Sign determination methods for the respiratory signal in data-driven PET gating

Patient respiratory motion during PET image acquisition leads to blurring in the reconstructed images and may cause significant artifacts, resulting in decreased lesion detectability, inaccurate standard uptake value calculation and incorrect treatment planning in radiation therapy. To reduce these effects data can be regrouped into (nearly) 'motion-free' gates prior to reconstruction by selecting the events with respect to the breathing phase. This gating procedure therefore needs a respiratory signal: on current scanners it is obtained from an external device, whereas with data driven (DD) methods it can be directly obtained from the raw PET data. DD methods thus eliminate the use of external equipment, which is often expensive, needs prior setup and can cause patient discomfort, and they could also potentially provide increased fidelity to the internal movement. DD methods have been recently applied on PET data showing promising results. However, many methods provide signals whose direction with respect to the physical motion is uncertain (i.e. their sign is arbitrary), therefore a maximum in the signal could refer either to the end-inspiration or end-expiration phase, possibly causing inaccurate motion correction. In this work we propose two novel methods, CorrWeights and CorrSino, to detect the correct direction of the motion represented by the DD signal, that is obtained by applying principal component analysis (PCA) on the acquired data. They only require the PET raw data, and they rely on the assumption that one of the major causes of change in the acquired data related to the chest is respiratory motion in the axial direction, that generates a cranio-caudal motion of the internal organs. We also implemented two versions of a published registration-based method, that require image reconstruction. The methods were first applied on XCAT simulations, and later evaluated on cancer patient datasets monitored by the Varian Real-time Position ManagementTM (RPM) device, selecting the lower chest bed positions. For each patient different time intervals were evaluated ranging from 50 to 300 s in duration. The novel methods proved to be generally more accurate than the registration-based ones in detecting the correct sign of the respiratory signal, and their failure rates are lower than 3% when the DD signal is highly correlated with the RPM. They also have the advantage of faster computation time, avoiding reconstruction. Moreover, CorrWeights is not specifically related to PCA and considering its simple implementation, it could easily be applied together with any DD method in clinical practice

Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

BACKGROUND: Current (18)F-NaF assessments of aortic valve microcalcification using (18)F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements. MATERIALS AND METHODS: Fourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUV(max) values measured in the aortic valve and maximum target-to-background ratios (TBR(max)) values after correcting for blood pool activity. RESULTS: Compared to standard and ECG-MC reconstructions, increases in both SUV(max) and TBR(max) were observed following 3 × MC (SUV(max): Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBR(max): Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R(2) value) to the AVCS when compared to the standard methods (SUV(max): Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBR(max): Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46). CONCLUSION: 3 × MC improves the correlation between the AVCS and SUV(max) and TBR(max) and should be considered in PET studies of aortic valves using (18)F-NaF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-022-00433-7

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Dual gated PET/CT imaging of heart

Coronary artery disease (CAD) resulting from atherosclerotic arterial changes, plaques, is a progressive process, which can be asymptomatic for many years. Asymptomatic CAD can cause a heart attack that leads to sudden death if the vulnerable coronary plaque ruptures and causes artery occlusion. The plaque inflammation plays an important role in the rupture susceptibility. Reliable anticipation of rupture is still clinically impossible for a single patient. Detection of the vulnerable coronary plaques before clinical signs remains a significant scientific challenge where positron emission tomography (PET) can play an important role. The aim of this dissertation was to find out whether a small, coronary plaque size, heart structures could be detected by a clinically available positron emission tomography and computed tomography (PET/CT) hybrid camera in realistically moving cardiac phantoms, a minipig model, and patients with CAD. Due to cardiac motions accurate detection of small heart structures are known to be problematic in PET imaging. Due to absence of commercial application at the beginning of the study, new dual gating method for cardiac PET imaging was developed and programmed that takes into account both contraction and respiratory induced cardiac motions. Cardiac phantom PET studies showed that small, active and moving plaques can be distinguished from myocardium activity and the gating methods improved the detection sensitivity and resolution of the plaques. In minipig and CAD patient cardiac PET studies small structures of myocardium and coronary arteries was detected more sensitive and accurately when using dual gating method than manufacturer gating methods. In cardiac patient PET study respiratory induced cardiac motions were shown to be linearly dependent with spirometry-measured respiratory volumes. Standard 3-lead electrocardiogram (ECG) measurement can be filtered by anesthesia monitor to detect lung impedance signal. In cardiac patient PET study this lung impedance signal were applied for respiratory gating. In this study was observed that the 3-lead ECG derived impedance signal gating method detects respiratory induced cardiac motion in PET as well as other externally used respiratory gating methods. In summary, the dual gated cardiac PET method is more sensitive and accurate to detect small cardiac structures, as coronary vessel wall pathology, than the commercial methods used in the study.Sydämen kaksoisliiketahdistettu PET/CT kuvantaminen Ateroskleroottisten valtimomuutosten, plakkien, seurauksena asteittain kehittyvä sepelvaltimotauti voi olla vuosia oireeton. Oireeton sepelvaltimotauti voi aiheuttaa äkkikuolemaan johtavan sydäninfarktin, mikäli sepelvaltimon seinämäplakin repeytymisestä aiheutuu verisuonen tukkiva hyytymä. Tutkimuksissa on osoitettu, että plakin tulehduksella on merkittävä rooli repeytymisalttiudelle. Repeytymisen luotettava ennakointi on yksittäisen potilaan kohdalla edelleen kliinisesti mahdotonta. Tulehtuneiden ja repeytymisalttiiden sepelvaltimoplakkien toteaminen ennen kliinisiä oireita on edelleen merkittävä tieteellinen haaste, missä positroniemissiotomografia (PET) kuvantamisella voi olla merkittävä rooli. Väitöskirjan tavoitteena oli selvittää, voidaanko kliinisessä käytössä olevalla positroniemissiotomografia ja tietokonetomografia (PET/TT) yhdistelmäkameralla havaita pieniä, sepelvaltimoplakkien kokoisia, sydämen rakenteita koneellisesti toimivissa todenmukaisissa sydänmalleissa, eläinmallissa ja sepelvaltimotautia sairastavilla potilailla. Sydämen pienten rakenteiden tarkka havaitseminen PET/TTkameroilla on haasteellista sydämen liikkumisen vuoksi. Tutkimuksessa kehitettiin ja ohjelmoitiin uusi sydämen PET-kuvantamisen liiketahdistusmenetelmä, joka ottaa huomioon sekä sydämen supistusliikkeen että hengitysliikkeen vaikutuksen sydämen PET kuvantamissa. Koneellisilla sydänmalleilla osoitettiin, että PET on riittävän herkkä havaitsemaan pieniä ja liikkuvia radioaktiivisia ”sepelvaltimoplakkeja”, ja että liiketahdistusmenetelmät parantavat plakkien havaitsemisherkkyyttä ja tarkkuutta. Eläinmallissa ja sepelvaltimotautipotilailla kaksoisliiketahdistusmenetelmän herkkyys ja tarkkuus havaita pieniä sydänlihaksen ja sepelvaltimoiden rakenteita todettiin kaupallisia tahdistusmenetelmiä paremmaksi. Potilastutkimuksissa todettiin hengityksen aiheuttama sydämen liike PET-kuvissa lineaarisesti riippuvaiseksi spirometrialla mitattujen hengitystilavuuksien kanssa. Tavallisesta 3-johtoisesta sydänsähkökäyrästä voidaan anestesiamonitorin avulla suodattaa keuhkojen impedanssisignaalia. Hengitysliikkeen aiheuttama potilaiden sydämen liike PETkuvissa havaittiin yhtä hyvin käyttämällä tätä keuhkojen impedanssisignaalia kuin muita yleisesti käytettäviä ulkoisia hengitystahdistussignaaleja. Todetaan, että kaksoisliiketahdistettu sydämen PET-kuvantamismenetelmä on tutkimuksessa käytettyjä kaupallisia menetelmiä herkempi ja tarkempi havaitsemaan sydämen pieniä rakenteita sekä sepelvaltimon seinämän tulehdusplakkeja

DEVELOPMENT AND APPLICATIONS OF FEATURE-GUIDED CARDIAC MOTION ESTIMATION METHODS FOR 4D CARDIAC PET

The aim of this dissertation research is to develop, implement and evaluate methods to extract useful information about cardiac motion and myocardial contractility from 4D cardiac PET images with much improved image quality. First, to reduce the influence of respiratory motion and improve the quality of cardiac PET images used in motion estimation, data-driven respiratory gating methods are proposed to allow accurate extraction of respiratory motion signal from the list-mode data. Time-of-flight PET information is incorporated into respiratory signal extraction, and background correction method is developed to improve the quality and accuracy of the extracted respiratory signal. The methods were applied and evaluated using clinical list-mode cardiac PET data. With improved image quality, anatomical feature such as papillary muscles and the interventricular sulcus become increasingly detectable in gated cardiac PET images. For more accurate cardiac motion estimation, these anatomical features in human heart were extracted and used in combination with a priori knowledge of cardiac function to guide the cardiac motion estimation process. Initial estimates of the cardiac motion vector field were obtained based on the motion of the features for the traditional optical-flow algorithm. For further improvement, motion of the anatomical feature was used as additional constraint in the motion estimation algorithm to reduce the effect of the classical aperture problem. Different from previous cardiac motion extraction and estimation studies that only provide qualitative evaluation of the motion estimation results due to unavailability of ground truth for clinical cardiac datasets, this study employed simulation data from a realistic digital phantom with known cardiac motion for both qualitative and quantitative evaluation. Motion estimation results from simulation data indicate the feature-based cardiac motion estimation method is able to improve the accuracy of the cardiac motion field estimates, especially for motion components parallel to edges and therefore difficult to estimate using the conventional optical-flow based method. The proposed research will allow PET imaging to provide unprecedented cardiac motion information in addition to its functional information thus improving diagnosis of cardiac diseases including perfusion and motion abnormalities, and patient care with reduced cost. Also, more accurate estimation of cardiac motion will help to further improve the quality of 4D cardiac PET imaging with cardiac motion compensation

Perspectives on Nuclear Medicine for Molecular Diagnosis and Integrated Therapy

nuclear medicine; diagnostic radiolog

A role for artificial intelligence in molecular imaging of infection and inflammation

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [F-18]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment

Imaging and Modeling of Myocardial Metabolism

Current imaging methods have focused on evaluation of myocardial anatomy and function. However, since myocardial metabolism and function are interrelated, metabolic myocardial imaging techniques, such as positron emission tomography, single photon emission tomography, and magnetic resonance spectroscopy present novel opportunities for probing myocardial pathology and developing new therapeutic approaches. Potential clinical applications of metabolic imaging include hypertensive and ischemic heart disease, heart failure, cardiac transplantation, as well as cardiomyopathies. Furthermore, response to therapeutic intervention can be monitored using metabolic imaging. Analysis of metabolic data in the past has been limited, focusing primarily on isolated metabolites. Models of myocardial metabolism, however, such as the oxygen transport and cellular energetics model and constraint-based metabolic network modeling, offer opportunities for evaluation interactions between greater numbers of metabolites in the heart. In this review, the roles of metabolic myocardial imaging and analysis of metabolic data using modeling methods for expanding our understanding of cardiac pathology are discussed

Quantitative PET and SPECT

Since the introduction of personalized medicine, the primary focus of imaging has moved from detection and diagnosis to tissue characterization, the determination of prognosis, prediction of treatment efficacy, and measurement of treatment response. Precision (personalized) imaging heavily relies on the use of hybrid technologies and quantitative imaging biomarkers. The growing number of promising theragnostics require accurate quantification for pre- and post-treatment dosimetry. Furthermore, quantification is required in the pharmacokinetic analysis of new tracers and drugs and in the assessment of drug resistance. Positron Emission Tomography (PET) is, by nature, a quantitative imaging tool, relating the time–activity concentration in tissues and the basic functional parameters governing the biological processes being studied. Recent innovations in single photon emission computed tomography (SPECT) reconstruction techniques have allowed for SPECT to move from relative/semi-quantitative measures to absolute quantification. The strength of PET and SPECT is that they permit whole-body molecular imaging in a noninvasive way, evaluating multiple disease sites. Furthermore, serial scanning can be performed, allowing for the measurement of functional changes over time during therapeutic interventions. This Special Issue highlights the hot topics on quantitative PET and SPECT