Publications by authors named "Ingrid Nijholt"

Objectives: Establishing paediatric DRLs is challenging due to sparse data availability. The objective was to assess paediatric fluoroscopic dose levels in Dutch clinical practice, as current diagnostic reference levels (DRLs) need updating following the European Guidelines on DRLs for Paediatric Imaging (PiDRL).

Material And Methods: Air Kerma-area Product (KAP) values were retrospectively collected from paediatric patients (0-18 years) who underwent fluoroscopic procedures in nine Dutch hospitals between 01-01-2017 and 01-06-2021.

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Purpose: Our understanding of the influence of preterm birth and related perinatal exposures on early brain development is limited, hampering personalized optimization of neuroprotective strategies. This study assesses the effect of gestational age (GA) at birth on brain volumes at term-equivalent age (TEA) in infants without overt brain injury born across the GA spectrum.

Methods: A cohort of infants born across the GA spectrum (25-40 weeks' gestation) underwent 3T brain MRI around TEA (40-46 weeks postmenstrual age).

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Background: In clinical practice, currently no standardised approach exists to determine which patients with cervical spine injury (CSI) on CT scan should receive continued cervical spine immobilisation and a neurosurgeon is generally consulted. Insights into the contribution of CT assessment and classification of CSI in determining the need for stabilising therapy could aid in standardising clinical practice in the emergency department. Standardising clinical practice for cervical spine immobilisation could potentially improve the patient flow in the emergency department by reducing delay in decision-making, particularly in hospitals without availability of immediate neurosurgical consultation.

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Purpose: Moderately preterm (MP) infants (32-33 weeks' gestation) are at increased risk for developmental problems compared to late preterm (LP) infants (34-36 weeks' gestation). Fiber bundle tractography remains an unexplored avenue to understanding this risk-difference between MP and LP infants. This study aimed to examine along-tract profile differences between MP and LP infants at term-equivalent age (TEA).

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Objective: To compare diagnostic accuracy of artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT with attending radiologists.

Design: Retrospective, diagnostic accuracy study.

Methods: AI analyzed 2368 scans from patients screened for C-spine fracture with CT (2007-2014, fracture prevalence 9.

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Article Synopsis
  • MRgFUS is a promising and safe treatment for uterine fibroids and adenomyosis, especially for women looking to preserve their fertility.
  • It has shown potential in relieving pain for conditions like endometriosis and recurrent gynecologic cancers, but further research is required.
  • Widespread reimbursement for MRgFUS is limited due to insufficient large-scale studies comparing it to standard treatment options.
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Introduction: The non-perfused volume divided by total fibroid load (NPV/TFL) is a predictive outcome parameter for MRI-guided high-intensity focused ultrasound (MR-HIFU) treatments of uterine fibroids, which is related to long-term symptom relief. In current clinical practice, the MR-HIFU outcome parameters are typically determined by visual inspection, so an automated computer-aided method could facilitate objective outcome quantification. The objective of this study was to develop and evaluate a deep learning-based segmentation algorithm for volume measurements of the uterus, uterine fibroids, and NPVs in MRI in order to automatically quantify the NPV/TFL.

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Background: A method for periprocedural contrast agent-free visualization of uterine fibroid perfusion could potentially shorten magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) treatment times and improve outcomes. Our goal was to test feasibility of perfusion fraction mapping by intravoxel incoherent motion (IVIM) modeling using diffusion-weighted MRI as method for visual evaluation of MR-HIFU treatment progression.

Methods: Conventional and T2-corrected IVIM-derived perfusion fraction maps were retrospectively calculated by applying two fitting methods to diffusion-weighted MRI data ( = 0, 50, 100, 200, 400, 600 and 800 s/mm at 1.

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In this review, a systematic literature search on the effectiveness and complication rates of ultrasound-guided and magnetic resonance-guided high-intensity focused ultrasound (USg-/MRgHIFU) for abdominal wall endometriosis (AWE) was conducted in six databases in May/June 2023. Original articles of (non)randomized trials, cohort studies, case-control studies and case series published in peer-reviewed journals were included. Of the included studies the level of evidence (LoE) and methodological quality using the ROBINS-I and IHE-QAT was assessed.

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Objectives: To assess the environmental impact of the non-invasive Magnetic Resonance image-guided High-Intensity Focused Ultrasound (MR-HIFU) treatment of uterine fibroids, we aimed to perform a full Life Cycle Assessment (LCA). However, as a full LCA was not feasible at this time, we evaluated the CO (carbon dioxide) emission from the MRI scanner, MR-HIFU device, and the medication used, and analyzed solid waste produced during treatment.

Methods: Our functional unit was one uterine fibroid MR-HIFU treatment.

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Background: To compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR).

Methods: Conventional CT and 130-keV monoenergetic images with and without O-MAR and DL-MAR images of 28 unilateral THA patients were reconstructed. Image quality, metal artifacts, and diagnostic confidence in bone, pelvic organs, and soft tissue adjacent to the prosthesis were jointly scored by two experienced musculoskeletal radiologists.

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Purpose: To evaluate the diagnostic performance and generalizability of the winning DL algorithm of the RSNA 2020 PE detection challenge to a local population using CTPA data from two hospitals.

Materials And Methods: Consecutive CTPA images from patients referred for suspected PE were retrospectively analysed. The winning RSNA 2020 DL algorithm was retrained on the RSNA-STR Pulmonary Embolism CT (RSPECT) dataset.

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Objective: Concerns have been raised about the effect of skin color on the accuracy of transcutaneous bilirubin (TcB) measurements, a widely used method for hyperbilirubinemia diagnosis in newborns. Literature is inconclusive, with both reported under- and overestimations of the TcB with increasing skin pigmentation. Therefore, the influence of skin color on TcB measurements was systematically evaluated in a controlled, in vitro setting.

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Purpose: Despite progressive implementation of image-guided point-shear wave elastography (pSWE) in guidelines as an alternative to transient elastography for the staging of fibrotic liver disease, pSWE is not widely adopted in clinical workflow. More information on reliability and validity of pSWE systems is needed. Therefore, we performed a phantom study to evaluate the validity and reliability of pSWE with ultrasound systems.

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Article Synopsis
  • The study aimed to evaluate the diagnostic accuracy of a deep learning AI for detecting cervical spine fractures on CT scans, comparing it to the performance of attending radiologists and identifying fractures requiring stabilizing therapy.
  • A total of 2,368 scans were analyzed, revealing that AI had a sensitivity of 71.5% and detected many fractures missed by radiologists, while radiologists had a higher sensitivity of 88.2% but missed fewer fractures in need of stabilizing therapy.
  • The findings suggest that while the AI missed more fractures overall, it was able to identify some that radiologists missed, including several critical injuries needing intervention.
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Objectives: Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT images may worsen. The goal of this study was to assess the performance of an established AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPI) to detect pulmonary embolism (PE) on VMI.

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Objectives: The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of radiologists alone in detecting incidental acute pulmonary embolism (PE) on routine portal venous contrast-enhanced chest computed tomography (CT).

Methods: CTs of 3089 consecutive patients referred to the radiology department for a routine contrast-enhanced chest CT between 27-5-2020 and 31-12-2020, were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The diagnostic performance of the AI was compared to the initial report.

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Objective: To review the effect of bladder outlet procedures on urodynamic outcomes and symptom scores in males with detrusor underactivity (DU) or acontractile detrusors (AD).

Materials And Methods: We performed a systematic review and meta-analysis of research publications derived from PubMed, Embase, Web of Science, and Ovid Medline to identify clinical studies of adult men with non-neurogenic DU or AD who underwent any bladder outlet procedure. Outcomes comprised the detrusor pressure at maximum flow (P Q ), maximum flow rate (Q ), international prostate symptom score (IPSS), and quality of life (QoL).

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Purpose: To generate and extend the evidence on the clinical validity of an artificial intelligence (AI) algorithm to detect acute pulmonary embolism (PE) on CT pulmonary angiography (CTPA) of patients suspected of PE and to evaluate the possibility of reducing the risk of missed findings in clinical practice with AI-assisted reporting.

Methods: Consecutive CTPA scan data of 3316 patients referred because of suspected PE between 24-2-2018 and 31-12-2020 were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The output of the AI was compared with the attending radiologists' report.

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Purpose: To develop a deep learning-based metal artifact reduction technique (dl-MAR) and quantitatively compare metal artifacts on dl-MAR-corrected CT-images, orthopedic metal artifact reduction (O-MAR)-corrected CT-images and uncorrected CT-images after sacroiliac (SI) joint fusion.

Methods: dl-MAR was trained on CT-images with simulated metal artifacts. Pre-surgery CT-images and uncorrected, O-MAR-corrected and dl-MAR-corrected post-surgery CT-images of twenty-five patients undergoing SI joint fusion were retrospectively obtained.

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Magnetic resonance imaging-guided high-intensity focused ultrasound (MR-HIFU) is an innovative treatment for patients with painful bone metastases. The adoption of MR-HIFU will be influenced by several factors beyond its effectiveness. To identify contextual factors affecting the adoption of MR-HIFU, we conducted a group concept mapping (GCM) study in four European countries.

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