Publications

2018
Brinker S, Preiswerk F, McDannold N. Platform for investigating spatial and intensity parameters for phase-sensitive MRI focal localization with steerable single element focused ultrasoud transducers through skull. ISMRM 26th Annual Meeting. 2018. brinker_et_al._-_2018_-_platform_for_investigating_spatial_and_intensity_p.pdf
Luo J, Frisken S, Machado I, Zhang M, Pieper S, Golland P, Toews M, Unadkat P, Sedghi A, Zhou H, et al. Using the variogram for vector outlier screening: application to feature-based image registration. International Journal of Computer Assisted Radiology and Surgery. 2018;13 (12) :1871–1880. Publisher's VersionAbstract
Purpose Matching points that are derived from features or landmarks in image data is a key step in some medical imaging applications. Since most robust point matching algorithms claim to be able to deal with outliers, users may place high confidence in the matching result and use it without further examination. However, for tasks such as feature-based registration in image-guided neurosurgery, even a few mismatches, in the form of invalid displacement vectors, could cause serious consequences. As a result, having an effective tool by which operators can manually screen all matches for outliers could substantially benefit the outcome of those applications. Methods We introduce a novel variogram-based outlier screening method for vectors. The variogram is a powerful geostatistical tool for characterizing the spatial dependence of stochastic processes. Since the spatial correlation of invalid displacement vectors, which are considered as vector outliers, tends to behave differently than normal displacement vectors, they can be efficiently identified on the variogram. Results We validate the proposed method on 9 sets of clinically acquired ultrasound data. In the experiment, potential outliers are flagged on the variogram by one operator and further evaluated by 8 experienced medical imaging researchers. The matching quality of those potential outliers is approximately 1.5 lower, on a scale from 1 (bad) to 5 (good), than valid displacement vectors. Conclusion The variogram is a simple yet informative tool. While being used extensively in geostatistical analysis, it has not received enough attention in the medical imaging field. We believe there is a good deal of potential for clinically applying the proposed outlier screening method. By way of this paper, we also expect researchers to find variogram useful in other medical applications that involve motion vectors analyses.
luo_et_al._-_2018_-_using_the_variogram_for_vector_outlier_screening_.pdf
Luo J, Toews M, Machado I, Frisken S, Zhang M, Preiswerk F, Sedghi A, Ding H, Pieper S, Golland P, et al. A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2018. Springer ; 2018. Publisher's Version luo_et_al._-_2018_-_a_feature-driven_active_framework_for_ultrasound-b.pdf
Preiswerk F, Cheng C-C, Luo J, Madore B. Synthesizing dynamic MRI using long-term recurrent convolutional networks, in Intl. Conf. on Machine Learning in Medical Imaging. Granada, Spain: Springer ; 2018 :8. arXiv preprintAbstract
A method is proposed for converting raw ultrasound signals of respiratory organ motion into high frame rate dynamic MRI using a long-term recurrent convolutional neural network. Ultrasound signals were acquired using a single-element transducer, referred to here as ‘organ-configuration motion’ (OCM) sensor, while sagittal MR images were simultaneously acquired. Both streams of data were used for training a cascade of convolutional layers, to extract relevant features from raw ultrasound, followed by a recurrent neural network, to learn its temporal dynamics. The network was trained with MR images on the output, and was employed to predict MR images at a temporal resolution of 100 frames per second, based on ultrasound input alone, without any further MR scanner input. The method was validated on 7 subjects.
Giger A, Stadelmann M, Preiswerk F, Jud C, De Luca V, Celicanin Z, Bieri O, Salomir R, Cattin P. Ultrasound-driven 4D MRI. Physics in Medicine and Biology. 2018. Publisher's Version giger_et_al._-_2018_-_ultrasound-driven_4d_mri.pdf
Preiswerk F, Sury M, Wortman J, Collins J, Duryea J. Application of Deep Learning for the Quantitative Assessment of Bone Marrow Lesions (BMLs). Proceedings of the International Workshop on Osteoarthritis Imaging. 2018. preiswerk_et_al._-_2018_-_application_of_deep_learning_for_the_quantitative_.pdf
Wu P-H, Cheng C-C, Preiswerk F, Madore B. Application of hybrid MR-ultrasound imaging to multi-baseline thermometry. ISMRM 26th Annual Meeting. 2018. wu_et_al._-_2018_-_application_of_hybrid_mr-ultrasound_imaging_to_mul.pdf
Cheng C-C, Preiswerk F, Madore B, Belsley G, Moore SC, Wu P-H, Foley Kijewski M, Campbell L, DiCarli MF. Ultrasound-based sensors for motion correction of PET data. Society of Nuclear Medicine And Molecular Imaging SNMMI. 2018. cheng_et_al._-_2018_-_ultrasound-based_sensors_for_motion_correction_of_.pdf
Madore B, Cheng C-C, Wu P-H, Preiswerk F. Ultrasound-based sensors for physiological motion monitoring. ISMRM 26th Annual Meeting. 2018. madore_et_al._-_2018_-_ultrasound-based_sensors_for_physiological_motion_.pdf
2017
Preiswerk F, Toews M, Cheng C-C, Hoge WS, Chiou J-yuan G, Mei C-S, Schaefer LF, Schwartz BM, Panych LP, Madore B. Hybrid MRI-Ultrasound acquisitions, and scannerless real-time imaging. ISMRM 25th Annual Meeting. 2017. preiswerk_et_al._-_2016_-_hybrid_mri-ultrasound_acquisitions_and_scannerles.pdf preiswerk_et_al._-_2017_-_hybrid_mri-ultrasound_acquisitions_and_scannerles.pdf
Wu P-H, Preiswerk F, Cheng C-C, Madore B. Hybrid MR-ultrasound acquisition for multi-baseline thermometry. ISMRM 25th Annual Meeting. 2017. wu_et_al._-_2017_-_hybrid_mr-ultrasound_acquisition_for_multi-baselin.pdf
Preiswerk F, Cai J, Cheng C-C, Hoge WS, Wu P-H, Panych LP, Madore B. RF-sensing for Trigger-based Synchronization of Auxiliary Devices, and Pulse-sequence Debugging. ISMRM 25th Annual Meeting. 2017. preiswerk_et_al._-_2017_-_rf-sensing_for_trigger-based_synchronization_of_.pdf
Hoge WS, Preiswerk F, Polimeni JR, Yengul SS, Ciris PA, Madore B. Ultrasound Monitoring of a Respiratory Phantom for the Development and Validation of Segmented EPI Reconstruction Methods. ISMRM 25th Annual Meeting. 2017. hoge_et_al._-_2017_-_ultrasound_monitoring_of_a_respiratory_phantom_for.pdf
Preiswerk F, Cheng C-C, Wu P-H, Panych LP, Madore B. Ultrasound-based Cardiac Gating for MRI. ISMRM 25th Annual Meeting. 2017. preiswerk_et_al._-_2017_-_ultrasound-based_cardiac_gating_for_mri.pdf
Jud C, Cattin PC, Preiswerk F. Statistical Respiratory Models for Motion Estimation. In: Zheng G, Li S, Szekely G Statistical Shape and Deformation Analysis. 1st ed. Elsevier ; 2017. Publisher's Version jud_et_al._-_2017_-_statistical_respiratory_models_for_motion_estimati.pdf
2016
Preiswerk F, Toews M, Cheng C-C, Chiou J-yuan G, Mei C-S, Schaefer LF, Hoge WS, Schwartz BM, Panych LP, Madore B. Hybrid MRI-Ultrasound acquisitions, and scannerless real-time imaging. Magnetic Resonance in Medicine. 2016. Publisher's Version preiswerk_et_al._-_2016_-_hybrid_mri-ultrasound_acquisitions_and_scannerles.pdf
Preiswerk F, Cheng C-C, Yengul SS, Panych LP, Madore B. Scannerless real-time MRI. ISMRM 24rd Annual Meeting. 2016 :863. preiswerk_et_al._-_2016_-_scannerless_real-time_mri.pdf
2015
Preiswerk F, Toews M, Hoge WS, Chiou J-yuan G, Panych LP, Wells WM, Madore B. Hybrid Utrasound and MRI Acquisitions for High-Speed Imaging of Respiratory Organ Motion, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Vol 9349. Cham: Springer International Publishing ; 2015 :315–322. Publisher's Version Download PDF
Jud C, Preiswerk F, Cattin PC. Respiratory Motion Compensation with Topology Independent Surrogates. MICCAI 2015 – Workshop on Imaging and Computer Assistance in Radiation Therapy. 2015. Download PDF
Celicanin Z, Bieri O, Preiswerk F, Cattin P, Scheffler K, Santini F. Simultaneous acquisition of image and navigator slices using CAIPIRINHA for 4D MRI. Magnetic Resonance in Medicine. 2015;73 (2) :669–676. Publisher's Version Download PDF

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