Pancreatic perfusion imaging method that reduces radiation dose and maintains image quality by combining volumetric perfusion CT with multiphasic contrast enhanced-CT

Utilization of relative evaluation of pancreatic perfusion CT parameters to support appropriate pancreatic adenocarcinoma diagnosis

OBJECTIVES

To investigate the effect of relative evaluation of perfusion computed tomography (PCT) parameters in the diagnosis of pancreatic adenocarcinoma (PAC).

METHODS

Of the 117 patients in which PCT was performed (May 2019 to June 2023), 99 patients with mass lesions (MLs), including 50 PAC and 11 patients with mass-forming pancreatitis (MFP), and 15 patients without MLs but with main pancreatic duct (MPD) abnormalities, including 6 PAC and 7 no diagnosis of malignancy (NDM) cases were enrolled in this study. Parameter values were obtained from parametric maps of blood flow (BF), blood volume (BV), and mean transit time (MTT) for the ML and abnormal MPD part (AMP), pancreas and spleen. Diagnostic performance was evaluated based on receiver operating characteristic analysis for absolute values and relative values for pancreas and spleen.

RESULTS

BFML, BVML, BFML/Pancreas, BFML/Spleen, BVML/Pancreas and BVML/Spleen were significantly lower in PAC than MFP cases. Areas under the curve (AUCs) for BFML, BFML/Pancreas, BFML/Spleen were 0.71 (sensitivity, 54 %; specificity, 91 %), 0.80 (sensitivity, 74 %; specificity, 82 %) and 0.79 (sensitivity, 68 %; specificity. 91 %), respectively. The AUCs for BVML, BVML/Pancreas, BVML/Spleen were 0.72 (sensitivity, 48 %; specificity, 100 %), 0.85 (sensitivity, 76 %; specificity, 91 %) and 0.87 (sensitivity, 76 %; specificity, 91 %), respectively, with significantly better diagnostic performance on relative evaluation (P < 0.05). BVAMP/Spleen and MTTAMP/Spleen were significantly higher in PAC than NDM cases, with AUCs of 1 (100 % sensitivity and specificity) and 0.91 (sensitivity, 86 %; specificity, 100 %), respectively.

CONCLUSIONS

Relative evaluation of PCT parameters is expected to contribute to more appropriate diagnosis of PAC.

Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma

CT perfusion (CTP) analysis is difficult to implement in clinical practice. Therefore, we investigated a novel semi-automated CTP AI biomarker and applied it to identify vascular phenotypes of pancreatic ductal adenocarcinoma (PDAC) and evaluate their association with overall survival (OS).

METHODS

From January 2018 to November 2022, 107 PDAC patients were prospectively included, who needed to undergo CTP and a diagnostic contrast-enhanced CT (CECT). We developed a semi-automated CTP AI biomarker, through a process that involved deformable image registration, a deep learning segmentation model of tumor and pancreas parenchyma volume, and a trilinear non-parametric CTP curve model to extract the enhancement slope and peak enhancement in segmented tumors and pancreas. The biomarker was validated in terms of its use to predict vascular phenotypes and their association with OS. A receiver operating characteristic (ROC) analysis with five-fold cross-validation was performed. OS was assessed with Kaplan-Meier curves. Differences between phenotypes were tested using the Mann-Whitney U test.

RESULTS

The final analysis included 92 patients, in whom 20 tumors (21%) were visually isovascular. The AI biomarker effectively discriminated tumor types, and isovascular tumors showed higher enhancement slopes (2.9 Hounsfield unit HU/s vs. 2.0 HU/s, p < 0.001) and peak enhancement (70 HU vs. 47 HU, p < 0.001); the AUC was 0.86. The AI biomarker's vascular phenotype significantly differed in OS (p < 0.01).

CONCLUSIONS

The AI biomarker offers a promising tool for robust CTP analysis. In PDAC, it can distinguish vascular phenotypes with significant OS prognostication.

Structural and functional imaging of brains

Analyzing the complex structures and functions of brain is the key issue to understanding the physiological and pathological processes. Although neuronal morphology and local distribution of neurons/blood vessels in the brain have been known, the subcellular structures of cells remain challenging, especially in the live brain. In addition, the complicated brain functions involve numerous functional molecules, but the concentrations, distributions and interactions of these molecules in the brain are still poorly understood. In this review, frontier techniques available for multiscale structure imaging from organelles to the whole brain are first overviewed, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), serial-section electron microscopy (ssEM), light microscopy (LM) and synchrotron-based X-ray microscopy (XRM). Specially, XRM for three-dimensional (3D) imaging of large-scale brain tissue with high resolution and fast imaging speed is highlighted. Additionally, the development of elegant methods for acquisition of brain functions from electrical/chemical signals in the brain is outlined. In particular, the new electrophysiology technologies for neural recordings at the single-neuron level and in the brain are also summarized. We also focus on the construction of electrochemical probes based on dual-recognition strategy and surface/interface chemistry for determination of chemical species in the brain with high selectivity and long-term stability, as well as electrochemophysiological microarray for simultaneously recording of electrochemical and electrophysiological signals in the brain. Moreover, the recent development of brain MRI probes with high contrast-to-noise ratio (CNR) and sensitivity based on hyperpolarized techniques and multi-nuclear chemistry is introduced. Furthermore, multiple optical probes and instruments, especially the optophysiological Raman probes and fiber Raman photometry, for imaging and biosensing in live brain are emphasized. Finally, a brief perspective on existing challenges and further research development is provided.

CT perfusion as a potential biomarker for pancreatic ductal adenocarcinoma during routine staging and restaging

PURPOSE

To evaluate the significance of CT perfusion parameters predicting response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Seventy patients with PDAC prospectively had CT perfusion acquisition incorporated into baseline multiphase staging CT. Twenty-eight who were naïve to therapy were retained for further investigation. Perfusion was performed 5-42.5 s after contrast, followed by parenchymal and portal venous phases. Blood flow (BF), blood volume (BV), and permeability surface area product (PS) were calculated using deconvolution algorithms. Patients were categorized as responders or non-responders per RECIST 1.1. Perfusion variables with AUC ≥ 0.70 in differentiating responders from non-responders were retained. Logistic regression was used to assess associations between baseline perfusion variables and response.

RESULTS

18 of 28 patients showed favorable response to therapy. Baseline heterogeneity variables in tumor max ROI were higher in non-responders than responders [median BF coefficient of variation (CV) 0.91 vs. 0.51 respectively, odds ratio (OR) 6.8 per one standard deviation (1-SD) increase, P = 0.047; median PS CV 1.6 vs. 0.68, OR 3.9 per 1-SD increase, P = 0.047; and median BV CV 0.75 vs. 0.54, OR = 4.0 per 1-SD increase, P = 0.047]. Baseline BV mean in tumor center was lower in non-responders than responders (median BV mean: 0.74 vs. 2.9 ml/100 g respectively, OR 0.28 per 1-SD increase, P = 0.047).

CONCLUSION

For patients with PDAC receiving neoadjuvant therapy, lower and more heterogeneous perfusion parameters correlated with an unfavorable response to therapy. Such quantitative information can be acquired utilizing a comprehensive protocol interleaving perfusion CT acquisition with standard of care multiphase CT scans using a single contrast injection, which could be used to identify surgical candidates and predict outcome.

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death with a 5-year survival rate of 10%. Quantitative CT perfusion (CTP) can provide additional diagnostic information compared to the limited accuracy of the current standard, contrast-enhanced CT (CECT). This systematic review evaluates CTP for diagnosis, grading, and treatment assessment of PDAC. The secondary goal is to provide an overview of scan protocols and perfusion models used for CTP in PDAC. The search strategy combined synonyms for 'CTP' and 'PDAC.' Pubmed, Embase, and Web of Science were systematically searched from January 2000 to December 2020 for studies using CTP to evaluate PDAC. The risk of bias was assessed using QUADAS-2. 607 abstracts were screened, of which 29 were selected for full-text eligibility. 21 studies were included in the final analysis with a total of 760 patients. All studies comparing PDAC with non-tumorous parenchyma found significant CTP-based differences in blood flow (BF) and blood volume (BV). Two studies found significant differences between pathological grades. Two other studies showed that BF could predict neoadjuvant treatment response. A wide variety in kinetic models and acquisition protocol was found among included studies. Quantitative CTP shows a potential benefit in PDAC diagnosis and can serve as a tool for pathological grading and treatment assessment; however, clinical evidence is still limited. To improve clinical use, standardized acquisition and reconstruction parameters are necessary for interchangeability of the perfusion parameters.