Automatic segmentation of extensor carpi ulnaris tendon and detection of tendinosis with convolutional neural networks.

Background: Extensor Carpi Ulnaris (ECU) tendinosis, a frequent cause of chronic wrist pain, requires prompt diagnosis to prevent disability. This study demonstrates the use of convolutional neural networks (CNNs) for automated detection and segmentation of the ECU tendon and tendinosis in 2D axial wrist MRI.

Purpose: To develop a CNN for the automated detection of ECU tendon and automatic delineation of tendinosis in 2D wrist MRI.

Factors influencing the reliability of a CT angiography-based deep learning method for infarct volume estimation.

Objectives: CT angiography (CTA)-based machine learning methods for infarct volume estimation have shown a tendency to overestimate infarct core and final infarct volumes (FIV). Our aim was to assess factors influencing the reliability of these methods.

Methods: The effect of collateral circulation on the correlation between convolutional neural network (CNN) estimations and FIV was assessed based on the Miteff system and hypoperfusion intensity ratio (HIR) in 121 patients with anterior circulation acute ischaemic stroke using Pearson correlation coefficients and median volumes.

Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection.

Background: Guidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches.

Methods: A total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included.

Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images.

Background: Early diagnosis of the potentially fatal but curable chronic pulmonary embolism (CPE) is challenging. We have developed and investigated a novel convolutional neural network (CNN) model to recognise CPE from CT pulmonary angiograms (CTPA) based on the general vascular morphology in two-dimensional (2D) maximum intensity projection images.

Methods: A CNN model was trained on a curated subset of a public pulmonary embolism CT dataset (RSPECT) with 755 CTPA studies, including patient-level labels of CPE, acute pulmonary embolism (APE), or no pulmonary embolism.

Stiffness-Controlled Hydrogels for 3D Cell Culture Models.

Nanofibrillated cellulose (NFC) hydrogel is a versatile biomaterial suitable, for example, for three-dimensional (3D) cell spheroid culturing, drug delivery, and wound treatment. By freeze-drying NFC hydrogel, highly porous NFC structures can be manufactured. We freeze-dried NFC hydrogel and subsequently reconstituted the samples into a variety of concentrations of NFC fibers, which resulted in different stiffness of the material, i.

Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach.

Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images.

Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei.

Background: The segmentation of 3D cell nuclei is essential in many tasks, such as targeted molecular radiotherapies (MRT) for metastatic tumours, toxicity screening, and the observation of proliferating cells. In recent years, one popular method for automatic segmentation of nuclei has been deep learning enhanced marker-controlled watershed transform. In this method, convolutional neural networks (CNNs) have been used to create nuclei masks and markers, and the watershed algorithm for the instance segmentation.

Brain Volumes and Abnormalities in Adults Born Preterm at Very Low Birth Weight.

Objectives: To assess radiographic brain abnormalities and investigate volumetric differences in adults born preterm at very low birth weight (<1500 g), using siblings as controls.

Study Design: We recruited 79 adult same-sex sibling pairs with one born preterm at very low birth weight and the sibling at term. We acquired 3-T brain magnetic resonance imaging from 78 preterm participants and 72 siblings.

Automatic CT Angiography Lesion Segmentation Compared to CT Perfusion in Ischemic Stroke Detection: a Feasibility Study.

In stroke imaging, CT angiography (CTA) is used for detecting arterial occlusions. These images could also provide information on the extent of ischemia. The study aim was to develop and evaluate a convolutional neural network (CNN)-based algorithm for detecting and segmenting acute ischemic lesions from CTA images of patients with suspected middle cerebral artery stroke.

Computed tomography angiography-based deep learning method for treatment selection and infarct volume prediction in anterior cerebral circulation large vessel occlusion.

Background: Computed tomography perfusion (CTP) is the mainstay to determine possible eligibility for endovascular thrombectomy (EVT), but there is still a need for alternative methods in patient triage.

Purpose: To study the ability of a computed tomography angiography (CTA)-based convolutional neural network (CNN) method in predicting final infarct volume in patients with large vessel occlusion successfully treated with endovascular therapy.

Materials And Methods: The accuracy of the CTA source image-based CNN in final infarct volume prediction was evaluated against follow-up CT or MR imaging in 89 patients with anterior circulation ischemic stroke successfully treated with EVT as defined by Thrombolysis in Cerebral Infarction category 2b or 3 using Pearson correlation coefficients and intraclass correlation coefficients.

Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study.

Background: Chronic pulmonary embolism (CPE) is a life-threatening disease easily misdiagnosed on computed tomography. We investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA).

Methods: Preoperative CTPA of 25 patients with CPE and 25 without pulmonary embolism were selected.

Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke.

Background: Computed tomography angiography (CTA) imaging is needed in current guideline-based stroke diagnosis, and infarct core size is one factor in guiding treatment decisions. We studied the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from CTA and compared the results to a CT perfusion (CTP)-based commercially available software (RAPID, iSchemaView).

Methods: We retrospectively selected 83 consecutive stroke cases treated with thrombolytic therapy or receiving supportive care that presented to Helsinki University Hospital between January 2018 and July 2019.

Rotation and torsion of the left ventricle with cardiovascular magnetic resonance tagging: comparison of two analysis methods.

Background: Left ventricle rotation and torsion are fundamental components of myocardial function, and several software packages have been developed for analysis of these components. The purpose of this study was to compare the suitability of two software packages with different technical principles for analysis of rotation and torsion of the left ventricle during systole.

Methods: A group of hypertrophic cardiomyopathy (HCM) patients (N = 14, age 43 ± 11 years), mutation carriers without hypertrophy (N = 10, age 34 ± 13 years), and healthy relatives (N = 12, age 43 ± 17 years) underwent a cardiovascular magnetic resonance examination, including spatial modulation of magnetization tagging sequences in basal and apical planes of the left ventricle.

3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke.

Background: The aim of this study was to investigate the feasibility of ischemic stroke detection from computed tomography angiography source images (CTA-SI) using three-dimensional convolutional neural networks.

Methods: CTA-SI of 60 patients with a suspected acute ischemic stroke of the middle cerebral artery were randomly selected for this study; 30 patients were used in the neural network training, and the subsequent testing was performed using the remaining 30 patients. The training and testing were based on manually segmented lesions.

Automated daily quality control analysis for mammography in a multi-unit imaging center.

Background: The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users.

Purpose: To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center.

Potential occupational exposures in diagnostic and interventional radiology: statistical modeling based on Finnish national dose registry data.

Background: Radiation worker categorization and exposure monitoring practices must be proportional to the current working environment.

Purpose: To analyze exposure data of Finnish radiological workers and to estimate the magnitude and frequency of their potential occupational radiation exposure, and to propose appropriate radiation worker categorization.

Material And Methods: Estimates of the probabilities of annual effective doses exceeding certain levels were obtained by calculating the survival function of a lognormal probability density function (PDF) fitted in the measured occupational exposure data.

Multicellular dosimetric chain for molecular radiotherapy exemplified with dose simulations on 3D cell spheroids.

Purpose: Absorbed radiation dose-response relationships are not clear in molecular radiotherapy (MRT). Here, we propose a voxel-based dose calculation system for multicellular dosimetry in MRT. We applied confocal microscope images of a spherical cell aggregate i.

Analysis of MCNP simulated gamma spectra of CdTe detectors for boron neutron capture therapy.

The next step in the boron neutron capture therapy (BNCT) is the real time imaging of the boron concentration in healthy and tumor tissue. Monte Carlo simulations are employed to predict the detector response required to realize single-photon emission computed tomography in BNCT, but have failed to correctly resemble measured data for cadmium telluride detectors. In this study we have tested the gamma production cross-section data tables of commonly used libraries in the Monte Carlo code MCNP in comparison to measurements.

Prompt gamma and neutron detection in BNCT utilizing a CdTe detector.

In this work, a novel sensor technology based on CdTe detectors was tested for prompt gamma and neutron detection using boronated targets in (epi)thermal neutron beam at FiR1 research reactor in Espoo, Finland. Dedicated neutron filter structures were omitted to enable simultaneous measurement of both gamma and neutron radiation at low reactor power (2.5 kW).

Data quality in fMRI and simultaneous EEG-fMRI.

Object: To evaluate functional magnetic resonance imaging (fMRI) and simultaneous electroencephalography (EEG)-fMRI data quality in an organization using several magnetic resonance imaging (MRI) systems.

Materials And Methods: Functional magnetic resonance imaging measurements were carried out twice with a uniform gel phantom on five different MRI systems with field strengths of 1.5 and 3.

Advanced brain dopamine transporter imaging in mice using small-animal SPECT/CT.

Background: Iodine-123-β-CIT, a single-photon emission computed tomography (SPECT) ligand for dopamine transporters (DATs), has been used for in vivo studies in humans, monkeys, and rats but has not yet been used extensively in mice. To validate the imaging and analysis methods for preclinical DAT imaging, wild-type healthy mice were scanned using 123I-β-CIT.

Methods: The pharmacokinetics and reliability of 123I-β-CIT in mice (n = 8) were studied with a multipinhole SPECT/CT camera after intravenous injection of 123I-β-CIT (38 ± 3 MBq).

Boron neutron capture therapy (BNCT) in Finland: technological and physical prospects after 20 years of experiences.

Boron Neutron Capture Therapy (BNCT) is a binary radiotherapy method developed to treat patients with certain malignant tumours. To date, over 300 treatments have been carried out at the Finnish BNCT facility in various on-going and past clinical trials. In this technical review, we discuss our research work in the field of medical physics to form the groundwork for the Finnish BNCT patient treatments, as well as the possibilities to further develop and optimize the method in the future.

Accuracy of the electron transport in mcnp5 and its suitability for ionization chamber response simulations: A comparison with the egsnrc and penelope codes.

Purpose: In this work, accuracy of the mcnp5 code in the electron transport calculations and its suitability for ionization chamber (IC) response simulations in photon beams are studied in comparison to egsnrc and penelope codes.

Methods: The electron transport is studied by comparing the depth dose distributions in a water phantom subdivided into thin layers using incident energies (0.05, 0.

Transition in occupational radiation exposure monitoring methods in diagnostic and interventional radiology.

Radiation exposure monitoring is a traditional keystone of occupational radiation safety measures in medical imaging. The aim of this study was to review the data on occupational exposures in a large central university hospital radiology organisation and propose changes in the radiation worker categories and methods of exposure monitoring. An additional objective was to evaluate the development of electronic personal dosimeters and their potential in the digitised radiology environment.