PhD candidate
e-mail: j [dot] sprem [at] umcutrecht [dot] nl
Phone: +31 88 75 56695
LinkedIn; Google Scholar
In 2012 Jurica obtained his Bachelor of Science in Computing degree at the University of Zagreb, Croatia (Faculty of Electrical Engineering and Computing). In 2014 he got his Master’s Degree in Information and Communication Technology at the University of Zagreb. His main interest lies in image processing and its application in medical imaging. In 2014 he started as a PhD-candidate at the Image Sciences Institute in UMC Utrecht where his main area of research is calcium scoring of plaque in coronary arteries under the project “Cardiovascular phenotype-genotype analysis within a CT based lung cancer screening trial“. He is currently working on decreasing the interscan variabilities using different machine learning approaches (deep learning).
Journal Articles |
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1. | J. Šprem, B.D. de Vos, N. Lessmann, R.W. van Hamersvelt, M.J.W. Greuter, P.A. de Jong, T. Leiner, M.A. Viergever, I. Išgum Coronary calcium scoring with partial volume correction in anthropomorphic thorax phantom and screening chest CT images Journal Article PLoS One, 13 (12), pp. e0209318, 2018. @article{Šprem2018b, title = {Coronary calcium scoring with partial volume correction in anthropomorphic thorax phantom and screening chest CT images}, author = {J. Šprem, B.D. de Vos, N. Lessmann, R.W. van Hamersvelt, M.J.W. Greuter, P.A. de Jong, T. Leiner, M.A. Viergever, I. Išgum}, url = {https://doi.org/10.1371/journal.pone.0209318}, year = {2018}, date = {2018-12-20}, journal = {PLoS One}, volume = {13}, number = {12}, pages = {e0209318}, abstract = {Introduction: The amount of coronary artery calcium determined in CT scans is a well established predictor of cardiovascular events. However, high interscan variability of coronary calcium quantification may lead to incorrect cardiovascular risk assignment. Partial volume effect contributes to high interscan variability. Hence, we propose a method for coronary calcium quantification employing partial volume Methods: Two phantoms containing artificial coronary artery calcifications and 293 subject chest CT scans were used. The first and second phantom contained nine calcifications and the second phantom contained three artificial arteries with three calcifications of different volumes, shapes and densities. The first phantom was scanned five times with and without extension rings. The second phantom was scanned three times without and with simulated cardiac motion (10 and 30 mm/s). Chest CT scans were acquired without ECG-synchronization and reconstructed using sharp and soft kernels. Coronary calcifications were annotated employing the clinically used intensity value thresholding (130 HU). Thereafter, a threshold separating each calcification from its background was determined using an Expectation-Maximization algorithm. Finally, for each lesion the partial content of calcification in each voxel was determined depending on its intensity and the determined threshold. Results: Clinical calcium scoring resulted in overestimation of calcium volume for medium and high density calcifications in the first phantom, and overestimation of calcium volume for high density and underestimation for low density calcifications in the second phantom. With induced motion these effects were further emphasized. The proposed quantification resulted in better accuracy and substantially lower over- and underestimation of calcium volume even in presence of motion. In chest CT, the agreement between calcium scores from the two reconstructions improved when proposed method was Conclusion: Compared with clinical calcium scoring, proposed quantification provides a better estimate of the true calcium volume in phantoms and better agreement in calcium scores between different subject scan reconstructions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Introduction: The amount of coronary artery calcium determined in CT scans is a well established predictor of cardiovascular events. However, high interscan variability of coronary calcium quantification may lead to incorrect cardiovascular risk assignment. Partial volume effect contributes to high interscan variability. Hence, we propose a method for coronary calcium quantification employing partial volume Methods: Two phantoms containing artificial coronary artery calcifications and 293 subject chest CT scans were used. The first and second phantom contained nine calcifications and the second phantom contained three artificial arteries with three calcifications of different volumes, shapes and densities. The first phantom was scanned five times with and without extension rings. The second phantom was scanned three times without and with simulated cardiac motion (10 and 30 mm/s). Chest CT scans were acquired without ECG-synchronization and reconstructed using sharp and soft kernels. Coronary calcifications were annotated employing the clinically used intensity value thresholding (130 HU). Thereafter, a threshold separating each calcification from its background was determined using an Expectation-Maximization algorithm. Finally, for each lesion the partial content of calcification in each voxel was determined depending on its intensity and the determined threshold. Results: Clinical calcium scoring resulted in overestimation of calcium volume for medium and high density calcifications in the first phantom, and overestimation of calcium volume for high density and underestimation for low density calcifications in the second phantom. With induced motion these effects were further emphasized. The proposed quantification resulted in better accuracy and substantially lower over- and underestimation of calcium volume even in presence of motion. In chest CT, the agreement between calcium scores from the two reconstructions improved when proposed method was Conclusion: Compared with clinical calcium scoring, proposed quantification provides a better estimate of the true calcium volume in phantoms and better agreement in calcium scores between different subject scan reconstructions. |
2. | J. Šprem; B.D. de Vos; N. Lessmann; P.A. de Jong; M.A. Viergever; I. Išgum Impact of automatically detected motion artifacts on coronary calcium scoring in chest CT Journal Article Journal of Medical Imaging, 5 (4), pp. 044007 , 2018. @article{Šprem2018, title = {Impact of automatically detected motion artifacts on coronary calcium scoring in chest CT}, author = {J. Šprem and B.D. de Vos and N. Lessmann and P.A. de Jong and M.A. Viergever and I. Išgum}, url = {https://doi.org/10.1117/1.JMI.5.4.044007}, year = {2018}, date = {2018-12-11}, journal = {Journal of Medical Imaging}, volume = {5}, number = {4}, pages = {044007 }, abstract = {The amount of coronary artery calcification (CAC) quantified in CT scans enables prediction of cardiovascular disease (CVD) risk. However, interscan variability of CAC quantification is high, especially in scans made without ECG synchronization. We propose a method for automatic detection of CACs that are severely affected by cardiac motion. Subsequently, we evaluate the impact of such CACs on CAC quantification and CVD risk determination. This study includes 1000 baseline and 585 one year follow-up low-dose chest CTs from the National Lung Screening Trial. 415 baseline scans are used to train and evaluate a convolutional neural network that identifies observer determined CACs affected by severe motion artifacts. Thereafter, 585 paired scans acquired at baseline and follow-up were used to evaluate the impact of severe motion artifacts on CAC quantification and risk categorization. Based on the CAC amount the scans were categorized into four standard CVD risk categories. The method identified CACs affected by severe motion artifacts with 85.2% accuracy. Moreover, reproducibility of CAC scores in scan pairs is higher in scans containing mostly CACs not affected by severe cardiac motion. Hence, the proposed method enables identification of scans affected by severe cardiac motion where CAC quantification may not be reproducible.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The amount of coronary artery calcification (CAC) quantified in CT scans enables prediction of cardiovascular disease (CVD) risk. However, interscan variability of CAC quantification is high, especially in scans made without ECG synchronization. We propose a method for automatic detection of CACs that are severely affected by cardiac motion. Subsequently, we evaluate the impact of such CACs on CAC quantification and CVD risk determination. This study includes 1000 baseline and 585 one year follow-up low-dose chest CTs from the National Lung Screening Trial. 415 baseline scans are used to train and evaluate a convolutional neural network that identifies observer determined CACs affected by severe motion artifacts. Thereafter, 585 paired scans acquired at baseline and follow-up were used to evaluate the impact of severe motion artifacts on CAC quantification and risk categorization. Based on the CAC amount the scans were categorized into four standard CVD risk categories. The method identified CACs affected by severe motion artifacts with 85.2% accuracy. Moreover, reproducibility of CAC scores in scan pairs is higher in scans containing mostly CACs not affected by severe cardiac motion. Hence, the proposed method enables identification of scans affected by severe cardiac motion where CAC quantification may not be reproducible. |
3. | J.S. Kuperus, C.F. Buckens, J. Šprem, F.C. Oner, P.A. de Jong, J. Verlaan The Natural Course of Diffuse Idiopathic Skeletal Hyperostosis in the Thoracic Spine of Adult Males Journal Article The Journal of Rheumatology, 2018, ISSN: 0315-162X. @article{Kuperus18, title = {The Natural Course of Diffuse Idiopathic Skeletal Hyperostosis in the Thoracic Spine of Adult Males}, author = {J.S. Kuperus, C.F. Buckens, J. Šprem, F.C. Oner, P.A. de Jong, J. Verlaan}, url = {http://www.jrheum.org/content/early/2018/04/09/jrheum.171091}, issn = {0315-162X}, year = {2018}, date = {2018-01-01}, journal = {The Journal of Rheumatology}, publisher = {The Journal of Rheumatology}, abstract = {Objective Diffuse idiopathic skeletal hyperostosis (DISH) is characterized by flowing bony bridges on the right side of the spine. Knowledge of the development of these spinal bridges is limited. The current longitudinal computed tomography (CT) study was designed to bridge this gap. Methods Chest CT scans from elderly males with 2 scans (interval >= 2.5 yrs) were retrospectively included. Using the Resnick criteria, a pre-DISH group and a definite DISH group were identified. A scoring system based on the completeness of a bone bridge (score 0–3), extent of fluency, and location of the new bone was created to evaluate the progression of bone formation. Results In total, 145 of 1367 subjects were allocated to the DISH groups with a mean followup period of 5 years. Overall prevalence of a complete bone bridge increased in the pre-DISH group (11.3% to 31.0%) and in the definite DISH group (45.0% to 55.8%). The mean bridge score increased significantly in both the pre-DISH and definite DISH group (p < 0.001). The new bone gradually became more flowing and expanded circumferentially. Conclusion Over the mean course of 5 years, the new bone developed from incomplete, pointy bone bridges to more flowing complete bridges. This suggests an ongoing and measurable bone-forming process that continues to progress, also in established cases of DISH.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Objective Diffuse idiopathic skeletal hyperostosis (DISH) is characterized by flowing bony bridges on the right side of the spine. Knowledge of the development of these spinal bridges is limited. The current longitudinal computed tomography (CT) study was designed to bridge this gap. Methods Chest CT scans from elderly males with 2 scans (interval >= 2.5 yrs) were retrospectively included. Using the Resnick criteria, a pre-DISH group and a definite DISH group were identified. A scoring system based on the completeness of a bone bridge (score 0–3), extent of fluency, and location of the new bone was created to evaluate the progression of bone formation. Results In total, 145 of 1367 subjects were allocated to the DISH groups with a mean followup period of 5 years. Overall prevalence of a complete bone bridge increased in the pre-DISH group (11.3% to 31.0%) and in the definite DISH group (45.0% to 55.8%). The mean bridge score increased significantly in both the pre-DISH and definite DISH group (p < 0.001). The new bone gradually became more flowing and expanded circumferentially. Conclusion Over the mean course of 5 years, the new bone developed from incomplete, pointy bone bridges to more flowing complete bridges. This suggests an ongoing and measurable bone-forming process that continues to progress, also in established cases of DISH. |
Inproceedings |
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1. | J. Šprem; B.D. de Vos; P.A. de Jong; M.A. Viergever; I. Isgum In: SPIE Medical Imaging, 2017. @inproceedings{Šprem2017-3103, title = {Classification of coronary artery calcifications according to motion artifacts in chest CT using a convolutional neural network}, author = {J. Šprem and B.D. de Vos and P.A. de Jong and M.A. Viergever and I. Isgum}, url = {https://doi.org/10.1117/12.2253669}, year = {2017}, date = {2017-02-13}, booktitle = {SPIE Medical Imaging}, series = {10133-27}, abstract = {Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events (CVEs). CAC can be quantified in chest CT scans acquired in lung screening. However, in these images the reproducibility of CAC quantification is compromised by cardiac motion artifacts that occur during scanning, which limits the reproducibility of CVE risk assessment. We present a system for detection of severe cardiac motion artifacts affecting CACs by using a convolutional neural network (CNN). This study included 125 chest CT scans from the National Lung Screening Trial (NLST). The images were acquired with CT scanners from four major CT scanner vendors (GE, Siemens, Philips, Toshiba) with varying tube voltage and slice thickness settings, and without ECG synchronization. An observer manually identified CAC lesions and labelled each CAC according to presence of cardiac motion (strongly affected, not affected). A CNN was designed to automatically label the identified CAC lesions according to the presence of cardiac motion by analyzing a patch from the axial CT slice around each CAC lesion. From 125 CT scans, 9201 CAC lesions were analyzed. 8001 lesions were used for training (19% positive) and the remaining 1200 (50% positive) were used for testing. The CNN achieved a classification accuracy of 85% (86% sensitivity, 84% specificity).}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events (CVEs). CAC can be quantified in chest CT scans acquired in lung screening. However, in these images the reproducibility of CAC quantification is compromised by cardiac motion artifacts that occur during scanning, which limits the reproducibility of CVE risk assessment. We present a system for detection of severe cardiac motion artifacts affecting CACs by using a convolutional neural network (CNN). This study included 125 chest CT scans from the National Lung Screening Trial (NLST). The images were acquired with CT scanners from four major CT scanner vendors (GE, Siemens, Philips, Toshiba) with varying tube voltage and slice thickness settings, and without ECG synchronization. An observer manually identified CAC lesions and labelled each CAC according to presence of cardiac motion (strongly affected, not affected). A CNN was designed to automatically label the identified CAC lesions according to the presence of cardiac motion by analyzing a patch from the axial CT slice around each CAC lesion. From 125 CT scans, 9201 CAC lesions were analyzed. 8001 lesions were used for training (19% positive) and the remaining 1200 (50% positive) were used for testing. The CNN achieved a classification accuracy of 85% (86% sensitivity, 84% specificity). |
Abstracts |
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1. | J. Šprem, B.D. de Vos, R. Vliegenthart, M.A. Viergever, P. A. de Jong, I. Isgum Increasing the Interscan Reproducibility of Coronary Calcium Scoring by Partial Volume Correction in Low-Dose non-ECG Synchronized CT: Phantom Study Abstract In: 2015. @booklet{Šprem2015, title = {Increasing the Interscan Reproducibility of Coronary Calcium Scoring by Partial Volume Correction in Low-Dose non-ECG Synchronized CT: Phantom Study}, author = {J. Šprem, B.D. de Vos, R. Vliegenthart, M.A. Viergever, P. A. de Jong, I. Isgum}, year = {2015}, date = {2015-11-29}, booktitle = {Radiological Society of North America}, month = {11}, keywords = {}, pubstate = {published}, tppubtype = {booklet} } |
PhD Theses |
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1. | J. Šprem Enhanced cardiovascular risk prediction by machine learning PhD Thesis Utrecht University, The Netherlands, 2019, ISBN: 978-94-6323-713-0. @phdthesis{Šprem2019, title = {Enhanced cardiovascular risk prediction by machine learning}, author = {J. Šprem}, isbn = {978-94-6323-713-0}, year = {2019}, date = {2019-07-11}, school = {Utrecht University, The Netherlands}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } |