Comparison of parametric imaging and SUV imaging with [68 Ga]Ga-PSMA-11 using dynamic total-body PET/CT in prostate cancer.

PURPOSE: Standardized uptake value (SUV) has been prevalently used to measure [68 Ga]Ga-PSMA-11 activity in prostate cancer, but it is susceptible to multiple factors. Parametric imaging allows for absolute quantification of tracer uptake and provides a better diagnostic accuracy that is crucial for lesion detection. However, the clinical significance of total-body parametric imaging of [68 Ga]Ga-PSMA-11 remains to be fully assessed. Therefore, the aim of our study is to delve into the diagnostic implications of total-body parametric imaging of [68 Ga]Ga-PSMA-11 PET/CT for patients with prostate cancer. METHODS: Twenty prostate cancer patients were included and underwent a dynamic total-body [68 Ga]Ga-PSMA-11 PET/CT scan. An irreversible two-tissue compartment model (2T3k) was fitted for each tissue time-to-activity curve, and the net influx rate (Ki) was obtained. The image quality and semi-quantitative analysis of lesion-to-background ratio (LBR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between parametric images and SUV images. RESULTS: Kinetic modeling using 2T3k demonstrated favorable model fitting in both normal organs and lesions. All of the lesions detected on SUV images (55-60 min) could be detected on Ki images. The correlation between Ki, SUVmean, and SUVmax in both normal organs and pathological lesions was found to be positive and statistically significant. Conversely, a moderate positive correlations were found between Ki and K1 (R = 0.69, P < 0.001; R = 0.61, P < 0.001) and Ki and k3 (R = 0.69, P < 0.001; R = 0.62, P < 0.001), in normal organs and pathological lesions, respectively. Visual assessment in Ki images showed less image noise and higher lesions conspicuity compared to SUV images. Ki image-derived LBR, SNR, and CBR of pathological lesions including primary tumors (PTs), lymph node metastases (LNMs) and bone metastases (BMs), exhibited remarkably higher folds (1.4-3.6 folds) compared to those derived from SUV of corresponding lesions. CONCLUSIONS: Total-body parametric imaging of [68 Ga]Ga-PSMA-11 enhanced lesion contrast and improved lesion detectability compared to SUV images. This may potentially serve as an imaging biomarker and theranostic tool for precise diagnosis and treatment evaluation in prostate cancer patients.

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.

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.

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.

Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner.

BACKGROUND: Accurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples, but the introduction of long axial field of view (LAFOV) PET systems enables the use of non-invasive image-derived input functions (IDIFs) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition, which is impractical in a busy clinical setting. Population-based input functions (PBIFs) have previously shown potential in accurate Patlak analysis of [18F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [18F]-FDG total body kinetic modeling. METHODS: Dynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [18F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the AUC of IDIF curve tails using various portions of image data (35-65, 40-65, 45-65, 50-65, and 55-65 min post-injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumor lesions and cerebral gray matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t* = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A comparison of parametric images generated using IDIF and sPBIFs was also performed. RESULTS: There was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P > 0.05). Excellent agreement was shown between Patlak Ki estimates obtained using sPBIF and IDIF. Using the sPBIF55-65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved < 15% precision error in Ki estimates in tumor lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 min of PET data as reference, Patlak Ki images generated using sPBIF55-65 with 20 min of PET data (t* = 45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB. CONCLUSION: We demonstrate the feasibility of performing accurate [18F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner.

Quantitation of dynamic total-body PET imaging: recent developments and future perspectives.

BACKGROUND: Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. CHALLENGES: The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. CONCLUSIONS: In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.

A deep neural network for parametric image reconstruction on a large axial field-of-view PET.

PURPOSE: The PET scanners with long axial field of view (AFOV) having ~ 20 times higher sensitivity than conventional scanners provide new opportunities for enhanced parametric imaging but suffer from the dramatically increased volume and complexity of dynamic data. This study reconstructed a high-quality direct Patlak Ki image from five-frame sinograms without input function by a deep learning framework based on DeepPET to explore the potential of artificial intelligence reducing the acquisition time and the dependence of input function in parametric imaging. METHODS: This study was implemented on a large AFOV PET/CT scanner (Biograph Vision Quadra) and twenty patients were recruited with 18F-fluorodeoxyglucose (18F-FDG) dynamic scans. During training and testing of the proposed deep learning framework, the last five-frame (25 min, 40-65 min post-injection) sinograms were set as input and the reconstructed Patlak Ki images by a nested EM algorithm on the vendor were set as ground truth. To evaluate the image quality of predicted Ki images, mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) were calculated. Meanwhile, a linear regression process was applied between predicted and true Ki means on avid malignant lesions and tumor volume of interests (VOIs). RESULTS: In the testing phase, the proposed method achieved excellent MSE of less than 0.03%, high SSIM, and PSNR of ~ 0.98 and ~ 38 dB, respectively. Moreover, there was a high correlation (DeepPET: [Formula: see text]= 0.73, self-attention DeepPET: [Formula: see text]=0.82) between predicted Ki and traditionally reconstructed Patlak Ki means over eleven lesions. CONCLUSIONS: The results show that the deep learning-based method produced high-quality parametric images from small frames of projection data without input function. It has much potential to address the dilemma of the long scan time and dependency on input function that still hamper the clinical translation of dynamic PET.

Reliability and Value of 3D Sequential QUantitative T1 -T2 -T2 * MAppings (SQUMA) MR Multi-Parametric Imaging in Characterizing Carotid Artery Atherosclerosis.

BACKGROUND: T1 , T2 , and T2 * mappings are seldom performed in a single examination, and their values in evaluating symptomatic atherosclerosis are lacking. PURPOSE: To perform three-dimensional (3D) quantitative T1 , T2 , and T2 * mappings (SQUMA) multi-parametric imaging for carotid vessel wall and evaluate its reliability and value in assessing carotid atherosclerosis. STUDY TYPE: Prospective. SUBJECTS: Eight healthy subjects and 20 patients with symptomatic carotid atherosclerosis. FIELD STRENGTH/SEQUENCE: 3 T, SQUMA imaging T1 -, T2 -, and T2 *-mapping, multi-contrast vessel wall imaging including T1 - and T2 -weighted, time-of-flight, and SNAP sequences. ASSESSMENT: SQUMA was acquired in all subjects and multi-contrast images were acquired in healthy subjects. T1 , T2 , and T2 * values and lumen area (LA), wall area (WA), mean wall thickness (MeanWT), and normalized wall index (NWI) of carotid arteries were measured. SQUMA and multi-contrast measurements were compared in healthy subjects and differences in SQUMA measurements between healthy subjects and patients were assessed. The discriminative value of SQUMA measurements for symptomatic vessel was determined. STATISTICAL TESTS: Paired t or Wilcoxon signed-rank test, independent t or Mann-Whitney U test, area under the receiver operating characteristic curve (AUC), intraclass correlation coefficients, and Bland-Altman plots. Statistically significant level, P < 0.05. RESULTS: There were no significant differences in LA (P = 0.340), WA (P = 0.317), MeanWT (P = 0.088), and NWI (P = 0.091) of carotid arteries between SQUMA and multi-contrast vessel wall images. The values of T2 (50.9 ± 2.9 msec vs. 44.5 ± 4.2 msec), T2 * (28.2 ± 4.3 msec vs. 24.7 ± 2.6 msec), WA (23.7 ± 4.6 mm2 vs. 36.2 ± 7.7 mm2 ), MeanWT (0.99 ± 0.05 mm vs. 1.50 ± 0.28 mm), and NWI (40.7 ± 3.0% vs. 53.8 ± 5.4%) of carotid arteries in healthy subjects were significantly different from those in atherosclerotic patients. The combination of quantitative T1 , T2 , and T2 * values and MeanWT showed greatest AUC (0.81; 95% CI: 0.65-0.92) in discriminating symptomatic vessels. DATA CONCLUSION: Carotid MR 3D quantitative multi-parametric imaging of SQUMA enables acquisition of T1 , T2 , and T2 * maps, reliably measuring carotid morphology and discriminating carotid symptomatic atherosclerosis. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Quantitative evaluation of the hemodynamic differences between ruptured and unruptured cerebral arteriovenous malformations using angiographic parametric imaging-derived radiomics features.

PURPOSE: Imaging features of cerebral arteriovenous malformations (AVMs) are mainly interpreted according to demographic and qualitative anatomical characteristics. This study aimed to use angiographic parametric imaging (API)-derived radiomics features to explore whether these features extracted from digital subtraction angiography (DSA) were associated with the hemorrhagic presentation of AVMs. METHODS: Patients with AVM were retrospectively evaluated. Among them, 80% were randomly assigned to a training dataset, and the remaining 20% were assigned to an independent test dataset. Radiomics features were extracted from DSA by API. Then, informative features were selected from radiomics features and clinical features using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. A model was constructed based on the selected features to classify the dichotomous hemorrhagic presentation in the training dataset. The model performance was evaluated in the test dataset with confusion matrix-related metrics. RESULTS: A total of 529 consecutive patients with AVMs between July 2011 and December 2020 were included in this study. After being selected by the LASSO algorithm and analyzed by multivariable logistic regression, three clinical features, namely, age (p = 0.01), nidus size (p < 0.001), and venous drainage patterns (p < 0.001), and four radiomics features were used to construct a model in the training dataset. On the independent test dataset, the model demonstrated a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 0.852, 0.844, 0.881, 0.809, and 0.849, respectively. CONCLUSION: The radiomics features extracted from DSA by API could be potential indicators for the hemorrhagic presentation of AVMs.

Quantitative Ultrasound: An Emerging Technology for Detecting, Diagnosing, Imaging, Evaluating, and Monitoring Disease.

Ultrasound has been a popular clinical imaging modality for decades. It is a well-established means of displaying the macroscopic anatomy of soft-tissue structures. While conventional ultrasound methods, i.e., B-mode and Doppler methods, are well proven and continue to advance technically in many ways, e.g., by extending into higher frequencies and taking advantage of harmonic phenomena in tissues, fundamentally new so-called quantitative ultrasound (QUS) technologies also are emerging and offer exciting promise for making significant improvements in clinical imaging and characterization of disease. These emerging quantitative methods include spectrum analysis, image statistics, elasticity imaging, contrast-agent methods, and flow-detection and -measurement techniques. Each provides independent information. When used alone, each can provide clinically valuable imaging capabilities; when combined with each other, their capabilities may be more powerful in many applications. Furthermore, all can be used fused with other imaging modalities, such as computed tomography (CT), magnetic-resonance (MR), positron-emission-tomography (PET), or single-photon emission computerized tomography (SPECT) imaging, to offer possibly even greater improvements in detecting, diagnosing, imaging, evaluating, and monitoring disease. This chapter focuses on QUS methods that are based on spectrum analysis and image statistics.

The Role of Contrast-Enhanced Ultrasound Plus Color Parametric Imaging in the Differential Diagnosis of Subpleural Pulmonary Lesions.

OBJECTIVES: To distinguish benign and malignant subpleural pulmonary lesions (SPLs) with contrast-enhanced ultrasound (CEUS) and color parametric imaging (CPI), and evaluate the role of CEUS plus CPI in the differential diagnosis of pathological types of SPLs. METHODS: One hundred and thirty-six patients underwent CEUS with a Logiq E9 XD Clear ultrasonic machine equipped with a 3.5- to 5.0-MHz C5-1 transducer in our center were enrolled in our study, including 27 cases of benign lesions and 109 cases of malignant lesions. The ultrasound contrast agent used in this study was SonoVue. CEUS images and CPI of all cases were reviewed and analyzed by the resident and staff radiologist groups separately. RESULTS: With CEUS alone, by both the two groups, the main enhancement pattern of benign SPLs was arborization (P < .001), while centripetal enhancement pattern occurred more frequently in malignant SPLs (P < .001). With CEUS plus CPI, by both the two groups, the main enhancement pattern of benign SPLs was arborization (P < .001), while those of malignant SPLs were centripetal (P < .001) and eccentric (P < .05). The diagnosis performance of CEUS plus CPI was significantly higher than that of CEUS alone in both the resident (area under the curve [AUC] = 0.857 vs 0.677, P < .001) and staff (AUC = 0.866 vs 0.681, P < .001) groups. Moreover, CPI offered remarkable inter-consistency improvements in the enhancement pattern determination between the two groups. CONCLUSION: The CEUS enhancement patterns would provide information of blood perfusion patterns in the differential diagnosis of benign and malignant SPLs. The diagnosis performance could be significantly improved by CEUS plus CPI compared with CEUS alone.

caliPER: A software for blood-free parametric Patlak mapping using PET/MRI input function.

Routine clinical use of absolute PET quantification techniques is limited by the need for serial arterial blood sampling for input function and more importantly by the lack of automated pharmacokinetic analysis tools that can be readily implemented in clinic with minimal effort. PET/MRI provides the ability for absolute quantification of PET probes without the need for serial arterial blood sampling using image-derived input functions (IDIFs). Here we introduce caliPER, a modular and scalable software for simplified pharmacokinetic modeling of PET probes with irreversible uptake or binding based on PET/MR IDIFs and Patlak Plot analysis. caliPER generates regional values or parametric maps of net influx rate (Ki) using reconstructed dynamic PET images and anatomical MRI aligned to PET for IDIF vessel delineation. We evaluated the performance of caliPER for blood-free region-based and pixel-wise Patlak analyses of [18F] FDG by comparing caliPER IDIF to serial arterial blood input functions and its application in imaging brain glucose hypometabolism in Frontotemporal dementia. IDIFs corrected for partial volume errors including spill-out and spill-in effects were similar to arterial blood input functions with a general bias of around 6-8%, even for arteries <5 mm. The Ki and cerebral metabolic rate of glucose estimated using caliPER IDIF were similar to estimates using arterial blood sampling (<2%) and within limits of whole brain values reported in literature. Overall, caliPER is a promising tool for irreversible PET tracer quantification and can simplify the ability to perform parametric analysis in clinical settings without the need for blood sampling.

Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps.

PURPOSE: A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast parametric maps. We sought to reduce the acquisition time for QSM and macromolecular tissue volume by acquiring both contrasts simultaneously by leveraging their redundancies. The joint virtual coil concept with GRAPPA (JVC-GRAPPA) was applied to reduce acquisition time further. METHODS: Three adult volunteers were imaged on a 3 Tesla scanner using a multi-echo 3D GRE sequence acquired at 3 head orientations. Macromolecular tissue volume, QSM, R2∗ , T1 , and proton density maps were reconstructed. The same sequence (GRAPPA R = 4) was performed in subject 1 with a single head orientation for comparison. Fully sampled data was acquired in subject 2, from which retrospective undersampling was performed (R = 6 GRAPPA and R = 9 JVC-GRAPPA). Prospective undersampling was performed in subject 3 (R = 6 GRAPPA and R = 9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. RESULTS: Subject 1's multi-orientation and single-orientation macromolecular tissue volume maps were not significantly different based on RMSE. For subject 2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject 3. Using QSM, R2∗ , and macromolecular tissue volume, the contributions of myelin and iron content to susceptibility were estimated. CONCLUSION: We have developed a novel strategy to simultaneously acquire data for the reconstruction of 5 intrinsically coregistered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 min using JVC-GRAPPA.

Parametric image generation with the uEXPLORER total-body PET/CT system through deep learning.

PURPOSE: Total-body dynamic positron emission tomography/computed tomography (PET/CT) provides much sensitivity for clinical imaging and research, bringing new opportunities and challenges regarding the generation of total-body parametric images. This study investigated parametric [Formula: see text] images directly generated from static PET images without an image-derived input function on a 2-m total-body PET/CT scanner (uEXPLORER) using a deep learning model to significantly reduce the dynamic scanning time and improve patient comfort. METHODS: [Formula: see text]F-Fluorodeoxyglucose ([Formula: see text]F-FDG) 2-m total-body PET/CT image pairs were acquired for 200 patients (scanned once) with two protocols: one parametric PET image (60 min, 0[Formula: see text]60 min) and one static PET image (10 min, range of 50[Formula: see text]60 min). A deep learning model was implemented to predict parametric [Formula: see text] images from the static PET images. Evaluation metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean square error (NMSE), were calculated for a 10-fold cross-validation assessment. Moreover, image quality was assessed by two nuclear medicine physicians in terms of clinical readings. RESULTS: The synthetic parametric PET images were qualitatively and quantitatively consistent with the reference images. In particular, the global mean SSIM between the synthetic and reference parametric [Formula: see text] images exceeded 0.9 across all test patients. On the other hand, the overall subjective quality of the synthetic parametric PET images was 4.00 ± 0.45 (the highest possible rating is 5) according to the two expert nuclear medicine physicians. CONCLUSION: The findings illustrated the feasibility of the proposed technique and its potential to reduce the required scanning duration for 2-m total-body dynamic PET/CT systems. Moreover, this study explored the potential of direct parametric image generation with uEXPLORER. Deep learning technologies may output high-quality synthetic parametric images, and the validation of clinical applications and the interpretability of network models still need further research in future works.

First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients.

PURPOSE: To investigate the kinetics of 18F-fluorodeoxyglucose (18F-FDG) by positron emission tomography (PET) in multiple organs and test the feasibility of total-body parametric imaging using an image-derived input function (IDIF). METHODS: Twenty-four oncological patients underwent dynamic 18F-FDG scans lasting 65 min using a long  axial FOV (LAFOV) PET/CT system. Time activity curves (TAC) were extracted from semi-automated segmentations of multiple organs, cerebral grey and white matter, and from vascular structures. The tissue and tumor lesion TACs were fitted using an irreversible two-tissue compartment (2TC) and a Patlak model. Parametric images were also generated using direct and indirect Patlak methods and their performances were evaluated. RESULTS: We report estimates of kinetic parameters and metabolic rate of glucose consumption (MRFDG) for different organs and tumor lesions. In some organs, there were significant differences between MRFDG values estimated using 2TC and Patlak models. No statistically significant difference was seen between MRFDG values estimated using 2TC and Patlak methods in tumor lesions (paired t-test, P = 0.65). Parametric imaging showed that net influx (Ki) images generated using direct and indirect Patlak methods had superior tumor-to-background ratio (TBR) to standard uptake value (SUV) images (3.1- and 3.0-fold mean increases in TBRmean, respectively). Influx images generated using the direct Patlak method had twofold higher contrast-to-noise ratio in tumor lesions compared to images generated using the indirect Patlak method. CONCLUSION: We performed pharmacokinetic modelling of multiple organs using linear and non-linear models using dynamic total-body 18F-FDG images. Although parametric images did not reveal more tumors than SUV images, the results confirmed that parametric imaging furnishes improved tumor contrast. We thus demonstrate the feasibility of total-body kinetic modelling and parametric imaging in basic research and oncological studies. LAFOV PET can enhance dynamic imaging capabilities by providing high sensitivity parametric images and allowing total-body pharmacokinetic analysis.

Simultaneous Denoising of Dynamic PET Images Based on Deep Image Prior.

Parametric imaging obtained from kinetic modeling analysis of dynamic positron emission tomography (PET) data is a useful tool for quantifying tracer kinetics. However, pixel-wise time-activity curves have high noise levels which lead to poor quality of parametric images. To solve this limitation, we proposed a new image denoising method based on deep image prior (DIP). Like the original DIP method, the proposed DIP method is an unsupervised method, in which no training dataset is required. However, the difference is that our method can simultaneously denoise all dynamic PET images. Moreover, we propose a modified version of the DIP method called double DIP (DDIP), which has two DIP architectures. The additional DIP model is used to generate high-quality input data for the second DIP model. Computer simulations were performed to evaluate the performance of the proposed DIP-based methods. Our simulation results showed that the DDIP method outperformed the single DIP method. In addition, the DDIP method combined with data augmentation could generate PET parametric images with superior image quality compared to the spatiotemporal-based non-local means filtering and high constrained backprojection. Our preliminary results show that our proposed DDIP method is a novel and effective unsupervised method for simultaneously denoising dynamic PET images.

Parametric imaging of dual-time window [18F]flutemetamol and [18F]florbetaben studies.

Optimal pharmacokinetic models for quantifying amyloid beta (Aß) burden using both [18F]flutemetamol and [18F]florbetaben scans have previously been identified at a region of interest (ROI) level. The purpose of this study was to determine optimal quantitative methods for parametric analyses of [18F]flutemetamol and [18F]florbetaben scans. Forty-six participants were scanned on a PET/MR scanner using a dual-time window protocol and either [18F]flutemetamol (N=24) or [18F]florbetaben (N=22). The following parametric approaches were used to derive DVR estimates: reference Logan (RLogan), receptor parametric mapping (RPM), two-step simplified reference tissue model (SRTM2) and multilinear reference tissue models (MRTM0, MRTM1, MRTM2), all with cerebellar grey matter as reference tissue. In addition, a standardized uptake value ratio (SUVR) was calculated for the 90-110 min post injection interval. All parametric images were assessed visually. Regional outcome measures were compared with those from a validated ROI method, i.e. DVR derived using RLogan. Visually, RPM, and SRTM2 performed best across tracers and, in addition to SUVR, provided highest AUC values for differentiating between Aß-positive vs Aß-negative scans ([18F]flutemetamol: range AUC=0.96-0.97 [18F]florbetaben: range AUC=0.83-0.85). Outcome parameters of most methods were highly correlated with the reference method (R2≥0.87), while lowest correlation were observed for MRTM2 (R2=0.71-0.80). Furthermore, bias was low (≤5%) and independent of underlying amyloid burden for MRTM0 and MRTM1. The optimal parametric method differed per evaluated aspect; however, the best compromise across aspects was found for MRTM0 followed by SRTM2, for both tracers. SRTM2 is the preferred method for parametric imaging because, in addition to its good performance, it has the advantage of providing a measure of relative perfusion (R1), which is useful for measuring disease progression.

Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network.

Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct reconstruction methods, which reconstruct parametric images directly from the raw PET data, have superior image quality due to better noise modeling and richer information extracted from the PET raw data. For low-dose scenarios, the advantages of direct methods are more obvious. However, the wide adoption of direct reconstruction is inevitably impeded by the excessive computational demand and deficiency of the accessible raw data. In addition, motion modeling inside dynamic PET image reconstruction raises more computational challenges for direct reconstruction methods. In this work, we focused on the 18F-FDG Patlak model, and proposed a data-driven approach which can estimate the motion corrected full-dose direct Patlak images from the dynamic PET reconstruction series, based on a proposed novel temporal non-local convolutional neural network. During network training, direct reconstruction with motion correction based on full-dose dynamic PET sinograms was performed to obtain the training labels. The reconstructed full-dose /low-dose dynamic PET images were supplied as the network input. In addition, a temporal non-local block based on the dynamic PET images was proposed to better recover the structural information and reduce the image noise. During testing, the proposed network can directly output high-quality Patlak parametric images from the full-dose /low-dose dynamic PET images in seconds. Experiments based on 15 full-dose and 15 low-dose 18F-FDG brain datasets were conducted and analyzed to validate the feasibility of the proposed framework. Results show that the proposed framework can generate better image quality than reference methods.

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

Clinical feasibility and impact of fully automated multiparametric PET imaging using direct Patlak reconstruction: evaluation of 103 dynamic whole-body 18F-FDG PET/CT scans.

PURPOSE: Functional imaging by standard whole-body (WB) 18F-flurodeoxyglucose (FDG) positron emission tomography (PET) is an integrated part of disease diagnostics. Recently, a clinical dynamic whole-body (D-WB) FDG PET/CT scanning protocols has been developed allowing for quantitative imaging of tissue metabolic rate of FDG (MRFDG). It was the purpose of this retrospective study to evaluate whether MRFDG imaging is feasible in a clinical setting and whether it improves lesion detectability. METHODS: One hundred nine patients representing a broad range of referral indications for FDG PET/CT were invited to undergo a D-WB FDG PET/CT scan. Two sets of images were produced: parametric images and standard static SUV images. Both sets of images were reviewed visually, and 310 individual lesions were quantitatively analysed using the target-to-background (TBR) and contrast-to-noise (CNR) metrics. RESULTS: One hundred three out of 109 patients completed the D-WB FDG PET/CT scan. There was no difference in the number of pathological lesions identified visually on the MRFDG and the SUV images, whereas MRFDG images yielded 4 fewer false positives than the SUV images. Quantitatively, MRFDG TBR was significantly higher than SUV TBR in 299/310 lesions, and better MRFDG CNR was found to facilitate the challenging reading of lesions with low SUV TBR. CONCLUSION: D-WB FDG PET/CT is feasible in a clinical setting and produces MRFDG images of good visual quality and superior lesion contrast. In addition, MRFDG images complement the standard SUV images providing better quantification and enhanced image reading. However, although MRFDG also reduced the number of false-positive findings, no additional malignant lesions were identified. The technique therefore appears to be best suited for select patient groups or possibly treatment response evaluation.