Variability in Manual Segmentation of Temporal Bone Structures in Cone Beam CT Images.

PURPOSE: Manual segmentation of anatomical structures is the accepted "gold standard" for labeling structures in clinical images. However, the variability in manual segmentation of temporal bone structures in CBCT images of the temporal bone has not been systematically evaluated using multiple reviewers. Therefore, we evaluated the intravariability and intervariability of manual segmentation of inner ear structures in CBCT images of the temporal bone. METHODS: Preoperative CBCTs scans of the inner ear were obtained from 10 patients who had undergone cochlear implant surgery. The cochlea, facial nerve, chorda tympani, mid-modiolar (MM) axis, and round window (RW) were manually segmented by five reviewers in two separate sessions that were at least 1 month apart. Interreviewer and intrareviewer variabilities were assessed using the Dice coefficient (DICE), volume similarity, mean Hausdorff Distance metrics, and visual review. RESULTS: Manual segmentation of the cochlea was the most consistent within and across reviewers with a mean DICE of 0.91 (SD = 0.02) and 0.89 (SD = 0.01) respectively, followed by the facial nerve with a mean DICE of 0.83 (SD = 0.02) and 0.80 (SD = 0.03), respectively. The chorda tympani had the greatest amount of reviewer variability due to its thin size, and the location of the centroid of the RW and the MM axis were also quite variable between and within reviewers. CONCLUSIONS: We observed significant variability in manual segmentation of some of the temporal bone structures across reviewers. This variability needs to be considered when interpreting the results in studies using one manual reviewer.

Further Validity Evidence for Patient-Specific Virtual Reality Temporal Bone Surgical Simulation.

OBJECTIVE: Patient-specific virtual reality (VR) simulation of cochlear implant (CI) surgery potentially enables preoperative rehearsal and planning. We aim to gather supporting validity evidence for patient-specific simulation through the analysis of virtual performance and comparison with postoperative imaging. METHODS: Prospective, multi-institutional study. Pre- and postoperative cone-beam CT scans of CI surgical patients were obtained and processed for patient-specific VR simulation. The virtual performances of five trainees and four attendings were recorded and (1) compared with volumes removed during actual surgery as determined in postoperative imaging, and (2) assessed using the Copenhagen Cochlear Implant Surgery Assessment Tool (CISAT) by two blinded raters. The volumes compared were cortical mastoidectomy, facial recess, and round window (RW) cochleostomy as well as violation of the facial nerve and chorda. RESULTS: Trainees drilled more volume in the cortical mastoidectomy and facial recess, whereas attendings drilled more volume for the RW cochleostomy and made more violations. Except for the cochleostomy, attendings removed volumes closer to that determined in postoperative imaging. Trainees achieved a higher CISAT performance score compared with attendings (22.0 vs. 18.4 points) most likely due to lack of certain visual cues. CONCLUSION: We found that there were differences in performance of trainees and attendings in patient-specific VR simulation of CI surgery as assessed by raters and in comparison with actual drilled volumes. The presented approach of volume comparison is novel and might be used for further validation of patient-specific VR simulation before clinical implementation for preoperative rehearsal in temporal bone surgery. LEVEL OF EVIDENCE: n/a Laryngoscope, 134:1403-1409, 2024.

Post-operative evaluation of computed tomography imaging following cochlear implantation.

PURPOSE: This study utilized an automated segmentation algorithm to assess the cochlear implant electrode array within the cochlea and investigate its impact on audiologic outcomes as measured by post-operative speech perception scores. Furthermore, manual evaluations of electrode placement were compared to automatic segmentation methods to determine their accuracy in predicting post-operative audiologic outcomes. MATERIALS AND METHODS: This retrospective chart review was conducted at a tertiary care referral center involving adult post-lingually deafened cochlear implant recipients implanted from 2015 to 2019. Patients with appropriate postoperative imaging and speech testing were included. Patients were excluded if non-English speaking, had a cognitive deficit, or a labyrinthine malformation. Automated and manual methods were used to analyze computed tomography (CT) scans and correlate the findings with post-operative speech perception scores and detection of electrode translocation. RESULTS: Among the 47 patients who met inclusion criteria, 15 had electrode translocations confirmed by automatic segmentation methods. Controlling for CI usage and pre-operative AzBio scores, patients with translocation exhibited significantly lower consonant-nucleus consonant (CNC) and AzBio scores at 6-months post-implantation compared to patients with ST insertions. Moreover, the number of translocated electrode contacts was significantly associated with post-operative CNC scores. Manual evaluations of electrode location were predictive but less sensitive to electrode translocations when compared with automated 3D segmentation. CONCLUSIONS: Placement of CI electrode contacts within ST without translocation into SV, leads to improved audiologic outcomes. Manual assessment of electrode placement via temporal bone CT, without 3D reconstruction, provides a less sensitive method to determine electrode placement than automated methods. LEVEL OF EVIDENCE: Level 4. LAY SUMMARY: This study investigated the impact of electrode placement on speech outcomes for cochlear implant recipients. Using advanced imaging techniques, the researchers compared automated and manual methods for evaluating electrode position and examined the relationship between electrode translocation and audiologic outcomes. The findings revealed that proper placement within the cochlea without translocation into inappropriate compartments inside the cochlea improves speech understanding. Manual evaluations were somewhat accurate but less sensitive in detecting translocations compared to automated methods, which offer more precise predictions of patient outcomes. These results contribute to our understanding of factors influencing cochlear implant success and highlight the importance of optimizing electrode placement for improved speech outcomes.

Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications.

Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellXTM robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellXTM system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.

Automating iPSC generation to enable autologous photoreceptor cell replacement therapy.

BACKGROUND: Inherited retinal degeneration is a leading cause of incurable vision loss in the developed world. While autologous iPSC mediated photoreceptor cell replacement is theoretically possible, the lack of commercially available technologies designed to enable high throughput parallel production of patient specific therapeutics has hindered clinical translation. METHODS: In this study, we describe the use of the Cell X precision robotic cell culture platform to enable parallel production of clinical grade patient specific iPSCs. The Cell X is housed within an ISO Class 5 cGMP compliant closed aseptic isolator (Biospherix XVivo X2), where all procedures from fibroblast culture to iPSC generation, clonal expansion and retinal differentiation were performed. RESULTS: Patient iPSCs generated using the Cell X platform were determined to be pluripotent via score card analysis and genetically stable via karyotyping. As determined via immunostaining and confocal microscopy, iPSCs generated using the Cell X platform gave rise to retinal organoids that were indistinguishable from organoids derived from manually generated iPSCs. In addition, at 120 days post-differentiation, single-cell RNA sequencing analysis revealed that cells generated using the Cell X platform were comparable to those generated under manual conditions in a separate laboratory. CONCLUSION: We have successfully developed a robotic iPSC generation platform and standard operating procedures for production of high-quality photoreceptor precursor cells that are compatible with current good manufacturing practices. This system will enable clinical grade production of iPSCs for autologous retinal cell replacement.

Pipeline for Automated Processing of Clinical Cone-Beam Computed Tomography for Patient-Specific Temporal Bone Simulation: Validation and Clinical Feasibility.

OBJECTIVE: Patient-specific simulation allows the surgeon to plan and rehearse the surgical approach ahead of time. Preoperative clinical imaging for this purpose requires time-consuming manual processing and segmentation of landmarks such as the facial nerve. We aimed to evaluate an automated pipeline with minimal manual interaction for processing clinical cone-beam computed tomography (CBCT) temporal bone imaging for patient-specific virtual reality (VR) simulation. STUDY DESIGN: Prospective image processing of retrospective imaging series. SETTING: Academic hospital. METHODS: Eleven CBCTs were selected based on quality and used for validation of the processing pipeline. A larger naturalistic sample of 36 CBCTs were obtained to explore parameters for successful processing and feasibility for patient-specific VR simulation.Visual inspection and quantitative metrics were used to validate the accuracy of automated segmentation compared with manual segmentation. Range of acceptable rotational offsets and translation point selection variability were determined. Finally, feasibility in relation to image acquisition quality, processing time, and suitability for VR simulation was evaluated. RESULTS: The performance of automated segmentation was acceptable compared with manual segmentation as reflected in the quantitative metrics. Total time for processing for new data sets was on average 8.3 minutes per data set; of this, it was less than 30 seconds for manual steps. Two of the 36 data sets failed because of extreme rotational offset, but overall the registration routine was robust to rotation and manual selection of a translational reference point. Another seven data sets had successful automated segmentation but insufficient suitability for VR simulation. CONCLUSION: Automated processing of CBCT imaging has potential for preoperative VR simulation but requires further refinement.

Automated Calculation of Cochlear Implant Electrode Insertion Parameters in Clinical Cone-Beam CT.

HYPOTHESIS: Automated processing of postoperative clinical cone-beam CT (CBCT) of cochlear implant (CI) patients can be used to accurately determine electrode contacts and integrated with an atlas-based mapping of cochlear microstructures to calculate modiolar distance, angular insertion distance, and scalar location of electrode contacts. BACKGROUND: Hearing outcomes after CI surgery are dependent on electrode placement. CBCT is increasingly used for in-office temporal bone imaging and might be routinely used for pre- and post-surgical evaluation. METHODS: Thirty-six matched pairs of pre- and postimplant CBCT scans were obtained. These were registered with an atlas to model cochlear microstructures in each dataset. Electrode contact center points were automatically determined using thresholding and electrode insertion parameters were calculated. Automated localization and calculation were compared with manual segmentation of contact center points as well as manufacturer specifications. RESULTS: Automated electrode contact detection aligned with manufacturer specifications of spacing and our algorithms worked for both distantly- and closely spaced arrays. The average difference between the manual and the automated selection was 0.15 mm, corresponding to a 1.875 voxel difference in each plane at the scan resolution. For each case, we determined modiolar distance, angular insertion depth, and scalar location. These calculations also resulted in similar insertion values using manual and automated contact points as well as aligning with electrode properties. CONCLUSION: Automated processing of implanted high-resolution CBCT images can provide the clinician with key information on electrode placement. This is one step toward routine use of clinical CBCT after CI surgery to inform and guide postoperative treatment.

Characterizing the Neuroimaging and Histopathological Correlates of Cerebral Small Vessel Disease in Spontaneously Hypertensive Stroke-Prone Rats.

Introduction: Spontaneously hypertensive stroke-prone rats (SHRSP) are used to model clinically relevant aspects of human cerebral small vessel disease (CSVD). To decipher and understand the underlying disease dynamics, assessment of the temporal progression of CSVD histopathological and neuroimaging correlates is essential. Materials and Methods: Eighty age-matched male SHRSP and control Wistar Kyoto rats (WKY) were randomly divided into four groups that were aged until 7, 16, 24 and 32 weeks. Sensorimotor testing was performed weekly. Brain MRI was acquired at each study time point followed by histological analyses of the brain. Results: Compared to WKY controls, the SHRSP showed significantly higher prevalence of small subcortical hyperintensities on T2w imaging that progressed in size and frequency with aging. Volumetric analysis revealed smaller intracranial and white matter volumes on brain MRI in SHRSP compared to age-matched WKY. Diffusion tensor imaging (DTI) showed significantly higher mean diffusivity in the corpus callosum and external capsule in WKY compared to SHRSP. The SHRSP displayed signs of motor restlessness compared to WKY represented by hyperactivity in sensorimotor testing at the beginning of the experiment which decreased with age. Distinct pathological hallmarks of CSVD, such as enlarged perivascular spaces, microbleeds/red blood cell extravasation, hemosiderin deposits, and lipohyalinosis/vascular wall thickening progressively accumulated with age in SHRSP. Conclusions: Four stages of CSVD severity in SHRSP are described at the study time points. In addition, we find that quantitative analyses of brain MRI enable identification of in vivo markers of CSVD that can serve as endpoints for interventional testing in therapeutic studies.

Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation.

White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical con-sequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs. However, T1-weighted images are typically not part of standard clinical scans which are acquired for patients with acute stroke. In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverages only FLAIR images with a spatially-registered white matter (WM) brain atlas to yield competitive WMH segmentation performance. Specifically, we designed a dual-path segmentation model with two novel connecting mechanisms, namely multi-input attention module (MAM) and attention fusion module (AFM) to fuse the information from two paths for accurate results. Experiments on two publicly available datasets show the effectiveness of the proposed BAGAU-Net. With only FLAIR images and WM brain atlas, BAGAU-Net outperforms the state-of-the-art method with T1-weighted images, paving the way for effective development of WMH segmentation. Availability: https://github.com/Ericzhang1/BAGAU-Net.

Patient-specific Virtual Temporal Bone Simulation Based on Clinical Cone-beam Computed Tomography.

OBJECTIVES: Patient-specific surgical simulation allows presurgical planning through three-dimensional (3D) visualization and virtual rehearsal. Virtual reality simulation for otologic surgery can be based on high-resolution cone-beam computed tomography (CBCT). This study aimed to evaluate clinicians' experience with patient-specific simulation of mastoid surgery. METHODS: Prospective, multi-institutional study. Preoperative temporal bone CBCT scans of patients undergoing cochlear implantation (CI) were retrospectively obtained. Automated processing and segmentation routines were used. Otologic surgeons performed a complete mastoidectomy with facial recess approach on the patient-specific virtual cases in the institution's temporal bone simulator. Participants completed surveys regarding the perceived accuracy and utility of the simulation. RESULTS: Twenty-two clinical CBCTs were obtained. Four attending otologic surgeons and 5 otolaryngology trainees enrolled in the study. The mean number of simulations completed by each participant was 16.5 (range 3-22). "Overall experience" and "usefulness for presurgical planning" were rated as "good," "very good," or "excellent" in 84.6% and 71.6% of the simulations, respectively. In 10.7% of simulations, the surgeon reported to have gained a significantly greater understanding of the patient's anatomy compared to standard imaging. Participants were able to better appreciate subtle anatomic findings after using the simulator for 60.4% of cases. Variable CBCT acquisition quality was the most reported limitation. CONCLUSION: Patient-specific simulation using preoperative CBCT is feasible and may provide valuable insights prior to otologic surgery. Establishing a CBCT acquisition protocol that allows for consistent segmentation will be essential for reliable surgical simulation. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:1855-1862, 2021.

Atlas-based segmentation of cochlear microstructures in cone beam CT.

PURPOSE: To develop an automated segmentation approach for cochlear microstructures [scala tympani (ST), scala vestibuli (SV), modiolus (Mod), mid-modiolus (Mid-Mod), and round window membrane (RW)] in clinical cone beam computed tomography (CBCT) images of the temporal bone for use in surgical simulation software and for preoperative surgical evaluation. METHODS: This approach was developed using the publicly available OpenEar (OE) Library that includes temporal bone specimens with spatially registered CBCT and 3D micro-slicing images. Five of these datasets were spatially aligned to our internal OSU atlas. An atlas of cochlear microstructures was created from one of the OE datasets. An affine registration of this atlas to the remaining OE CBCT images was used for automatically segmenting the cochlear microstructures. Quantitative metrics and visual review were used for validating the automatic segmentations. RESULTS: The average DICE metrics were 0.77 and 0.74 for the ST and SV, respectively. The average Hausdorff distance (AVG HD) was 0.11 mm and 0.12 mm for both scalae. The mean distance between the centroids for the round window was 0.32 mm, and the mean AVG HD was 0.09 mm. The mean distance and angular rotation between the mid-modiolar axes were 0.11 mm and 9.8 degrees, respectively. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer. CONCLUSIONS: An atlas-based approach using 3D micro-slicing data and affine spatial registration in the cochlear region was successful in segmenting cochlear microstructures of temporal bone anatomy for use in simulation software and potentially for pre-surgical planning and rehearsal.

Segmentation of Temporal Bone Anatomy for Patient-Specific Virtual Reality Simulation.

OBJECTIVES: Virtual reality (VR) simulation for patient-specific pre-surgical planning and rehearsal requires accurate segmentation of key surgical landmark structures such as the facial nerve, ossicles, and cochlea. The aim of this study was to explore different approaches to segmentation of temporal bone surgical anatomy for patient-specific VR simulation. METHODS: De-identified, clinical computed tomography imaging of 9 pediatric patients aged 3 months to 12 years were obtained retrospectively. The patients represented normal anatomy and key structures were manually segmented using open source software. The OTOPLAN (CAScination AG, Bern, Switzerland) otological planning software was used for guided segmentation. An atlas-based algorithm was used for computerized, automated segmentation. Experience with the different approaches as well as time and resulting models were compared. RESULTS: Manual segmentation was time consuming but also the most flexible. The OTOPLAN software is not designed specifically for our purpose and therefore the number of structures that can be segmented is limited, there was some user-to-user variation as well as volume differences compared with manual segmentation. The atlas-based automated segmentation potentially allows a full range of structures to be segmented and produces segmentations comparable to those of manual segmentation with a processing time that is acceptable because of the minimal user interaction. CONCLUSION: Segmentation is fundamental for patient-specific VR simulation for pre-surgical planning and rehearsal in temporal bone surgery. The automated segmentation algorithm currently offers the most flexible and feasible approach and should be implemented. Further research is needed in relation to cases of abnormal anatomy. LEVEL OF EVIDENCE: 4.

Breast Cancer Stem Cells: Biomarkers, Identification and Isolation Methods, Regulating Mechanisms, Cellular Origin, and Beyond.

Despite recent advances in diagnosis and treatment, breast cancer (BC) is still a major cause of cancer-related mortality in women. Breast cancer stem cells (BCSCs) are a small but significant subpopulation of heterogeneous breast cancer cells demonstrating strong self-renewal and proliferation properties. Accumulating evidence has proved that BCSCs are the driving force behind BC tumor initiation, progression, metastasis, drug resistance, and recurrence. As a heterogeneous disease, BC contains a full spectrum of different BC subtypes, and different subtypes of BC further exhibit distinct subtypes and proportions of BCSCs, which correspond to different treatment responses and disease-specific outcomes. This review summarized the current knowledge of BCSC biomarkers and their clinical relevance, the methods for the identification and isolation of BCSCs, and the mechanisms regulating BCSCs. We also discussed the cellular origin of BCSCs and the current advances in single-cell lineage tracing and transcriptomics and their potential in identifying the origin and lineage development of BCSCs.

Automated in-process characterization and selection of cell-clones for quality and efficient cell manufacturing.

Delivery of safe, effective and reliable cellular therapies, whether based on mesenchymal stromal cells (MSCs) or induced pluripotent stem cells (iPSCs), demand standardization of cell culture protocols. There is a need to develop automation platform that enables the users to generate culture expanded human cell populations that improves the quality and reduces batch-to-batch variation with respect to biological potential. Cell X™ robot was designed to address these current challenges in the cell fabrication industry. It utilizes non-invasive large field of view quantitative image analysis to guide an automated process of targeted "biopsy" (cells or media), "picking" (selection) of desired cells or colonies, or "weeding" (removal) of undesired cells, thus providing an unprecedented ability to acquire quantitative measurement in a complex heterogeneous cell environment "in process" and then to act on those measurements to define highly reproducible methods for cell and colony "management" based on application specific critical quality attributes to improve the quality of the manufactured cell lines and cell products.

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging.

Metastatic spread of cancer is an unfortunate consequence of disease progression, aggressive cancer subtypes, and/or late diagnosis. Brain metastases are particularly devastating, difficult to treat, and confer a poor prognosis. While the precise incidence of brain metastases in the United States remains hard to estimate, it is likely to increase as extracranial therapies continue to become more efficacious in treating cancer. Thus, it is necessary to identify and develop novel therapeutic approaches to treat metastasis at this site. To this end, intracranial injection of cancer cells has become a well-established method in which to model brain metastasis. Previously, the inability to directly measure tumor growth has been a technical hindrance to this model; however, increasing availability and quality of small animal imaging modalities, such as magnetic resonance imaging (MRI), are vastly improving the ability to monitor tumor growth over time and infer changes within the brain during the experimental period. Herein, intracranial injection of murine mammary tumor cells into immunocompetent mice followed by MRI is demonstrated. The presented injection approach utilizes isoflurane anesthesia and a stereotactic setup with a digitally controlled, automated drill and needle injection to enhance precision, and reduce technical error. MRI is measured over time using a 9.4 Tesla instrument in The Ohio State University James Comprehensive Cancer Center Small Animal Imaging Shared Resource. Tumor volume measurements are demonstrated at each time point through use of ImageJ. Overall, this intracranial injection approach allows for precise injection, day-to-day monitoring, and accurate tumor volume measurements, which combined greatly enhance the utility of this model system to test novel hypotheses on the drivers of brain metastases.

Atlas-based segmentation of temporal bone surface structures.

PURPOSE: To develop a time-efficient automated segmentation approach that could identify surface structures on the temporal bone for use in surgical simulation software and preoperative surgical training. METHODS: An atlas-based segmentation approach was developed to segment the tegmen, sigmoid sulcus, exterior auditory canal, interior auditory canal, and posterior canal wall in normal temporal bone CT images. This approach was tested in images of 20 cadaver bones (10 left, 10 right). The results of the automated segmentation were compared to manual segmentation using quantitative metrics of similarity, Mahalanobis distance, average Hausdorff distance, and volume similarity. RESULTS: The Mahalanobis distance was less than 0.232 mm for all structures. The average Hausdorff distance was less than 0.464 mm for all structures except the posterior canal wall and external auditory canal for the right bones. Volume similarity was 0.80 or greater for all structures except the sigmoid sulcus that was 0.75 for both left and right bones. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer. CONCLUSIONS: An atlas-based approach using a deformable registration of a Gaussian-smoothed temporal bone image and refinements using surface landmarks was successful in segmenting surface structures of temporal bone anatomy for use in pre-surgical planning and training.

Quantifying proliferative and surface marker heterogeneity in colony-founding connective tissue progenitors and their progeny using time-lapse microscopy.

Connective tissue progenitors (CTPs) are defined as the heterogeneous population of tissue-resident stem and progenitor cells that are capable of proliferating and differentiating into connective tissue phenotypes. The prevalence and variation in clonal progeny of CTPs can be characterized using a colony formation assay. However, colony assays do not directly assess the characteristics of the colony-founding CTP. We performed large, field-of-view, time-lapse microscopy to manually track colonies back to the founding cells. Image processing and analysis was used to characterize the colonies and their founding cells. We found that the traditional colony-forming unit (CFU) assay underestimates the number of founding cells as colonies can be formed by more than one founding cell. After 6 days in culture, colonies do not completely express CD73, CD90, and CD105. Heterogeneity in colony cells was characterized by two cell populations, proliferative and spread cells. Regression modelling of duration of lag phase and doubling time by cell marker suggests the presence of CD90 and CD105 in CTP subpopulations with different proliferative capabilities. From mathematical modelling of clonal colonies, we quantitatively characterized proliferation, migration, and cell marker expression rates to identify desirable clones for selection. Direct assessment of colony formation parameters led to more accurate assessment of CFU heterogeneity. Furthermore, these parameters can be used to quantify the diversity and hierarchy of stem and progenitor cells from a cell source or tissue for tissue engineering applications.

Human Atrial Fibrillation Drivers Resolved With Integrated Functional and Structural Imaging to Benefit Clinical Mapping.

OBJECTIVES: This study sought to improve atrial fibrillation (AF) driver identification by integrating clinical multielectrode mapping with driver fingerprints defined by high-resolution ex vivo 3-dimensional (3D) functional and structural imaging. BACKGROUND: Clinical multielectrode mapping of AF drivers suffers from variable contact, signal processing, and structural complexity within the 3D human atrial wall, raising questions on the validity of such drivers. METHODS: Sustained AF was mapped in coronary-perfused explanted human hearts (n = 11) with transmural near-infrared optical mapping (∼0.3 mm2 resolution). Simultaneously, custom FIRMap catheters (∼9 × 9 mm2 resolution) mapped endocardial and epicardial surfaces, which were analyzed by Focal Impulse and Rotor Mapping activation and Rotational Activity Profile (Abbott Labs, Chicago, Illinois). Functional maps were integrated with contrast-enhanced cardiac magnetic resonance imaging (∼0.1 mm3 resolution) analysis of 3D fibrosis architecture. RESULTS: During sustained AF, near-infrared optical mapping identified 1 to 2 intramural, spatially stable re-entrant AF drivers per heart. Driver targeted ablation affecting 2.2 ± 1.1% of the atrial surface terminated and prevented AF. Driver regions had significantly higher phase singularity density and dominant frequency than neighboring nondriver regions. Focal Impulse and Rotor Mapping had 80% sensitivity to near-infrared optical mapping-defined driver locations (16 of 20), and matched 14 of 20 driver visualizations: 10 of 14 re-entries seen with Rotational Activity Profile; and 4 of 6 breakthrough/focal patterns. Focal Impulse and Rotor Mapping detected 1.1 ± 0.9 false-positive rotational activity profiles per recording, but these regions had lower intramural contrast-enhanced cardiac magnetic resonance imaging fibrosis than did driver regions (14.9 ± 7.9% vs. 23.2 ± 10.5%; p < 0.005). CONCLUSIONS: The study revealed that both re-entrant and breakthrough/focal AF driver patterns visualized by surface-only clinical multielectrodes can represent projections of 3D intramural microanatomic re-entries. Integration of multielectrode mapping and 3D fibrosis analysis may enhance AF driver detection, thereby improving the efficacy of driver-targeted ablation.

Three-dimensional Integrated Functional, Structural, and Computational Mapping to Define the Structural "Fingerprints" of Heart-Specific Atrial Fibrillation Drivers in Human Heart Ex Vivo.

BACKGROUND: Structural remodeling of human atria plays a key role in sustaining atrial fibrillation (AF), but insufficient quantitative analysis of human atrial structure impedes the treatment of AF. We aimed to develop a novel 3-dimensional (3D) structural and computational simulation analysis tool that could reveal the structural contributors to human reentrant AF drivers. METHODS AND RESULTS: High-resolution panoramic epicardial optical mapping of the coronary-perfused explanted intact human atria (63-year-old woman, chronic hypertension, heart weight 608 g) was conducted during sinus rhythm and sustained AF maintained by spatially stable reentrant AF drivers in the left and right atrium. The whole atria (107×61×85 mm3) were then imaged with contrast-enhancement MRI (9.4 T, 180×180×360-µm3 resolution). The entire 3D human atria were analyzed for wall thickness (0.4-11.7 mm), myofiber orientations, and transmural fibrosis (36.9% subendocardium; 14.2% midwall; 3.4% subepicardium). The 3D computational analysis revealed that a specific combination of wall thickness and fibrosis ranges were primarily present in the optically defined AF driver regions versus nondriver tissue. Finally, a 3D human heart-specific atrial computer model was developed by integrating 3D structural and functional mapping data to test AF induction, maintenance, and ablation strategies. This 3D model reproduced the optically defined reentrant AF drivers, which were uninducible when fibrosis and myofiber anisotropy were removed from the model. CONCLUSIONS: Our novel 3D computational high-resolution framework may be used to quantitatively analyze structural substrates, such as wall thickness, myofiber orientation, and fibrosis, underlying localized AF drivers, and aid the development of new patient-specific treatments.

Automated MicroSPECT/MicroCT Image Analysis of the Mouse Thyroid Gland.

BACKGROUND: The ability of thyroid follicular cells to take up iodine enables the use of radioactive iodine (RAI) for imaging and targeted killing of RAI-avid thyroid cancer following thyroidectomy. To facilitate identifying novel strategies to improve 131I therapeutic efficacy for patients with RAI refractory disease, it is desired to optimize image acquisition and analysis for preclinical mouse models of thyroid cancer. METHODS: A customized mouse cradle was designed and used for microSPECT/CT image acquisition at 1 hour (t1) and 24 hours (t24) post injection of 123I, which mainly reflect RAI influx/efflux equilibrium and RAI retention in the thyroid, respectively. FVB/N mice with normal thyroid glands and TgBRAFV600E mice with thyroid tumors were imaged. In-house CTViewer software was developed to streamline image analysis with new capabilities, along with display of 3D voxel-based 123I gamma photon intensity in MATLAB. RESULTS: The customized mouse cradle facilitates consistent tissue configuration among image acquisitions such that rigid body registration can be applied to align serial images of the same mouse via the in-house CTViewer software. CTViewer is designed specifically to streamline SPECT/CT image analysis with functions tailored to quantify thyroid radioiodine uptake. Automatic segmentation of thyroid volumes of interest (VOI) from adjacent salivary glands in t1 images is enabled by superimposing the thyroid VOI from the t24 image onto the corresponding aligned t1 image. The extent of heterogeneity in 123I accumulation within thyroid VOIs can be visualized by 3D display of voxel-based 123I gamma photon intensity. CONCLUSIONS: MicroSPECT/CT image acquisition and analysis for thyroidal RAI uptake is greatly improved by the cradle and the CTViewer software, respectively. Furthermore, the approach of superimposing thyroid VOIs from t24 images to select thyroid VOIs on corresponding aligned t1 images can be applied to studies in which the target tissue has differential radiotracer retention from surrounding tissues.