Evaluation of renal allograft function early after transplantation with diffusion-weighted MR imaging

To determine the inter-patient variability of apparent diffusion coefficients (ADC) and concurrent micro-circulation contributions from diffusion-weighted MR imaging (DW-MRI) in renal allografts early after transplantation, and to obtain initial information on whether these measures are altered in histologically proven acute allograft rejection (AR).

A Portable Image Overlay Projection Device for Computer-Aided Open Liver Surgery

Image overlay projection is a form of augmented reality that allows surgeons to view underlying anatomical structures directly on the patient surface. It improves intuitiveness of computer-aided surgery by removing the need for sight diversion between the patient and a display screen and has been reported to assist in 3-D understanding of anatomical structures and the identification of target and critical structures. Challenges in the development of image overlay technologies for surgery remain in the projection setup. Calibration, patient registration, view direction, and projection obstruction remain unsolved limitations to image overlay techniques. In this paper, we propose a novel, portable, and handheld-navigated image overlay device based on miniature laser projection technology that allows images of 3-D patient-specific models to be projected directly onto the organ surface intraoperatively without the need for intrusive hardware around the surgical site. The device can be integrated into a navigation system, thereby exploiting existing patient registration and model generation solutions. The position of the device is tracked by the navigation system’s position sensor and used to project geometrically correct images from any position within the workspace of the navigation system. The projector was calibrated using modified camera calibration techniques and images for projection are rendered using a virtual camera defined by the projectors extrinsic parameters. Verification of the device’s projection accuracy concluded a mean projection error of 1.3 mm. Visibility testing of the projection performed on pig liver tissue found the device suitable for the display of anatomical structures on the organ surface. The feasibility of use within the surgical workflow was assessed during open liver surgery. We show that the device could be quickly and unobtrusively deployed within the sterile environment.

A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease

OBJECT: The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients. METHODS: Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces. RESULTS: Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus. CONCLUSIONS: This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

Comparative evaluation of the My5-FU™ immunoassay and LC-MS/MS in monitoring the 5-fluorouracil plasma levels in cancer patients

BACKGROUND: Chemotherapies of solid tumors commonly include 5-fluorouracil (5-FU). With standard doses of 5-FU, substantial inter-patient variability has been observed in exposure levels and treatment response. Recently, improved outcomes in colorectal cancer patients due to pharmacokinetically guided 5-FU dosing were reported. We aimed at establishing a rapid and sensitive method for monitoring 5-FU plasma levels in cancer patients in our routine clinical practice. METHODS: Performance of the Saladax My5-FU™ immunoassay was evaluated on the Roche Cobas® Integra 800 analyzer. Subsequently, 5-FU concentrations of 247 clinical plasma samples obtained with this assay were compared to the results obtained by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and other commonly used clinical analyzers (Olympus AU400, Roche Cobas c6000, and Thermo Fisher CDx90). RESULTS: The My-FU assay was successfully validated on the Cobas Integra 800 analyzer in terms of linearity, precision, accuracy, recovery, interference, sample carryover, and dilution integrity. Method comparison between the Cobas Integra 800 and LC-MS/MS revealed a proportional bias of 7% towards higher values measured with the My5-FU assay. However, when the Cobas Integra 800 was compared to three other clinical analyzers in addition to LC-MS/MS including 50 samples representing the typical clinical range of 5-FU plasma concentrations, only a small proportional bias (≤1.6%) and a constant bias below the limit of detection was observed. CONCLUSIONS: The My5-FU assay demonstrated robust and highly comparable performance on different analyzers. Therefore, the assay is suitable for monitoring 5-FU plasma levels in routine clinical practice and may contribute to improved efficacy and safety of commonly used 5-FU-based chemotherapies.

Carriage of the PNPLA3 rs738409 C >G polymorphism confers an increased risk of non-alcoholic fatty liver disease associated hepatocellular carcinoma.

BACKGROUND & AIMS Subtle inter-patient genetic variation and environmental factors combine to determine disease progression in non-alcoholic fatty liver disease (NAFLD). Carriage of the PNPLA3 rs738409 c.444C >G minor allele (encoding the I148M variant) has been robustly associated with advanced NAFLD. Although most hepatocellular carcinoma (HCC) is related to chronic viral hepatitis or alcoholic liver disease, the incidence of NAFLD-related HCC is increasing. We examined whether rs738409 C >G was associated with HCC-risk in patients with NAFLD. METHODS PNPLA3 rs738409 genotype was determined by allelic discrimination in 100 European Caucasians with NAFLD-related HCC and 275 controls with histologically characterised NAFLD. RESULTS Genotype frequencies were significantly different between NAFLD-HCC cases (CC=28, CG=43, GG=29) and NAFLD-controls (CC=125, CG=117, GG=33) (p=0.0001). In multivariate analysis adjusted for age, gender, diabetes, BMI, and presence of cirrhosis, carriage of each copy of the rs738409 minor (G) allele conferred an additive risk for HCC (adjusted OR 2.26 [95% CI 1.23-4.14], p=0.0082), with GG homozygotes exhibiting a 5-fold [1.47-17.29], p=0.01 increased risk over CC. When compared to the UK general population (1958 British Birth Cohort, n=1476), the risk-effect was more pronounced (GC vs. CC: unadjusted OR 2.52 [1.55-4.10], p=0.0002; GG vs. CC: OR 12.19 [6.89-21.58], p<0.0001). CONCLUSIONS Carriage of the PNPLA3 rs738409 C >G polymorphism is not only associated with greater risk of progressive steatohepatitis and fibrosis but also of HCC. If validated, these findings suggest that PNPLA3 genotyping has the potential to contribute to multi-factorial patient-risk stratification, identifying those to whom HCC surveillance may be targeted.

TM6SF2 rs58542926 influences hepatic fibrosis progression in patients with non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is an increasingly common condition, strongly associated with the metabolic syndrome, that can lead to progressive hepatic fibrosis, cirrhosis and hepatic failure. Subtle inter-patient genetic variation and environmental factors combine to determine variation in disease progression. A common non-synonymous polymorphism in TM6SF2 (rs58542926 c.449 C>T, p.Glu167Lys) was recently associated with increased hepatic triglyceride content, but whether this variant promotes clinically relevant hepatic fibrosis is unknown. Here we confirm that TM6SF2 minor allele carriage is associated with NAFLD and is causally related to a previously reported chromosome 19 GWAS signal that was ascribed to the gene NCAN. Furthermore, using two histologically characterized cohorts encompassing steatosis, steatohepatitis, fibrosis and cirrhosis (combined n=1,074), we demonstrate a new association, independent of potential confounding factors (age, BMI, type 2 diabetes mellitus and PNPLA3 rs738409 genotype), with advanced hepatic fibrosis/cirrhosis. These findings establish new and important clinical relevance to TM6SF2 in NAFLD.

High-accuracy patient-to-image registration for the facilitation of image-guided robotic microsurgery on the head

Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.

Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Single fluoroscopic image based 2D/3D reconstruction of a scaled, patient-specific vertebral model

Purpose Accurate three-dimensional (3D) models of lumbar vertebrae can enable image-based 3D kinematic analysis. The common approach to derive 3D models is by direct segmentation of CT or MRI datasets. However, these have the disadvantages that they are expensive, timeconsuming and/or induce high-radiation doses to the patient. In this study, we present a technique to automatically reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image. Methods Our technique is based on a hybrid 2D/3D deformable registration strategy combining a landmark-to-ray registration with a statistical shape model-based 2D/3D reconstruction scheme. Fig. 1 shows different stages of the reconstruction process. Four cadaveric lumbar spine segments (total twelve lumbar vertebrae) were used to validate the technique. To evaluate the reconstruction accuracy, the surface models reconstructed from the lateral fluoroscopic images were compared to the associated ground truth data derived from a 3D CT-scan reconstruction technique. For each case, a surface-based matching was first used to recover the scale and the rigid transformation between the reconstructed surface model Results Our technique could successfully reconstruct 3D surface models of all twelve vertebrae. After recovering the scale and the rigid transformation between the reconstructed surface models and the ground truth models, the average error of the 2D/3D surface model reconstruction over the twelve lumbar vertebrae was found to be 1.0 mm. The errors of reconstructing surface models of all twelve vertebrae are shown in Fig. 2. It was found that the mean errors of the reconstructed surface models in comparison to their associated ground truths after iterative scaled rigid registrations ranged from 0.7 mm to 1.3 mm and the rootmean squared (RMS) errors ranged from 1.0 mm to 1.7 mm. The average mean reconstruction error was found to be 1.0 mm. Conclusion An accurate, scaled 3D reconstruction of the lumbar vertebra can be obtained from a single lateral fluoroscopic image using a statistical shape model based 2D/3D reconstruction technique. Future work will focus on applying the reconstructed model for 3D kinematic analysis of lumbar vertebrae, an extension of our previously-reported imagebased kinematic analysis. The developed method also has potential applications in surgical planning and navigation.

Real-Time Multimodal Retinal Image Registration for a Computer Assisted Laser Photocoagulation System

An algorithm for the real-time registration of a retinal video sequence captured with a scanning digital ophthalmoscope (SDO) to a retinal composite image is presented. This method is designed for a computer-assisted retinal laser photocoagulation system to compensate for retinal motion and hence enhance the accuracy, speed, and patient safety of retinal laser treatments. The procedure combines intensity and feature-based registration techniques. For the registration of an individual frame, the translational frame-to-frame motion between preceding and current frame is detected by normalized cross correlation. Next, vessel points on the current video frame are identified and an initial transformation estimate is constructed from the calculated translation vector and the quadratic registration matrix of the previous frame. The vessel points are then iteratively matched to the segmented vessel centerline of the composite image to refine the initial transformation and register the video frame to the composite image. Criteria for image quality and algorithm convergence are introduced, which assess the exclusion of single frames from the registration process and enable a loss of tracking signal if necessary. The algorithm was successfully applied to ten different video sequences recorded from patients. It revealed an average accuracy of 2.47 ± 2.0 pixels (∼23.2 ± 18.8 μm) for 2764 evaluated video frames and demonstrated that it meets the clinical requirements.

Multimodal target correction by local bone registration: a PET/CT evaluation

PET/CT guidance for percutaneous interventions allows biopsy of suspicious metabolically active bone lesions even when no morphological correlation is delineable in the CT images. Clinical use of PET/CT guidance with conventional step-by-step technique is time consuming and complicated especially in cases in which the target lesion is not shown in the CT image. Our recently developed multimodal instrument guidance system (IGS) for PET/CT improved this situation. Nevertheless, bone biopsies even with IGS have a trade-off between precision and intervention duration which is proportional to patient and personnel exposure to radiation. As image acquisition and reconstruction of PET may take up to 10 minutes, preferably only one time consuming combined PET/CT acquisition should be needed during an intervention. In case of required additional control images in order to check for possible patient movements/deformations, or to verify the final needle position in the target, only fast CT acquisitions should be performed. However, for precise instrument guidance accounting for patient movement and/or deformation without having a control PET image, it is essential to be able to transfer the position of the target as identified in the original PET/CT to a changed situation as shown in the control CT.

Femur Specific Polyaffine Model to Regularize the Log-Domain Demons Registration

Osteoarticular allograft transplantation is a popular treatment method in wide surgical resections with large defects. For this reason hospitals are building bone data banks. Performing the optimal allograft selection on bone banks is crucial to the surgical outcome and patient recovery. However, current approaches are very time consuming hindering an efficient selection. We present an automatic method based on registration of femur bones to overcome this limitation. We introduce a new regularization term for the log-domain demons algorithm. This term replaces the standard Gaussian smoothing with a femur specific polyaffine model. The polyaffine femur model is constructed with two affine (femoral head and condyles) and one rigid (shaft) transformation. Our main contribution in this paper is to show that the demons algorithm can be improved in specific cases with an appropriate model. We are not trying to find the most optimal polyaffine model of the femur, but the simplest model with a minimal number of parameters. There is no need to optimize for different number of regions, boundaries and choice of weights, since this fine tuning will be done automatically by a final demons relaxation step with Gaussian smoothing. The newly developed synthesis approach provides a clear anatomically motivated modeling contribution through the specific three component transformation model, and clearly shows a performance improvement (in terms of anatomical meaningful correspondences) on 146 CT images of femurs compared to a standard multiresolution demons. In addition, this simple model improves the robustness of the demons while preserving its accuracy. The ground truth are manual measurements performed by medical experts.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.