Ari-Petteri Ronkainen's research while affiliated with Kuopio University Hospital and other places

What is this page?


This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile.

It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.

If you're a ResearchGate member, you can follow this page to keep up with this author's work.

If you are this author, and you don't want us to display this page anymore, please let us know.

Publications (3)


Fig. 1 Experimental design and data collection workflow (PG proteoglycan). Panel 1 (yellow = CNTRL, green (top = IL, bottom = SA), orange (top = COL24h, middle = COL90m, bottom = TRP30m)
Fig. 2 Safranin-O stained histological sections. A Healthy controls (CNTRL), B surface abrasion (SA), C impact loading (IL), D collagenase 24 h (COL24h), E collagenase 90 min (COL90m), and F trypsin 30 min (TRP30m)
Fig. 3 A Acquired raw spectra with outliers (fingerprint region). B preprocessed spectra with their mean
Fig. 5 Percentage decrease in reference properties for each damage group relative to control group (CNTRL control, IL impact loading, SA surface abrasion, COL24h collagen 24 h, COL90m collagen 90 min, TRP30m trypsin 30 min)
Spearman correlations and normalized root-mean-square error (RMSE) for test and cross-validation (CV) across R1 and the combina- tion of R2 and R3
Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties
  • Article
  • Full-text available

June 2023

·

81 Reads

Annals of Biomedical Engineering

Eslam Shehata

·

·

·

[...]

·

Isaac O. Afara

Objective To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). Design Visually normal bovine patellae ( n = 12) were used in this study. Osteochondral plugs ( n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared ( n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties. Results The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%). Conclusion RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS.

Download
Share

Two commercial cylindrical CBCT phantoms (A and B) and one custom phantom with commercial electron density rods (C) were imaged with all scanners
Examples of ROI placement in the phantom measurements. A Uniformity phantom and the five ROIs used in the calculations. B Edge detection ROI for ESF measurement and MTF calculation. C Representative axial slice from the HU-value phantom with ROIs. D Breast and trabecular bone rod ROIs for LCV calculations, and water, trabecular bone, and background ROIs for CNR measurements
Example axial images of the uniformity phantom acquired with the default and ULD protocols for each scanner
Modulation transfer functions for each scanner at different dose levels
Example axial images of the low contrast phantom acquired with the default and ULD protocols for each scanner
A dose–neutral image quality comparison of different CBCT and CT systems using paranasal sinus imaging protocols and phantoms

September 2022

·

104 Reads

·

5 Citations

European Archives of Oto-Rhino-Laryngology

Purpose To compare the image quality produced by equivalent low-dose and default sinus imaging protocols of a conventional dental cone-beam computed tomography (CBCT) scanner, an extremity CBCT scanner and a clinical multidetector computed tomography (MDCT) scanner. Methods Three different phantoms were scanned using dose–neutral ultra-low-dose and low-dose sinus imaging protocols, as well as default sinus protocols of each device. Quantified parameters of image quality included modulation transfer function (MTF) to characterize the spatial response of the imaging system, contrast-to-noise ratio, low contrast visibility, image uniformity and Hounsfield unit accuracy. MTF was calculated using the line spread and edge spread functions (LSF and ESF). Results The dental CBCT had superior performance over the extremity CBCT in each studied parameter at similar dose levels. The MDCT had better contrast-to-noise ratio, low contrast visibility and image uniformity than the CBCT scanners. However, the CBCT scanners had better resolution compared to the MDCT. Accuracy of HU values for different materials was on the same level between the dental CBCT and MDCT, but substantially poorer performance was observed with the extremity CBCT. Conclusions The studied dental CBCT scanner showed superior performance over the studied extremity CBCT scanner when using dose–neutral imaging protocols. In case a dental CBCT is not available, the given extremity CBCT is still a viable option as it provides the benefit of high resolution over a conventional MDCT.


Assessment of ejection fraction and heart perfusion using myocardial perfusion single-photon emission computed tomography in Finland and Estonia: a multicenter phantom study

June 2020

·

19 Reads

·

3 Citations

Nuclear Medicine Communications

Objectives: Myocardial SPECT/CT imaging is frequently performed to assess myocardial perfusion and dynamic parameters of heart function, such as ejection fraction (EF). However, potential pitfalls exist in the imaging chain that can unfavorably affect diagnosis and treatment. We performed a national cardiac quality control study to investigate how much SPECT/CT protocols vary between different nuclear medicine units in Finland, and how this may affect the heart perfusion and EF values. Methods: Altogether, 21 nuclear medicine units participated with 27 traditional SPECT/CT systems and two cardiac-centered IQ-SPECT systems. The reproducibility of EF and the uniformity of perfusion were studied using a commercial dynamic heart phantom. SPECT/CT acquisitions were performed and processed at each participating unit using their own clinical protocol and with a standardized protocol. The effects of acquisition protocols and analysis routines on EF estimates and uniformity of perfusion were studied. Results: Considerable variation in EF estimates and in the uniformity of perfusion were observed between the units. Uniformity of perfusion was improved in some units after applying the higher count-statistic standard acquisition protocol. EF estimates varied more due to differences in analysis routines than as a result of different acquisition protocols. The results obtained with the two IQ-SPECT systems differed substantially from the traditional multipurpose cameras. Conclusion: On average, the EF and heart perfusion were accurately estimated by SPECT/CT, but high errors could be produced if the acquisition and analysis routines were poorly optimized. Eight of the 21 participants altered their imaging protocol after this quality control tour.

Citations (2)


... Micro-CT has a high accuracy in micrometers for trabecular parameters and is considered the "gold standard" for bone morphology and micro-structure, such as bone volume and bone fraction, but is limited to the ex vivo bone model [35]. Computed tomography (CT) is a well-established method able to provide a larger field of view, superior signal and contrast-to-noise ratios, and more accurate Hounsfield unit values when compared to CBCT [36]. CBCT has advantages over conventional CT and DXA as it is more affordable, emits a lower radiation dosage, has faster acquisition, and yields higher image quality, but variations found are associated with the use of different CBCT units, different voxel values, different imaging parameters, and different positioning sites, and by measuring different bone regions [37]. ...

Reference:

Osseodensification vs. Conventional Osteotomy: A Case Series with Cone Beam Computed Tomography
A dose–neutral image quality comparison of different CBCT and CT systems using paranasal sinus imaging protocols and phantoms

European Archives of Oto-Rhino-Laryngology

... While the diagnostic and prognostic advantages of dedicated cardiac SPECT systems are already well reported [1, 2, 16-18, 23, 26], the clinical use of general-purpose SPECT systems equipped with cardiac specific collimators is still very relevant [2,15,16,27]. In case of the heart-focused collimators the imaging performance [11,13,28,29] along with the accurate clinical use [12,[30][31][32][33][34] has been already thoroughly investigated. Furthermore, the image quality performance of these systems was compared with CZT based and conventional SPECT using both phantom and patient scans [35]. ...

Assessment of ejection fraction and heart perfusion using myocardial perfusion single-photon emission computed tomography in Finland and Estonia: a multicenter phantom study
  • Citing Article
  • June 2020

Nuclear Medicine Communications