I received my B.Sc. degree in Biomedical Engineering (EE), in 2012, M.Sc. in Biomedical Imaging from Åbo Akademi BioImaging Master's Degree Program, and a Ph.D. degree in Medical Physics and Engineering from the University of Turku, Finland, in 2014 and 2018, respectively. Prior to joining Stanford University, I worked as a Principal Lecturer in Artificial Intelligence at Turku University of Applied Sciences (TUAS), Senior Researcher at the University of Turku, Research Scientist in Precordior Company, and Graduate Researcher at Turku PET Center in Finland. I am a scientist with academic and industrial interests and collaboration experiences with various universities and companies. My research interests focus on physiological monitoring for human health, multi-facet data processing, and analysis, medical imaging, applied machine learning, deep learning, computer vision, robotics, and autonomous systems, privacy-preserving ML, and statistical analysis. I routinely use my technical background in Biomedical Engineering and Artificial Intelligence to tackle all aspects of a project and deliver high-quality results. In addition to a diverse technical repertoire, I have excellent written and oral communication skills gained through the regular presentation of my research work and interaction with people in a variety of different roles - from students I have worked with, to professionals with whom I discuss experimental results and “next steps.”
Honors & Awards
Postdoctoral Fellowship, Finnish Cultural Foundation (2022-2023)
Postdoctoral Fellowship (PET/MRI Motion Estimation and Correction), Ulla Tuominen Foundation (Postdoc Pool) (2021-2022)
Postdoctoral Grant (MEMS-Based Head Motion Tracking PET/MRI), Turku University Central Hospital Education and Research Foundation (2020)
Smartphone Motion Processing for Atrial Fibrillation Detection, Finnish Foundation for Science and Technology (2018)
Wearable Motion Processing System for Detecting Heart Arrhythmia Using MEMS Sensors, Nokia Foundation Awards (2017)
Smartphone Motion Processing for Atrial Fibrillation Detection, Finnish Foundation for Science and Technology (2017)
Wearable Motion Processing System for Detecting Heart Arrhythmia Using MEMS Sensors, Nokia Foundation Awards (2016)
Doctoral Studies Scholarships, Faculty of Medicine, University of Turku (2015-2017)
IEEE Young Trainee Grant, IEEE NSS/MIC (2016)
Young Researcher award, University of Turku Foundation (2015)
Ph.D., University of Turku, Medical Physics and Engineering (2018)
M.Sc., Åbo Akademi University, Biomedical Imaging (2014)
B.Sc., Iran, Biomedical Engineering (2012)
Craig Levin, Postdoctoral Faculty Sponsor
Tero Koivisto, Mojtaba JAFARI TADI, Mikko Pänkäälä, Juuso Blomster, Juhani Airaksinen, Antti Saraste. "United States Patent US20210338108A1 Apparatus for producing information indicative of cardiac abnormality", Precordior Oy, Nov 4, 2021
Mojtaba Jafaritadi, Juhani Airaksinen, Tero Koivisto, Mikko Pänkäälä, Tuomas Valtonen. "United States Patent US20180303382A1 Method and apparatus for producing information indicative of cardiac condition", Precordior Oy, Oct 25, 2018
Current Research and Scholarly Interests
Dr. Jafaritadi is working on the device- and data-driven motion estimation systems for brain PET imaging. His research focuses on motion tracking using multidimensional MEMS motion sensors, signal processing, and machine learning. He is also interested in working on data-driven motion correction and image enhancement for PET/MRI using deep neural networks.
MEMS-based Head Motion Tracking for Brain PET/MRI
This project considers Microelectromechanical (MEMS) sensors for head motion tracking in clinical PET/MRI studies.
Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification
JOURNAL OF NUCLEAR CARDIOLOGY
Dual-gating reduces respiratory and cardiac motion effects but increases noise. With motion correction, motion is minimized and image quality preserved. We applied motion correction to create end-diastolic respiratory motion corrected images from dual-gated images.[18F]-fluorodeoxyglucose ([18F]-FDG) PET images of 13 subjects were reconstructed with 4 methods: non-gated, dual-gated, motion corrected, and motion corrected with 4D-CT (MoCo-4D). Image quality was evaluated using standardized uptake values, contrast ratio, signal-to-noise ratio, coefficient of variation, and contrast-to-noise ratio. Motion minimization was evaluated using myocardial wall thickness.MoCo-4D showed improvement for contrast ratio (2.83 vs 2.76), signal-to-noise ratio (27.5 vs 20.3) and contrast-to-noise ratio (14.5 vs 11.1) compared to dual-gating. The uptake difference between MoCo-4D and non-gated images was non-significant (P > .05) for the myocardium (2.06 vs 2.15 g/mL), but significant (P < .05) for the blood pool (.80 vs .86 g/mL). Non-gated images had the lowest coefficient of variation (27.3%), with significant increase for all other methods (31.6-32.5%). MoCo-4D showed smallest myocardial wall thickness (16.6 mm) with significant decrease compared to non-gated images (20.9 mm).End-diastolic respiratory motion correction and 4D-CT resulted in improved motion minimization and image quality over standard dual-gating.
View details for DOI 10.1007/s12350-021-02769-6
View details for Web of Science ID 000692062800003
View details for PubMedID 34476780
- Learning to Denoise Gated Cardiac PET Images Using Convolutional Neural Networks IEEE ACCESS 2021; 9: 145886-145899
A novel dual gating approach using joint inertial sensors: implications for cardiac PET imaging
PHYSICS IN MEDICINE AND BIOLOGY
2017; 62 (20): 8080–8101
Positron emission tomography (PET) is a non-invasive imaging technique which may be considered as the state of art for the examination of cardiac inflammation due to atherosclerosis. A fundamental limitation of PET is that cardiac and respiratory motions reduce the quality of the achieved images. Current approaches for motion compensation involve gating the PET data based on the timing of quiescent periods of cardiac and respiratory cycles. In this study, we present a novel gating method called microelectromechanical (MEMS) dual gating which relies on joint non-electrical sensors, i.e. tri-axial accelerometer and gyroscope. This approach can be used for optimized selection of quiescent phases of cardiac and respiratory cycles. Cardiomechanical activity according to echocardiography observations was investigated to confirm whether this dual sensor solution can provide accurate trigger timings for cardiac gating. Additionally, longitudinal chest motions originating from breathing were measured by accelerometric- and gyroscopic-derived respiratory (ADR and GDR) tracking. The ADR and GDR signals were evaluated against Varian real-time position management (RPM) signals in terms of amplitude and phase. Accordingly, high linear correlation and agreement were achieved between the reference electrocardiography, RPM, and measured MEMS signals. We also performed a Ge-68 phantom study to evaluate possible metal artifacts caused by the integrated read-out electronics including mechanical sensors and semiconductors. The reconstructed phantom images did not reveal any image artifacts. Thus, it was concluded that MEMS-driven dual gating can be used in PET studies without an effect on the quantitative or visual accuracy of the PET images. Finally, the applicability of MEMS dual gating for cardiac PET imaging was investigated with two atherosclerosis patients. Dual gated PET images were successfully reconstructed using only MEMS signals and both qualitative and quantitative assessments revealed encouraging results that warrant further investigation of this method.
View details for DOI 10.1088/1361-6560/aa8b09
View details for Web of Science ID 000412386800002
View details for PubMedID 28880843
Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables
2017; 7: 6823
Gyrocardiography (GCG) is a new non-invasive technique for assessing heart motions by using a sensor of angular motion - gyroscope - attached to the skin of the chest. In this study, we conducted simultaneous recordings of electrocardiography (ECG), GCG, and echocardiography in a group of subjects consisting of nine healthy volunteer men. Annotation of underlying fiducial points in GCG is presented and compared to opening and closing points of heart valves measured by a pulse wave Doppler. Comparison between GCG and synchronized tissue Doppler imaging (TDI) data shows that the GCG signal is also capable of providing temporal information on the systolic and early diastolic peak velocities of the myocardium. Furthermore, time intervals from the ECG Q-wave to the maximum of the integrated GCG (angular displacement) signal and maximal myocardial strain curves obtained by 3D speckle tracking are correlated. We see GCG as a promising mechanical cardiac monitoring tool that enables quantification of beat-by-beat dynamics of systolic time intervals (STI) related to hemodynamic variables and myocardial contractility.
View details for DOI 10.1038/s41598-017-07248-y
View details for Web of Science ID 000406610000074
View details for PubMedID 28754888
View details for PubMedCentralID PMC5533710
- Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 455-469
A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography
2021; 21 (12)
We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation technique. The presented method can be used to detect respiratory cycles and estimate their lengths with state-of-the-art accuracy when compared to other IMU-based methods, and is the first based on commercial MEMS devices, which can estimate quantitatively both the magnitude and the phase of respiratory motion from the abdomen and chest regions. For the considered test group consisting of eight subjects with acute myocardial infarction, our method achieved the absolute breathing rate error per minute of 0.44 ± 0.23 1/min, and the absolute amplitude error of 0.24 ± 0.09 cm, when compared to the clinically used respiratory motion estimation technique. The presented method could be used to simplify the logistics related to respiratory motion estimation in PET imaging studies, and also to enable multi-position motion measurements for advanced organ motion estimation.
View details for DOI 10.3390/s21123983
View details for Web of Science ID 000666365800001
View details for PubMedID 34207864
View details for PubMedCentralID PMC8228885
- Classification of Atrial Fibrillation and Acute Decompensated Heart Failure Using Smartphone Mechanocardiography: A Multilabel Learning Approach IEEE SENSORS JOURNAL 2020; 20 (14): 7957–68
Investigating the estimation of cardiac time intervals using gyrocardiography
2020; 41 (5): 055004
Assessment of cardiac time intervals (CTIs) is essential for monitoring cardiac performance. Recently, gyrocardiography (GCG) has been introduced as a non-invasive technology for cardiac monitoring. GCG measures the chest's angular precordial vibrations caused by myocardium wall motion using a gyroscope sensor attached to the sternum. In this study, we investigated the accuracy and reproducibility of estimating CTIs from the GCG recordings of 50 adults.We proposed five fiducial points for the GCG waveforms associated with the opening and closure of aortic and mitral valves. Two annotators annotated the suggested points on each cardiac cycle. The points were compared to the corresponding opening and closing of cardiac valves delineated on Tissue Doppler imaging (TDI) recordings. The fiducial points were annotated on seismocardiography (SCG) and impedance cardiography (ICG) signals recorded simultaneously.For estimating the timing of mitral valve closure, aortic valve opening, aortic valve closure, and mitral valve opening, 40%, 67%, 75%, and 70% of GCG annotations fell in the corresponding echocardiography ranges, respectively. The results showed moderate-to-excellent (r = 0.4-0.92; p-value < 0.01) correlation between the measured and the reference CTls. A myocardial performance index (Tei index) adapted using joint GCG and SCG resulted in a moderate correlation (r = 0.4; p-value < 0.001).The findings showed that the CTIs can be easily measured using GCG. Also, we found that using SCG and GCG recordings together could provide an opportunity to estimate CTIs more accurately, and make it possible to calculate the Tei index as an indicator of myocardial performance.
View details for DOI 10.1088/1361-6579/ab87b2
View details for Web of Science ID 000541882200001
View details for PubMedID 32268315
A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating.
Sensors (Basel, Switzerland)
2019; 19 (19)
Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.
View details for DOI 10.3390/s19194137
View details for PubMedID 31554282
View details for PubMedCentralID PMC6811750
- Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms IEEE SENSORS JOURNAL 2019; 19 (6): 2230–42
- Reliability of Self-Applied Smartphone Mechanocardiography for Atrial Fibrillation Detection IEEE ACCESS 2019; 7: 146801–12
- Stand-Alone Heartbeat Detection in Multidimensional Mechanocardiograms IEEE SENSORS JOURNAL 2019; 19 (1): 234–42
Multiclass Classifier based Cardiovascular Condition Detection Using Smartphone Mechanocardiography
2018; 8: 9344
Cardiac translational and rotational vibrations induced by left ventricular motions are measurable using joint seismocardiography (SCG) and gyrocardiography (GCG) techniques. Multi-dimensional non-invasive monitoring of the heart reveals relative information of cardiac wall motion. A single inertial measurement unit (IMU) allows capturing cardiac vibrations in sufficient details and enables us to perform patient screening for various heart conditions. We envision smartphone mechanocardiography (MCG) for the use of e-health or telemonitoring, which uses a multi-class classifier to detect various types of cardiovascular diseases (CVD) using only smartphone's built-in internal sensors data. Such smartphone App/solution could be used by either a healthcare professional and/or the patient him/herself to take recordings from their heart. We suggest that smartphone could be used to separate heart conditions such as normal sinus rhythm (SR), atrial fibrillation (AFib), coronary artery disease (CAD), and possibly ST-segment elevated myocardial infarction (STEMI) in multiclass settings. An application could run the disease screening and immediately inform the user about the results. Widespread availability of IMUs within smartphones could enable the screening of patients globally in the future, however, we also discuss the possible challenges raised by the utilization of such self-monitoring systems.
View details for DOI 10.1038/s41598-018-27683-9
View details for Web of Science ID 000435536100020
View details for PubMedID 29921933
View details for PubMedCentralID PMC6008477
A Miniaturized Low Power Biomedical Sensor Node for Clinical Research and Long Term Monitoring of Cardiovascular Signals
View details for Web of Science ID 000424890101180
A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms
2016; 37 (11): 1885–1909
Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and ≃0.6%), left (99.3%, 98.8% and ≃0.001%) and right (99.53%, 99.3% and ≃0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r > 0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.
View details for DOI 10.1088/0967-3334/37/11/1885
View details for Web of Science ID 000385498000001
View details for PubMedID 27681033