The ribs unfolded - a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients.

OBJECTIVE: To assess a radiologist's detection rate of rib fractures in trauma CT when reading curved planar reformats (CPRs) of the ribs compared to reading standard MPRs. METHODS: Two hundred and twenty trauma CTs (146 males, 74 females) were retrospectively subjected to a software algorithm to generate CPRs of the ribs. Patients were split into two equal groups. Sixteen patients were excluded due to insufficient segmentation, leaving 107 patients in group A and 97 patients in group B. Two radiologists independently evaluated group A using CPRs and group B using standard MPRs. Two different radiologists reviewed both groups with the inverse methods setting. Results were compared to a standard of reference created by two senior radiologists. RESULTS: The reference standard identified 361 rib fractures in 61 patients. Reading CPRs showed a significantly higher overall sensitivity (P < 0.001) for fracture detection than reading standard MPRs, with 80.9% (584/722) and 71.5% (516/722), respectively. Mean reading time was significantly shorter for CPRs (31.3 s) compared to standard MPRs (60.7 s; P < 0.001). CONCLUSION: Using CPRs for the detection of rib fractures accelerates the reading of trauma patient chest CTs, while offering an increased overall sensitivity compared to conventional standard MPRs. KEY POINTS: • In major blunt trauma, rib fractures are diagnosed with Computed Tomography. • Image processing can unfold all ribs into a single plane. • Unfolded ribs can be read twice as fast as axial images. • Unfolding the ribs allows a more accurate diagnosis of rib fractures.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Strategies for brain shift evaluation.

For the analysis of the brain shift phenomenon different strategies were applied. In 32 glioma cases pre- and intraoperative MR datasets were acquired in order to evaluate the maximum displacement of the brain surface and the deep tumor margin. After rigid registration using the software of the neuronavigation system, a direct comparison was made with 2D- and 3D visualizations. As a result, a great variability of the brain shift was observed ranging up to 24 mm for cortical displacement and exceeding 3 mm for the deep tumor margin in 66% of all cases. Following intraoperative imaging the neuronavigation system was updated in eight cases providing reliable guidance. For a more comprehensive analysis a voxel-based nonlinear registration was applied. Aiming at improved speed of alignment we performed all interpolation operations with 3D texture mapping based on OpenGL functions supported in graphics hardware. Further acceleration was achieved with an adaptive refinement of the underlying control point grid focusing on the main deformation areas. For a quick overview the registered datasets were evaluated with different 3D visualization approaches. Finally, the results were compared to the initial measurements contributing to a better understanding of the brain shift phenomenon. Overall, the experiments clearly demonstrate that deformations of the brain surface and deeper brain structures are uncorrelated.

Remote computing environment compensating for brain shift.

OBJECTIVE: Anatomical and functional image data become invalid during an operation due to brain shift. Compensation is achieved by using intraoperative imaging to update anatomical information. To accelerate the registration and visualization of pre- and intraoperative image data, the presented work focuses on remote computing capabilities. The underlying framework efficiently combines local desktop computers and remote high-end graphics workstations exploiting expensive hardware. METHODS: By performing all computations on the remote computer, the MR volumes are rigidly aligned via voxel-based registration. Using graphics hardware for acceleration, all interpolation operations are performed with 3D texture-mapping hardware. A new approach then transforms functional markers from preoperative measurements to the intraoperative situation using an automatic tracking algorithm to identify corresponding sulci. Communicating Java viewers are suggested for analyzing the results interactively on a local computer, with all calculations being performed exclusively on the remote computer. RESULTS: The suggested approach was successfully applied in 5 cases using MR data containing functional markers of MEG and fMRI measurements identifying eloquent brain areas. Remote large-scale graphics hardware was thereby efficiently made available for fast registration and interactive direct volume rendering in neurosurgery. CONCLUSION: Overall, the presented framework demonstrates efficient access of expensive high-end hardware remotely controlled by thin clients, and further emphasizes the need to compensate for brain shift in functional neuronavigation.

Fast and adaptive finite element approach for modeling brain shift.

OBJECTIVE: In this paper we introduce a finite element-based strategy for simulation of brain deformation occurring during neurosurgery. The phenomenon, known as brain shift, causes a decrease in the accuracy of neuronavigation systems that rely on preoperatively acquired data. This can be compensated for with a computational model of the brain deformation process. By applying model calculations to preoperative images, an update within the operating room can be performed. METHODS: One of the crucial concerns in the context of developing a physical-based model is the choice of governing equations describing the physics of the phenomenon. In this work, deformation of brain tissue is expressed in terms of a 3D consolidation model for a linearly elastic and porous fluid. The next crucial issue is ensuring stable calculations within the chosen model. For this purpose, we developed a special technique for generating the underlying geometry for the simulation. With this technique an unstructured grid consisting of regular tetrahedra is created, whereupon time-dependent finite element simulation is performed in an adaptive manner. RESULTS: We applied our algorithm to preoperative MR scans and investigated the value of the method. Due to the adaptivity of the method, only 5-10% of the computing time was needed as compared to traditional finite element approaches based on a uniformly subdivided grid. The results of the experiments were compared to the corresponding intraoperative MR scans. A close match between the computed deformation of the brain and the displacement resulting from the intraoperative data was observed. CONCLUSION: A model-based approach for the simulation of brain shift is presented. In this computational model the brain tissue is described as an elastic and porous material using Biot consolidation theory. Validating experiments conducted with MR data provided promising results.

Estimating a patient surface model for optimizing the medical scanning workflow.

In this paper, we present the idea of equipping a tomographic medical scanner with a range imaging device (e.g. a 3D camera) to improve the current scanning workflow. A novel technical approach is proposed to robustly estimate patient surface geometry by a single snapshot from the camera. Leveraging the information of the patient surface geometry can provide significant clinical benefits, including automation of the scan, motion compensation for better image quality, sanity check of patient movement, augmented reality for guidance, patient specific dose optimization, and more. Our approach overcomes the technical difficulties resulting from suboptimal camera placement due to practical considerations. Experimental results on more than 30 patients from a real CT scanner demonstrate the robustness of our approach.

ADR--Anatomy-Driven Reformation.

Dedicated visualization methods are among the most important tools of modern computer-aided medical applications. Reformation methods such as Multiplanar Reformation or Curved Planar Reformation have evolved as useful tools that facilitate diagnostic and therapeutic work. In this paper, we present a novel approach that can be seen as a generalization of Multiplanar Reformation to curved surfaces. The main concept is to generate reformatted medical volumes driven by the individual anatomical geometry of a specific patient. This process generates flat views of anatomical structures that facilitate many tasks such as diagnosis, navigation and annotation. Our reformation framework is based on a non-linear as-rigid-as-possible volumetric deformation scheme that uses generic triangular surface meshes as input. To manage inevitable distortions during reformation, we introduce importance maps which allow controlling the error distribution and improving the overall visual quality in areas of elevated interest. Our method seamlessly integrates with well-established concepts such as the slice-based inspection of medical datasets and we believe it can improve the overall efficiency of many medical workflows. To demonstrate this, we additionally present an integrated visualization system and discuss several use cases that substantiate its benefits.

Venous tree separation in the liver: graph partitioning using a non-ising model.

Entangled tree-like vascular systems are commonly found in the body (e.g., in the peripheries and lungs). Separation of these systems in medical images may be formulated as a graph partitioning problem given an imperfect segmentation and specification of the tree roots. In this work, we show that the ubiquitous Ising-model approaches (e.g., Graph Cuts, Random Walker) are not appropriate for tackling this problem and propose a novel method based on recursive minimal paths for doing so. To motivate our method, we focus on the intertwined portal and hepatic venous systems in the liver. Separation of these systems is critical for liver intervention planning, in particular when resection is involved. We apply our method to 34 clinical datasets, each containing well over a hundred vessel branches, demonstrating its effectiveness.

Automatic contrast phase estimation in CT volumes.

We propose an automatic algorithm for phase labeling that relies on the intensity changes in anatomical regions due to the contrast agent propagation. The regions (specified by aorta, vena cava, liver, and kidneys) are first detected by a robust learning-based discriminative algorithm. The intensities inside each region are then used in multi-class LogitBoost classifiers to independently estimate the contrast phase. Each classifier forms a node in a decision tree which is used to obtain the final phase label. Combining independent classification from multiple regions in a tree has the advantage when one of the region detectors fail or when the phase training example database is imbalanced. We show on a dataset of 1016 volumes that the system correctly classifies native phase in 96.2% of the cases, hepatic dominant phase (92.2%), hepatic venous phase (96.7%), and equilibrium phase (86.4%) in 7 seconds on average.

Dose reduction for semi-automated volumetry of hepatic metastasis in MDCT studies.

OBJECTIVES: To investigate the performance of semi-automated measurements (RECIST, volume) of hepatic metastases in multidetector-row computed tomography (MDCT) under normal-dose- and simulated low-dose-protocols. MATERIALS AND METHODS: Thirty-five patients (67 +/- 13 years) with a total of 79 hepatic metastases underwent 16-MDCT (120 kv, 160 mAseff, pitch 1, 3 mm slice thickness, 2 mm reconstruction increment, B30f standard soft tissue kernel) for either initial staging or therapy monitoring. Corresponding raw data from these standard-dose scans were simulated at lower radiation doses of 80/60/40 mAseff (Somatom Noise Vers.6.1 beta, Siemens Healthcare, Forchheim, Germany). A semi-automated software tool (SyngoCT Oncology, Siemens Healthcare, Forchheim, Germany) was applied to each dose setting to evaluate size parameters (RECIST, volume). These measurements were compared by applying repeated-measures analysis of variance and displayed graphically. RESULTS: For RECIST measurements no statistically significant differences were found between standard dose (Mean RECIST diameter: 20.46 +/- 8.37 mm) and different simulated low radiation doses (80 mAseff: 20.95 +/- 8.20 mm/60 mAseff: 20.50 +/- 8.35 mm/40 mAseff: 19.95 +/- 8.16 mm): P = 0.0774.Statistically significant differences of volume quantification (P < 0.05) could be found between standard-(3.60 +/- 4.63 mL) and simulated lowest dose of 40 mAseff (3.17 +/- 4.08 mL), whereas there was no difference (P > 0.05) between 160 mAseff- and either 80 mAseff-(3.46 +/- 4.31 mL) or 60 mAseff-protocols (3.44 +/- 4.35 mL). CONCLUSIONS: Software-assisted assessment of RECIST criteria and volume demonstrated valid performances under different dose-settings in MDCT; therefore, substantial radiation dose reduction could be possible with the use of semi-automated measurements in follow-up studies.

A generic probabilistic active shape model for organ segmentation.

Probabilistic models are extensively used in medical image segmentation. Most of them employ parametric representations of densities and make idealizing assumptions, e.g., normal distribution of data. Often, such assumptions are inadequate and limit a broader application. We propose here a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric density estimates. In particular, a nearest neighbor boundary appearance model is complemented by a cascade of boosted classifiers for region information and combined with a shape model based on Parzen density estimation. Image and shape terms are integrated into a single level set equation. Our approach has been evaluated for 3-D liver segmentation using a public data base originating from a competition (http://sliver07.org). With an average surface distance of 1.0 mm and an average volume overlap error of 6.5%, it outperforms other automatic methods and provides accuracy close to interactive ones. Since no adaptions specific to liver segmentation have been made, our probabilistic active shape model can be applied to other segmentation tasks easily.

Comparison and evaluation of methods for liver segmentation from CT datasets.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.