Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Artificial Intelligence in PET: An Industry Perspective

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients

Study of a convergent subsetized list-mode EM reconstruction algorithm

Abstract-We have implemented a convergent subsetized (CS) list-mode reconstruction algorithm, based on previous work [1]- [3] on complete-data OS-EM reconstruction. The first step of the convergent algorithm is exactly equivalent (unlike the histogrammode case) to the regular subsetized list-mode EM algorithm, while the second and final step takes the form of additive updates in image space. A hybrid algorithm based on the ordinary and the convergent algorithms is also proposed, and is shown to combine the advantages of the two algorithms: it is able to reach a higher image quality in fewer iterations while maintaining the convergent behavior, making the hybrid approach a good alternative to the ordinary subsetized list-mode EM algorithm. Reconstructions using various LOR-driven projection techniques (Siddon method, trilinear and bilinear interpolation) were considered and it was demonstrated that in terms of FWHM, the Siddon technique is inferior to the other two algorithms, with the bilinear interpolation technique performing nearly similarly as the trilinear while being considerably faster

Deep learning-based Dosimetry in Radionuclide Therapy:Is It Worth the Effort?

We propose a novel unified framework to perform whole-body voxel-level dosimetry taking into account patient-specific tissue heterogeneity and activity distribution using Monte Carlo (MC) simulations and deep learning algorithms. We extended the core idea of the voxel-scale MIRD dosimetry formalism previously validated for positron emitters used in diagnostic imaging (18F) to radionuclides with complex decay schemes used in therapy (177Lu). In this context, we trained a model to predict the deposited energy distribution obtained from MC simulations (specific S-values), while two-paired input channels consist of density map and dose distribution kernel in soft-tissue (single S-value) are fed into the network. Transfer learning was applied using our previous 18F model fine-tuned on 177Lu dataset. Accordingly, whole-body dose maps were constructed through convolving specific S-values into time-integrated activity distribution obtained from SPECT images. The Deep Neural Network (DNN) predicted dose map was compared with the reference (Monte Carlo-based) and two MIRD-based methods, including single-voxel S-value (SSV) and multiple-voxel S-value (MSV) approaches. The results demonstrated that DNN, MSV and SSV show a comparable performance against the MC approach in soft tissue. However, in small size heterogeneous boundaries (lumbar region), DNN outperformed other approaches achieving lower bias (4%) compared to MSV (26%) and SSV (30%) techniques.</p

Low dose radiation therapy and convalescent plasma:How a hybrid method may maximize benefits for covid-19 patients

Physicians and scientists around the world are aggressively attempting to develop effective treatment strategies. The treatment goal is to reduce the fatality rate in 15% to 20% of individuals infected with SARS-CoV-2 who develop severe inflammatory conditions that can lead to pneumonia, and acute respiratory distress syndrome. These conditions are major causes of death in these patients. Convalescent plasma (CP) col-lected from patients recovered from the novel corona virus disease (COVID-19) has been considered as an effective treatment method for COVID-19. Moreover, low-dose radiation therapy (LDRT) for COVID-19 pneumonia was historically used to treat pneumonia during the first half of the 20th century. The concept of LDRT for CO-VID-19 pneumonia was first introduced in March 2020. Later scientists from Cana-da, Spain, United States, Germany and France also confirmed the potential efficacy of LDRT for treatment of COVID-19 pneumonia. The rationale behind introducing LDRT as an effective treatment method for pneumonia in COVID-19 patients is not only due to its anti-inflammatory effect, but also in optimization of the activity of the immune system. Moreover, LDRT, unlike other treatment methods such as antiviral drugs, does not have the key disadvantage of exerting a significant selective pressure on the SARS-CoV-2 virus and hence does not lead to evolution of the virus through mutations. Given these considerations, we believe that a hybrid treatment including both CP and LDRT can trigger synergistic responses that will help healthcare providers in mitigating today’s COVID-19 pandemic.</p

Analytic System Matrix Resolution Modeling in PET: An Application to Rb-82 Cardiac Imaging

Abstract-An area of growing interest in PET imaging has been that of incorporating increasingly more accurate system matrix elements into the reconstruction task, thus arriving at images of higher quality. This work explores application of an analytic approach which individually models and combines the various resolution degrading phenomenon in PET (inter-crystal scattering, inter-crystal penetration, photon non-collinearity and positron range), and does not require extensive experimental measurements and/or simulations. The approach is able to produce considerable enhancements in image quality. The reconstructed resolution is seen to improve from 5.1mm-7.7mm across the field-of-view (FoV) to ≈3.5mm nearly uniformly across the FoV. Furthermore, phantom studies indicate clearly improved images, while similar significant improvements are seen for the particular task of Rb-82 cardiac imaging. Keywords: Positron emission tomography, Image reconstruction, Image enhancement, Positrons, Compton scattering. I. OVERVIEW AND MOTIVATION In PET imaging, four processes are responsible for degrading image resolution: positron range, photon non-collinearity, intercrystal scattering as well as penetration. Aside from improvements to PET detection (hardware), different reconstruction approaches have been proposed in the literature to model the aforementioned factors, with the aim of improving image resolution. First, let us consider an image with J basis functions (usually voxels) and a histogrammed dataset with I projection bins. We then denote the system matrix as P=(p ij ) I×J , where each element p ij models the probability that an event generated in voxel j is detected along line-of-response (LOR) i. Next, one may decompose [1] the system matrix into three components Here, the matrix B=(b ij ) J×J is used to account for imagebased blurring effects, while the matrix G=(g ij ) I×J contains the geometric probability terms relating each voxel j to an LOR i. In addition, the matrix W=(w ij ) I×I can be used to account for sensitivity variations (i.e. due to attenuation and normalization) as well inter-crystal blurring effects. An approach [2], [3] has been to model overall resolution blurring entirely into the image-space component B of the system matrix. This approach is very straight-forward to implement, and produces images of higher quality. However, the method is somewhat ad hoc and in particular does not model the varying degrees of inter-crystal blurring in the projection space. The method is thus not suited to model the parallax effect. An approach developed in A more accurate approach An alternative approach [5]- A new approach has been to make very accurate noncollimated A new approach is investigated in this work, which takes the approach of analytically modeling each of the resolution degrading phenomenon, followed by their combination in the overall system matrix, thus not requiring extensive simulations or experimental measurements, and producing significantly improved image qualities. We describe each of these next. II. DESCRIPTION OF METHOD A. Positron Range In the seminal work of Palmer and Brownel

Comparative assessment of different energy mapping methods for generation of 511-KEV attenuation map from ct images in pet/ct systems: a phantom study

The use of X-ray CT images for CT-based attenuation correction (CTAC) of PET data results in the decrease of overall scanning time and creates a noise-free attenuation map (μmap). The linear attenuation coefficient (LAC) measured with CT is calculated at the x-ray energy rather than at the 511 keV. It is therefore necessary to convert the linear attenuation coefficients obtained from the CT scan to those corresponding to the 511 keV. Several conversion strategies have been developed including scaling, segmentation, hybrid, bilinear and dual-energy decomposition methods. The aim of this study is to compare the accuracy of different energy mapping methods for generation of attenuation map form CT images. An in-house made polyethylene phantom with different concentrations of K2HPO4 was used in order to quantitatively measure the accuracy of the nominated methods, using quantitative analysis of created μmaps. The generated μmaps using different methods compared with theoretical values calculated using XCOM cross section library. Accurate quantitative analysis showed that for low concentrations of K2HPO4 all these methods produce acceptable attenuation maps at 511 keV, but for high concentration of K2HPO4 the last three methods produced the lowest errors (10.1 in hybrid, 9.8 in bilinear, and 4.7 in dual energy method). The results also showed that in dual energy method, combination of 80 and 140 kVps produces the least error (4.2) compared to other combinations of kVps. ©2008 IEEE

Is correction for metallic artefacts mandatory in cardiac SPECT/CT imaging in the presence of pacemaker and implantable cardioverter defibrillator leads?

Introduction: Metallic artifacts due to pacemaker/ implantable cardioverter defibrillator (ICD) leads in CT images can produce artifactual uptake in cardiac SPECT/CT images. The aim of this study was to determine the influence of the metallic artifacts due to pacemaker and ICD leads on myocardial SPECT/CT imaging. Methods: The study included 9 patients who underwent myocardial perfusion imaging (MPI). A cardiac phantom with an inserted solid defect was used. The SPECT images were corrected for attenuation using both artifactual CT and CT corrected using metal artifact reduction (MAR). VOI-based analysis was performed in artifactual regions. Results: In phantom studies, mean-of-relative-difference in white-region, between artifact-free attenuation-map without/with MAR were changed from 9.2 and 2.1 to 3.7 and 1.2 for ICD and pacemaker lead, respectively. However, these values for typical patient were 9.7±7.0 and 3.8±2.4 for ICD and pacemaker leads respectively, in white-region. MAR effectively reduces the artifacts in white-regions while this reduction is not significant in black-regions. Conclusion: Following application of MAR, visual and quantification analyses revealed that while quality of CT images were significantly improved, the improvements in the SPECT/CT images were not as pronounced or significant. Therefore cardiac SPECT images corrected for attenuation using CT in the presence of metallic-leads can be interpreted without correction for metal artefacts. © 2018 Tehran University of Medical Sciences. All rights reserved