Dr. Jouke Dijkstra

Most recent publications

Efficacy of human experts and an automated segmentation algorithm in quantifying disease pathology in coronary computed tomography angiography: A head-to-head comparison with intravascular ultrasound imaging
Çap M, Ramasamy A, Parasa R, Tanboga IH, Maung S, Morgan K, Yap NAL, Abou Gamrah M, Sokooti H, Kitslaar P, Reiber JHC, Dijkstra J, Torii R, Moon JC, Mathur A, Baumbach A, Pugliese F and Bourantas CV
Coronary computed tomography angiography (CCTA) analysis is currently performed by experts and is a laborious process. Fully automated edge-detection methods have been developed to expedite CCTA segmentation however their use is limited as there are concerns about their accuracy. This study aims to compare the performance of an automated CCTA analysis software and the experts using near-infrared spectroscopy-intravascular ultrasound imaging (NIRS-IVUS) as a reference standard.
A novel deep learning model for a computed tomography diagnosis of coronary plaque erosion
Park S, Yuki H, Niida T, Suzuki K, Kinoshita D, McNulty I, Broersen A, Dijkstra J, Lee H, Kakuta T, Ye JC and Jang IK
Patients with acute coronary syndromes caused by plaque erosion might be managed conservatively without stenting. Currently, the diagnosis of plaque erosion requires an invasive imaging procedure. We sought to develop a deep learning (DL) model that enables an accurate diagnosis of plaque erosion using coronary computed tomography angiography (CTA). A total of 532 CTA scans from 395 patients were used to develop a DL model: 426 CTA scans from 316 patients for training and internal validation, and 106 separate scans from 79 patients for validation. Momentum Distillation-enhanced Composite Transformer Attention (MD-CTA), a novel DL model that can effectively process the entire set of CTA scans to diagnose plaque erosion, was developed. The novel DL model, compared to the convolution neural network, showed significantly improved AUC (0.899 [0.841-0.957] vs. 0.724 [0.622-0.826]), sensitivity (87.1 [70.2-96.4] vs. 71.0 [52.0-85.8]), and specificity (85.3 [75.3-92.4] vs. 68.0 [56.2-78.3]), respectively, for the patient-level prediction. Similar results were obtained at the slice-level prediction AUC (0.897 [0.890-0.904] vs. 0.757 [0.744-0.770]), sensitivity (82.2 [79.8-84.3] vs. 68.9 [66.2-71.6]), and specificity (80.1 [79.1-81.0] vs. 67.3 [66.3-68.4]), respectively. This newly developed DL model enables an accurate CT diagnosis of plaque erosion, which might enable cardiologists to provide tailored therapy without invasive procedures.Clinical Trial Registration: http://www.clinicaltrials.gov , NCT04523194.
Semi-automatic standardized analysis method to objectively evaluate near-infrared fluorescent dyes in image-guided surgery
Dijkhuis TH, Bijlstra OD, Warmerdam MI, Faber RA, Linders DGJ, Galema HA, Broersen A, Dijkstra J, Kuppen PJK, Vahrmeijer AL and Mieog JSD
Near-infrared fluorescence imaging still lacks a standardized, objective method to evaluate fluorescent dye efficacy in oncological surgical applications. This results in difficulties in translation between preclinical to clinical studies with fluorescent dyes and in the reproduction of results between studies, which in turn hampers further clinical translation of novel fluorescent dyes.
Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images
Zhang X, Broersen A, Sokooti H, Ramasamy A, Kitslaar P, Parasa R, Karaduman M, Mohammed ASAJ, Bourantas CV and Dijkstra J
The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.
Two Facets of Shear Stress Post Drug Coating Balloon: Angiography Versus Optical Coherence Tomography Fusion Approach
Poon EKW, Ninomiya K, Kageyama S, Guo X, Reimers B, Torii R, Dijkstra J, Bourantas CV, Reiber JHC, Barlis P, Onuma Y and Serruys PW