A learning-based material decomposition pipeline for multi-energy x-ray imaging - Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Xia, Yan; Choi, Jang-Hwan; Chen, Shuqing; Hu, Shiyang; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Medical Physics, 2019)
PurposeBenefiting from multi-energy x-ray imaging technology, material decomposition facilitates the characterization of different materials in x-ray imaging. However, the performance of material decomposition is limited by the accuracy of the decomposition model. Due to the presence of nonideal effects in x-ray imaging systems, it is difficult to explicitly build the imaging system models for material decomposition. As an alternative, this paper explores the feasibility of using machine learning approaches for material decomposition tasks.
CRIS-Team WoS Importer:2019-03-12

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

An analytical approach for the simulation of realistic low-dose fluoroscopic images - Hariharan, Sai Gokul; Strobel, Norbert; Kaethner, Christian; Kowarschik, Markus; Fahrig, Rebecca; Navab, Nassir (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts.MethodWe introduce a novel noise simulation method based on an X-ray image formation model. The method makes use of the system parameters associated with low- and high-dose X-ray image acquisitions, such as system gain and electronic noise, to preserve the image noise characteristics of low-dose images.ResultsWe have compared several corresponding regions of the associated real and simulated low-dose imagesobtained from two different imaging systemsvisually as well as statistically, using a two-sample Kolmogorov-Smirnov test at 5% significance. In addition to being visually similar, the hypothesis that the corresponding regionsfrom 80 pairs of real and simulated low-dose regionsbelonging to the same distribution has been accepted in 81.43% of the cases.ConclusionThe results suggest that the simulated low-dose images obtained using the proposed method are almost indistinguishable from real low-dose images. Since extensive calibration procedures required in previous methods can be avoided using the proposed approach, it allows an easy adaptation to different X-ray imaging systems. This in turn leads to an increased diversity of the training data for potential learning-based methods.
CRIS-Team WoS Importer:2019-04-02

Automatic Motion Estimation and Compensation Framework forWeight-bearing C-arm CT scans using Fiducial Markers - Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Maier, Andreas; Fahrig, Rebecca (IFMBE Proceedings, 2015)
UnivIS-Import:2018-09-06:Pub.2015.tech.IMMD.IMMD5.automa_2

Bayesian optimization to design a novel x-ray shaping device - Whelan, Brendan; Trovati, Stefania; Wang, Jinghui; Fahrig, Rebecca; Maxim, Peter G.; Hanuka, Adi; Shumail, Muhammad; Tantawi, Sami; Merrick, Julian; Perl, Joseph; Keall, Paul; Loo, Billy W. (Medical Physics, 2022)
Purpose In radiation therapy, x-ray dose must be precisely sculpted to the tumor, while simultaneously avoiding surrounding organs at risk. This requires modulation of x-ray intensity in space and/or time. Typically, this is achieved using a multi leaf collimator (MLC)-a complex mechatronic device comprising over one hundred individually powered tungsten 'leaves' that move in or out of the radiation field as required. Here, an all-electronic x-ray collimation concept with no moving parts is presented, termed "SPHINX": Scanning Pencil-beam High-speed Intensity-modulated X-ray source. SPHINX utilizes a spatially distributed bremsstrahlung target and collimator array in conjunction with magnetic scanning of a high energy electron beam to generate a plurality of small x-ray "beamlets." Methods A simulation framework was developed in Topas Monte Carlo incorporating a phase space electron source, transport through user defined magnetic fields, bremsstrahlung x-ray production, transport through a SPHINX collimator, and dose in water. This framework was completely parametric, meaning a simulation could be built and run for any supplied geometric parameters. This functionality was coupled with Bayesian optimization to find the best parameter set based on an objective function which included terms to maximize dose rate for a user defined beamlet width while constraining inter-channel cross talk and electron contamination. Designs for beamlet widths of 5, 7, and 10 mm(2) were generated. Each optimization was run for 300 iterations and took approximately 40 h on a 24-core computer. For the optimized 7-mm model, a simulation of all beamlets in water was carried out including a linear scanning magnet calibration simulation. Finally, a back-of-envelope dose rate formalism was developed and used to estimate dose rate under various conditions. Results The optimized 5-, 7-, and 10-mm models had beamlet widths of 5.1 , 7.2 , and 10.1 mm(2) and dose rates of 3574, 6351, and 10 015 Gy/C, respectively. The reduction in dose rate for smaller beamlet widths is a result of both increased collimation and source occlusion. For the simulation of all beamlets in water, the scanning magnet calibration reduced the offset between the collimator channels and beam centroids from 2.9 +/- 1.9 mm to 0.01 +/- 0.03 mm. A slight reduction in dose rate of approximately 2% per degree of scanning angle was observed. Based on a back-of-envelope dose rate formalism, SPHINX in conjunction with next-generation linear accelerators has the potential to achieve substantially higher dose rates than conventional MLC-based delivery, with delivery of an intensity modulated 100 x 100 mm(2) field achievable in 0.9 to 10.6 s depending on the beamlet widths used. Conclusions Bayesian optimization was coupled with Monte Carlo modeling to generate SPHINX geometries for various beamlet widths. A complete Monte Carlo simulation for one of these designs was developed, including electron beam transport of all beamlets through scanning magnets, x-ray production and collimation, and dose in water. These results demonstrate that SPHINX is a promising candidate for sculpting radiation dose with no moving parts, and has the potential to vastly improve both the speed and robustness of radiotherapy delivery.
CRIS-Team WoS Importer:2022-08-26

Data-driven estimation of noise variance stabilization parameters for low-dose X-ray images - Hariharan, Sai Gokul; Strobel, Norbert; Kaethner, Christian; Kowarschik, Markus; Fahrig, Rebecca; Navab, Nassir (Physics in Medicine and Biology, 2020)
Purpose. Denoising X-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT). To calculate the GAT, the system gain as well as the variance of electronic noise are required. Unfortunately, these parameters are difficult to predict from the X-ray tube settings in clinical practice, because the system gain observed at the detector depends on the beam hardening caused by the patient. Materials and Methods. We propose a data-driven method for estimating the parameters required to carry out an NVS using the GAT. It utilizes the energy compaction property of the discrete cosine transform to obtain the NVS parameters using a robust regression approach relying on a linear Poisson-Gaussian model. The method has been experimentally validated with respect to beam hardening as well as denoising performance for different dose and scatter levels. Results. Across a range of low-dose X-ray settings, the proposed robust regression approach has estimated both system gain and electronic noise level with an average error of only 4.2%. When used to perform a GAT followed by the denoising of low-dose X-ray images, performance gains of 5% for peak-signal-to-noise ratio and 4% for structural similarity index can be obtained. Conclusion. The parameters needed to calculate the GAT can be estimated efficiently and robustly using a data-driven approach. The improved parameter estimation method facilitates a more accurate GAT-based NVS and, hence, better denoising of low-dose X-ray images when algorithms designed for additive Gaussian noise are applied.
CRIS-Team Scopus Importer:2020-12-11
Keywords: Low-dose X-ray imaging; Noise level function estimation; Noise variance stabilization

Epipolar consistency conditions for motion correction in weight-bearing imaging - Bier, Bastian; Aichert, André; Felsner, Lina; Unberath, Mathias; Levenstone, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin 2017 Algorithmen Systeme Anwendungen (Bildverarbeitung für die Medizin 2017), 2017)

Fast Pose Verification for High-Speed Radiation Therapy - Maier, Andreas; Westphal, Susanne; Geimer, Tobias; Maxim, Peter G.; King, Gregory; Schueler, Emil; Fahrig, Rebecca; Loo, Billy (Bildverarbeitung für die Medizin 2017: Algorithmen - Systeme - Anwendungen, 2017)
This paper discusses fast pose verification for radiation therapy on a new high-speed radiation therapy device. The PHASER system follows the idea of 4th generation CT imaging and allows fast 360° treatment using a steerable electron beam. Doing so, dose delivery is possible in few seconds. A major problem, however, is fast verification of the patient pose during treatment. In this paper, we suggest to use a projection-based approach that can be evaluated quickly and allows an accuracy below 1 mm as shown by our simulation study based on planning data from six 4D CT data sets.
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.fastpo

Feasibility of Motion Compensation using Inertial Measurement in C-arm CT - Maier, Jennifer; Aichert, André; Mehringer, Wolfgang; Bier, Bastian; Eskofier, Björn; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Bonaretti, Serena; Maier, Andreas (Conference Record of the 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2018)

Generation of synthetic Image Data for the Evaluation of Brain Shift Compensation Methods - Bayer, Siming; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.genera_1

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery - Bayer, Siming; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin (International Journal of Biomedical Imaging, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.intrao

Joint Calibration and Motion Estimation in Weight-Bearing Cone-Beam CT of the Knee Joint using Fiducial Markers - Syben-Leisner, Christopher; Bier, Bastian; Berger, Martin; Aichert, André; Fahrig, Rebecca; Gold, Garry; Levenston, Marc; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.jointc_3
Keywords: C-arm CBCT; Image Reconstruction; Knee-Joint Imaging; Motion Estimation; Self-Calibration

Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations - Hariharan, Sai Gokul; Kaethner, Christian; Strobel, Norbert; Kowarschik, Markus; Albarqouni, Shadi; Fahrig, Rebecca; Navab, Nassir (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
X-ray guidance is an integral part of interventional procedures, but the exposure to ionizing radiation poses a non-negligible threat to patients and clinical staff. Unfortunately, a reduction in the X-ray dose results in a lower signal-to-noise ratio, which may impair the quality of X-ray images. To ensure an acceptable image quality while keeping the X-ray dose as low as possible, it is common practice to use denoising techniques. However, at very low dose levels, the application of conventional denoising techniques may lead to undesirable artifacts or oversmoothing. On the other hand, supervised learning techniques have outperformed conventional techniques in producing suitable results, provided aligned pairs of associated high- and low-dose X-ray images are available. Unfortunately, it is neither acceptable nor possible to acquire such image pairs during a clinical intervention. To enable the use of learning-based methods for the denoising of X-ray images, we propose a novel strategy that involves the use of model-based simulations of low-dose X-ray images during the training phase. We utilize a data-driven normalization step that increases the robustness of the proposed approach to varying amounts of signal-dependent noise associated with different X-ray image acquisition protocols. A quantitative and qualitative analysis based on clinical and phantom data shows that the proposed strategy outperforms well-established conventional X-ray image denoising methods. It also indicates that the proposed approach allows for a significant dose reduction without sacrificing important image information.
CRIS-Team Scopus Importer:2019-12-10
Keywords: Deep learning; Low-dose X-ray image denoising; Noise simulation

Left ventricle segmentation in LGE-MRI using multiclass learning - Kurzendorfer, Tanja; Breininger, Katharina; Steidl, Stefan; Maier, Andreas; Fahrig, Rebecca (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
Cardiovascular diseases are the major cause of death worldwide. Magnetic resonance imaging (MRI) is often used for the diagnosis of cardiac diseases because of its good soft tissue contrast. Furthermore, the fibrosis characterization of the myocardium can be important for accurate diagnosis and treatment planning. The clinical gold standard to visualize myocardial scarring is late gadolinium enhanced (LGE) MRI. However, the challenge arises in the accurate segmentation of the endocardial and epicardial border because of the smooth transition between the blood pool and scarred myocardium, as contrast agent accumulates in the damaged tissue and leads to hyper-enhancements. An exact segmentation, is essential for the scar tissue quantification. We propose a deep learning-based method to segment the left ventricle's endocardium and epicardium in LGE-MRI. To this end, a multi-scale fully convolutional neural network with skip-connections (U-Net) and residual units is applied to solve the multiclass segmentation problem. As a loss function, weighted cross-entropy is used. The network is trained on 70 clinical LGE MRI sequences, validated with 5, and evaluated with 26 data sets. The approach yields a mean Dice coefficient of 0.90 for the endocard and 0.87 for the epicard. The proposed method segments the endocardium and epicardium of the left ventricle fully automatically with a high accuracy.
CRIS-Team Scopus Importer:2019-07-12
Keywords: Convolutional Neural Network; Late Gadolinium Enhanced; Left Ventricle; Segmentation

Marker-free motion correction in weight-bearing cone-beam CT of the knee joint - Berger, Martin; Müller, Kerstin; Aichert, André; Unberath, Mathias; Thies, Justus; Choi, Jang-Hwan; Fahrig, Rebecca; Maier, Andreas (Medical Physics, 2016)
Purpose: To allow for a purely image-based motion estimation and compensation in weight-bearing cone-beam computed tomography of the knee joint. Methods: Weight-bearing imaging of the knee joint in a standing position poses additional requirements for the image reconstruction algorithm. In contrast to supine scans, patient motion needs to be estimated and compensated. The authors propose a method that is based on 2D/3D registration of left and right femur and tibia segmented from a prior, motion-free reconstruction acquired in supine position. Each segmented bone is first roughly aligned to the motion-corrupted reconstruction of a scan in standing or squatting position. Subsequently, a rigid 2D/3D registration is performed for each bone to each of K projection images, estimating 6x4xK motion parameters. The motion of individual bones is combined into global motion fields using thin-plate-spline extrapolation. These can be incorporated into a motion-compensated reconstruction in the backprojection step. The authors performed visual and quantitative comparisons between a state-of-the-art marker-based (MB) method and two variants of the proposed method using gradient correlation (GC) and normalized gradient information (NGI) as similarity measure for the 2D/3D registration. Results: The authors evaluated their method on four acquisitions under different squatting positions of the same patient. All methods showed substantial improvement in image quality compared to the uncorrected reconstructions. Compared to NGI and MB, the GC method showed increased streaking artifacts due to misregistrations in lateral projection images. NGI and MB showed comparable image quality at the bone regions. Because the markers are attached to the skin, the MB method performed better at the surface of the legs where the authors observed slight streaking of the NGI and GC methods. For a quantitative evaluation, the authors computed the universal quality index (UQI) for all bone regions with respect to the motion-free reconstruction. The authors quantitative evaluation over regions around the bones yielded a mean UQI of 18.4 for no correction, 53.3 and 56.1 for the proposed method using GC and NGI, respectively, and 53.7 for the MB reference approach. In contrast to the authors registration-based corrections, the MB reference method caused slight nonrigid deformations at bone outlines when compared to a motion-free reference scan. Conclusions: The authors showed that their method based on the NGI similarity measure yields reconstruction quality close to the MB reference method. In contrast to the MB method, the proposed method does not require any preparation prior to the examination which will improve the clinical workflow and patient comfort. Further, the authors found that the MB method causes small, nonrigid deformations at the bone outline which indicates that markers may not accurately reflect the internal motion close to the knee joint. Therefore, the authors believe that the proposed method is a promising alternative to MB motion management.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.marker
Keywords: GRK-1773

Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging - Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Chen, Shuqing; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Radiation and Plasma Medical Sciences, 2018)
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.materi

Monocular multi-person pose estimation: A survey - Dos Reis, Eduardo Souza; Seewald, Lucas Adams; Antunes, Rodolfo Stoffel; Rodrigues, Vinicius Facco; Righi, Rodrigo Da Rosa; Da Costa, Cristiano Andre; Da Silveira Jr, Luiz Gonzaga; Eskofier, Björn; Maier, Andreas; Horz, Tim; Fahrig, Rebecca (Pattern Recognition, 2021)
Multi-person pose estimation in unconstrained scenarios, with an unknown number of individuals, is a main step towards scene understanding and action recognition. Due to the recent advancements on the architecture of convolutional networks, body part detectors are now accurate and estimate poses in real-time for both single-and multi-person scenes. In contrast, assigning detected body parts to coherent human poses when there are multiple persons interacting is an arduous task. To name a few of the challenges that arise in such scenes: person-to-person occlusion, truncated body parts, and more sources for double counting. Recently, the community contributed towards solving most of them. Hence, it would be interesting to analyze and compile successful approaches from current literature into research trends, and identify possible gaps for future works. To the best of our knowledge, there is no up-to-date review on the main advancements in the field that target this particular set of challenges. This survey fills this gap by reviewing the main breakthroughs on multi-person pose estimation over the last decade and summarizing their impact on the state-of-the-art. Regarding scientific contributions, we propose a novel taxonomy that categorizes the reviewed methods according to their main contributions to the pose estimation pipeline, lists the main datasets and evaluation metrics to train new models, and provides insights on the best entries of publicly available benchmarks. (c) 2021 Elsevier Ltd. All rights reserved.
CRIS-Team WoS Importer:2021-06-25
Keywords: Human pose estimation; Multi-person pose estimation; Markerless body part detectors; Human pose tracking

Motion Compensation for Cone-Beam CT using Fourier Consistency Conditions - Berger, Martin; Xia, Yan; Aichinger, Wolfgang; Mentl, Katrin; Unberath, Mathias; Aichert, André; Riess, Christian; Hornegger, Joachim; Fahrig, Rebecca; Maier, Andreas (Physics in Medicine and Biology, 2017)
Keywords: cone-beam CT; Fourier consistency conditions; motion correction; motion estimation

Noise reduction method in low-dose CT data combining neural networks and an iterative reconstruction technique - Choi, Dahim; Kim, Juhee; Chae, Seung Hoon; Baek, Jongduk; Maier, Andreas; Fahrig, Rebecca; Park, Hyun Seok; Choi, Jang Hwan (Proceedings of SPIE - The International Society for Optical Engineering, 2019)
Improving image quality from low-dose CT image and keeping diagnostic features is integral to lowering the amount of exposure to radiation and its potential risks. Noise reduction methods using deep neural network have been developed and displayed impressive performance, but there are limitations on noise remnants, blurring on high-frequency edge, and artifacts occurrence. To increase noise reduction performance and deal with those issues simultaneously, we have implemented block-based REDCNN model and applied patch-based Landweber-type iteration to images passed through REDCNN model. The model successfully smooths noise on CT images which are imposed Gaussian and Poisson noise, and outperforms noise reduction by other state-of-the-art deep neural network models. We also have tested the effect of repetition of an iterative reconstruction, changing a step size and the number of iteration.
CRIS-Team Scopus Importer:2019-05-02
Keywords: Deep Neural Networks; Landweber iteration; Low-dose CT; Noise Reduction; REDCNN

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

Precision Learning: Reconstruction Filter Kernel Discretization - Syben-Leisner, Christopher; Stimpel, Bernhard; Breininger, Katharina; Würfl, Tobias; Fahrig, Rebecca; Dörfler, Arnd; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.precis_0

Precision Learning: Towards Use of Known Operators in Neural Networks - Maier, Andreas; Schebesch, Frank; Syben-Leisner, Christopher; Würfl, Tobias; Steidl, Stefan; Choi, Jang-Hwan; Fahrig, Rebecca (2018 24rd International Conference on Pattern Recognition (ICPR), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.precis_5

Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system - Hariharan, Sai Gokul; Kaethner, Christian; Strobel, Norbert; Kowarschik, Markus; DiNitto, Julie; Albarqouni, Shadi; Fahrig, Rebecca; Navab, Nassir (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: 2D digital subtraction angiography (DSA) has become an important technique for interventional neuroradiology tasks, such as detection and subsequent treatment of aneurysms. In order to provide high-quality DSA images, usually undiluted contrast agent and a high X-ray dose are used. The iodinated contrast agent puts a burden on the patients’ kidneys while the use of high-dose X-rays expose both patients and medical staff to a considerable amount of radiation. Unfortunately, reducing either the X-ray dose or the contrast agent concentration usually results in a sacrifice of image quality. Materials and methods: To denoise a frame, the proposed spatiotemporal denoising method utilizes the low-rank nature of a spatially aligned temporal sequence where variation is introduced by the flow of contrast agent through a vessel tree of interest. That is, a constrained weighted rank-1 approximation of the stack comprising the frame to be denoised and its temporal neighbors is computed where the weights are used to prevent the contribution of non-similar pixels toward the low-rank approximation. The method has been evaluated using a vascular flow phantom emulating cranial arteries into which contrast agent can be manually injected (Vascular Simulations Replicator, Vascular Simulations, Stony Brook NY, USA). For the evaluation, image sequences acquired at different dose levels as well as different contrast agent concentrations have been used. Results: Qualitative and quantitative analyses have shown that with the proposed approach, the dose and the concentration of the contrast agent could both be reduced by about 75%, while maintaining the required image quality. Most importantly, it has been observed that the DSA images obtained using the proposed method have the closest resemblance to typical DSA images, i.e., they preserve the typical image characteristics best. Conclusion: Using the proposed denoising approach, it is possible to improve the image quality of low-dose DSA images. This improvement could enable both a reduction in contrast agent and radiation dose when acquiring DSA images, thereby benefiting patients as well as clinicians. Since the resulting images are free from artifacts and as the inherent characteristics of the images are also preserved, the proposed method seems to be well suited for clinical images as well.
CRIS-Team Scopus Importer:2019-05-14
Keywords: Digital subtraction angiography; Low-dose X-ray sequences; Spatiotemporal denoising; Weighted low-rank approximation

Preliminary Study Investigating Brain Shift Compensation using 3D CBCT Cerebral Vascular Images - Bayer, Siming; Schaffert, Roman; Ravikumar, Nishant; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin; Tong, Xiaoguang; Wang, Hu (Bildverarbeitung für die Medizin 2018, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.prelim_3

Pulse Pileup Analysis for a Double-Sided Silicon Strip Detector Using Variable Pulse Shapes - Wang, Jinghui; Chen, Linchuan; Persson, Mats; Rajbhandary, Paurakh L.; Kandlakunta, Praneeth; Carini, Gabriella; Fahrig, Rebecca (IEEE Transactions on Nuclear Science, 2019)
Due to pulse pileup, photon counting detectors (PCDs) suffer from count loss and energy distortion when operating in high count rate environments. In this paper, we studied the pulse pileup of a double-sided silicon strip detector (DSSSD) to evaluate its potential application in a mammography system. We analyzed the pulse pileup using pulses of varied shapes, where the shape of the pulse depends on the location of photon interaction within the detector. To obtain the shaped pulses, first, transient currents for photons interacting at different locations were simulated using a Technology Computer-Aided Design (TCAD) software. Next, the currents were shaped by a CR-RC2 shaping circuit, calculated using Simulink. After obtaining these pulses, both the different orders of pileup and the energy spectrum were calculated by taking into account the following two factors: 1) spatial distribution of photon interactions within the detector and 2) time interval distribution between successive photons under a given photon flux. We found that for a DSSSD with a thickness of 300 mu m, a pitch of 25 mu m, and a strip length of 1 cm, under a bias voltage of 50 V, the variable pulse shape model predicts that the fraction free of pileup can be >90% under a photon flux of 3.75 Mcps/mm(2) (million counts per second per square mm). The DSSSD is a promising candidate for digital mammography applications.
CRIS-Team WoS Importer:2019-07-09

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

Scatter Correction for C-Arm CT Using Primary Modulation - Bier, Bastian; Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Ritschl, Ludwig; Kachelriess, Marc; Fahrig, Rebecca; Maier, Andreas (Proceedings of the 4th International Conference on Image Formation in X-ray Computed Tomography, 2016)
UnivIS-Import:2018-09-11:Pub.2016.tech.IMMD.IMMD5.scatte_8

Scatter correction using a primary modulator on a clinical angiography C-arm CT system - Bier, Bastian; Berger, Martin; Maier, Andreas; Kachelriess, Marc; Ritschl, Ludwig; Müller, Kerstin; Choi, Jang-Hwan; Fahrig, Rebecca (Medical Physics, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.scatte
Keywords: C-Arm; CBCT; Primary Modulator; Scatter Correction

Science and practice of imaging physics through 50 years of SPIE Medical Imaging conferences - Wang, Adam; Cunningham, Ian; Danielsson, Mats; Fahrig, Rebecca; Flohr, Thomas; Hoeschen, Christoph; Noo, Frederic; Sabol, John M.; Siewerdsen, Jeffrey H.; Tingberg, Anders; Yorkston, John; Zhao, Wei; et al. (Journal of Medical Imaging, 2022)
Purpose: For 50 years now, SPIE Medical Imaging (MI) conferences have been the premier forum for disseminating and sharing new ideas, technologies, and concepts on the physics of MI.
CRIS-Team WoS Importer:2022-11-25

Smooth Ride: Low-Pass Filtering of Manual Segmentations Improves Consensus - Maier, Jennifer; Black, Marianne; Hall, Mary; Choi, Jang-Hwan; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas (Bildverarbeitung für die Medizin 2019, 2019)