Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics.

The objective is to evaluate the performance of blood test results, radiomics, and a combination of the two data types on the prediction of the 24-h oxygenation support need for the Coronavirus disease 2019 (COVID-19) patients. In this retrospective cohort study, COVID-19 patients with confirmed real-time reverse transcription-polymerase chain reaction assay (RT-PCR) test results between February 2020 and August 2021 were investigated. Initial blood cell counts, chest radiograph, and the status of oxygenation support used within 24 h were collected (n = 290; mean age, 45 ± 19 years; 125 men). Radiomics features from six lung zones were extracted. Logistic regression and random forest models were developed using the clinical-only, radiomics-only, and combined data. Ten repeats of fivefold cross-validation with bootstrapping were used to identify the input features and models with the highest area under the receiver operating characteristic curve (AUC). Higher AUCs were achieved when using only radiomics features compared to using only clinical features (0.94 ± 0.03 vs. 0.88 ± 0.04). The best combined model using both radiomics and clinical features achieved highest in the cross-validation (0.95 ± 0.02) and test sets (0.96 ± 0.02). In comparison, the best clinical-only model yielded AUCs of 0.88 ± 0.04 in cross-validation and 0.89 ± 0.03 in test set. Both radiomics and clinical data can be used to predict 24-h oxygenation support need for COVID-19 patients with AUC > 0.88. Moreover, the combination of both data types further improved the performance.

Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients.

To explore the feasibility of combined radiomics of post-treatment I-131 total body scan (TBS) and clinical parameter to predict successful ablation in low-risk papillary thyroid carcinoma (PTC) patients. Data of low-risk PTC patients who underwent total/near total thyroidectomy and I-131 ablation 30 mCi between April 2015 and July 2021 were retrospectively reviewed. The clinical factors studied included age, sex, and pre-ablative serum thyroglobulin (Tg). Radiomic features were extracted via PyRadiomics, and radiomic feature selection was performed. The predictive performance for successful ablation of the clinical parameter, radiomic, and combined models (radiomics combined with clinical parameter) was calculated using the area under the receiver operating characteristic curve (AUC). One hundred and thirty patients were included. Successful ablation was achieved in 77 patients (59.2%). The mean pre-ablative Tg in the unsuccessful group (15.50 ± 18.04 ng/ml) was statistically significantly higher than those in the successful ablation group (7.12 ± 7.15 ng/ml). The clinical parameter, radiomic, and combined models produced AUCs of 0.66, 0.77, and 0.87 in the training sets, and 0.65, 0.69, and 0.78 in the validation sets, respectively. The combined model produced a significantly higher AUC than that of the clinical parameter (p < 0.05). Radiomic analysis of the post-treatment TBS combined with pre-ablative serum Tg showed a significant improvement in the predictive performance of successful ablation in low-risk PTC patients compared to the use of clinical parameter alone.Thai Clinical Trials Registry TCTR identification number is TCTR20230816004 ( https://www.thaiclinicaltrials.org/show/TCTR20230816004 ).

Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma.

BACKGROUND: The prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment. PURPOSE: To investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC. MATERIALS AND METHODS: A retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme. RESULTS: Integrating radiomics with clinical data significantly enhanced the predictive power, yielding a C-index of 0.788 ± 0.066 to 0.848 ± 0.079 for the combined model versus 0.745 ± 0.082 to 0.766 ± 0.083 for clinical data alone (p<0.05). Multimodality radiomics combined with clinical data offered the highest performance. Despite the absence of EBV DNA, radiomics integration significantly improved survival predictions (C-index ranging from 0.770 ± 0.070 to 0.831 ± 0.083 in combined model versus 0.727 ± 0.084 to 0.734 ± 0.088 in clinical model, p<0.05). CONCLUSIONS: The combination of multimodality radiomic features from CT and MR images could offer superior predictive performance for OS, PFS, and DMFS compared to relying on conventional clinical data alone.

A multivariable normal tissue complication probability model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients in the modern radiotherapy era.

Radiation-induced hypothyroidism (RHT) is a common long-term complication for nasopharyngeal carcinoma (NPC) survivors. A model using clinical and dosimetric factors for predicting risk of RHT could suggest a proper dose-volume parameters for the treatment planning in an individual level. We aim to develop a multivariable normal tissue complication probability (NTCP) model for RHT in NPC patients after intensity-modulated radiotherapy or volumetric modulated arc therapy. The model was developed using retrospective clinical data and dose-volume data of the thyroid and pituitary gland based on a standard backward stepwise multivariable logistic regression analysis and was then internally validated using 10-fold cross-validation. The final NTCP model consisted of age, pretreatment thyroid-stimulating hormone and mean thyroid dose. The model performance was good with an area under the receiver operating characteristic curve of 0.749 on an internal (200 patients) and 0.812 on an external (25 patients) validation. The mean thyroid dose at ≤45 Gy was suggested for treatment plan, owing to an RHT incidence of 2% versus 61% in the >45 Gy group.

Improved prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma using pre-treatment CT radiomics.

When planning radiation therapy, late effects due to the treatment should be considered. One of the most common complications of head and neck radiation therapy is hypothyroidism. Although clinical and dosimetric data are routinely used to assess the risk of hypothyroidism after radiation, the outcome is still unsatisfactory. Medical imaging can provide additional information that improves the prediction of hypothyroidism. In this study, pre-treatment computed tomography (CT) radiomics features of the thyroid gland were combined with clinical and dosimetric data from 220 participants to predict the occurrence of hypothyroidism within 2 years after radiation therapy. The findings demonstrated that the addition of CT radiomics consistently and significantly improves upon conventional model, achieving the highest area under the receiver operating characteristic curve (AUCs) of 0.81 ± 0.06 with a random forest model. Hence, pre-treatment thyroid CT imaging provides useful information that have the potential to improve the ability to predict hypothyroidism after nasopharyngeal radiation therapy.

Dual-Tracer Parathyroid Imaging Using Joint SPECT Reconstruction.

Purpose: We assessed the lesion detection performance of the dual-tracer parathyroid SPECT imaging using the joint reconstruction method. Materials and Methods: Thirty-six noise realizations were created from SPECT projections collected from an in-house neck phantom to emulate 99mTc-pertechnetate/99mTc-sestamibi parathyroid SPECT datasets. Difference images representing parathyroid lesions were reconstructed using the subtraction and the joint methods whose corresponding optimal iteration was defined as the iteration which maximized the channelized Hotelling observer signal-to-noise ratio (CHO-SNR). The joint method whose initial estimate was derived from the subtraction method at optimal iteration (the joint-AltInt method) was also assessed. In a study of 36 patients, a human-observer lesion-detection study was performed using difference images from the three methods at optimal iteration and the subtraction method with four iterations. The area under the receiver operating characteristic curve (AUC) was calculated for each method. Results: In the phantom study, both the joint-AltInt method and the joint method improved SNR compared to the subtraction method at their optimal iteration by 444% and 81%, respectively. In the patient study, the joint-AltInt method yielded the highest AUC of 0.73 as compared with 0.72, 0.71, and 0.64 from the joint method, the subtraction method at optimal iteration, and the subtraction method at four iterations. At a specificity of at least 0.70, the joint-AltInt method yielded significantly higher sensitivity than the other methods (0.60 vs 0.46, 042, and 0.42; p < 0.05). Conclusions: The joint reconstruction method yielded higher lesion detectability than the conventional method and holds promise for dual-tracer parathyroid SPECT imaging.

Depressive symptoms due to stroke are strongly predicted by the volume and location of the cerebral infarction, white matter hyperintensities, hypertension, and age: A precision nomothetic psychiatry analysis.

OBJECTIVES: To delineate the effects of white matter hyperintensities (WMHs) as measured by Fluid-attenuated inversion recovery (FLAIR) and infarction volume as measured by Diffusion-weighted imaging (DWI) on post-stroke depression symptoms. METHODS: Baseline National Institutes of Health Stroke Score (NIHSS) and Modified Rankin Scale (mRS) scores, and FLAIR and DWI MRIs to assess WMHs and acute infarct volumes, respectively, were assessed in 47 patients (≥55 years) with acute ischemic stroke and 17 normal controls. The Montgomery-Åsberg Depression Rating Scale (MDRS) was assessed three months after the stroke. RESULTS: The MADRS score was significantly increased in stroke patients as compared with normal controls. The MADRS scale is not unidimensional and cannot be used as an accurate indicator of depression severity in stroke patients. Three months after stroke, key depressive (sadness and inability to feel) and concentration-tension symptoms, and lassitude are significantly predicted by the infarct volume. Right side infarction strongly predicts key depressive symptoms and left side infarction strongly predicts concentration-tension and lassitude scores. Total WMHs significantly predict key depressive and concentration-tension symptoms, and lassitude, with these effects being mediated by right and left DWI stroke volumes and associated disabilities. CONCLUSIONS: Interactions between age, hypertension, a chronic atherosclerotic process, and acute stroke account for the onset of key depressive symptoms three months after the acute infarct. Chronic and acute neuro-immune and neuro-oxidative stress pathways associated with the formation of WMHs and acute stroke may explain the incidence of post-stroke key depressive and concentration-tension symptoms, and lassitude.

Tumor Prognostic Prediction of Nasopharyngeal Carcinoma Using CT-Based Radiomics in Non-Chinese Patients.

PURPOSE: We aimed to construct predictive models for the overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) for nasopharyngeal carcinoma (NPC) patients by using CT-based radiomics. MATERIALS AND METHODS: We collected data from 197 NPC patients. For each patient, radiomic features were extracted from the CT image acquired at pretreatment via PyRadiomics. Feature selection was performed in two steps. First, features with high inter-observer variability based on multiple tumor delineations were excluded. Then, stratified bootstrappings were performed to identify feature combinations that most frequently achieved the highest (i) area under the receiver operating characteristic curve (AUC) for predicting 3-year OS, PFS, and DMFS or (ii) Harrell's C-index for predicting time to event. Finally, regularized logistic regression and Cox proportional hazard models with the most frequently selected feature combinations as input were tuned using cross-validation. Additionally, we examined the robustness of the constructed model to variation in tumor delineation by simulating 100 realizations of radiomic feature values to mimic features extracted from different tumor boundaries. RESULTS: The combined model that used both radiomics and clinical features yielded significantly higher AUC and Harrell's C-index than models using either feature set alone for all outcomes (p < 0.05). The AUCs and Harrell's C-indices of the clinical-only and radiomics-only models ranged from 0.758 ± 0.091 to 0.789 ± 0.082 and from 0.747 ± 0.062 to 0.767 ± 0.074, respectively. In comparison, the combined models achieved AUC of 0.801 ± 0.075 to 0.813 ± 0.078 and Harrell's C-indices of 0.779 ± 0.066 to 0.796 ± 0.069. The results showed that our models were robust to variation in tumor delineation with the coefficient of variation ranging from 4.8% to 6.4% and from 6.7% to 9.3% for AUC and Harrell's C-index, respectively. CONCLUSION: Our results demonstrated that using CT-based radiomic features together with clinical features provided superior NPC prognostic prediction than using either clinical or radiomic features alone.

Impact of reconstruction parameters on lesion detection and localization in joint ictal/inter-ictal SPECT reconstruction.

PURPOSE: Previously, a joint ictal/inter-ictal SPECT reconstruction was proposed to reconstruct a differential image representing the change of brain SPECT image from an inter-ictal to an ictal study. The so-called joint method yielded better performance for epileptic foci localization than the conventional subtraction method. In this study, we evaluated the performance of different reconstruction settings of the joint reconstruction of ictal/inter-ictal SPECT data, which creates a differential image showing the difference between ictal and inter-ictal images, in lesion detection and localization in epilepsy imaging. METHODS: Differential images reconstructed from phantom data using the joint and the subtraction methods were compared based on lesion detection performance (channelized Hotelling observer signal-to-noise ratio (SNRCHO) averaged across four lesion-to-background contrast levels) at the optimal iteration. The joint-initial method which was the joint method that was initialized by the subtraction method at optimal iteration was also used to reconstruct differential images. These three methods with respective optimal iteration and the subtraction method with four iterations were applied to epileptic patient datasets. A human observer lesion localization study was performed based on localization receiver operating characteristic (LROC) analysis. RESULTS: From the phantom study, at their respective optimal iteration, the joint method yielded an improvement in lesion detection performance over the subtraction method of 26%, which increased to 145% when using the joint-initial method. From the patient study, the joint-initial method yielded the highest area under the LROC curve as compared with those of the joint and the subtraction methods with optimal iteration and with 4 iterations (0.44 vs 0.41, 0.39 and 0.36, respectively). CONCLUSIONS: In lesion detection and localization, the joint method at optimal iteration outperformed the subtraction method at optimal iteration and at iteration typically used in clinical practice. Furthermore, initialization by the subtraction method improved the performance of the joint method.

Pharmacokinetic Modeling of 18F-FDOPA PET in the Human Brain for Early Parkinson's Disease

Objectives: Early detection is essential for the treatment approaches of Parkinson's disease (PD). Clinical criteria alone may be insufficient to distinguish early PD from other conditions. This study aimed to investigate the transfer rate constants of 6-18F-fluoro-L-dopa (18F-FDOPA) in positron emission tomography (PET) brain images as a sensitive parameter to detect early PD. Methods: Retrospective 18F-FDOPA PET data of five patients with early PD were collected. PET data were acquired for 90 min after intravenous injection of 306-379 MBq 18F-FDOPA, and reconstructed into a series of 18 five-minute frames. Reoriented PET images were coregistered and normalized with the PET brain template on the statistical parametric mapping. The 18F-FDOPA activity concentrations were measured in the striatum, caudate, and putamen on both sides: Contralateral (as PD) and ipsilateral (as control) to the main motor symptoms. The pharmacokinetic model was generated using the SAAM II simulation software. The transfer rate constants across the blood-brain barrier (forward, K1 and reverse, k2) and decarboxylation rate constants (k3) were estimated in these regions. Results: The activity uptakes in the contralateral striatum (0.0323%±0.0091%) and putamen (0.0169%±0.0054%) were significantly lower than the control (0.0353%±0.0086%, 0.0199%±0.0054%, respectively). The K1 and k3 were significantly lower in the contralateral striatum and putamen (p<0.05). There were no significant differences in any transfer rate constants in the caudate. Conclusion: The transfer rate constants (K1 and k3) of 18F-FDOPA on the contralateral striatum and putamen were significantly lower than the control. These biokinetic data could be potential indicators for quantitative detection of early PD diagnosis.

Determination of radiation dose and low-dose protocol for digital chest tomosynthesis using radiophotoluminescent (RPL) glass dosimeters.

PURPOSE: This study aimed to determine a low-dose protocol for digital chest tomosynthesis (DTS). METHODS: Five simulated nodules with a CT number of approximately 100 HU with size diameter of 3, 5, 8, 10, and 12 mm were inserted into an anthropomorphic chest phantom (N1 Lungman model), and then scanned by DTS system (Definium 8000) with varying tube voltage, copper filter thickness, and dose ratio. Three radiophotoluminescent (RPL) glass dosimeters, type GD-352 M with a dimension of 1.5 × 12 mm, were used to measure the entrance surface air kerma (ESAK) in each protocol. The effective dose (ED) was calculated using the recorded total dose-area-product (DAP). The signal-to-noise ratio (SNR) was determined for qualitative image quality evaluation. The image criteria and nodule detection capability were scored by two experienced radiologists. The selected low-dose protocol was further applied in a clinical study with 30 pulmonary nodule follow-up patients. RESULTS: The average ESAK obtained from the standard default protocol was 1.68 ± 0.15 mGy, while an ESAK of 0.47 ± 0.02 mGy was found for a low-dose protocol. The EDs for the default and low-dose protocols were 313.98 ± 0.72 µSv and 100.55 ± 0.28 µSv, respectively. There were small non-significant differences in the image criteria and nodule detection scoring between the low-dose and default protocols interpreted by two radiologists. The effective dose of 98.87 ± 0.08 µSv was obtained in clinical study after applying the low-dose protocol. CONCLUSIONS: The low-dose protocol obtained in this study can substantially reduce radiation dose while preserving an acceptable image quality compared to the standard protocol.

Antral contraction rate estimation from dynamic antral scintigraphy using Hilbert-Huang transform.

Conventional analysis of the gastric antral contraction rate (ACR) utilizes the Fourier transform (FT) which does not effectively capture the non-stationary property of dynamic antral scintigraphy (DAS). In this study, we showed that application of Hilbert-Huang transform (HHT) on DAS yielded better estimates of ACR. Specifically, the time activity curves were extracted from the DAS data of 18 healthy volunteers and subjected to FT and HHT analyses. Comparison of the mean, standard deviation (SD), and root mean square error (RMSE) of ACR estimated by both methods showed that the proposed HHT method yielded significantly smaller SD (p<0.00001), smaller relative SD (13.3% versus 53.7%) and RMSE (0.72 cpm versus 1.59 cpm). Moreover, the HHT method also achieved lower relative SD of the frequency values from the intrinsic mode functions. Overall results indicated that the HHT method outperformed the conventional FT method in estimating the ACR from DAS. We anticipate that our approach will lead to development of effective noninvasive diagnoses of gastrointestinal tract diseases using DAS.

Preoperative parathyroid localization using joint planar imaging.

The conventional approach for preoperative parathyroid localization with the dual 99mTc-sestamibi (99mTc-MIBI) and 99mTc-pertechnetate (99mTcO4- ) scintigraphy technique obtains the differential image by subtracting images from the two scans; the former depicts both the thyroid and parathyroid glands while the latter depicts the thyroid only. In this study, we developed a novel method based on Poisson noise modeling and maximum-likelihood estimation to generate the differential image in an iterative fashion using both planar images jointly. We demonstrated improved performance of our joint method as compared with the subtraction method in both phantom and patient studies. In the phantom study, we acquired two noise-free planar datasets using 99mTc on an in-house thyroid phantom and a "lesion" bead (representing a parathyroid gland) with the same attenuation background as the thyroid phantom. These two planar datasets were combined and scaled to simulate noise-free clinical MIBI (four lesion-to-background contrast (LBC) values: 1.2, 1.3, 1.4 and 1.5), and 99mTcO4- datasets. One-hundred Poisson noise realizations were generated for each datasets. The mean and standard deviation (SD) of the lesion contrast in the differential images were computed for both the subtraction and the joint methods. We also applied both the subtraction and the joint methods to one parathyroid patient dataset. The voxel-wise mean-to-SD ratios in four hyperfunctioning parathyroid lesions were calculated. The phantom results showed that the joint method at the 50th iteration yielded a significant SD reduction compared with the subtraction method ranging from 20% to 45% (p <; 0.05). Similarly, the voxel-wise mean-to-SD ratios were substantially improved in the patient study from 0.40-1.60 (subtraction) to 2.68-3.16 (joint).

Impact of injection time on migration of SPECT seizure onset in temporal lobe epilepsy.

In this work, we investigated an impact of injection time on migration of seizure-onset in ictal/interictal single photon emission computed tomography (SPECT) study for patients with temporal lobe epilepsy. We selected 33 patients with refractory temporal lobe epilepsy who underwent ictal/interictal SPECT studies and had preoperative intracranial EEG result or surgical resection which was used as reference for seizure location. We divided all patients into two groups, which are the fast and the delayed groups; the delayed group comprised patients with injection time more than a cutoff time and vice versa. Using the subtraction ictal-interictal SPECT co-registered with SPECT (SISCOS) with varied Z-threshold (1.0, 1.5, 2.0 and 2.5), a method similar to subtraction ictal SPECT co-registered to magnetic resonance imaging (MRI) (SISCOM), a seizure-onset region in the SISCOS image was localized at the region with maximum sum of Z-scores. For each pair of cutoff time and Z-threshold, we determined the migratory proportion which was defined as the proportion of patients whose seizure-onset location based on SISCOS image was discordant with the reference. At cutoff time of 32-35 seconds and the Z-threshold of 2.0, the migratory proportion values were 7/26 (26.9%) and 5/7 (71.4%) in the fast and the delayed groups, respectively. At the same range of cutoff time with the Z-threshold of 2.5, the migratory proportion was 8/26 (30.8%) in the fast group while the proportion was 5/7 (71.4%) in the delayed group. Using Fisher's exact test, the migratory proportion values at the Z-threshold of 2.0 and 2.5 were significantly different between the fast and the delayed groups (p = 0.0709 and 0.0838, respectively), suggesting that patients with temporal lobe epilepsy who undergo an ictal/interictal SPECT study with injection time longer than 35 seconds tend to have seizure-onset zone migration in the SISCOS analysis with the traditionally-used Z-threshold of 2.0.

Analysis of dynamic antral scintigraphy using empirical mode decomposition.

In this work, we proposed to use the empirical mode decomposition (EMD) to analyze signals from dynamic antral scintigraphy (DAS) for antral contraction rate estimation. The proposed EMD-Fourier method analyzes a DAS time-activity curve (TAC) which is called a DAS signal by estimating the frequency from the Fourier transform of each intrinsic mode function (IMF), thus yielding multiple frequency values for each TAC as opposed to one frequency value obtained from the conventional Fourier method. Twenty-three TACs extracted from DAS data acquired on twenty-three healthy volunteers were analyzed using both the EMD-Fourier method and the Fourier method. The mean and standard deviation of frequency across all volunteers were computed. The result showed that the EMD-Fourier method provided a reduction of SD-to-mean ratio from the Fourier method ranging from 64.3% to 85.8%. The mean frequencies from the third IMF in the EMD-Fourier method and from the Fourier method were 2.76 and 3.44 cycles per minute were close to a widely-used normal antral contraction rate (3.0 cycles per minute), while the EMD-Fourier method yielded significant SD reduction (from 4.77 to 0.57: p <; 0.0001). Moreover, it was found that the first IMF yielded a frequency estimate of 11.26 ± 2.2 cycles per minute, while the second IMF yielded a frequency estimate of 4.3 ± 1.3 cycles per minute, which are similar to the contraction rates of the duodenum and the large intestine, respectively.

Magnetic Resonance-based Motion Correction for Quantitative PET in Simultaneous PET-MR Imaging.

Motion degrades image quality and quantitation of PET images, and is an obstacle to quantitative PET imaging. Simultaneous PET-MR offers a tool that can be used for correcting the motion in PET images by using anatomic information from MR imaging acquired concurrently. Motion correction can be performed by transforming a set of reconstructed PET images into the same frame or by incorporating the transformation into the system model and reconstructing the motion-corrected image. Several phantom and patient studies have validated that MR-based motion correction strategies have great promise for quantitative PET imaging in simultaneous PET-MR.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies.

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans-each containing 1/8th of the total number of events-were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of [Formula: see text], the tracer transport rate (ml · min-1 · ml-1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced [Formula: see text] maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced [Formula: see text] estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Joint reconstruction of Ictal/inter-ictal SPECT data for improved epileptic foci localization.

PURPOSE: To improve the performance for localizing epileptic foci, we have developed a joint ictal/inter-ictal SPECT reconstruction method in which ictal and inter-ictal SPECT projections are simultaneously reconstructed to obtain the differential image. METHODS: We have developed a SPECT reconstruction method that jointly reconstructs ictal and inter-ictal SPECT projection data. We performed both phantom and patient studies to evaluate the performance of our joint method for epileptic foci localization as compared with the conventional subtraction method in which the differential image is obtained by subtracting the inter-ictal image from the co-registered ictal image. Two low-noise SPECT projection datasets were acquired using 99m Tc and a Hoffman head phantom at two different positions and orientations. At one of the two phantom locations, a low-noise dataset was also acquired using a 99m Tc-filled 3.3-cm sphere with a cold attenuation background identical to the Hoffman phantom. These three datasets were combined and scaled to mimic low-noise clinical ictal (three different lesion-to-background contrast levels: 1.25, 1.55, and 1.70) and inter-ictal scans. For each low-noise dataset, 25 noise realizations were generated by adding Poisson noise to the projections. The mean and standard deviation (SD) of lesion contrast in the differential images were computed using both the conventional subtraction and our joint methods. We also applied both methods to the 35 epileptic patient datasets. Each differential image was presented to two nuclear medicine physicians to localize a lesion and specify a confidence level. The readers' data were analyzed to obtain the localized-response receiver operating characteristic (LROC) curves for both the subtraction and joint methods. RESULTS: For the phantom study, the difference between the mean lesion contrast in the differential images obtained using the conventional subtraction versus our joint method decreases as the iteration number increases. Compared with the conventional subtraction approach, the SD reduction of lesion contrast at the 10th iteration using our joint method ranges from 54.7% to 68.2% (P < 0.0005), and 33.8% to 47.9% (P < 0.05) for 2 and 4 million total inter-ictal counts, respectively. In the patient study, our joint method increases the area under LROC from 0.24 to 0.34 and from 0.15 to 0.20 for the first and second reader, respectively. We have demonstrated improved performance of our method as compared to the standard subtraction method currently used in clinical practice. CONCLUSION: The proposed joint ictal/inter-ictal reconstruction method yields better performance for epileptic foci localization than the conventional subtraction method.

Numerical observer for atherosclerotic plaque classification in spectral computed tomography.

Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dual-energy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre-Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all [Formula: see text]). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all [Formula: see text]) for FBP-Ramp images and 53.2% to 67.7% (all [Formula: see text]) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications.

Quantification and normalization of noise variance with sparsity regularization to enhance diffuse optical tomography.

Conventional reconstruction of diffuse optical tomography (DOT) is based on the Tikhonov regularization and the white Gaussian noise assumption. Consequently, the reconstructed DOT images usually have a low spatial resolution. In this work, we have derived a novel quantification method for noise variance based on the linear Rytov approximation of the photon diffusion equation. Specifically, we have implemented this quantification of noise variance to normalize the measurement signals from all source-detector channels along with sparsity regularization to provide high-quality DOT images. Multiple experiments from computer simulations and laboratory phantoms were performed to validate and support the newly developed algorithm. The reconstructed images demonstrate that quantification and normalization of noise variance with sparsity regularization (QNNVSR) is an effective reconstruction approach to greatly enhance the spatial resolution and the shape fidelity for DOT images. Since noise variance can be estimated by our derived expression with relatively limited resources available, this approach is practically useful for many DOT applications.