MR imaging-guided interventions for treatment of low back pain and for diagnosis and treatment of soft tissue and bony spinal lesions have been shown to be feasible, effective, and safe. Advantages of this technique include the absence of ionizing radiation, the high tissue contrast, and multiplanar imaging options. Recent advancements in MR imaging systems allow improved image qualities and real-time guidance. One exciting application is MR imaging-guided cryotherapy of spinal lesions, including treating such lesions as benign osteoid osteomas and malignant metastatic disease in patients who are not good surgical candidates. This particular technique shows promise for local tumor control and pain relief in appropriate patients.
This paper presents a fully-actuated robotic system for percutaneous prostate therapy under continuously acquired live magnetic resonance imaging (MRI) guidance. The system is composed of modular hardware and software to support the surgical workflow of intra-operative MRI-guided surgical procedures. We present the development of a 6-degree-of-freedom (DOF) needle placement robot for transperineal prostate interventions. The robot consists of a 3-DOF needle driver module and a 3-DOF Cartesian motion module. The needle driver provides needle cannula translation and rotation (2-DOF) and stylet translation (1-DOF). A custom robot controller consisting of multiple piezoelectric motor drivers provides precision closed-loop control of piezoelectric motors and enables simultaneous robot motion and MR imaging. The developed modular robot control interface software performs image-based registration, kinematics calculation, and exchanges robot commands and coordinates between the navigation software and the robot controller with a new implementation of the open network communication protocol OpenIGTLink. Comprehensive compatibility of the robot is evaluated inside a 3-Tesla MRI scanner using standard imaging sequences and the signal-to-noise ratio (SNR) loss is limited to 15%. The image deterioration due to the present and motion of robot demonstrates unobservable image interference. Twenty-five targeted needle placements inside gelatin phantoms utilizing an 18-gauge ceramic needle demonstrated 0.87 mm root mean square (RMS) error in 3D Euclidean distance based on MRI volume segmentation of the image-guided robotic needle placement procedure.
Described here are the results from the profiling of the proteins arginine vasopressin (AVP) and adrenocorticotropic hormone (ACTH) from normal human pituitary gland and pituitary adenoma tissue sections, using a fully automated droplet-based liquid-microjunction surface-sampling-HPLC-ESI-MS-MS system for spatially resolved sampling, HPLC separation, and mass spectrometric detection. Excellent correlation was found between the protein distribution data obtained with this method and data obtained with matrix-assisted laser desorption/ionization (MALDI) chemical imaging analyses of serial sections of the same tissue. The protein distributions correlated with the visible anatomic pattern of the pituitary gland. AVP was most abundant in the posterior pituitary gland region (neurohypophysis), and ATCH was dominant in the anterior pituitary gland region (adenohypophysis). The relative amounts of AVP and ACTH sampled from a series of ACTH-secreting and non-secreting pituitary adenomas correlated with histopathological evaluation. ACTH was readily detected at significantly higher levels in regions of ACTH-secreting adenomas and in normal anterior adenohypophysis compared with non-secreting adenoma and neurohypophysis. AVP was mostly detected in normal neurohypophysis, as expected. This work reveals that a fully automated droplet-based liquid-microjunction surface-sampling system coupled to HPLC-ESI-MS-MS can be readily used for spatially resolved sampling, separation, detection, and semi-quantitation of physiologically-relevant peptide and protein hormones, including AVP and ACTH, directly from human tissue. In addition, the relative simplicity, rapidity, and specificity of this method support the potential of this basic technology, with further advancement, for assisting surgical decision-making. Graphical Abstract Mass spectrometry based profiling of hormones in human pituitary gland and tumor thin tissue sections.
Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements, a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SPArse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimaging study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications.
Performing intraoperative cardiovascular procedures inside an MR imaging scanner can potentially provide substantial advantage in clinical outcomes by reducing the risk and increasing the success rate relative to the way such procedures are performed today, in which the primary surgical guidance is provided by X-ray fluoroscopy, by electromagnetically tracked intraoperative devices, and by ultrasound. Both noninvasive and invasive cardiologists are becoming increasingly familiar with the capabilities of MR imaging for providing anatomic and physiologic information that is unequaled by other modalities. As a result, researchers began performing animal (preclinical) interventions in the cardiovascular system in the early 1990s.
OBJECTIVE. The aim of this study was to assess whether computer-assisted detection-processed MRI kinetics data can provide further information on the biologic aggressiveness of breast tumors. MATERIALS AND METHODS. We identified 194 newly diagnosed invasive breast cancers presenting as masses on contrast-enhanced MRI by a HIPAA-compliant pathology database search. Computer-assisted detection-derived data for the mean and median peak signal intensity percentage increase, most suspicious kinetic curve patterns, and volumetric analysis of the different kinetic patterns by mean percentage tumor volume were compared against the different hormonal receptor (estrogen-receptor [ER], progesterone-receptor [PR], ERRB2 (HER2/neu), and triple-receptor expressivity) and histologic grade subgroups, which were used as indicators of tumor aggressiveness. RESULTS. The means and medians of the peak signal intensity percentage increase were higher in ER-negative, PR-negative, and triple-negative (all p ≤ 0.001), and grade 3 tumors (p = 0.011). Volumetric analysis showed higher mean percentage volume of rapid initial enhancement in biologically more aggressive ER-negative, PR-negative, and triple-negative tumors compared with ER-positive (64% vs 53.6%, p = 0.013), PR-positive (65.4% vs 52.5%, p = 0.001), and nontriple-negative tumors (65.3% vs 54.6%, p = 0.028), respectively. A higher mean percentage volume of rapid washout component was seen in ERRB2-positive tumors compared with ERRB2-negative tumors (27.5% vs 17.9%, p = 0.020). CONCLUSION. Peak signal intensity percentage increase and volume analysis of the different kinetic patterns of breast tumors showed correlation with hormonal receptor and histologic grade indicators of cancer aggressiveness. Computer-assisted detection-derived MRI kinetics data have the potential to further characterize the aggressiveness of an invasive cancer.
Magnetic Resonance (MR) imaging provides excellent image quality at a high cost and low frame rate. Ultrasound (US) provides poor image quality at a low cost and high frame rate. We propose an instance-based learning system to obtain the best of both worlds: high quality MR images at high frame rates from a low cost single-element US sensor. Concurrent US and MRI pairs are acquired during a relatively brief offine learning phase involving the US transducer and MR scanner. High frame rate, high quality MR imaging of respiratory organ motion is then predicted from US measurements, even after stopping MRI acquisition, using a probabilistic kernel regression framework. Experimental results show predicted MR images to be highly representative of actual MR images.
BACKGROUND: To evaluate the information gain by the application of both non-contrast and contrast enhanced computed tomography (CT) with extended mediastinal display window settings in the evaluation of pure ground glass nodules (pGGNs) and or mixed ground glass nodules (mGGNs) in the context of pre-invasive or early stage lung adenocarcinoma. METHODS: One hundred and fifty patients with ground glass nodules (GGNs) and mGGNs, with contrast enhanced CT scans within 2 weeks of thoracic surgery were included in the study. Quantitative evaluation of all nodules was performed in a conventional mediastinal window (CMW) and an extended mediastinal window (EMW) both on non-contrast images and contrast-enhanced images. RESULTS: Contrast-enhanced images with CMW demonstrated amplification of solid portion in 23 (43%), 41 (77%) with EMW out of 53 minimally invasive adenocarcinoma (MIA) nodules, and in 34 of 37 (91%) of invasive adenocarcinoma (IAC) nodules. Using the increase in size of solid portion of the nodule measured on the enhanced CT images with EMW, area under the receiver operating characteristic (ROC) curve of 0.872 and 0.899 was utilized for differentiating between the pre-invasive nodules and MIA and between MIA and IAC nodules, respectively. Statistically significant differences existed between the pre-invasive and the MIA groups, and MIA and the IAC groups in smaller nodules (P<0.01). CONCLUSIONS: Comparative quantitative analysis of the pre and post contrast images can help differentiate between atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), MIAs, and IACs. Extension of the CT mediastinal window setting improves the evaluation of small GGNs, and can augment the diagnostic accuracy when evaluating small pGGNs and mGGNs.
BACKGROUND: Skull base tumors frequently encase or invade adjacent normal neurovascular structures. For this reason, optimal tumor resection with incomplete knowledge of patient anatomy remains a challenge. METHODS: To determine the accuracy and utility of image-based preoperative segmentation in skull base tumor resections, we performed a prospective study. Ten patients with skull base tumors underwent preoperative 3T magnetic resonance imaging, which included thin section three-dimensional (3D) space T2, 3D time of flight, and magnetization-prepared rapid acquisition gradient echo sequences. Imaging sequences were loaded in the neuronavigation system for segmentation and preoperative planning. Five different neurovascular landmarks were identified in each case and measured for accuracy using the neuronavigation system. Each segmented neurovascular element was validated by manual placement of the navigation probe, and errors of localization were measured. RESULTS: Strong correspondence between image-based segmentation and microscopic view was found at the surface of the tumor and tumor-normal brain interfaces in all cases. The accuracy of the measurements was 0.45 ± 0.21 mm (mean ± standard deviation). This information reassured the surgeon and prevented vascular injury intraoperatively. Preoperative segmentation of the related cranial nerves was possible in 80% of cases and helped the surgeon localize involved cranial nerves in all cases. CONCLUSION: Image-based preoperative vascular and neural element segmentation with 3D reconstruction is highly informative preoperatively and could increase the vigilance of neurosurgeons for preventing neurovascular injury during skull base surgeries. Additionally, the accuracy found in this study is superior to previously reported measurements. This novel preliminary study is encouraging for future validation with larger numbers of patients.
BACKGROUND: To develop a technique to noninvasively estimate stroke volume in real time during magnetic resonance imaging (MRI)-guided procedures, based on induced magnetohydrodynamic voltages (VMHD) that occur in ECG recordings during MRI exams, leaving the MRI scanner free to perform other imaging tasks. Because of the relationship between blood flow (BF) and VMHD, we hypothesized that a method to obtain stroke volume could be derived from extracted VMHD vectors in the vectorcardiogram (VCG) frame of reference (VMHDVCG).
METHODS AND RESULTS: To estimate a subject-specific BF-VMHD model, VMHDVCG was acquired during a 20-s breath-hold and calibrated versus aortic BF measured using phase-contrast magnetic resonance in 10 subjects (n=10) and 1 subject diagnosed with premature ventricular contractions. Beat-to-beat validation of VMHDVCG-derived BF was performed using real-time phase-contrast imaging in 7 healthy subjects (n=7) during 15-minute cardiac exercise stress tests and 30 minutes after stress relaxation in 3T MRIs. Subject-specific equations were derived to correlate VMHDVCG with BF at rest and validated using real-time phase-contrast. An average error of 7.22% and 3.69% in stroke volume estimation, respectively, was found during peak stress and after complete relaxation. Measured beat-to-beat BF time history derived from real-time phase-contrast and VMHD was highly correlated using a Spearman rank correlation coefficient during stress tests (0.89) and after stress relaxation (0.86).
CONCLUSIONS: Accurate beat-to-beat stroke volume and BF were estimated using VMHDVCG extracted from intra-MRI 12-lead ECGs, providing a means to enhance patient monitoring during MR imaging and MR-guided interventions.
Contrast-enhanced breast MR imaging is increasingly being used to diagnose breast cancer and to perform biopsy procedures. The American Cancer Society has advised women at high risk for breast cancer to have breast MR imaging screening as an adjunct to screening mammography. This article places special emphasis on biopsy and operative planning involving MR imaging and reviews use of breast MR imaging in monitoring response to neoadjuvant chemotherapy. Described are peer-reviewed data on currently accepted MR imaging-guided procedures for addressing benign and malignant breast diseases, including intraoperative imaging.
Several advantages of MR imaging compared with other imaging modalities have provided the rationale for increased attention to MR-guided interventions, including its excellent soft tissue contrast, its capability to show both anatomic and functional information, and no use of ionizing radiation. An important aspect of MR-guided intervention is to provide visualization and navigation of interventional devices relative to the surrounding tissues. This article focuses on the methods for MR-guided active tracking in catheter-based interventions. Practical issues about implementation of active catheter tracking in a clinical setting are discussed and several current application examples are highlighted.
PURPOSE: In gynecologic cancers, magnetic resonance (MR) imaging is the modality of choice for visualizing tumors and their surroundings because of superior soft-tissue contrast. Real-time MR guidance of catheter placement in interstitial brachytherapy facilitates target coverage, and would be further improved by providing intraprocedural estimates of dosimetric coverage. A major obstacle to intraprocedural dosimetry is the time needed for catheter trajectory reconstruction. Herein the authors evaluate an active MR tracking (MRTR) system which provides rapid catheter tip localization and trajectory reconstruction. The authors assess the reliability and spatial accuracy of the MRTR system in comparison to standard catheter digitization using magnetic resonance imaging (MRI) and CT. METHODS: The MRTR system includes a stylet with microcoils mounted on its shaft, which can be inserted into brachytherapy catheters and tracked by a dedicated MRTR sequence. Catheter tip localization errors of the MRTR system and their dependence on catheter locations and orientation inside the MR scanner were quantified with a water phantom. The distances between the tracked tip positions of the MRTR stylet and the predefined ground-truth tip positions were calculated for measurements performed at seven locations and with nine orientations. To evaluate catheter trajectory reconstruction, fifteen brachytherapy catheters were placed into a gel phantom with an embedded catheter fixation framework, with parallel or crossed paths. The MRTR stylet was then inserted sequentially into each catheter. During the removal of the MRTR stylet from within each catheter, a MRTR measurement was performed at 40 Hz to acquire the instantaneous stylet tip position, resulting in a series of three-dimensional (3D) positions along the catheter's trajectory. A 3D polynomial curve was fit to the tracked positions for each catheter, and equally spaced dwell points were then generated along the curve. High-resolution 3D MRI of the phantom was performed followed by catheter digitization based on the catheter's imaging artifacts. The catheter trajectory error was characterized in terms of the mean distance between corresponding dwell points in MRTR-generated catheter trajectory and MRI-based catheter digitization. The MRTR-based catheter trajectory reconstruction process was also performed on three gynecologic cancer patients, and then compared with catheter digitization based on MRI and CT. RESULTS: The catheter tip localization error increased as the MRTR stylet moved further off-center and as the stylet's orientation deviated from the main magnetic field direction. Fifteen catheters' trajectories were reconstructed by MRTR. Compared with MRI-based digitization, the mean 3D error of MRTR-generated trajectories was 1.5 ± 0.5 mm with an in-plane error of 0.7 ± 0.2 mm and a tip error of 1.7 ± 0.5 mm. MRTR resolved ambiguity in catheter assignment due to crossed catheter paths, which is a common problem in image-based catheter digitization. In the patient studies, the MRTR-generated catheter trajectory was consistent with digitization based on both MRI and CT. CONCLUSIONS: The MRTR system provides accurate catheter tip localization and trajectory reconstruction in the MR environment. Relative to the image-based methods, it improves the speed, safety, and reliability of the catheter trajectory reconstruction in interstitial brachytherapy. MRTR may enable in-procedural dosimetric evaluation of implant target coverage.
OBJECTIVES: This study was conducted to validate the accuracy of image-based pre-operative segmentation using the gold standard endoscopic and microscopic findings for localization and pre-operative diagnosis of the offensive vessel. PATIENTS AND METHODS: Fourteen TN and 6 HS cases were randomly selected. All patients had 3T MRI, which included thin-sectioned 3D space T2, 3D Time of Flight and MPRAGE Sequences. Imaging sequences were loaded in BrainLab iPlanNet and fused. Individual segmentation of the affected cranial nerves and the compressing vascular structure was performed by a neurosurgeon, and the results were compared with the microscopic and endoscopic findings by two blinded neurosurgeons. For each case, at least three neurovascular landmarks were targeted. Each segmented neurovascular element was validated by manual placement of the navigation probe over each target, and errors of localization were measured in mm. RESULTS: All patients underwent retro-sigmoid craniotomy and MVD using both microscope and endoscope. Based on image segmentation, the compressing vessel was identified in all cases except one, which was also negative intraoperatively. Perfect correspondence was found between image-based segmentation and endoscopic and microscopic images and videos (Dice coefficient of 1). Measurement accuracy was 0.45±0.21mm (mean±SD). CONCLUSION: Image-based segmentation is a promising method for pre-operative identification and localization of offending blood vessels causing HFS and TN. Using this method may prevent some unnecessary explorations on especially atypical cases with no vascular contacts. However, negative pre-operative image segmentation may not preclude one from exploration in classic cases of TN or HFS. A multicenter study with larger number of cases is recommended.
To enhance neuro-navigation, high quality pre-operative images must be registered onto intra-operative configuration of the brain. Therefore evaluation of the degree to which structures may remain misaligned after registration is critically important. We consider two Hausdorff Distance (HD)-based evaluation approaches: the edge-based HD (EBHD) metric and the Robust HD (RHD) metric as well as various commonly used intensity-based similarity metrics such as Mutual Information (MI), Normalised Mutual Information (NMI), Entropy Correlation Coefficient (ECC), Kullback-Leibler Distance (KLD) and Correlation Ratio (CR). We conducted the evaluation by applying known deformations to simple sample images and real cases of brain shift. We conclude that the intensity-based similarity metrics such as MI, NMI, ECC, KLD and CR do not correlate well with actual alignment errors, and hence are not useful for assessing misalignment. On the contrary, the EBHD and the RHD metrics correlated well with actual alignment errors; however, they have been found to underestimate the actual misalignment. We also note that it is beneficial to present HD results as a percentile-HD curve rather than a single number such as the 95-percentile HD. Percentile-HD curves present the full range of alignment errors and also facilitate the comparison of results obtained using different approaches. Furthermore, the qualities that should be possessed by an ideal evaluation metric were highlighted. Future studies could focus on developing such an evaluation metric.
We present a proof of concept study designed to support the clinical development of mass spectrometry imaging (MSI) for the detection of pituitary tumors during surgery. We analyzed by matrix-assisted laser desorption/ionization (MALDI) MSI six nonpathological (NP) human pituitary glands and 45 hormone secreting and nonsecreting (NS) human pituitary adenomas. We show that the distribution of pituitary hormones such as prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) in both normal and tumor tissues can be assessed by using this approach. The presence of most of the pituitary hormones was confirmed by using MS/MS and pseudo-MS/MS methods, and subtyping of pituitary adenomas was performed by using principal component analysis (PCA) and support vector machine (SVM). Our proof of concept study demonstrates that MALDI MSI could be used to directly detect excessive hormonal production from functional pituitary adenomas and generally classify pituitary adenomas by using statistical and machine learning analyses. The tissue characterization can be completed in fewer than 30 min and could therefore be applied for the near-real-time detection and delineation of pituitary tumors for intraoperative surgical decision-making.
RATIONALE AND OBJECTIVES: Assess the impact of implementing a structured report template and a computer-aided diagnosis (CAD) tool on the quality of prostate multiparametric magnetic resonance imaging (mp-MRI) reports.
MATERIALS AND METHODS: Institutional Review Board approval was obtained for this Health Insurance Portability and Accountability Act-compliant study performed at an academic medical center. The study cohort included all prostate mp-MRI reports (n = 385) finalized 6 months before and after implementation of a structured report template and a CAD tool (collectively the information technology [IT] tools) integrated into the picture archiving and communication system workstation. Primary outcome measure was quality of prostate mp-MRI reports. An expert panel of our institution's subspecialty-trained abdominal radiologists defined prostate mp-MRI report quality as optimal, satisfactory, or unsatisfactory based on documentation of nine variables. Reports were reviewed to extract the predefined quality variables and determine whether the IT tools were used to create each report. Chi-square and Student's t tests were used to compare report quality before and after implementation of IT tools.
RESULTS: The overall proportion of optimal or satisfactory reports increased from 29.8% (47/158) to 53.3% (121/227) (P < .001) after implementing the IT tools. Although the proportion of optimal or satisfactory reports increased among reports generated using at least one of the IT tools (47/158 = [29.8%] vs. 105/161 = [65.2%]; P < .001), there was no change in quality among reports generated without use of the IT tools (47/158 = [29.8%] vs. 16/66 = [24.2%]; P = .404).
CONCLUSIONS: The use of a structured template and CAD tool improved the quality of prostate mp-MRI reports compared to free-text report format and subjective measurement of contrast enhancement kinetic curve.
BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop. METHODS: Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods. RESULTS: The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues. CONCLUSIONS: The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.
This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.
We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm. We report segmentation results for abdominal organs in whole-body CT and in contrast-enhanced CT images. The accuracy of our method compares favorably to common multi-atlas segmentation while offering a speed-up of about three orders of magnitude. Furthermore, keypoint transfer requires no training phase or registration to an atlas. The algorithm's robustness enables the segmentation of scans with highly variable field-of-view.
OBJECTIVE: The purpose of this article is to report the translational process of an implantable microdevice platform with an emphasis on the technical and engineering adaptations for patient use, regulatory advances, and successful integration into clinical workflow. METHODS: We developed design adaptations for implantation and retrieval, established ongoing monitoring and testing, and facilitated regulatory advances that enabled the administration and examination of a large set of cancer therapies simultaneously in individual patients. RESULTS: Six applications for oncology studies have successfully proceeded to patient trials, with future applications in progress. CONCLUSION: First-in-human translation required engineering design changes to enable implantation and retrieval that fit with existing clinical workflows, a regulatory strategy that enabled both delivery and response measurement of up to 20 agents in a single patient, and establishment of novel testing and quality control processes for a drug/device combination product without clear precedents. SIGNIFICANCE: This manuscript provides a real-world account and roadmap on how to advance from animal proof-of-concept into the clinic, confronting the question of how to use research to benefit patients.
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.
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.
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.
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 [Formula: see text] mm, [Formula: see text] 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 ([Formula: see text]) 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.