Quantitative Comparison of Color-Coded Parametric Imaging Technologies Based on Digital Subtraction and Digital Variance Angiography: A Retrospective Observational Study.

The evaluation of hemodynamic conditions in critical limb-threatening ischemia (CLTI) patients is inevitable in endovascular interventions. In this study, the performance of color-coded digital subtraction angiography (ccDSA) and the recently developed color-coded digital variance angiography (ccDVA) was compared in the assessment of key time parameters in lower extremity interventions. The observational study included 19 CLTI patients who underwent peripheral vascular intervention at our institution in 2020. Pre- and post-dilatational images were retrospectively processed and analyzed by a commercially available ccDSA software (Kinepict Medical Imaging Tool 6.0.3; Kinepict Health Ltd., Budapest, Hungary) and by the recently developed ccDVA technology. Two protocols were applied using both a 4 and 7.5 frames per second acquisition rate. Time-to-peak (TTP) parameters were determined in four pre- and poststenotic regions of interest (ROI), and ccDVA values were compared to ccDSA read-outs. The ccDVA technology provided practically the same TTP values as ccDSA (r = 0.99, R2 = 0.98, p < 0.0001). The correlation was extremely high independently of the applied protocol or the position of ROI; the r value was 0.99 (R2 = 0.98, p < 0.0001) in all groups. A similar correlation was observed in the change in passage time (r = 0.98, R2 = 0.96, p < 0.0001). The color-coded DVA technology can reproduce the same hemodynamic data as a commercially available DSA-based software; therefore, it has the potential to be an alternative decision-supporting tool in catheter labs.

Washout-parametric imaging with Sonazoid for enhanced differentiation of focal liver lesions.

PURPOSE: The study aimed to compare the diagnostic performance of washout-parametric imaging (WOPI) with that of conventional contrast-enhanced ultrasound (cCEUS) in differentiating focal liver lesions (FLLs). METHODS: A total of 181 FLLs were imaged with contrast-enhanced ultrasound using Sonazoid, and the recordings were captured for 10 minutes in a prospective setting. WOPI was constructed from three images, depicting the arterial phase (peak enhancement), the early portal venous phase (1-minute post-injection), and the vasculo-Kupffer phase (5 or 10 minutes post-injection). The intensity variations in these images were color-coded and superimposed to produce a single image representing the washout timing across the lesions. From the 181 FLLs, 30 hepatocellular carcinomas (HCCs), 30 non-HCC malignancies, and 30 benign lesions were randomly selected for an observer study. Both techniques (cCEUS and WOPI) were evaluated by four off-site readers. They classified each lesion as benign or malignant using a continuous rating scale, with the endpoints representing "definitely benign" and "definitely malignant." The diagnostic performance of cCEUS and WOPI was compared using the area under the receiver operating characteristic curve (AUC) with the DeLong test. Interobserver agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS: The difference in average AUC values between WOPI and cCEUS was 0.0062 (95% confidence interval, -0.0161 to 0.0285), indicating no significant difference between techniques. The interobserver agreement was higher for WOPI (ICC, 0.77) than cCEUS (ICC, 0.67). CONCLUSION: The diagnostic performance of WOPI is comparable to that of cCEUS in differentiating FLLs, with superior interobserver agreement.

Assessment of dual time point protocols to produce parametric Ki images in FDG PET/CT: A virtual clinical study.

PURPOSE: This simulation study investigated the feasibility of generating Patlak Ki images using a dual time point (DTP-Ki) scan protocol involving two 3-min/bed routine static PET scans and, subsequently, assessed DTP-Ki performance for an optimal DTP scan time frame combination, against conventional Patlak Ki estimated from complete 0-93 min dynamic PET data. METHODS: Six realistic heterogeneous tumors of different characteristic spatiotemporal [18F]FDG uptake distributions for three noise levels commonly found in clinical studies and 20 noise realizations (N = 360 samples) were produced by analytic simulations of the XCAT phantom. Subsequently, DTP-Ki images were generated by performing standard linear indirect Patlak analysis with t* ≥ 12 $ \ge 12$ -min (Patlakt* = 12) using a scaled population-based input function (sPBIF) model on 66 combinations of early and late 3-min/bed static whole-body PET reconstructed images. All DTP-Ki images were evaluated against respective DTP-Ki images estimated with Patlakt* = 12 and 0-93 min individual input functions (iIFs) and against gold standard Ki images estimated with Patlakt* = 12, 0-93 min iIFs and tissue time activity curves from all reconstructed WB passes 12-93 min post injection. The optimal combination of early and late frames, in terms of attaining the highest correlation between DTP-Ki with sPBIF and gold standard Ki was also determined from a set of 66 different combinations of 2-min early and late frames. Moreover, the performance of DTP-Ki with sPBIF was compared against that of the retention index (RI) in terms of their correlation to the gold standard Ki. Finally, the feasibility and practicality of DTP protocol in the clinic were assessed through the analysis of nine patients. RESULTS: High correlations (>0.9) were observed between DTP-Ki values from sPBIF and those from iIFs for all evaluated DTP protocols while the mean AUC difference between sPBIF and iIFs was less than 10%. The percentage difference of mean values between DTP-Ki from sPBIF and from iIFs was less than 1%. DTP Ki from sPBIF exhibited significantly higher correlation with gold standard Ki, in contrast to RI, across all 66 DTP protocols (p < 0.05 using the two-tailed t-test by Williams) with the highest correlation attained for the 50-53-min early + 90-93-min late scan time frames (optimal DTP protocol). CONCLUSION: Feasibility of generating Patlak Ki [18F] FDG images from an early and a late post injection 3-min/bed routine static scan using a population-based input function model was demonstrated and an optimal DTP scan protocol was determined. The results indicated high correlations between DTP-Ki and gold-standard Ki images that are significantly larger than those between RI and gold-standard Ki.

Pixel-based small-window parametric ultrasound imaging for liver tumor characterization.

Purpose: Characterizing liver tumors remains a challenge in clinical practice. Ultrasound parametric imaging based on statistical distribution can enhance image contrast compared with B-mode imaging, requiring scatterers following specific distributions. This study proposes a pixel-based small-window parametric ultrasound imaging method using weighted horizontally normalized Shannon entropy (WhNSE) and fuzzy entropy (FE) to improve detectability liver tumor. Methods: Pixel-based parametric imaging requires a sliding window to traverse across the B-mode image with the step of one pixel, while calculating the entropy by the pixel values in the window. The entropy is assigned to the center pixel of the sliding window. The entropy image is obtained after getting the entropy values of all pixels. FE and WhNSE are two novel entropies first applied to parametric imaging. The detection abilities of regions of interest (ROI) and the contrast-to-noise ratio (CNR) were evaluated through simulations and clinical explorations. Results: In simulations, FE imaging showed the highest improvement in detecting hyperechoic ROIs, with a CNR gain up to 457.31% (p < 0.01) in simulations. WhNSE imaging demonstrated the best performance in hyperechoic ROI detection, with a CNR of 1.607 ± 0.816 (p = 0.05), significantly higher than B-mode images. Conclusions: The proposed pixel-based parametric imaging method based on fuzzy entropy and weighted horizontally normalized Shannon entropy both effectively enhance the contrast and detectability of ultrasound images. The imaging enhancement method of the pixel-based fuzzy entropy imaging with proper parameters got better detection performance, due to the consideration of the relationship of neighboring pixels.

Impact of different parametric Patlak imaging approaches and comparison with a 2-tissue compartment pharmacokinetic model with a long axial field-of-view (LAFOV) PET/CT in oncological patients.

AIM: The recently introduced Long-Axial-Field-of-View (LAFOV) PET-CT scanners allow for the first-time whole-body dynamic- and parametric imaging. Primary aim of this study was the comparison of direct and indirect Patlak imaging as well as the comparison of different time frames for Patlak calculation with the LAFOV PET-CT in oncological patients. Secondary aims of the study were lesion detectability and comparison of Patlak analysis with a two-tissue-compartment model (2TCM). METHODOLOGY: 50 oncological patients with 346 tumor lesions were enrolled in the study. All patients underwent [18F]FDG PET/CT (skull to upper thigh). Here, the Image-Derived-Input-Function) (IDIF) from the descending aorta was used as the exclusive input function. Four sets of images have been reviewed visually and evaluated quantitatively using the target-to-background (TBR) and contrast-to-noise ratio (CNR): short-time (30 min)-direct (STD) Patlak Ki, short-time (30 min)-indirect (STI) Patlak Ki, long-time (59.25 min)-indirect (LTI) Patlak Ki, and 50-60 min SUV (sumSUV). VOI-based 2TCM was used for the evaluation of tumor lesions and normal tissues and compared with the results of Patlak model. RESULTS: No significant differences were observed between the four approaches regarding the number of tumor lesions. However, we found three discordant results: a true positive liver lesion in all Patlak Ki images, a false positive liver lesion delineated only in LTI Ki which was a hemangioma according to MRI and a true negative example in a patient with an atelectasis next to a lung tumor. STD, STI and LTI Ki images had superior TBR in comparison with sumSUV images (2.9-, 3.3- and 4.3-fold higher respectively). TBR of LTI Ki were significantly higher than STD Ki. VOI-based k3 showed a 21-fold higher TBR than sumSUV. Parameters of different models vary in their differential capability between tumor lesions and normal tissue like Patlak Ki which was better in normal lung and 2TCM k3 which was better in normal liver. 2TCM Ki revealed the highest correlation (r = 0.95) with the LTI Patlak Ki in tumor lesions group and demonstrated the highest correlation with the STD Patlak Ki in all tissues group and normal tissues group (r = 0.93 and r = 0.74 respectively). CONCLUSIONS: Dynamic [18F]-FDG with the new LAFOV PET/CT scanner produces Patlak Ki images with better lesion contrast than SUV images, but does not increase the lesion detection rate. The time window used for Patlak imaging plays a more important role than the direct or indirect method. A combination of different models, like Patlak and 2TCM may be helpful in parametric imaging to obtain the best TBR in the whole body in future.

Accurate brain pharmacokinetic parametric imaging using the blood input function extracted from the cavernous sinus.

Brain pharmacokinetic parametric imaging based on dynamic positron emission tomography (PET) scan is valuable in the diagnosis of brain tumor and neurodegenerative diseases. For short-axis PET system, standard blood input function (BIF) of the descending aorta is not acquirable during the dynamic brain scan. BIF extracted from the intracerebral vascular is inaccurate, making the brain parametric imaging task challenging. This study introduces a novel technique tailored for brain pharmacokinetic parameter imaging in short-axis PET in which the head BIF (hBIF) is acquired from the cavernous sinus. The proposed method optimizes the hBIF within the Patlak model via data fitting, curve correction and Patlak graphical model rewriting. The proposed method was built and evaluated using dynamic PET datasets of 67 patients acquired by uEXPLORER PET/CT, among which 64 datasets were used for data fitting and model construction, and 3 were used for method testing; using cross-validation, a total of 15 patient datasets were finally used to test the model. The performance of the new method was evaluated via visual inspection, root-mean-square error (RMSE) measurements and VOI-based accuracy analysis using linear regression and Person's correlation coefficients (PCC). Compared to directly using the cavernous sinus BIF directly for parameter imaging, the new method achieves higher accuracy in parametric analysis, including the generation of Patlak plots closer to the standard plots, better visual effects and lower RMSE values in the Ki (P = 0.0012) and V (P = 0.0042) images. VOI-based analysis shows regression lines with slopes closer to 1 (P = 0.0019 for Ki ) and smaller intercepts (P = 0.0085 for V). The proposed method is capable of achieving accurate brain pharmacokinetic parametric imaging using cavernous sinus BIF with short-axis PET scan. This may facilitate the application of this imaging technology in the clinical diagnosis of brain diseases.

Effect of singular value decomposition on removing injection variability in 2D quantitative angiography: An in silico and in vitro phantoms study.

BACKGROUND: Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD-based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations. PURPOSE: Building on the identified limitations in QA, the study aims to adapt SVD-based deconvolution techniques from CTP to QA for IAs. This adaptation seeks to capitalize on the high temporal resolution of QA, despite its two-dimensional nature, to enhance the consistency and accuracy of hemodynamic parameter assessment. The goal is to develop a method that can reliably assess hemodynamic conditions in IAs, independent of injection variables, for improved neurovascular diagnostics. MATERIALS AND METHODS: The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using computational fluid dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time-density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Next, correlations between QA parameters, injection duration, and inlet velocity were assessed for unconvolved and deconvolved data for all SVD methods. Additionally, we performed an in vitro study, to complement our in silico investigation. We generated a 2D DSA using a flow circuit design for a patient-specific internal carotid artery phantom. The DSA showcases factors like x-ray artifacts, noise, and patient motion. We evaluated QA parameters for the in vitro phantoms using different SVD variants and established correlations between QA parameters, injection duration, and velocity for unconvolved and deconvolved data. RESULTS: The different SVD algorithm variants showed strong correlations between flow and deconvolution-adjusted QA parameters. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment. CONCLUSION: Implementing SVD-based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters.

Color-coded parametric imaging support display of vessel hemorrhage-an in vitro experiment and clinical validation study.

Background: Digital Subtraction Angiography (DSA) is currently the most effective diagnostic method for vascular diseases, but it is still subject to various factors, resulting in uncertain diagnosis. Therefore, a new technology is needed to help clinical doctors improve diagnostic accuracy and efficiency. Purpose: The objective of the study was to investigate the effect of utilizing color-coded parametric imaging techniques on the accuracy of identifying active bleeding through DSA, the widely accepted standard for diagnosing vascular disorders. Methods: Several variables can delay the diagnosis and treatment of active bleeding with DSA. To resolve this, we carried out an in vitro simulation experiment to simulate vascular hemorrhage and utilized five color-coded parameters (area under curve, time to peak, time-of-arrival, transit time, and flow rate of contrast agent) to determine the optimal color coding parameters. We then verified it in a clinical study. Results: Five different color-coded parametric imaging methods were compared and the time-of-arrival color coding was the most efficient technique for diagnosing active hemorrhage, with a statistically significant advantage (P < 0.001). In clinical study, 135 patients (101 with confirmed bleeding and 34 with confirmed no bleeding) were collected. For patients whose bleeding could not be determined using DSA alone (55/101) and whose no bleeding could not be diagnosed by DSA alone (35/55), the combination of time-of-arrival color parametric imaging was helpful for diagnosis, with a statistically significant difference (P < 0.01 and P = 0.01). Conclusions: The time-of-arrival color coding imaging method is a valuable tool for detecting active bleeding. When combined with DSA, it improves the visual representation of active hemorrhage and improves the efficiency of diagnosis.

Differential Diagnosis of Pathological Type of Peripheral Lung Cancer with Multimodal Contrast-Enhanced Ultrasound.

OBJECTIVE: The goal of the work described here was to investigate the role of multimodal contrast-enhanced ultrasound in the differential diagnosis of peripheral lung cancer. METHODS: From April 2017 to July 2021, 109 patients with confirmed pulmonary malignant lesions who underwent CEUS examination were involved in our study. Seven patients were excluded because of the short duration of CEUS video or unsatisfactory imaging. Finally,102 patients with peripheral lung cancer were enrolled in this study. The maximum diameter of the lesions ranged from 1.6 to 13.0 cm (mean 6.2 ± 2.3 cm). On the basis of the pathological results, the patients were divided into the small cell lung cancer (SCLC) group and non-small cell lung cancer (NSCLC) group (including adenocarcinoma, lung squamous cell carcinoma and large cell neuroendocrine carcinoma). A Logiq E9 ultrasonic machine equipped with a 3.5 to 5.0 MHz C5-1 probe was used. Patient clinical information, CEUS features, CPI patterns and TIC parameters were analyzed and compared between different groups. Statistical analyses were performed with SPSS software and MedCalc software. The receiver operating characteristic curve was plotted. RESULTS: In the differential diagnosis of SCLC and NSCLC, color parametric imaging indicated great performance. NSCLC exhibited a centripetal enhancement pattern more frequently (72.7%), while SCLC exhibited an eccentric enhancement pattern more frequently (92.9%) (p < 0.001). In the differential diagnosis of adenocarcinoma and squamous cell carcinoma, logistic regression analysis revealed that patient age of onset ≤60 y, difference in arrival time between lung and tumor ≤3.8 s, drop time of the time-intensity curve >23.2 s and absence of internal necrosis on CEUS were independent predictors for adenocarcinoma (area under the curve = 0.861). CONCLUSION: In our study, multimodal contrast-enhanced ultrasound provided useful information in the differential diagnosis between small cell lung cancer and non-small cell lung cancer, especially between adenocarcinoma and squamous cell carcinoma.

Clinical Implementation of Total-Body PET in China.

Total-body (TB) PET/CT is a groundbreaking tool that has brought about a revolution in both clinical application and scientific research. The transformative impact of TB PET/CT in the realms of clinical practice and scientific exploration has been steadily unfolding since its introduction in 2018, with implications for its implementation within the health care landscape of China. TB PET/CT's exceptional sensitivity enables the acquisition of high-quality images in significantly reduced time frames. Clinical applications have underscored its effectiveness across various scenarios, emphasizing the capacity to personalize dosage, scan duration, and image quality to optimize patient outcomes. TB PET/CT's ability to perform dynamic scans with high temporal and spatial resolution and to perform parametric imaging facilitates the exploration of radiotracer biodistribution and kinetic parameters throughout the body. The comprehensive TB coverage offers opportunities to study interconnections among organs, enhancing our understanding of human physiology and pathology. These insights have the potential to benefit applications requiring holistic TB assessments. The standard topics outlined in The Journal of Nuclear Medicine were used to categorized the reviewed articles into 3 sections: current clinical applications, scan protocol design, and advanced topics. This article delves into the bottleneck that impedes the full use of TB PET in China, accompanied by suggested solutions.

Optimization and impact of sensitivity mode on abbreviated scan protocols with population-based input function for parametric imaging of [18F]-FDG for a long axial FOV PET scanner.

BACKGROUND: The long axial field of view, combined with the high sensitivity of the Biograph Vision Quadra PET/CT scanner enables the precise deviation of an image derived input function (IDIF) required for parametric imaging. Traditionally, this requires an hour-long dynamic PET scan for [18F]-FDG, which can be significantly reduced by using a population-based input function (PBIF). In this study, we expand these examinations and include the scanner's ultra-high sensitivity (UHS) mode in comparison to the high sensitivity (HS) mode and evaluate the potential for further shortening of the scan time. METHODS: Patlak Ki and DV estimates were determined by the indirect and direct Patlak methods using dynamic [18F]-FDG data of 6 oncological patients with 26 lesions (0-65 min p.i.). Both sensitivity modes for different number/duration of PET data frames were compared, together with the potential of using abbreviated scan durations of 20, 15 and 10 min by using a PBIF. The differences in parametric images and tumour-to-background ratio (TBR) due to the shorter scans using the PBIF method and between the sensitivity modes were assessed. RESULTS: A difference of 3.4 ± 7.0% (Ki) and 1.2 ± 2.6% (DV) was found between both sensitivity modes using indirect Patlak and the full IDIF (0-65 min). For the abbreviated protocols and indirect Patlak, the UHS mode resulted in a lower bias and higher precision, e.g., 45-65 min p.i. 3.8 ± 4.4% (UHS) and 6.4 ± 8.9% (HS), allowing shorter scan protocols, e.g. 50-65 min p.i. 4.4 ± 11.2% (UHS) instead of 7.3 ± 20.0% (HS). The variation of Ki and DV estimates for both Patlak methods was comparable, e.g., UHS mode 3.8 ± 4.4% and 2.7 ± 3.4% (Ki) and 14.4 ± 2.7% and 18.1 ± 7.5% (DV) for indirect and direct Patlak, respectively. Only a minor impact of the number of Patlak frames was observed for both sensitivity modes and Patlak methods. The TBR obtained with direct Patlak and PBIF was not affected by the sensitivity mode, was higher than that derived from the SUV image (6.2 ± 3.1) and degraded from 20.2 ± 12.0 (20 min) to 10.6 ± 5.4 (15 min). Ki and DV estimate images showed good agreement (UHS mode, RC: 6.9 ± 2.3% (Ki), 0.1 ± 3.1% (DV), peak signal-to-noise ratio (PSNR): 64.5 ± 3.3 dB (Ki), 61.2 ± 10.6 dB (DV)) even for abbreviated scan protocols of 50-65 min p.i. CONCLUSIONS: Both sensitivity modes provide comparable results for the full 65 min dynamic scans and abbreviated scans using the direct Patlak reconstruction method, with good Ki and DV estimates for 15 min short scans. For the indirect Patlak approach the UHS mode improved the Ki estimates for the abbreviated scans.

Dual-time-point dynamic 68Ga-PSMA-11 PET/CT for parametric imaging generation in prostate cancer.

PURPOSE: To investigate the optimal dual-time-point (DTP) approaches using dynamic 68Ga-PSMA-11 PET/CT imaging to generate parametric images for prostate cancer patients. METHODS: Fifteen patients with prostate cancer were intravenously administered 68Ga-PSMA-11 of 181.9 ± 47.2 MBq, followed by an immediate 60 min dynamic PET/CT scan. List-mode data were reconstructed into 25 timeframes (6 × 10 s, 8 × 30 s, and 11 × 300 s) and corrected for motion and partial volume effect. DTP parametric images were generated using different interval time points of 5 min and 10 min, with a minimum of 30 min time interval. Net influx rates (Ki) were calculated through the fitting of a single irreversible two-tissue compartmental model. Intraclass correlation coefficient (ICC) values between DTP protocols and 60 min Ki were obtained. Lesion-to-background ratios (LBRs) of Ki and standardized uptake value (SUV) images in each DTP protocol were determined. RESULTS: The DTP protocol of 5-10 min with a 40-45 min interval showed the highest ICC of 0.988 compared with the 60 min Ki, whereas the ICC values for the intervals of 0-5 min with 55-60 min and 0-10 min with 50-60 min were 0.941. The LBRs of the 60 min Ki, 5-10 min with 40-45 min Ki, 0-5 min with 55-60 min Ki, 0-10 min with 50-60 min Ki, SUVmean, and SUVmax images were 29.53 ± 27.33, 13.05 ± 15.28, 45.15 ± 53.11, 45.52 ± 70.31, 19.77 ± 23.43, and 25.06 ± 30.07, respectively. CONCLUSION: The 0-5 min with 55-60 min DTP parametric imaging exhibits a comparable Ki to 60 min parametric imaging and remarkable image quality and contrast than SUV imaging, enhancing prostate cancer diagnosis while maintaining time efficiency.

Parametric net influx rate imaging of 68Ga-DOTATATE in patients with neuroendocrine tumors: assessment of lesion detectability.

OBJECTIVES: There has been developed a clinical dynamic total-body 68Ga-DOTATATE PET/CT imaging protocol that allows quantitative imaging of net influx rate (Ki). Using qualitative and quantitative analyses of clinical studies, this retrospective study aims to assess whether parametric Ki images improve lesion detectability. METHODS: Using a 194-cm axial field-of-view PET/CT scanner, 52 patients with neuroendocrine tumors underwent a 60-min dynamic total-body 68Ga-DOTATATE scan. Parametric Ki images and static standardized uptake value (SUV) images were generated. In addition to visual inspection of both sets of images, a quantitative analysis of 249 individual lesions was conducted using the target-to-background (TBR) metric. RESULTS: There were 52 patients who underwent dynamic total-body 68Ga-DOTATATE PET/CT scans. A total of 249 lesions were evaluated, of which 66 lesions were biopsy-proven and 183 lesions were unproven. Ki images produced two fewer false positives than the SUV images. Overall, our results from 66 proven NET lesions suggested similar sensitivity (98.5%) but improved accuracy (from 95.6 to 97.1%) and potentially enhanced specificity with Ki over SUV imaging. Besides, there was no difference in the number of pathological lesions identified visually in both images. However, Ki TBR was significantly higher than SUV TBR quantitatively (P < 0.001). CONCLUSIONS: Patlak Ki imaging provides nuclear physicians with a PET image with higher tumor contrast which may enhance confidence in diagnosis with possibly reduced false positive results, albeit an equivalent detectability, compared to static SUV image.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

Characterization of kidneys in patients with systemic sclerosis by multi-parametric magnetic resonance quantitative imaging.

PURPOSE: To determine the usefulness of multiparametric magnetic resonance (MR) quantitative imaging in characterizing the kidneys in systemic sclerosis (SSc) patients. MATERIAL AND METHODS: Forty-six SSc patients (47.9 ± 12.8 years, 40 females) and 22 age- and sex- matched healthy volunteers (46.1 ± 13.8 years, 20 females) were recruited and underwent renal MR imaging by acquiring blood oxygen level dependent and saturated multi-delay renal arterial spin labeling (SAMURAI) sequences. The T2* value, T1 value, renal blood flow (RBF), arterial bolus arrival time (aBAT), and tissue bolus arrival time (tBAT) of renal cortex were measured and compared among diffuse cutaneous SSc (dcSSc) and limited cutaneous SSc (lcSSc) groups and healthy controls using One-way ANOVA and analyzed by logistic regression. RESULTS: Compared to healthy volunteers, SSc patients with normal estimated glomerular filtration rate (n = 40) had significantly lower T2* value (P = 0.026) in the left renal cortex, longer T1 value (right: P = 0.015; left: P = 0.023), lower RBF (right: P < 0.001; left: P < 0.001), and shorter tBAT (right: P < 0.001; left: P = 0.005) in both right and left renal cortex after adjusting for demographics. The dcSSc patients (n = 23) had significantly lower RBF in both right (226.7 ± 65.2 mL/100 g/min vs. 278.2 ± 73.5 mL/100 g/min, P = 0.022) and left (194.5 ± 71.5 mL/100 g/min vs. 252.7 ± 84.4 mL/100 g/min, P = 0.020) renal cortex compared to the lcSSc patients (n = 23) after adjusting for demographics, but the significance of the difference was attenuated after further adjusting for modified Rodnan skin score and digital ulcers. CONCLUSION: Multi-parametric MR quantitative imaging, particularly multi-delay ASL perfusion imaging, is a useful technique for characterizing the kidneys and classification of SSc patients.

Multiple ultrasonic parametric imaging for the detection and monitoring of high-intensity focused ultrasound ablation.

Numerous quantitative ultrasound imaging techniques have demonstrated superior monitoring performance for thermal ablation when compared to conventional ultrasonic B-mode imaging. However, the absence of comparative studies involving various quantitative ultrasound imaging techniques hinders further clinical exploration. In this study, we simultaneously reconstructed ultrasonic Nakagami imaging, ultrasonic horizontally normalized Shannon entropy (hNSE) imaging, and ultrasonic differential attenuation coefficient intercept (DACI) imaging from ultrasound backscattered envelope data collected during high-intensity focused ultrasound ablation treatment. We comprehensively investigated their performance differences through qualitative and quantitative analyses, including the calculation of contrast-to-noise ratios (CNR) for ultrasonic images, receiver operating characteristic (ROC) analysis with corresponding indicators, the analysis of lesion area fitting relationships, and computational time consumption comparison. The mean CNR of hNSE imaging was 10.98 ± 4.48 dB, significantly surpassing the 3.82 ± 1.40 dB (p < 0.001, statistically significant) of Nakagami imaging and the 2.45 ± 0.74 dB (p < 0.001, statistically significant) of DACI imaging. This substantial difference underscores that hNSE imaging offers the highest contrast resolution for lesion recognition. Furthermore, we evaluated the ability of multiple ultrasonic parametric imaging to detect thermal ablation lesions using ROC curves. The area under the curve (AUC) for hNSE was 0.874, exceeding the values of 0.848 for Nakagami imaging and 0.832 for DACI imaging. Additionally, hNSE imaging exhibited the strongest linear correlation coefficient (R = 0.92) in the comparison of lesion area fitting, outperforming Nakagami imaging (R = 0.87) and DACI imaging (R = 0.85). hNSE imaging also performs best in real-time monitoring with each frame taking 6.38 s among multiple ultrasonic parametric imaging. Our findings unequivocally demonstrate that hNSE imaging excels in monitoring HIFU ablation treatment and holds the greatest potential for further clinical exploration.

Exploring the nigrostriatal and digestive interplays in Parkinson's disease using dynamic total-body [11C]CFT PET/CT.

PURPOSE: Dynamic total-body imaging enables new perspectives to investigate the potential relationship between the central and peripheral regions. Employing uEXPLORER dynamic [11C]CFT PET/CT imaging with voxel-wise simplified reference tissue model (SRTM) kinetic modeling and semi-quantitative measures, we explored how the correlation pattern between nigrostriatal and digestive regions differed between the healthy participants as controls (HC) and patients with Parkinson's disease (PD). METHODS: Eleven participants (six HCs and five PDs) underwent 75-min dynamic [11C]CFT scans on a total-body PET/CT scanner (uEXPLORER, United Imaging Healthcare) were retrospectively enrolled. Time activity curves for four nigrostriatal nuclei (caudate, putamen, pallidum, and substantia nigra) and three digestive organs (pancreas, stomach, and duodenum) were obtained. Total-body parametric images of relative transporter rate constant (R1) and distribution volume ratio (DVR) were generated using the SRTM with occipital lobe as the reference tissue and a linear regression with spatial-constraint algorithm. Standardized uptake value ratio (SUVR) at early (1-3 min, SUVREP) and late (60-75 min, SUVRLP) phases were calculated as the semi-quantitative substitutes for R1 and DVR, respectively. RESULTS: Significant differences in estimates between the HC and PD groups were identified in DVR and SUVRLP of putamen (DVR: 4.82 ± 1.58 vs. 2.58 ± 0.53; SUVRLP: 4.65 ± 1.36 vs. 2.84 ± 0.67; for HC and PD, respectively, both p < 0.05) and SUVREP of stomach (1.12 ± 0.27 vs. 2.27 ± 0.65 for HC and PD, respectively; p < 0.01). In the HC group, negative correlations were observed between stomach and substantia nigra in both the R1 and SUVREP values (r=-0.83, p < 0.05 for R1; r=-0.94, p < 0.01 for SUVREP). Positive correlations were identified between pancreas and putamen in both DVR and SUVRLP values (r = 0.94, p < 0.01 for DVR; r = 1.00, p < 0.001 for SUVRLP). By contrast, in the PD group, no correlations were found between the aforementioned target nigrostriatal and digestive areas. CONCLUSIONS: The parametric images of R1 and DVR generated from the SRTM model, along with SUVREP and SUVRLP, were proposed to quantify dynamic total-body [11C]CFT PET/CT in HC and PD groups. The distinction in correlation patterns of nigrostriatal and digestive regions between HC and PD groups identified by R1 and DVR, or SUVRs, may provide new insights into the disease mechanism.

PET KinetiX-A Software Solution for PET Parametric Imaging at the Whole Field of View Level.

Kinetic modeling represents the ultimate foundations of PET quantitative imaging, a unique opportunity to better characterize the diseases or prevent the reduction of drugs development. Primarily designed for research, parametric imaging based on PET kinetic modeling may become a reality in future clinical practice, enhanced by the technical abilities of the latest generation of commercially available PET systems. In the era of precision medicine, such paradigm shift should be promoted, regardless of the PET system. In order to anticipate and stimulate this emerging clinical paradigm shift, we developed a constructor-independent software package, called PET KinetiX, allowing a faster and easier computation of parametric images from any 4D PET DICOM series, at the whole field of view level. The PET KinetiX package is currently a plug-in for Osirix DICOM viewer. The package provides a suite of five PET kinetic models: Patlak, Logan, 1-tissue compartment model, 2-tissue compartment model, and first pass blood flow. After uploading the 4D-PET DICOM series into Osirix, the image processing requires very few steps: the choice of the kinetic model and the definition of an input function. After a 2-min process, the PET parametric and error maps of the chosen model are automatically estimated voxel-wise and written in DICOM format. The software benefits from the graphical user interface of Osirix, making it user-friendly. Compared to PMOD-PKIN (version 4.4) on twelve 18F-FDG PET dynamic datasets, PET KinetiX provided an absolute bias of 0.1% (0.05-0.25) and 5.8% (3.3-12.3) for KiPatlak and Ki2TCM, respectively. Several clinical research illustrative cases acquired on different hybrid PET systems (standard or extended axial fields of view, PET/CT, and PET/MRI), with different acquisition schemes (single-bed single-pass or multi-bed multipass), are also provided. PET KinetiX is a very fast and efficient independent research software that helps molecular imaging users easily and quickly produce 3D PET parametric images from any reconstructed 4D-PET data acquired on standard or large PET systems.

Characteristics of whole-body dynamic 18F-FDG PET/CT Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters.

Background: Dynamic course of flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) Patlak muti-parametric imaging spatial distribution in the targeted tissues may reveal highly useful clinical information about the tissue's metabolic properties. The characteristics of the Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters may provide reference for the clinical application of this new technology. Methods: A total of 27 patients with pathologically diagnosed lung cancer underwent whole-body dynamic 18F-FDG PET/CT examination before treatment. Parametric images of metabolic rate of FDG (MRFDG) and Patlak intercept (or distribution volume; DV) were generated using Patlak reconstruction. The values of primary lung cancer lesions, target-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were investigated using contour delineation and boundary delineation. Statistical analysis was performed to analyze the relationship between multi-parametric images and clinicopathological features, and to compare the effects of contour delineation and boundary delineation on quantitative parameters. Results: MRFDG images showed higher TBR and CNR than did standardized uptake value (SUV) images. There were significant differences in MRFDG-max, MRFDG-mean, and MRFDG-peak among groups with different tumor diameters and pathology types (P<0.05). Moreover, the metabolic parameters of MRFDG were higher in patients with tumor diameters ≥3 cm and squamous carcinoma. The differences of the maximum and peak values of MRFDG and DV were not statistically significant in the different outlining method subgroups (all P>0.05). However, the difference of the mean values of MRFDG and DV were statistically significant in the different outline method groupings (all P<0.05). Conclusions: Dynamic 18F-FDG PET/CT Patlak multi-parametric imaging can obtain quantitative values for lung cancer with high TBR and CNR. Moreover, the multi-parameters are various from different pathology types to tumor size. Different delineation methods have a greater influence on the mean value of quantitative parameters.

Estimation of kinetic parameters in dynamic FDG PET imaging based on shortened protocols: a virtual clinical study.

This study investigated the estimation of kinetic parameters and production of related parametric Ki images in FDG PET imaging using the proposed shortened protocol (three 3-min/bed routine static images) by means of the simulated annealing (SA) algorithm. Six realistic heterogeneous tumors and various levels of [18F] FDG uptake were simulated by the XCAT phantom. An irreversible two-tissue compartment model (2TCM) using population-based input function was employed. By keeping two routine clinical scans fixed (60-min and 90-min post injection), the effect of the early scan time on optimizing the estimation of the pharmacokinetic parameters was investigated. The SA optimization algorithm was applied to estimate micro- and macro-parameters (K1, k2, k3, Ki). The minimum bias for most parameters was observed at a scan time of 20-min, which was < 10%. A highly significant correlation (> 0.9) as well as limited bias (< 10%) were observed between kinetic parameters generated from two methods [two-tissue compartment full dynamic scan (2TCM-full) and two-tissue compartment by SA algorithm (2TCM-SA)]. The analysis showed a strong correlation (> 0.8) between (2TCM-SA) Ki and SUV images. In addition, the tumor-to-background ratio (TBR) metric in the parametric (2TCM-SA) Ki images was significantly higher than SUV, although the SUV images provide better Contrast-to-noise ratio relative to parametric (2TCM-SA) Ki images. The proposed shortened protocol by the SA algorithm can estimate the kinetic parameters in FDG PET scan with high accuracy and robustness. It was also concluded that the parametric Ki images obtained from the 2TCM-SA as a complementary image of the SUV possess more quantification information than SUV images and can be used by the nuclear medicine specialist. This method has the potential to be an alternative to a full dynamic PET scan.