruct thee-dimensional (3D) models of soft tissue surfaces in real-time and preserve high-resolution color textures. EMDQ-SLAM uses the expectation maximization and dual quaternion (EMDQ) algorithm combined with SURF features to track the camera motion and estimate tissue deformation between video frames. To overcome the problem of accumulative errors over time, we have integrated a g2o-based graph optimization method that combines the EMDQ mismatch removal and as-rigid-as-possible (ARAP) smoothing methods. Finally, the multi-band blending (MBB) algorithm has been used to obtain high resolution color textures with real-time performance. Experimental results demonstrate that our method outperforms two state-of-the-art non-rigid SLAM methods: MISSLAM and DefSLAM. Quantitative evaluation shows an average error in the range of 0.8-2.2 mm for different cases.
Xu Z, Yan J, Luo J, Wells W, Li X, Jagadeesan J. Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks. Proc IEEE Int Symp Biomed Imaging. 2021;2021.Abstract
The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization. In the deep learning era, researchers proposed many approaches to automatically learn the similarity metric, which has been shown effective in improving registration performance. However, for the regularization term, most existing multimodal registration approaches still use a hand-crafted formula to impose artificial properties on the estimated deformation field. In this work, we propose a unimodal cyclic regularization training pipeline, which learns task-specific prior knowledge from simpler unimodal registration, to constrain the deformation field of multimodal registration. In the experiment of abdominal CT-MR registration, the proposed method yields better results over conventional regularization methods, especially for severely deformed local regions.
Yeh F-C, Irimia A, de Bastos DCA, Golby AJ. Tractography Methods and Findings in Brain Tumors and Traumatic Brain Injury. Neuroimage. 2021;245 :118651.Abstract
White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.
Drakopoulos F, Tsolakis C, Angelopoulos A, Liu Y, Yao C, Kavazidi KR, Foroglou N, Fedorov A, Frisken S, Kikinis R, et al. Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems. Front Digit Health. 2021;2 :613608.Abstract
Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT. Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting. Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.
Marin B-M, Porath KA, Jain S, Kim M, Conage-Pough JE, Oh J-H, Miller CL, Talele S, Kitange GJ, Tian S, et al. Heterogeneous Delivery Across the Blood-Brain Barrier Limits the Efficacy of an EGFR-Targeting Antibody Drug Conjugate in Glioblastoma. Neuro Oncol. 2021;23 (12) :2042-53.Abstract
BACKGROUND: Antibody drug conjugates (ADCs) targeting the epidermal growth factor receptor (EGFR), such as depatuxizumab mafodotin (Depatux-M), is a promising therapeutic strategy for glioblastoma (GBM) but recent clinical trials did not demonstrate a survival benefit. Understanding the mechanisms of failure for this promising strategy is critically important. METHODS: PDX models were employed to study efficacy of systemic vs intracranial delivery of Depatux-M. Immunofluorescence and MALDI-MSI were performed to detect drug levels in the brain. EGFR levels and compensatory pathways were studied using quantitative flow cytometry, Western blots, RNAseq, FISH, and phosphoproteomics. RESULTS: Systemic delivery of Depatux-M was highly effective in nine of 10 EGFR-amplified heterotopic PDXs with survival extending beyond one year in eight PDXs. Acquired resistance in two PDXs (GBM12 and GBM46) was driven by suppression of EGFR expression or emergence of a novel short-variant of EGFR lacking the epitope for the Depatux-M antibody. In contrast to the profound benefit observed in heterotopic tumors, only two of seven intrinsically sensitive PDXs were responsive to Depatux-M as intracranial tumors. Poor efficacy in orthotopic PDXs was associated with limited and heterogeneous distribution of Depatux-M into tumor tissues, and artificial disruption of the BBB or bypass of the BBB by direct intracranial injection of Depatux-M into orthotopic tumors markedly enhanced the efficacy of drug treatment. CONCLUSIONS: Despite profound intrinsic sensitivity to Depatux-M, limited drug delivery into brain tumor may have been a key contributor to lack of efficacy in recently failed clinical trials.
Schilling KG, Rheault F, Petit L, Hansen CB, Nath V, Yeh F-C, Girard G, Barakovic M, Rafael-Patino J, Yu T, et al. Tractography Dissection Variability: What Happens When 42 Groups Dissect 14 White Matter Bundles on the Same Dataset?. Neuroimage. 2021;243 :118502.Abstract
White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
Abdelmoula WM, Lopez BG-C, Randall EC, Kapur T, Sarkaria JN, White FM, Agar JN, Wells WM, Agar NYR. Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks. Nat Commun. 2021;12 (1) :5544.Abstract
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.
Basu SS, Stopka SA, Abdelmoula WM, Randall EC, Gimenez-Cassina Lopez B, Regan MS, Calligaris D, Lu FF, Norton I, Mallory MA, et al. Interim Clinical Trial Analysis of Intraoperative Mass Spectrometry for Breast Cancer Surgery. NPJ Breast Cancer. 2021;7 (1) :116.Abstract
Optimal resection of breast tumors requires removing cancer with a rim of normal tissue while preserving uninvolved regions of the breast. Surgical and pathological techniques that permit rapid molecular characterization of tissue could facilitate such resections. Mass spectrometry (MS) is increasingly used in the research setting to detect and classify tumors and has the potential to detect cancer at surgical margins. Here, we describe the ex vivo intraoperative clinical application of MS using a liquid micro-junction surface sample probe (LMJ-SSP) to assess breast cancer margins. In a midpoint analysis of a registered clinical trial, surgical specimens from 21 women with treatment naïve invasive breast cancer were prospectively collected and analyzed at the time of surgery with subsequent histopathological determination. Normal and tumor breast specimens from the lumpectomy resected by the surgeon were smeared onto glass slides for rapid analysis. Lipidomic profiles were acquired from these specimens using LMJ-SSP MS in negative ionization mode within the operating suite and post-surgery analysis of the data revealed five candidate ions separating tumor from healthy tissue in this limited dataset. More data is required before considering the ions as candidate markers. Here, we present an application of ambient MS within the operating room to analyze breast cancer tissue and surgical margins. Lessons learned from these initial promising studies are being used to further evaluate the five candidate biomarkers and to further refine and optimize intraoperative MS as a tool for surgical guidance in breast cancer.
Fichtinger G, Mousavi P, Ungi T, Fenster A, Abolmaesumi P, Kronreif G, Ruiz-Alzola J, Ndoye A, Diao B, Kikinis R. Design of an Ultrasound-Navigated Prostate Cancer Biopsy System for Nationwide Implementation in Senegal. J Imaging. 2021;7 (8) :154.Abstract
This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on "frugal technology", intended to be affordable for low-middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.
Sprugnoli G, Rossi S, Rotenberg A, Pascual-Leone A, El-Fakhri G, Golby AJ, Santarnecchi E. Personalised, Image-Guided, Noninvasive Brain Stimulation in Gliomas: Rationale, Challenges and Opportunities. EBioMedicine. 2021;70 :103514.Abstract
Malignant brain tumours are among the most aggressive human cancers, and despite intensive efforts made over the last decades, patients' survival has scarcely improved. Recently, high-grade gliomas (HGG) have been found to be electrically integrated with healthy brain tissue, a communication that facilitates tumour mitosis and invasion. This link to neuronal activity has provided new insights into HGG pathophysiology and opened prospects for therapeutic interventions based on electrical modulation of neural and synaptic activity in the proximity of tumour cells, which could potentially slow tumour growth. Noninvasive brain stimulation (NiBS), a group of techniques used in research and clinical settings to safely modulate brain activity and plasticity via electromagnetic or electrical stimulation, represents an appealing class of interventions to characterise and target the electrical properties of tumour-neuron interactions. Beyond neuronal activity, NiBS may also modulate function of a range of substrates and dynamics that locally interacts with HGG (e.g., vascular architecture, perfusion and blood-brain barrier permeability). Here we discuss emerging applications of NiBS in patients with brain tumours, covering potential mechanisms of action at both cellular, regional, network and whole-brain levels, also offering a conceptual roadmap for future research to prolong survival or promote wellbeing via personalised NiBS interventions.
Fedorov A, Longabaugh WJR, Pot D, Clunie DA, Pieper S, Aerts HJWL, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, et al. NCI Imaging Data Commons. Cancer Res. 2021;81 (16) :4188-93.Abstract
The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot{trade mark, serif}. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of de-identified imaging data and to support integrated analyses with non-imaging data. We achieve this goal by co-locating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research.
Basu SS, Agar NYR. Bringing Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging to the Clinics. Clin Lab Med. 2021;41 (2) :309-24.Abstract
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is an emerging analytical technique that promises to change tissue-based diagnostics. This article provides a brief introduction to MALDI MSI as well as clinical diagnostic workflows and opportunities to apply this powerful approach. It describes various MALDI MSI applications, from more clinically mature applications such as cancer to emerging applications such as infectious diseases and drug distribution. In addition, it discusses the analytical considerations that need to be considered when bringing these approaches to different diagnostic problems and settings.
Banach A, King F, Masaki F, Tsukada H, Hata N. Visually Navigated Bronchoscopy Using Three Cycle-Consistent Generative Adversarial Network for Depth Estimation. Med Image Anal. 2021;73 :102164.Abstract
[Background] Electromagnetically Navigated Bronchoscopy (ENB) is currently the state-of-the art diagnostic and interventional bronchoscopy. CT-to-body divergence is a critical hurdle in ENB, causing navigation error and ultimately limiting the clinical efficacy of diagnosis and treatment. In this study, Visually Navigated Bronchoscopy (VNB) is proposed to address the aforementioned issue of CT-to-body divergence. [Materials and Methods] We extended and validated an unsupervised learning method to generate a depth map directly from bronchoscopic images using a Three Cycle-Consistent Generative Adversarial Network (3cGAN) and registering the depth map to preprocedural CTs. We tested the working hypothesis that the proposed VNB can be integrated to the navigated bronchoscopic system based on 3D Slicer, and accurately register bronchoscopic images to pre-procedural CTs to navigate transbronchial biopsies. The quantitative metrics to asses the hypothesis we set was Absolute Tracking Error (ATE) of the tracking and the Target Registration Error (TRE) of the total navigation system. We validated our method on phantoms produced from the pre-procedural CTs of five patients who underwent ENB and on two ex-vivo pig lung specimens. [Results] The ATE using 3cGAN was 6.2 +/- 2.9 [mm]. The ATE of 3cGAN was statistically significantly lower than that of cGAN, particularly in the trachea and lobar bronchus (p < 0.001). The TRE of the proposed method had a range of 11.7 to 40.5 [mm]. The TRE computed by 3cGAN was statistically significantly smaller than those computed by cGAN in two of the five cases enrolled (p < 0.05). [Conclusion] VNB, using 3cGAN to generate the depth maps was technically and clinically feasible. While the accuracy of tracking by cGAN was acceptable, the TRE warrants further investigation and improvement.
Steinmann S, Lyall AE, Langhein M, Nägele FL, Rauh J, Cetin-Karayumak S, Zhang F, Mussmann M, Billah T, Makris N, et al. Sex-Related Differences in White Matter Asymmetry and Its Implications for Verbal Working Memory in Psychosis High-Risk State. Front Psychiatry. 2021;12 :686967.Abstract
Objective: Sexual dimorphism has been investigated in schizophrenia, although sex-specific differences among individuals who are at clinical high-risk (CHR) for developing psychosis have been inconclusive. This study aims to characterize sexual dimorphism of language areas in the brain by investigating the asymmetry of four white matter tracts relevant to verbal working memory in CHR patients compared to healthy controls (HC). HC typically show a leftward asymmetry of these tracts. Moreover, structural abnormalities in asymmetry and verbal working memory dysfunctions have been associated with neurodevelopmental abnormalities and are considered core features of schizophrenia. Methods: Twenty-nine subjects with CHR (17 female/12 male) for developing psychosis and twenty-one HC (11 female/10 male) matched for age, sex, and education were included in the study. Two-tensor unscented Kalman filter tractography, followed by an automated, atlas-guided fiber clustering approach, were used to identify four fiber tracts related to verbal working memory: the superior longitudinal fasciculi (SLF) I, II and III, and the superior occipitofrontal fasciculus (SOFF). Using fractional anisotropy (FA) of tissue as the primary measure, we calculated the laterality index for each tract. Results: There was a significantly greater right>left asymmetry of the SLF-III in CHR females compared to HC females, but no hemispheric difference between CHR vs. HC males. Moreover, the laterality index of SLF-III for CHR females correlated negatively with Backward Digit Span performance, suggesting a greater rightward asymmetry was associated with poorer working memory functioning. Conclusion: This study suggests increased rightward asymmetry of the SLF-III in CHR females. This finding of sexual dimorphism in white matter asymmetry in a language-related area of the brain in CHR highlights the need for a deeper understanding of the role of sex in the high-risk state. Future work investigating early sex-specific pathophysiological mechanisms, may lead to the development of novel personalized treatment strategies aimed at preventing transition to a more chronic and difficult-to-treat disorder.
Langbein BJ, Szczepankiewicz F, Westin C-F, Bay C, Maier SE, Kibel AS, Tempany CM, Fennessy FM. A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer. Invest Radiol. 2021;56 (12) :845-53.Abstract
OBJECTIVES: The objectives of this exploratory study were to investigate the feasibility of multidimensional diffusion magnetic resonance imaging (MddMRI) in assessing diffusion heterogeneity at both a macroscopic and microscopic level in prostate cancer (PCa). MATERIALS AND METHODS: Informed consent was obtained from 46 subjects who underwent 3.0-T prostate multiparametric MRI, complemented with a prototype spin echo-based MddMRI sequence in this institutional review board-approved study. Prostate cancer tumors and comparative normal tissue from each patient were contoured on both apparent diffusion coefficient and MddMRI-derived mean diffusivity (MD) maps (from which microscopic diffusion heterogeneity [MKi] and microscopic diffusion anisotropy were derived) using 3D Slicer. The discriminative ability of MddMRI-derived parameters to differentiate PCa from normal tissue was determined using the Friedman test. To determine if tumor diffusion heterogeneity is similar on macroscopic and microscopic scales, the linear association between SD of MD and mean MKi was estimated using robust regression (bisquare weighting). Hypothesis testing was 2 tailed; P values less than 0.05 were considered statistically significant. RESULTS: All MddMRI-derived parameters could distinguish tumor from normal tissue in the fixed-effects analysis (P < 0.0001). Tumor MKi was higher (P < 0.05) compared with normal tissue (median, 0.40; interquartile range, 0.29-0.52 vs 0.20-0.18; 0.25), as was tumor microscopic diffusion anisotropy (0.55; 0.36-0.81 vs 0.20-0.15; 0.28). The MKi could not be predicted (no significant association) by SD of MD. There was a significant correlation between tumor volume and SD of MD (R2 = 0.50, slope = 0.008 μm2/ms per millimeter, P < 0.001) but not between tumor volume and MKi. CONCLUSIONS: This explorative study demonstrates that MddMRI provides novel information on MKi and microscopic anisotropy, which differ from measures at the macroscopic level. MddMRI has the potential to characterize tumor tissue heterogeneity at different spatial scales.
Bastos DCDA, Juvekar P, Tie Y, Jowkar N, Pieper S, Wells WM, Bi WL, Golby A, Frisken S, Kapur T. Challenges and Opportunities of Intraoperative 3D Ultrasound With Neuronavigation in Relation to Intraoperative MRI. Front Oncol. 2021;11 :656519.Abstract
Introduction: Neuronavigation greatly improves the surgeons ability to approach, assess and operate on brain tumors, but tends to lose its accuracy as the surgery progresses and substantial brain shift and deformation occurs. Intraoperative MRI (iMRI) can partially address this problem but is resource intensive and workflow disruptive. Intraoperative ultrasound (iUS) provides real-time information that can be used to update neuronavigation and provide real-time information regarding the resection progress. We describe the intraoperative use of 3D iUS in relation to iMRI, and discuss the challenges and opportunities in its use in neurosurgical practice. Methods: We performed a retrospective evaluation of patients who underwent image-guided brain tumor resection in which both 3D iUS and iMRI were used. The study was conducted between June 2020 and December 2020 when an extension of a commercially available navigation software was introduced in our practice enabling 3D iUS volumes to be reconstructed from tracked 2D iUS images. For each patient, three or more 3D iUS images were acquired during the procedure, and one iMRI was acquired towards the end. The iUS images included an extradural ultrasound sweep acquired before dural incision (iUS-1), a post-dural opening iUS (iUS-2), and a third iUS acquired immediately before the iMRI acquisition (iUS-3). iUS-1 and preoperative MRI were compared to evaluate the ability of iUS to visualize tumor boundaries and critical anatomic landmarks; iUS-3 and iMRI were compared to evaluate the ability of iUS for predicting residual tumor. Results: Twenty-three patients were included in this study. Fifteen patients had tumors located in eloquent or near eloquent brain regions, the majority of patients had low grade gliomas (11), gross total resection was achieved in 12 patients, postoperative temporary deficits were observed in five patients. In twenty-two iUS was able to define tumor location, tumor margins, and was able to indicate relevant landmarks for orientation and guidance. In sixteen cases, white matter fiber tracts computed from preoperative dMRI were overlaid on the iUS images. In nineteen patients, the EOR (GTR or STR) was predicted by iUS and confirmed by iMRI. The remaining four patients where iUS was not able to evaluate the presence or absence of residual tumor were recurrent cases with a previous surgical cavity that hindered good contact between the US probe and the brainsurface. Conclusion: This recent experience at our institution illustrates the practical benefits, challenges, and opportunities of 3D iUS in relation to iMRI.
Meyer A, Mehrtash A, Rak M, Bashkanov O, Langbein B, Ziaei A, Kibel AS, Tempany CM, Hansen C, Tokuda J. Domain Adaptation for Segmentation of Critical Structures for Prostate Cancer Therapy. Sci Rep. 2021;11 (1) :11480.Abstract
Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model's performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ([Formula: see text] for all structures using a Wilcoxon's signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method's generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted.
Zhou H, Jayender J. Real-Time Nonrigid Mosaicking of Laparoscopy Images. IEEE Trans Med Imaging. 2021;40 (6) :1726-36.Abstract
The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time. The key algorithm of this 2D non-rigid SLAM system is the expectation maximization and dual quaternion (EMDQ) algorithm, which can generate smooth and dense deformation field from sparse and noisy image feature matches in real-time. An uncertainty-based loop closing method has been proposed to reduce the accumulative errors. To achieve real-time performance, both CPU and GPU parallel computation technologies are used for dense mosaicking of all pixels. Experimental results on in vivo and synthetic data demonstrate the feasibility and accuracy of our non-rigid mosaicking method.
Madore B, Preiswerk F, Bredfeldt JS, Zong S, Cheng C-C. Ultrasound-based Sensors to Monitor Physiological Motion. Med Phys. 2021;48 (7) :3614-22.Abstract
PURPOSE: Medical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as "organ configuration motion" (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing-related signals. METHODS: By design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from magnetic resonance imaging (MRI) and optical tracking signals in five and eight volunteers, respectively. RESULTS: Breathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively. CONCLUSION: The present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.
Chaudhuri S, Fowler MJ, Baker C, Stopka SA, Regan MS, Sablatura L, Broughton CW, Knight BE, Stabenfeldt SE, Agar NYR, et al. β-Cyclodextrin-poly (β-Amino Ester) Nanoparticles Are a Generalizable Strategy for High Loading and Sustained Release of HDAC Inhibitors. ACS Appl Mater Interfaces. 2021;13 (18) :20960-73.Abstract
Therapeutic development of histone deacetylase inhibitors (HDACi) has been hampered by a number of barriers to drug delivery, including poor solubility and inadequate tissue penetration. Nanoparticle encapsulation could be one approach to improve the delivery of HDACi to target tissues; however, effective and generalizable loading of HDACi within nanoparticle systems remains a long-term challenge. We hypothesized that the common terminally ionizable moiety on many HDACi molecules could be capitalized upon for loading in polymeric nanoparticles. Here, we describe the simple, efficient formulation of a novel library of β-cyclodextrin-poly (β-amino ester) networks (CDN) to achieve this goal. We observed that network architecture was a critical determinant of CDN encapsulation of candidate molecules, with a more hydrophobic core enabling effective self-assembly and a PEGylated surface enabling high loading (up to ∼30% w/w), effective self-assembly of the nanoparticle, and slow release of drug into aqueous media (up to 24 days) for the model HDACi panobinostat. We next constructed a library of CDNs to encapsulate various small, hydrophobic, terminally ionizable molecules (panobinostat, quisinostat, dacinostat, givinostat, bortezomib, camptothecin, nile red, and cytarabine), which yielded important insights into the structural requirements for effective drug loading and CDN self-assembly. Optimized CDN nanoparticles were taken up by GL261 cells in culture and a released panobinostat was confirmed to be bioactive. Panobinostat-loaded CDNs were next administered by convection-enhanced delivery (CED) to mice bearing intracranial GL261 tumors. These studies confirm that CDN encapsulation enables a higher deliverable dose of drug to effectively slow tumor growth. Matrix-assisted laser desorption/ionization (MALDI) analysis on tissue sections confirms higher exposure of tumor to drug, which likely accounts for the therapeutic effects. Taken in sum, these studies present a novel nanocarrier platform for encapsulation of HDACi via both ionic and hydrophobic interactions, which is an important step toward better treatment of disease via HDACi therapy.

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