A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model - Zhong, Xia; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose With the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to automate and accelerate the clinical imaging workflow, manage patient dose, and provide navigation assistance for computed tomography diagnostic imaging. In addition to diagnostic imaging, a patient surface model has also tremendous potential to simplify interventional imaging. For example, if the anatomy of a patient was known, a robotic angiography system could be automatically positioned such that the organ of interest is positioned in the system’s iso-center offering a good and flexible view on the underlying patient anatomy quickly and without any additional X-ray dose. Method To enable such functionality in a clinical context with sufficiently high accuracy, we present an extension of our previous patient surface model by adding internal anatomical landmarks associated with certain (main) bones of the human skeleton, in particular the spine. We also investigate different approaches to positioning of these landmarks employing CT datasets with annotated internal landmarks as training data. The general pipeline of our proposed method comprises the following steps: First, we train an active shape model using an existing avatar database and segmented CT surfaces. This stage also includes a gravity correction procedure, which accounts for shape changes due to the fact that the avatar models were obtained in standing position, while the CT data were acquired with patients in supine position. Second, we match the gravity-corrected avatar patient surface models to surfaces segmented from the CT datasets. In the last step, we derive the spatial relationships between the patient surface model and internal anatomical landmarks. Result We trained and evaluated our method using cross-validation using 20 datasets, each containing 50 internal landmarks. We further compared the performance of four different generalized linear models’ setups to describe the positioning of the internal landmarks relative to the patient surface. The best mean estimation error over all the landmarks was achieved using lasso regression with a mean error of 12.19 ± 6.98 mm. Conclusion Considering that interventional X-ray imaging systems can have detectors covering an area of about 200 mm × 266 mm (20 cm × 27 cm) at iso-center, this accuracy is sufficient to facilitate automatic positioning of the X-ray system
Keywords: Patient modeling; Anatomical landmark; Statistical shape model; Interventional X-ray Imaging

Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach - Bayer, Siming; Zhong, Xia; Fu, Weilin; Ravikumar, Nishant; Maier, Andreas (Algorithmen – Systeme – Anwendungen. Proceedings des Workshops, 2020)
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results.However, the results are difficult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result.The results show that the proposed registration network reduces the initial target registration error up to 95 %<br

An anthropomorphic deformable phantom for brain shift simulation - Bayer, Siming; Wydra, Adrian; Zhong, Xia; Ravikumar, Nishant; Strumia, Maddalena; Schaffert, Roman; Ostermeier, Martin; Fahrig, Rebecca; Maier, Andreas (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2018)
The prominent soft tissue deformation during a neurosurgical procedure, the so called brain shift phenomenon, affects the accuracy of the surgery greatly. Although the feasibility of numerous intraoperative modalities is investigated for the brain shift compensation, another state-of-the-art interventional imaging modality – C-Arm CT is rarely studied in this context. Due to the lack of clinical data, a suitable anthropomorphic deformable phantom is indispensable. The purpose of this study is to describe and determine the characteristics of a multi-modal deformable brain phantom made out of polyurethane and ceramic composite bone material. The phantom is made of six parts: skin, skull, brain, blood vessels, ventricles and an inflatable tumor. Fluid can be filled into the blood vessels, the ventricles and the tumor in order to simulate the brain shift phenomenon. Such an anthropomorphic phantom is both visible with MR and C-Arm CT scanner. In order to evaluate the clinical relevance of our phantom, we induce deformations with different degrees by in- and deflated the embedded tumor in 5 steps. Both MR and C-Arm CT (cone beam CT) images are acquired in each step. The induced deformation is estimated by performing mono-modal deformable registration with symmetric normalization method (SyN) both on MR and cone beam CT (CBCT) acquisitions. The maximum displacement estimated with MR and CBCT pair is 29.6mm and 28.1mm. This result correlates the clinical findings.<br
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.ananth_9

Automatic Multi-organ Segmentation in Dual Energy CT (DECT) with Dedicated 3D Fully Convolutional DECT Networks - Chen, Shuqing; Zhong, Xia; Hu, Shiyang; Dorn, Sabrina; Kachelriess, Marc; Lell, Michael; Maier, Andreas (Medical Physics, 2020)
Purpose Dual‐energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single‐energy CT (SECT). Recent research shows that automatic multi‐organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. Methods In the scope of this work, we proposed four three‐dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. Results Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual‐source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross‐validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual‐energy spectra and outperform deep learning for SECT. Conclusions The results of the cross‐validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.
Keywords: Multi-organ; DECT; FCN; U-Net; deep learning

Automatic Multi-Organ Segmentation in Dual Energy CT using 3D Fully Convolutional Network - Chen, Shuqing; Zhong, Xia; Hu, Shiyang; Dorn, Sabrina; Kachelriess, Marc; Lell, Michael; Maier, Andreas (MIDL, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.automa_98

Comparison of Default Patient Surface Model Estimation Methods - Zhong, Xia; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen - Systeme - Anwendungen, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.compar_5

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images - Zhong, Xia; Amrehn, Mario; Ravikumar, Nishant; Chen, Shuqing; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Scientific Reports, 2021)
In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.
CRIS-Team Scopus Importer:2021-02-26

Effects of Tissue Material Properties on X-Ray Image, Scatter and Patient Dose A Monte Carlo Simulation - Roser, Philipp; Birkhold, Annette; Zhong, Xia; Stepina, Elizaveta; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik aktuell, 2019)
With increasing patient and staff X-ray radiation awareness, many efforts have been made to develop accurate patient dose estimation methods. To date, Monte Carlo (MC) simulations are considered golden standard to simulate the interaction of X-ray radiation with matter. However, sensitivity of MC simulation results to variations in the experimental or clinical setup of image guided interventional procedures are only limited studied. In particular, the impact of patient material compositions is poorly investigated. This is mainly due to the fact, that these methods are commonly validated in phantom studies utilizing a single anthropomorphic phantom. In this study, we therefore investigate the impact of patient material parameters mapping on the outcome of MC X-ray dose simulations. A computation phantom geometry is constructed and three different commonly used material composition mappings are applied. We used the MC toolkit Geant4 to simulate X-ray radiation in an interventional setup and compared the differences in dose deposition, scatter distributions and resulting X-ray images. The evaluation shows a discrepancy between different material composition mapping up to 20% concerning directly irradiated organs. These results highlight the need for standardization of material composition mapping for MC simulations in a clinical setup.
CRIS-Team Scopus Importer:2019-05-14

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge - Maier, Andreas; Maier, Andreas; Wein, Wolfgang; Shams, Roozbeh; Kadoury, Samuel; Drobny, David; Modat, Marc; Reinertsen, Ingerid; Rivaz, Hassan; Chabanas, Matthieu; Fortin, Maryse; MacHado, Ines; et al. (IEEE Transactions on Medical Imaging, 2020)
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.
CRIS-Team Scopus Importer:2020-03-27
Keywords: brain; brain shift; MRI; Registration; tumor; ultrasound

Generation of Personalized Computational Phantoms Using Only Patient Metadata - Zhong, Xia; Strobel, Norbert; Sanders, James Chester; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.genera_4

Imitation learning network for fundus image registration using a divide-and-conquer approach - Bayer, Siming; Zhong, Xia; Fu, Weilin; Ravikumar, Nishant; Maier, Andreas (Informatik aktuell, 2020)
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results. However, the results are diffcult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result. The results show that the proposed registration network reduces the initial target registration error up to 95%.
CRIS-Team Scopus Importer:2020-04-21

Improved Semi-Automatic Basket Catheter Reconstruction from two X-Ray Views - Zhong, Xia; Hoffmann, Matthias; Strobel, Norbert; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.improv_0

Improving Generalization Capability of Multiorgan Segmentation Models Using Dual-Energy CT - Chen, Shuqing; Zhong, Xia; Dorn, Sabrina; Ravikumar, Nishant; Tao, Qinghua; Huang, Xiaolin; Lell, Michael; Kachelriess, Marc; Maier, Andreas (IEEE Transactions on Radiation and Plasma Medical Sciences, 2022)
Multiorgan segmentation in computed tomography (CT) images is essential for a variety of clinical applications. Due to variations in acquisition protocols, clinical data differ in terms of soft-tissue contrast, noise, and artifacts. Devising automatic multiorgan segmentation approaches, which are generalized to data acquired using different CT protocols, are challenging and essential when conducting any multicenter/scanner analyses. In this study, we investigate the use of dual-energy CT (DECT) images to train a fully convolutional segmentation network which is generalized to CT images acquired using different protocols (i.e., at different energy levels and using a variety of reconstruction kernels) from different CT scanners. Furthermore, a novel image fusion approach in the frequency domain is proposed and compared to state-of-the-art fusion approaches, in terms of the segmentation quality achieved by the network. Overall, the experiments indicate that the generalization capability of the segmentation network is improved using DECT image fusion. The proposed fusion method outperforms all single-energy CT approaches. It provided a significant improvement in segmentation accuracy, ranging from 16.0% to 23.35% with p ≤ 0.03. Furthermore, two image fusion methods statistically significantly improve segmentation quality in the abdominal organs compared to simply using all available DECT data.
CRIS-Team Scopus Importer:2022-01-14
Keywords: Data augmentation; deep learning; dual-energy computed tomography (DECT); multiorgan segmentation

Markerless Coil Classification and Localization in a Routine MRI Examination Setting using an RGB-D Camera - Nadar, Gayathri; Zhong, Xia; Maier, Andreas (Bildverarbeitung für die Medizin, 2018)
In a routine MRI scan, a radio-frequency (RF) coil must be selected and placed around the region of interest (ROI). This is a crucial step in the workflow as the accurate coil placement is paramount for obtaining high-quality images. However, in the existing workflow, the position of the coil placement on the patient is estimated empirically by the medical technical assistant (MTA). This routine coil placement process has two shortcomings. On the one hand, the expertise of MTA in coil placement, taking the anatomical difference between patients into account, have a huge impact on the accuracy of the coil placement, and subsequently the image quality. On the other hand, the risk of selecting and placing the incorrect coil should be also be acknowledged. To improve the current workflow and provide feedback ahead of the MRI scans, we use an RGB-D camera to acquire extra information. Using the depth images taken before and after placing the coil, we propose a novel method to classify the coil type and localize the coil position during the coil placement process such that the MTA can place the coil correctly and accurately. We trained and evaluated our method over 100 synthetic data sets. We used two types of coils and placed and deformed them differently according to the anatomical region. The evaluation shows that we can classify the coil type without any error, and localize the coil with a mean translational error of 7.1 cm and mean rotation angle error of 0.025 rad.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.marker

Patient surface model and internal anatomical landmarks embedding - Zhong, Xia; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin, 2018)
The patient surface model has shown to be a useful asset to improve existing diagnostic and interventional tasks in a clinical environment. For example, in combination with RGB-D cameras, a patient surface model can be used to automate and accelerate the diagnostic imaging workflow, manage patient dose, and provide navigation assistance. A shortcoming of today’s patient surface models, however, is that, internal anatomical landmarks are not present. In this paper, we introduce a method to estimate internal anatomical landmarks based on the surface model of a patient. Our method relies on two major steps. First, we fit a template surface model is to a segmented surface of a CT dataset with annotated internal landmarks using keypoint and feature descriptor based rigid alignment and atlas-based non-rigid registration. In a second step, we find for each internal landmark a neighborhood on the template surface and learn a generalized linear embedding between neighboring surface vertices in the template and the internal landmark. We trained and evaluated our method using cross-validation in 20 datasets over 50 internal landmarks. We compared the performance of four different generalized linear models. The best mean estimation error over all the landmarks was achieved using the lasso regression method with a mean error of 12.19 ± 6.98 mm.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.patien_2

Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation - Zhong, Xia; Roser, Philipp; Bayer, Siming; Ravikumar, Nishant; Strobel, Norbert; Birkhold, Annette; Horz, Tim; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (BVM, 2019)
Fluoroscopy is used in a wide variety of examinations and procedures to diagnose or treat patients in modern pediatric medicine. Although these image guided interventions have many advantages in treating pediatric patients, understanding the deterministic and long term stochastic effects of ionizing radiation are of particular importance for this patient demographic. Therefore, quantitative estimation and visualization of radiation exposure distribution, and dose accumulation over the course of a procedure, is crucial for intra-procedure dose tracking and long term monitoring for risk assessment. Personalized pediatric models are necessary for precise determination of patient-X-ray interactions. One way to obtain such a model is to collect data from a population of pediatric patients, establish a population based generative pediatric model and use the latter for skin dose estimation. In this paper, we generate a population model for pediatric patient using data acquired by two RGB-D cameras from different views. A generative atlas was established using template registration. We evaluated the registered templates and generative atlas by computing the mean vertex error to the associated point cloud. The evaluation results show that the mean vertex error reduced from 25.2 ± 12.9 mm using an average surface model to 18.5 ± 9.4mm using specifically estimated pediatric surface model using the generated atlas. Similarly, the dose estimation error was halved from 10.6 ± 8.5% using the average surface model to 5.9 ± 9.3% using the personalized surface estimates.<br

Physics-driven learning of x-ray skin dose distribution in interventional procedures - Roser, Philipp; Zhong, Xia; Birkhold, Annette; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Medical Physics, 2019)
Purpose: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to patients’ skin, x-ray imaging devices are equipped with online air kerma monitoring components. Traditionally, such measurements have been used to estimate skin entrance dose by (a) estimating air kerma at the interventional reference point (IRP), (b) forward projecting the dose distribution, and (c) considering a backscatter factor among other correction factors. Unfortunately, the complicated interaction between incident x-ray photons, secondary electrons, and skin tissue cannot be properly accounted for by assuming a linear relationship between forward projected air kerma and a backscatter factor. Gold standard skin dose models are therefore determined using Monte Carlo (MC) techniques. However, MC simulations are computationally complex in general and possible acceleration mainly depends on the employed hardware and variance reduction techniques. To obtain reliable and fast dose estimates, we propose to combine MC-based simulations with learning-based methods. Methods: The basic idea of our method is to approximate the radiation physics to calculate a first-order exposure estimate quickly. This initial estimate is then refined using prior knowledge derived from MC simulations. To this end, the primary photon propagation inside a voxelized patient model is estimated using a less accurate but fast photon ray casting (RC) simulation based on the Beer–Lambert law. The results of the RC simulation are then fed into a convolutional neural network (CNN), which maps the propagation of primary photons to the dose deposition inside the patient model. Additionally, the patient model itself including anatomy and material properties, such as mass density and mass energy-absorption coefficients, are fed into the CNN as well. The CNN is trained using smoothed results of MC simulations as output and RC simulations of identical imaging settings and patient models as input. Results: In total, 163 MC and associated RC simulations are carried out for the head, thorax, abdomen, and pelvis in three different voxel phantoms. We used 1e8 or 1e9 primarily emitted photons sampled from a 125 kV peak voltage spectrum, respectively. Edge-preserving smoothing (EPS) is applied to reduce (a) general stochastic uncertainties and (b) stochastic uncertainty concerning MC simulations of less primary photons. The CNN is trained using seven imaging settings of the abdomen in a single phantom. Testing its performance on the remaining datasets, the CNN is capable of estimating skin dose with an error of below 10% for the majority of test cases. Conclusion: The combination of deep neural networks and MC simulation of particle physics has the potential to decrease the computational complexity of accurate skin dose estimation. The proposed approach can provide dose distributions in under one second when running on high-end hardware. On lower cost hardware, it took up to 2 min to arrive at the same result. This makes our approach applicable in high-end environments as well as in budget solutions. Furthermore, the number of primary photons only affects the training time, while the execution time is independent of the number of primary photons.<br
Keywords: convolutional neural network; dose estimation; interventional X-ray imaging; Monte Carlo simulation

Pitfalls in interventional X-ray organ dose assessment—combined experimental and computational phantom study: application to prostatic artery embolization - Roser, Philipp; Birkhold, Annette; Zhong, Xia; Ochs, Philipp; Stepina, Elizaveta; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose With X-ray radiation protection and dose management constantly gaining interest in interventional radiology, novel procedures often undergo prospective dose studies using anthropomorphic phantoms to determine expected reference organ-equivalent dose values. Due to inherent uncertainties, such as impact of exact patient positioning, generalized geometry of the phantoms, limited dosimeter positioning options, and composition of tissue-equivalent materials, these dose values might not allow for patient-specific risk assessment. Therefore, first the aim of this study is to quantify the influence of these parameters on local X-ray dose to evaluate their relevance in the assessment of patient-specific organ doses. Second, this knowledge further enables validating a simulation approach, which allows employing physiological material models and patient-specific geometries. Methods Phantom dosimetry experiments using MOSFET dosimeters were conducted reproducing imaging scenarios in prostatic arterial embolization (PAE). Associated organ-equivalent dose of prostate, bladder, colon, and skin was determined. Dose deviation induced by possible small displacements of the patient was reproduced by moving the X-ray source. Dose deviation induced by geometric and material differences was investigated by analyzing two different commonly used phantoms. We reconstructed the experiments using Monte Carlo (MC) simulations, a reference male geometry, and different material properties to validate simulations and experiments against each other. Results Overall, MC-simulated organ dose values are in accordance with the measured ones for the majority of cases. Marginal displacements of X-ray source relative to the phantoms lead to deviations of 6–135% in organ dose values, while skin dose remains relatively constant. Regarding the impact of phantom material composition, underestimation of internal organ dose values by 12–20% is prevalent in all simulated phantoms. Skin dose, however, can be estimated with low deviation of 1–8% at least for two materials. Conclusions Prospective reference dose studies might not extend to precise patient-specific dose assessment. Therefore, online organ dose assessment tools, based on advanced patient modeling and MC methods, are desirabl
Keywords: Anthropomorphic phantom; Dosimetry; MOSFET; Monte Carlo simulation; Prostatic artery embolization

Resolve Intraoperative Brain Shift as Imitation Game - Zhong, Xia; Bayer, Siming; Ravikumar, Nishant; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (MICCAI Challenge 2018 for Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2018), 2018)
Soft tissue deformation induced by craniotomy and tissue manipulation (brain shift) limits the use of preoperative image overlay in an image-guided neurosurgery, and therefore reduces the accuracy of the surgery as a consequence. An inexpensive modality to compensate for the brain shift in real-time is Ultrasound (US). The core subject of research in this context is the non-rigid registration of preoperative MR and intraoperative US images. In this work, we propose a learning based approach to address this challenge. Resolving intraoperative brain shift is considered as an imitation game, where the optimal action (displacement) for each landmark on MR is trained with a multi-task network. The result shows a mean target error of 1.21 ± 0.55 mm.<br

Semi-Automatic Basket Catheter Reconstruction from Two X-Ray Views - Zhong, Xia; Hoffmann, Matthias; Strobel, Norbert; Maier, Andreas (Pattern Recognition, 2015)
Ablation guided by focal impulse and rotor mapping (FIRM) is a novel treatment option for atrial fibrillation, a frequent heart arrhythmia. This procedure is performed minimally invasively and at least partially under fluoroscopic guidance. It involves a basket catheter comprising 64 electrodes. The 3-D position of these electrodes is important during treatment. We propose a novel model-based method for 3-D reconstruction of this catheter using two X-ray images taken from different views. Our approach requires only little user interaction. An evaluation of the method found that the electrodes of the basket catheter can be reconstructed with a median error of 1.5 mm for phantom data and 3.4 mm for clinical data.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.semiau_8

Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning - Roser, Philipp; Zhong, Xia; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Scattered radiation is a major concern impacting X-ray image-guided procedures in two ways. First, back-scatter significantly contributes to patient (skin) dose during complicated interventions. Second, forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. While conventionally employed anti-scatter grids improve image quality by blocking X-rays, the additional attenuation due to the anti-scatter grid at the detector needs to be compensated for by a higher patient entrance dose. This also increases the room dose affecting the staff caring for the patient. For skin dose quantification, back-scatter is usually accounted for by applying pre-determined scalar back-scatter factors or linear point spread functions to a primary kerma forward projection onto a patient surface point. However, as patients come in different shapes, the generalization of conventional methods is limited. Here, we propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector as well as the back-scatter affecting the patient skin dose. Knowing the forward-scatter, we can correct X-ray projections, while a good estimate of the back-scatter component facilitates an improved skin dose assessment. To simultaneously estimate forward-scatter as well as back-scatter, we propose a multi-task approach for joint back- and forward-scatter estimation by combining X-ray physics with neural networks. We show that, in theory, highly accurate scatter estimation in both cases is possible. In addition, we identify research directions for our multi-task framework and learning-based scatter estimation in general.<br
Keywords: X-ray scatter Skin dose Multi-task learning