Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI

The diagnostic value of advanced tracer kinetic models in evaluating high grade gliomas recurrence and treatment response using dynamic contrast-enhanced MRI

Background

The purpose of this study was to investigate the diagnostic value of advanced tracer kinetic models (TKMs) in differentiating HGGs recurrence and treatment response.

Methods

A total of 52 HGGs were included. DCE images were analyzed using the following TKMs: distributed parameter (DP), tissue homogeneity (TH), Brix's two-compartment (Brix) and extended-Tofts model (ETM), yielding the following parameters: cerebral blood flow (CBF), mean transit time (MTT), plasma volume (Vp), extravascular volume (Ve), vascular permeability (PS) and first-pass extraction ratio (E) in advanced TKMs (DP, TH and Brix); Ktrans, Ve, Vp and Kep in ETM. Two delineation methods were conducted (routine scans and parameter heat maps). The differences between two MRI scanners were compared. Mann-Whitney U test was used to assess the difference of parameter values. Diagnostic performance was assessed using the method of the receiver operating characteristic (ROC) curves, with the areas under the ROC curves (AUC) to determine the discriminating power of DCE parameters between recurrent tumor group and treatment response group . P<0.05 indicates statistical significance.

Results

The difference on the normalized kinetic parameter value (with respect to contralateral normal-appearing white matter) between two MRI scanners was statistically insignificant (P>0.05). MTT and Vp of advanced TKMs were higher in recurrent than in treatment response group (P<0.05). For ROI delineated on parameter heat maps, MTT(DP) attained the best performance with AUC 0.88, followed by MTT(TH) and Vp (DP, Brix) with AUCs around 0.80 (0.81, 0.80, 0.79 respectively). The best performance in ETM was Vp (AUC = 0.73).

Conclusion

MTT (DP, TH), and Vp (DP, Brix) could be potential quantitative imaging biomarkers in distinguishing recurrence and treatment response in HGGs.

State of the art of CT myocardial perfusion

Coronary computed tomography angiography (CCTA) is a powerful tool to rule out coronary artery disease (CAD). In the last decade, myocardial perfusion CT (CTP) technique has been developed for the evaluation of myocardial ischemia, thereby increasing positive predictive value for diagnosis of obstructive CAD. A diagnostic strategy combining CCTA and perfusion acquisitions provides both anatomical coronary evaluation and functional evaluation of the stenosis, increasing the specificity and the positive predictive value of cardiac CT. This could improve risk stratification and guide revascularization procedures, reducing unnecessary diagnostic procedures in invasive coronary angiography. Two different acquisitions protocol have been developed for CTP. Static CTP allows a qualitative or semiquantitative evaluation of myocardial perfusion using a single scan during the first pass of iodinated contrast material in the myocardium. Dynamic CTP is capable of a quantitative evaluation of perfusion through multiple acquisitions, providing direct measure of the myocardial blood flow. For both, CTP acquisition hyperemia is reached using stressor agents such as adenosine or regadenoson. CTP in addition to CCTA acquisition shows good diagnostic accuracy compared to invasive fractional flow reserve (FFR). Furthermore, the evaluation of late iodine enhancement (LIE) could be performed allowing the detection of myocardial infarction.

Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer

PURPOSE

To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer.

METHODS

This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed.

RESULTS

Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05).

CONCLUSION

IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.

Noninvasive ROS imaging and drug delivery monitoring in the tumor microenvironment

Reactive oxygen species (ROS) that are overproduced in certain tumors can be considered an indicator of oxidative stress levels in the tissue. Here, we report a magnetic resonance imaging (MRI)-based probe capable of detecting ROS levels in the tumor microenvironment (TME) using ROS-responsive manganese ion (Mn2+)-chelated, biotinylated bilirubin nanoparticles (Mn@bt-BRNPs). These nanoparticles are disrupted in the presence of ROS, resulting in the release of free Mn2+, which induces T1-weighted MRI signal enhancement. Mn@BRNPs show more rapid and greater MRI signal enhancement in high ROS-producing A549 lung carcinoma cells compared with low ROS-producing DU145 prostate cancer cells. A pseudo three-compartment model devised for the ROS-reactive MRI probe enables mapping of the distribution and concentration of ROS within the tumor. Furthermore, doxorubicin-loaded, cancer-targeting ligand biotin-conjugated Dox/Mn@bt-BRNPs show considerable accumulation in A549 tumors and also effectively inhibit tumor growth without causing body weight loss, suggesting their usefulness as a new theranostic agent. Collectively, these findings suggest that Mn@bt-BRNPs could be used as an imaging probe capable of detecting ROS levels and monitoring drug delivery in the TME with potential applicability to other inflammatory diseases.

Comparison of lung lesion assessment using free-breathing dynamic contrast-enhanced 1.5-T MRI with a golden-angle radial stack-of-stars VIBE sequence and CT

BACKGROUND

Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung.

PURPOSE

To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference.

MATERIAL AND METHODS

In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis.

RESULTS

The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower Ktrans, Ve, and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group (P < 0.05).

CONCLUSION

Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions.

Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging

Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.

Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling

PURPOSE

Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied.

METHODS

The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution.

RESULTS

The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s.

CONCLUSION

We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.

Longitudinal Magnetic Resonance Imaging with ROS-Responsive Bilirubin Nanoparticles Enables Monitoring of Nonalcoholic Steatohepatitis Progression to Cirrhosis

Despite the vital importance of monitoring the progression of nonalcoholic fatty liver disease (NAFLD) and its progressive form, nonalcoholic steatohepatitis (NASH), an efficient imaging modality that is readily available at hospitals is currently lacking. Here, a new magnetic-resonance-imaging (MRI)-based imaging modality is presented that allows for efficient and longitudinal monitoring of NAFLD and NASH progression. The imaging modality uses manganese-ion (Mn2+)-chelated bilirubin nanoparticles (Mn@BRNPs) as a reactive-oxygen-species (ROS)-responsive MRI imaging probe. Longitudinal T1-weighted MR imaging of NASH model mice is performed after injecting Mn@BRNPs intravenously. The MR signal enhancement in the liver relative to muscle gradually increases up to 8 weeks of NASH progression, but decreases significantly as NASH progresses to the cirrhosis-like stage at weeks 10 and 12. A new dual input pseudo-three-compartment model is developed to provide information on NASH stage with a single MRI scan. It is also demonstrated that the ROS-responsive Mn@BRNPs can be used to monitor the efficacy of potential anti-NASH drugs with conventional MRI. The findings suggest that the ROS-responsive Mn@BRNPs have the potential to serve as an efficient MRI contrast for monitoring NASH progression and its transition to the cirrhosis-like stage.

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors

BACKGROUND

To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs).

METHODS

This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve.

RESULTS

TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone.

CONCLUSIONS

Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

The staging of nonalcoholic fatty liver disease fibrosis: A comparative study of MR elastography and the quantitative DCE-MRI exchange model

Objectives

To evaluate the efficacy and image processing time of the dynamic contrast-enhanced MRI (DCE-MRI) exchange model in liver fibrosis staging and compare it to the efficacy of magnetic resonance elastography (MRE).

Methods

The subjects were 45 patients with nonalcoholic fatty liver disease (NAFLD) who underwent MRE and DCE-MRI in our hospital. Liver biopsy results were available for all patients. Spearman rank correlation coefficients were used to compare the correlations among MRE, DCE-MRI and liver fibrosis parameters. Quantitative DCE-MRI parameters, MRE-derived liver stiffness measurement (LSM), and the results of a combined DCE-MRI + MRE logistic regression model were compared in terms of the area under the receiver operating characteristic curve (AUC). We also compared the scanning and postprocessing times of the MRE and DCE-MRI techniques.

Results

The correlation coefficients between the following parameters of interest and liver fibrosis were as follows: capillary permeability-surface area product (PS; DCE-MRI parameter), -0.761; portal blood flow (Fp; DCE-MRI parameter), -0.754; MRE-LSM, 0.835. Some DCE-MRI parameters (PS, Fp) had slightly greater AUC values than MRE-LSM for diagnosing the presence or absence of liver fibrosis, and the combined model had the highest AUC value for all stages except F4, but there was no significant difference in the diagnostic efficacy of the DCE-MRI, MRE, and combined models for any stage of fibrosis. The average scanning times for MRE and DCE-MRI were 17 s and 330 s, respectively, and the average postprocessing times were 45.5 s and 342.7 s, respectively.

Conclusions

In the absence of MRE equipment, DCE-MRI represents an alternative technique. However, MRE is a quicker and simpler method for assessing fibrosis than DCE-MRI in the clinic.

Model discovery approach enables non-invasive measurement of intra-tumoral fluid transport in dynamic MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to non-invasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that it will contribute to the better understanding of cancer progression and therapeutic response.

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function

PURPOSE

In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time.

MATERIALS AND METHOD

A total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) Ca-25min, using DCE-MRI data of 25 min with individually sampled AIF (Ca); (ii) Ca-10min, using truncated 10min data with AIF (Ca); and (iii) Cp-10min, using truncated 10 min data with CIF (Cp). The PS estimates from the Ca-25min method were used as reference standard values to assess the accuracy of the Ca-10min and Cp-10min methods in estimating the PS values.

RESULTS

When compared to the reference method(Ca-25min), the Ca-10min and Cp-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10-4 min-1) with the Ca-10min, while it was reduced to 1.63 ± 2.25 (x10-4 min-1) with the Cp-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the Cp-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group.

CONCLUSIONS

We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10^-3, 2.8 × 10^-3, and >3.0 ×10^-2 mm²/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.

Quantifying the changes in the tumour vascular micro-environment in spinal metastases treated with stereotactic body radiotherapy - a single arm prospective study

BACKGROUND

The primary objective was to quantify changes in vascular micro-environment in spinal metastases (SM) patients treated with stereotactic body radiotherapy (SBRT) with multi-parametric dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). The secondary objective was to study plasma biomarkers related to endothelial apoptosis.

PATIENTS AND METHODS

Patients were imaged with DCE-MRI at baseline/1-week/12-weeks post-SBRT. Metrics including normalised time-dependent leakage (Ktrans), permeability surface product (PS), fractional plasma volume (Vp), extracellular volume (Ve) and perfusion (F) were estimated using distributed parameter model. Serum acid sphingomyelinase (ASM) and sphingosine-1-phosphate (S1P) were quantified using ELISA. Clinical outcomes including physician-scored and patient-reported toxicity were collected.

RESULTS

Twelve patients (with varying primary histology) were recruited, of whom 10 underwent SBRT. Nine patients (with 10 lesions) completed all 3 imaging assessment timepoints. One patient died due to pneumonia (unrelated) before follow-up scans were performed. Median SBRT dose was 27 Gy (range: 24-27) over 3 fractions (range: 2-3). Median follow-up for alive patients was 42-months (range: 22.3-54.3), with local control rate of 90% and one grade 2 or higher toxicity (vertebral compression fracture). In general, we found an overall trend of reduction at 12-weeks in all parameters (Ktrans/PS/Vp/Ve/F). Ktrans and PS showed a reduction as early as 1-week. Ve/Vp/F exhibited a slight rise 1-week post-SBRT before reducing below the baseline value. There were no significant changes, post-SBRT, in plasma biomarkers (ASM/S1P).

CONCLUSIONS

Tumour vascular micro-environment (measured by various metrics) showed a general trend towards downregulation post-SBRT. It is likely that vascular-mediated cell killing contributes to excellent local control rates seen with SBRT. Future studies should evaluate the effect of SBRT on primary-specific spinal metastases (e.g., renal cell carcinoma).

Evaluation of tracer kinetic parameters in cervical cancer using dynamic contrast-enhanced MRI as biomarkers in terms of biological relevance, diagnostic performance and inter-center variability

Objectives

This study assessed the clinical value of parameters derived from dynamic contrast-enhanced (DCE) MRI with respect to correlation with angiogenesis and proliferation of cervical cancer, performance of diagnosis and reproducibility of DCE-MRI parameters across MRI scanners.

Materials and Methods

A total of 113 patients with cervical carcinoma from two centers were included in this retrospective study. The DCE data were centralized and processed using five tracer kinetic models (TKMs) (Tofts, Ex-Tofts, ATH, SC, and DP), yielding the following parameters: volume transfer constant (Ktrans), extravascular extracellular volume (Ve), fractional volume of vascular space (Vp), blood flow (Fp), and permeability surface area product (PS). CD34 counts and Ki-67 PI (proliferation index) of cervical cancer and normal cervix tissue were obtained using immunohistochemical staining in Center 1.

Results

CD34 count and Ki-67 PI in cervical cancer were significantly higher than in normal cervix tissue (p<0.05). Parameter Ve from each TKM was significantly smaller in cervical cancer tissue than in normal cervix tissue (p<0.05), indicating the higher proliferation of cervical cancer cells. Ve of each TKM attained the largest AUC to diagnose cervical cancer. The distributions of DCE parameters for both cervical cancer and normal cervix tissue were not significantly different between two centers (P>0.05).

Conclusion

Parameter Ve was similar to the expression of Ki-67 in revealing the proliferation of tissue cells, attained good performance in diagnosis of cervical cancer, and demonstrated consistent findings on measured values across centers.

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.

Quantitative Dynamic-Enhanced MRI and Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Prediction of the Pathological Response to Neoadjuvant Chemotherapy and the Prognosis in Locally Advanced Gastric Cancer

Background

This study aimed to explore the predictive value of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters for the response to neoadjuvant chemotherapy (NCT) in locally advanced gastric cancer (LAGC) patients, and the relationship between the prediction results and patients’ prognosis, so as to provide a basis for clinical individualized precision treatment.

Methods

One hundred twenty-nine newly diagnosed LAGC patients who underwent IVIM-DWI and DCE-MRI pretreatment were enrolled in this study. Pathological tumor regression grade (TRG) served as the reference standard of NCT response evaluation. The differences in DCE-MRI and IVIM-DWI parameters between pathological responders (pR) and pathological non-responders (pNR) groups were analyzed. Univariate and multivariate logistic regressions were used to identify independent predictive parameters for NCT response. Prediction models were built with statistically significant quantitative parameters and their combinations. The performance of these quantitative parameters and models was evaluated using receiver operating characteristic (ROC) analysis. Clinicopathological variables, DCE-MRI and IVIM-DWI derived parameters, as well as the prediction model were analyzed in relation to 2-year recurrence-free survival (RFS) by using Cox proportional hazards model. RFS was compared using the Kaplan–Meier method and the log-rank test.

Results

Sixty-nine patients were classified as pR and 60 were pNR. K^trans, k_ep, and v_e values in the pR group were significantly higher, while ADC_standard and D values were significantly lower than those in the pNR group. Multivariate logistic regression analysis demonstrated that K^trans, k_ep, v_e, and D values were independent predictors for NCT response. The combined predictive model, which consisted of DCE-MRI and IVIM-DWI, showed the best prediction performance with an area under the curve (AUC) of 0.922. Multivariate Cox regression analysis showed that ypStage III and NCT response predicted by the IVIM-DWI model were independent predictors of poor RFS. The IVIM-DWI model could significantly stratify median RFS (52 vs. 15 months) and 2-year RFS rate (72.3% vs. 21.8%) of LAGC.

Conclusion

Pretreatment DCE-MRI quantitative parameters K^trans, k_ep, v_e, and IVIM-DWI parameter D value were independent predictors of NCT response for LAGC patients. The regression model based on baseline DCE-MRI, IVIM-DWI, and their combination could help RFS stratification of LAGC patients.

Endometrial carcinoma: use of tracer kinetic modeling of dynamic contrast-enhanced MRI for preoperative risk assessment

Background

To compare two tracer kinetic models in predicting of preoperative risk types in endometrial carcinoma (EC) using DCE-MRI.

Methods

A prospective study of patients with EC was conducted with institutional ethics approval and written informed consent. DCE-MRI data was analyzed using the extended Tofts (ET) and the distributed parameter (DP) models. DCE parameters blood flow (F), mean transit time, blood volume (Vp), extravascular extracellular volume (Ve), permeability surface area product (PS), extraction fraction, transfer constant (Ktrans), and efflux rate (Kep) between high- and low-risk EC were compared using the Mann–Whitney test. Bland–Altman analysis was utilized to compare parameter consistency and Spearman test to assess parameter correlation. Diagnostic performance of DCE parameters was analyzed by receiver-operating characteristic curve and compared with traditional MRI assessment.

Results

Fifty-one patients comprised the study group. Patients with high-risk EC exhibited significantly lower Ktrans, Kep, F, Vp and PS (P < 0.001). ET-derived Ktrans and DP-derived F attained AUC of 0.92 and 0.91, respectively. Bland–Altman analysis showed that the consistency of Ve or Vp between the two models was low (P < 0.001) while Spearman test showed a strong correlation (r = 0.719, 0.871). Both Ktrans and F showed higher accuracy in predicting EC risk types than traditional MRI assessment.

Conclusions

Kinetic parameters derived from DCE-MRI revealed a more hypovascular microenvironment for high risk EC than to low- risk ones, providing potential imaging biomarkers in preoperative risk assessment that might improve individualized surgical planning and management of EC.

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

Review of diffusion-weighted imaging and dynamic contrast-enhanced MRI for multiple myeloma and its precursors (monoclonal gammopathy of undetermined significance and smouldering myeloma)

The last decades, increasing research has been conducted on dynamic contrast-enhanced and diffusion-weighted MRI techniques in multiple myeloma and its precursors. Apart from anatomical sequences which are prone to interpretation errors due to anatomical variants, other pathologies and subjective evaluation of signal intensities, dynamic contrast-enhanced and diffusion-weighted MRI provide additional information on microenvironmental changes in bone marrow and are helpful in the diagnosis, staging and follow-up of plasma cell dyscrasias. Diffusion-weighted imaging provides information on diffusion (restriction) of water molecules in bone marrow and in malignant infiltration. Qualitative evaluation by visually assessing images with different diffusion sensitising gradients and quantitative evaluation of the apparent diffusion coefficient are studied extensively. Dynamic contrast-enhanced imaging provides information on bone marrow vascularisation, perfusion, capillary resistance, vascular permeability and interstitial space, which are systematically altered in different disease stages and can be evaluated in a qualitative and a (semi-)quantitative manner. Both diffusion restriction and abnormal dynamic contrast-enhanced MRI parameters are early biomarkers of malignancy or disease progression in focal lesions or in regions with diffuse abnormal signal intensities. The added value for both techniques lies in better detection and/or characterisation of abnormal bone marrow otherwise missed or misdiagnosed on anatomical MRI sequences. Increased detection rates of focal lesions or diffuse bone marrow infiltration upstage patients to higher disease stages, provide earlier access to therapy and slower disease progression and allow closer monitoring of high-risk patients. Despite promising results, variations in imaging protocols, scanner types and post-processing methods are large, thus hampering universal applicability and reproducibility of quantitative imaging parameters. The myeloma response assessment and diagnosis system and the international myeloma working group provide a systematic multicentre approach on imaging and propose which parameters to use in multiple myeloma and its precursors in an attempt to overcome the pitfalls of dynamic contrast-enhanced and diffusion-weighted imaging.Single sentence summary statementDiffusion-weighted imaging and dynamic contrast-enhanced MRI provide important additional information to standard anatomical MRI techniques for diagnosis, staging and follow-up of patients with plasma cell dyscrasias, although some precautions should be taken on standardisation of imaging protocols to improve reproducibility and application in multiple centres.

Imaging methods to evaluate tumor microenvironment factors affecting nanoparticle drug delivery and antitumor response

Standard small molecule and nanoparticulate chemotherapies are used for cancer treatment; however, their effectiveness remains highly variable. One reason for this variable response is hypothesized to be due to nonspecific drug distribution and heterogeneity of the tumor microenvironment, which affect tumor delivery of the agents. Nanoparticle drugs have many theoretical advantages, but due to variability in tumor microenvironment (TME) factors, the overall drug delivery to tumors and associated antitumor response are low. The nanotechnology field would greatly benefit from a thorough analysis of the TME factors that create these physiological barriers to tumor delivery and treatment in preclinical models and in patients. Thus, there is a need to develop methods that can be used to reveal the content of the TME, determine how these TME factors affect drug delivery, and modulate TME factors to increase the tumor delivery and efficacy of nanoparticles. In this review, we will discuss TME factors involved in drug delivery, and how biomedical imaging tools can be used to evaluate tumor barriers and predict drug delivery to tumors and antitumor response.

Estimation of fractional myocardial blood volume and water exchange using ferumoxytol-enhanced magnetic resonance imaging

Fractional myocardial blood volume (fMBV) estimated using ferumoxytol-enhanced magnetic resonance imaging (MRI) (FE-MRI) has the potential to capture a hemodynamic response to myocardial hypoperfusion during contrast steady state without reliance on gadolinium chelates. Ferumoxytol has a long intravascular half-life and its use for steady-state MRI is off-label. The aim of this prospective study was to optimize and evaluate a two-compartment model for estimation of fMBV based on FE-MRI. Nine healthy swine and one swine with artificially induced single-vessel coronary stenosis underwent MRI on a 3.0 T clinical magnet. Myocardial longitudinal spin-lattice relaxation rate (R1) was measured using the 5(3)3(3)3 modified Look-Locker inversion recovery (MOLLI) sequence before and at contrast steady state following seven ferumoxytol infusions (0.125-4.0 mg/kg). fMBV and water exchange were estimated using a two-compartment model. Model-fitted fMBV was compared to simple fast-exchange fMBV approximation and percent change in pre- and postferumoxytol R1. Dose undersampling schemes were investigated to reduce acquisition duration. Variation in fMBV was assessed using one-way analysis of variance. Fast-exchange fMBV and ferumoxytol dose undersampling were evaluated using Bland-Altman analysis. Healthy normal swine showed a mean mid-ventricular fMBV of 7.2 ± 1.4% and water exchange rate of 11.3 ± 5.1 s-1 . There was intersubject variation in fMBV (p < 0.05) without segmental variation (p = 0.387). fMBV derived from eight-dose and four-dose sampling schemes had no significant bias (mean difference = 0.07, p = 0.541, limits of agreement -1.04% [-1.45, -0.62%] to 1.18% [0.77, 1.59%]). Pixel-wise fMBV in one swine model with coronary artery stenosis showed elevated fMBV in ischemic segments (apical anterior: 11.90 ± 4.00%, apical septum: 16.10 ± 5.71%) relative to remote segments (apical inferior: 9.59 ± 3.35%, apical lateral: 9.38 ± 2.35%). A two-compartment model based on FE-MRI using the MOLLI sequence may enable estimation of fMBV in studies of ischemic heart disease. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 2.

Morphological and functional assessment of the uterus: "one-stop shop imaging" using a compressed-sensing accelerated, free-breathing T1-VIBE sequence

BACKGROUND

The combination of motion-insensitive, high-temporal, and spatial resolution imaging with evaluation of quantitative perfusion has the potential to increase the diagnostic capabilities of magnetic resonance imaging (MRI) in the female pelvis.

PURPOSE

To compare a free-breathing compressed-sensing VIBE (fbVIBE) with flexible temporal resolution (range = 4.6-13.8 s) with breath-hold VIBE (bhVIBE) and to evaluate the potential value of quantifying uterine perfusion.

MATERIAL AND METHODS

A total of 70 datasets from 60 patients (bhVIBE: n = 30; fbVIBE: n = 40) were evaluated by two radiologists. Only temporally resolved reconstruction (fbVIBE) was performed on 30 of the fbVIBE datasets. For a subset (n = 10) of the fbVIBE acquisitions, a time- and motion-resolved reconstruction (mrVIBE) was evaluated. Image quality (IQ), artifacts, diagnostic confidence (DC), and delineation of uterine structures (DoS) were graded on Likert scales (IQ/DC/DoS: 1 (non-diagnostic) to 5 (perfect); artifacts: 1 (no artifacts) to 5 (severe artifacts)). A Tofts model was applied for perfusion analysis. Ktrans was obtained in the myometrium (Mm), junctional zone (Jz), and cervix (Cx).

RESULTS

The median IQ/DoS/DC scores of fbVIBE (4/5/5 κ >0.7-0.9) and bhVIBE (4/4/4; κ = 0.5-0.7; P > 0.05) were high, but Artifacts were graded low (fbVIBE/bhVIBE: 2/2; κ = 0.6/0.5; P > 0.05). Artifacts were only slightly improved by the additional motion-resolved reconstruction (fbVIBE/mrVIBE: 2/1.5; P = 0.08); fbVIBE was preferred in most cases (7/10). Significant differences of Ktrans values were found between Cx, Jz, and Mm (0.12/0.21/0.19; P < 0.05).

CONCLUSION

The fbVIBE sequence allows functional and morphological assessment of the uterus at comparable IQ to bhVIBE.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging as Imaging Biomarker for Vascular Normalization Effect of Infigratinib in High-FGFR-Expressing Hepatocellular Carcinoma Xenografts

PURPOSE

Overexpression of fibroblast growth factor receptor (FGFR) contributes to tumorigenesis, metastasis, and poor prognosis of hepatocellular carcinoma (HCC). Infigratinib-a pan-FGFR inhibitor-potently suppresses the growth of high-FGFR-expressing HCCs in part via alteration of the tumor microenvironment and vessel normalization. In this study, we aim to assess the utility of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) as a non-invasive imaging technique to detect microenvironment changes associated with infigratinib and sorafenib treatment in high-FGFR-expressing HCC xenografts.

PROCEDURES

Serial DCE-MRIs were performed on 12 nude mice bearing high-FGFR-expressing patient-derived HCC xenografts to quantify tumor microenvironment pre- (day 0) and post-treatment (days 3, 6, 9, and 15) of vehicle, sorafenib, and infigratinib. DCE-MRI data were analyzed using extended generalized kinetic model and two-compartment distributed parameter model. After treatment, immunohistochemistry stains were performed on the harvested tumors to confirm DCE-MRI findings.

RESULTS

By treatment day 15, infigratinib induced tumor regression (70 % volume reduction from baseline) while sorafenib induced relative growth arrest (185 % volume increase from baseline versus 694 % volume increase from baseline of control). DCE-MRI analysis revealed different changes in microcirculatory parameters upon exposure to sorafenib versus infigratinib. While sorafenib induced microenvironment changes similar to those of rapidly growing tumors, such as a decrease in blood flow (F), fractional intravascular volume (vp), and permeability surface area product (PS), infigratinib induced the exact opposite changes as early as day 3 after treatment: increase in F, vp, and PS.

CONCLUSIONS

Our study demonstrated that DCE-MRI is a reliable non-invasive imaging technique to monitor tumor microcirculatory response to FGFR inhibition and VEGF inhibition in high-FGFR-expressing HCC xenografts. Furthermore, the microcirculatory changes from FGFR inhibition manifested early upon treatment initiation and were reliably detected by DCE-MRI, creating possibilities of combinatorial therapy for synergistic effect.

Dynamic contrast-enhanced MRI in oncology: how we do it

Magnetic resonance imaging (MRI) is particularly attractive for clinical application in perfusion imaging thanks to the absence of ionizing radiation and limited volumes of contrast agent (CA) necessary. Dynamic contrast-enhanced MRI (DCE-MRI) involves sequentially acquiring T1-weighted images through an organ of interest during the passage of a bolus administration of CA. It is a particularly flexible approach to perfusion imaging as the signal intensity time course allows not only rapid qualitative assessment, but also quantitative measures of intrinsic perfusion and permeability parameters. We examine aspects of the T1-weighted image series acquisition, CA administration, post-processing that constitute a DCE-MRI study in clinical practice, before considering some heuristics that may aid in interpreting the resulting contrast enhancement time series. While qualitative DCE-MRI has a well-established role in the diagnostic assessment of a range of tumours, and a central role in MR mammography, clinical use of quantitative DCE-MRI remains limited outside of clinical trials. The recent publication of proposals for standardized acquisition and analysis protocols for DCE-MRI by the Quantitative Imaging Biomarker Alliance may be an opportunity to consolidate and advance clinical practice.

Application of Distributed Parameter Model to Assessment of Glioma IDH Mutation Status by Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Previous studies using contrast-enhanced imaging for glioma isocitrate dehydrogenase (IDH) mutation assessment showed promising yet inconsistent results, and this study attempts to explore this problem by using an advanced tracer kinetic model, the distributed parameter model (DP). Fifty-five patients with glioma examined using dynamic contrast-enhanced imaging sequence at a 3.0 T scanner were retrospectively reviewed. The imaging data were processed using DP, yielding the following parameters: blood flow F, permeability-surface area product PS, fractional volume of interstitial space Ve, fractional volume of intravascular space Vp, and extraction ratio E. The results were compared with the Tofts model. The Wilcoxon test and boxplot were utilized for assessment of differences of model parameters between IDH-mutant and IDH-wildtype gliomas. Spearman correlation r was employed to investigate the relationship between DP and Tofts parameters. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and quantified using the area under the ROC curve (AUC). Results showed that IDH-mutant gliomas were significantly lower in F (P = 0.018), PS (P < 0.001), Vp (P < 0.001), E (P < 0.001), and Ve (P = 0.002) than IDH-wildtype gliomas. In differentiating IDH-mutant and IDH-wildtype gliomas, Vp had the best performance (AUC = 0.92), and the AUCs of PS and E were 0.82 and 0.80, respectively. In comparison, Tofts parameters were lower in K^trans (P = 0.013) and Ve (P < 0.001) for IDH-mutant gliomas. No significant difference was observed in Kep (P = 0.525). The AUCs of K^trans, Ve, and Kep were 0.69, 0.79, and 0.55, respectively. Tofts-derived Ve showed a strong correlation with DP-derived Ve (r > 0.9, P < 0.001). K^trans showed a weak correlation with F (r < 0.3, P > 0.16) and a very weak correlation with PS (r < 0.06, P > 0.8), both of which were not statistically significant. The findings by DP revealed a tissue environment with lower vascularity, lower vessel permeability, and lower blood flow in IDH-mutant than in IDH-wildtype gliomas, being hostile to cellular differentiation of oncogenic effects in IDH-mutated gliomas, which might help to explain the better outcomes in IDH-mutated glioma patients than in glioma patients of IDH-wildtype. The advantage of DP over Tofts in glioma DCE data analysis was demonstrated in terms of clearer elucidation of tissue microenvironment and better performance in IDH mutation assessment.

Comparison of blind deconvolution- and Patlak analysis-based methods for determining vascular permeability

This study describes a computational algorithm to determine vascular permeability constants from time-lapse imaging data without concurrent knowledge of the arterial input function. The algorithm is based on "blind" deconvolution of imaging data, which were generated with analytical and finite-element models of bidirectional solute transport between a capillary and its surrounding tissue. Compared to the commonly used Patlak analysis, the blind algorithm is substantially more accurate in the presence of solute delay and dispersion. We also compared the performance of the blind algorithm with that of a simpler one that assumed unidirectional transport from capillary to tissue [as described in Truslow et al., Microvasc. Res. 90, 117-120 (2013)]. The algorithm based on bidirectional transport was more accurate than the one based on unidirectional transport for more permeable vessels and smaller extravascular distribution volumes, and less accurate for less permeable vessels and larger extravascular distribution volumes. Our results indicate that blind deconvolution is superior to Patlak analysis for permeability mapping under clinically relevant conditions, and can thus potentially improve the detection of tissue regions with a compromised vascular barrier.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

BACKGROUND

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.

PURPOSE

To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.

FIELD STRENGTH/SEQUENCE

3T multishell diffusion-weighted sequence.

ASSESSMENT

Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.

STATISTICAL TESTS

Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.

RESULTS

The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm² /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order.

DATA CONCLUSION

The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Advances in functional and molecular MRI technologies in chronic liver diseases

MRI has emerged as the most comprehensive non-invasive diagnostic tool for liver diseases. In recent years, the value of MRI in hepatology has been significantly enhanced by a wide range of contrast agents, both clinically available and under development, that add functional information to anatomically detailed morphological images, or increase the distinction between normal and pathological tissues by targeting molecular and cellular events. Several classes of contrast agents are available for contrast-enhanced hepatic MRI, including i) conventional non-specific extracellular fluid contrast agents for assessing tissue perfusion; ii) hepatobiliary-specific contrast agents that are taken up by functioning hepatocytes and excreted through the biliary system for evaluating hepatobiliary function; iii) superparamagnetic iron oxide particles that accumulate in Kupffer cells; and iv) novel molecular contrast agents that are biochemically targeted to specific molecular/cellular processes for staging liver diseases or detecting treatment responses. The use of different functional and molecular MRI methods enables the non-invasive assessment of disease burden, progression, and treatment response in a variety of liver diseases. A high diagnostic performance can be achieved with MRI by combining imaging biomarkers.

Dynamic contrast-enhanced magnetic resonance imaging for monitoring the anti-angiogenesis efficacy in a C6 glioma rat model

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is useful in predicting responses to angiogenic therapy of malignant tumors.

PURPOSE

To observe the dynamics of DCE-MRI parameters in evaluating early effects of antiangiogenic therapy in a C6 glioma rat model.

MATERIAL AND METHODS

The Bevacizumab or vehicle treatment was started from the 14th day after glioma model was established. The treated and control groups (n = 13 per group) underwent DCE-MRI scans on days 0, 1, 3, 5, and 7 after treatment. Tumor volume was calculated according to T2-weighted images. Hematoxylin and eosin, microvessel density (MVD), and proliferating cell nuclear antigen (PCNA) examination were performed on day 7. The MRI parameters between the two groups were compared and correlations with immunohistochemical scores were analyzed.

RESULTS

The average tumor volume of treated group was significantly lower than that of control group on day 7 (81.764 ± 1.043 vs. 103.634 ± 3.868 mm³, P = 0.002). K^trans and K_ep decreased in the treated group while they increased in the control group. The differences were observed on day 5 (K^trans: 0.045 ± 0.018 vs. 0.093 ± 0.014 min^-1, P < 0.001; K_ep: 0.062 ± 0.018 vs. 0.134 ± 0.047 min^-1, P = 0.005) and day 7 (K^trans: 0.032 ± 0.010 vs. 0.115 ± 0.025 min^-1, P < 0.001; K_ep: 0.045 ± 0.016 vs. 0.144 ± 0.042 min^-1, P < 0.001). The difference of V_e was observed on day 5 (0.847 ± 0.248 vs. 0.397 ± 0.151, P = 0.009) and 7 (0.920 ± 0.154 vs. 0.364 ± 0.105, P = 0.006). K^trans and K_ep showed positive correlations with MVD and V_e showed negative correlation with PCNA.

CONCLUSION

DCE-MRI can assess the changes of early effects of anti-angiogenic therapy in preclinical practice.

Quantitative imaging for radiotherapy purposes

Quantitative imaging biomarkers show great potential for use in radiotherapy. Quantitative images based on microscopic tissue properties and tissue function can be used to improve contouring of the radiotherapy targets. Furthermore, quantitative imaging biomarkers might be used to predict treatment response for several treatment regimens and hence be used as a tool for treatment stratification, either to determine which treatment modality is most promising or to determine patient-specific radiation dose. Finally, patient-specific radiation doses can be further tailored to a tissue/voxel specific radiation dose when quantitative imaging is used for dose painting. In this review, published standards, guidelines and recommendations on quantitative imaging assessment using CT, PET and MRI are discussed. Furthermore, critical issues regarding the use of quantitative imaging for radiation oncology purposes and resultant pending research topics are identified.

DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction

Pharmacokinetic parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time course data enable the physio-biological interpretation of tissue angiogenesis. This study aims to develop machine learning approaches for cervical carcinoma prediction based on pharmacokinetic parameters. The performance of individual parameters was assessed in terms of their efficacy in differentiating cancerous tissue from normal cervix tissue. The effect of combining parameters was evaluated using the following two approaches: the first approach was based on support vector machines (SVMs) to combine the parameters from one pharmacokinetic model or across several models; the second approach was based on a novel method called APITL (artificial pharmacokinetic images for transfer learning), which was designed to fully utilize the comprehensive pharmacokinetic information acquired from DCE-MRI data. A "winner-takes-all" strategy was employed to consolidate the slice-wise prediction into subject-wise prediction. Experiments were carried out with a dataset comprising 36 patients with cervical cancer and 17 healthy subjects. The results demonstrated that parameter V_e, representing volume fraction of the extracellular extravascular space (EES), attained high discriminative power regardless of the pharmacokinetic model used for estimation. An approximately 10% improvement in the accuracy was achieved with the SVM approach. The APITL method further outperformed SVM and attained a subject-wise prediction accuracy of 94.3%. Our experiment demonstrated that APITL could predict cervical carcinoma with high accuracy and had potential in clinical applications.

Dynamic Contrast-Enhanced MR Imaging of Nonenhancing T2 High-Signal-Intensity Lesions in Baseline and Posttreatment Glioblastoma: Temporal Change and Prognostic Value

BACKGROUND AND PURPOSE

The prognostic value of dynamic contrast-enhanced MR imaging on nonenhancing T2 high-signal-intensity lesions in patients with glioblastoma has not been thoroughly elucidated to date. We evaluated the temporal change and prognostic value for progression-free survival of dynamic contrast-enhanced MR imaging-derived pharmacokinetic parameters on nonenhancing T2 high-signal-intensity lesions in patients with glioblastoma before and after standard treatment, including gross total surgical resection.

MATERIALS AND METHODS

This retrospective study included 33 patients who were newly diagnosed with glioblastoma and treated with gross total surgical resection followed by concurrent chemoradiation therapy and adjuvant chemotherapy with temozolomide in a single institution. All patients underwent dynamic contrast-enhanced MR imaging before surgery as a baseline and after completion of maximal surgical resection and concurrent chemoradiation therapy. On the whole nonenhancing T2 high-signal-intensity lesion, dynamic contrast-enhanced MR imaging-derived pharmacokinetic parameters (volume transfer constant [K ^trans], volume of extravascular extracellular space [v _e]_, and blood plasma volume [v_p ]) were calculated. The Cox proportional hazards regression model analysis was performed to determine the histogram features or percentage changes of pharmacokinetic parameters related to progression-free survival.

RESULTS

Baseline median K ^trans, baseline first quartile K ^trans, and posttreatment median K ^trans were significant independent variables, as determined by univariate analysis (P < .05). By multivariate Cox regression analysis including methylation status of O⁶-methylguanine-DNA methyltransferase, baseline median K ^trans was determined to be the significant independent variable and was negatively related to progression-free survival (hazard ratio = 1.48, P = .003).

CONCLUSIONS

Baseline median K ^trans from nonenhancing T2 high-signal-intensity lesions could be a potential prognostic imaging biomarker in patients undergoing gross total surgical resection followed by standard therapy for glioblastoma.

Quantitative MRI for Assessment of Treatment Outcomes in a Rabbit VX2 Hepatic Tumor Model

Globally, primary and secondary liver cancer is one of the most common cancer types, accounting 8.2% of deaths worldwide in 2018. One of the key strategies to improve the patient's prognosis is the early diagnosis, when liver function is still preserved. In hepatocellular carcinoma (HCC), the typical wash-in/wash-out pattern in conventional magnetic resonance imaging (MRI) reaches a sensitivity of 60% and specificity of 96-100%. However, in recent years functional MRI sequences such as hepatocellular-specific gadolinium-based dynamic-contrast enhanced MRI, diffusion-weighted imaging (DWI), and magnetic resonance spectroscopy (MRS) have been demonstrated to improve the evaluation of treatment success and thus the therapeutic decision-making and the patient's outcome. In the preclinical research setting, the VX2 liver rabbit tumor, which once originated from a virus-induced anaplastic squamous cell carcinoma, has played a longstanding role in experimental interventional oncology. Especially the high tumor vascularity allows assessing the treatment response of locoregional interventions such as radiofrequency ablation (RFA) and transcatheter arterial embolization (TACE). Functional MRI has been used to monitor the tumor growth and viability following interventional treatment. Besides promising results, a comprehensive overview of functional MRI sequences used so far in different treatment setting is lacking, thus lowering the comparability of study results. This review offers a comprehensive overview of study protocols, results, and limitations of quantitative MRI sequences applied to evaluate the treatment outcome of VX2 hepatic tumor models, thus generating a unique basis for future MRI studies and potential translation into the clinical setting. Level of Evidence: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2019. J. Magn. Reson. Imaging 2020;52:668-685.

A Comparative Study of Two-Compartment Exchange Models for Dynamic Contrast-Enhanced MRI in Characterizing Uterine Cervical Carcinoma

A variety of tracer kinetic methods have been employed to assess tumor angiogenesis. The Standard two-Compartment model (SC) used in cervix carcinoma was less frequent, and Adiabatic Approximation to the Tissue Homogeneity (AATH) and Distributed Parameter (DP) model are lacking. This study compares two-compartment exchange models (2CXM) (AATH, SC, and DP) for determining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in cervical cancer, with the aim of investigating the potential of various parameters derived from 2CXM for tumor diagnosis and exploring the possible relationship between these parameters in patients with cervix cancer. Parameters (tissue blood flow, F_p; tissue blood volume, V_p; interstitial volume, V_e; and vascular permeability, PS) for regions of interest (ROI) of cervix lesions and normal cervix tissue were estimated by AATH, SC, and DP models in 36 patients with cervix cancer and 17 healthy subjects. All parameters showed significant differences between lesions and normal tissue with a P value less than 0.05, except for PS from the AATH model, F_p from the SC model, and V_p from the DP model. Parameter V_e from the AATH model had the largest AUC (r = 0.85). Parameters F_p and V_p from SC and DP models and V_e and PS from AATH and DP models were highly correlated, respectively, (r > 0.8) in cervix lesions. Cervix cancer was found to have a very unusual microcirculation pattern, with over-growth of cancer cells but without evident development of angiogenesis. V_e has the best performance in identifying cervix cancer. Most physiological parameters derived from AATH, SC, and DP models are linearly correlated in cervix cancer.

On the potential use of dynamic contrast-enhanced (DCE) MRI parameters as radiomic features of cervical cancer

PURPOSE

To evaluate whether the analysis of high-temporal resolution DCE-MRI by various tracer kinetic models could yield useful radiomic features in discriminating cervix carcinoma and normal cervix tissue.

METHODS

Forty-three patients (median age 51 yr; range 26-78 yr) diagnosed with cervical cancer based on postoperative pathology were enrolled in this study with informed consent. DCE-MRI data with temporal resolution of 2 s were acquired and analyzed using the Tofts (TOFTS), extended Tofts (EXTOFTS), conventional two-compartment (CC), adiabatic tissue homogeneity (ATH), and distributed parameter (DP) models. Ability of all kinetic parameters in distinguishing tumor from normal tissue was assessed using Mann-Whitney U test and receiver operating characteristic (ROC) curves. Repeatability of parameter estimates due to sampling of arterial input functions (AIFs) was also studied using intraclass correlation (ICC) analysis.

RESULTS

Fractional extravascular, extracellular volume (Ve) of all models were significantly smaller in cervix carcinoma than normal cervix tissue, and were associated with large values of area under ROC curve (AUC 0.884-0.961). Capillary permeability PS derived from the ATH, CC, and DP models also yielded large AUC values (0.730, 0.860, and 0.797). Transfer constant Ktrans derived from TOFTS and EXTOFTS models yielded smaller AUC (0.587 and 0.701). Repeatability of parameters derived from all models was robust to AIF sampling, with ICC coefficients typically larger than 0.80.

CONCLUSIONS

With the use of high-temporal resolution DCE-MRI, all tracer kinetic models could reflect pathophysiological differences between cervix carcinoma and normal tissue (with significant differences in Ve and PS) and potentially yield radiomic features with diagnostic value.

Dynamic contrast-enhanced MRI of malignant pleural mesothelioma: a comparative study of pharmacokinetic models and correlation with mRECIST criteria

Background

Malignant pleural mesothelioma (MPM) is a rare and aggressive thoracic malignancy that is difficult to cure. Dynamic contrast-enhanced (DCE) MRI is a functional imaging technique used to analyze tumor microvascular properties and to monitor therapy response. Purpose of this study was to compare two tracer kinetic models, the extended Tofts (ET) and the adiabatic approximation tissue homogeneity model (AATH) for analysis of DCE-MRI and examine the value of the DCE parameters to predict response to chemotherapy in patients with MPM.

Method

This prospective, longitudinal, single tertiary radiology center study was conducted between October 2013 and July 2015. Patient underwent DCE-MRI studies at three time points: prior to therapy, during and after cisplatin-based chemotherapy. The images were analyzed using ET and AATH models. In short-term follow-up, the patients were classified as having disease control or progressive disease according to modified response evaluation criteria in solid tumors (mRECIST) criteria.

Receiver operating characteristic curve analysis was used to examine specificity and sensitivity of DCE parameters for predicting response to therapy. Comparison tests were used to analyze whether derived parameters are interchangeable between the two models.

Results

Nineteen patients form the study population. The results indicate that the derived parameters are not interchangeable between the models.

Significant correlation with response to therapy was found for AATH-calculated median pre-treatment efflux rate (k_ep) showing sensitivity of 83% and specificity of 100% (AUC 0.9). ET-calculated maximal pre-treatment k_ep showed 100% sensitivity and specificity for predicting treatment response during the early phase of the therapy and reached a favorable trend to significant prognostic value post-therapy.

Conclusion

Both models show potential in predicting response to therapy in MPM. High pre-treatment k_ep values suggest MPM disease control post-chemotherapy.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0189-5) contains supplementary material, which is available to authorized users.

Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated. Existing methods for BAT estimation are tailored to tissue concentration curves, which have a fast upslope to the peak as frequently observed in patient data. However, they may give poor results for curves that do not have this characteristic shape such as tissue concentration curves of small animals. In this paper, we propose a method for BAT estimation of signals that do not have a fast upslope to their peak. The model is based on splines which are able to adapt to a large variety of concentration curves. Furthermore, the method estimates BATs on a continuous time scale. All relevant model parameters are automatically determined by generalized cross validation. We use simulated concentration curves of small animal and patient settings to assess the accuracy and robustness of our approach. The proposed method outperforms a state-of-the-art method for small animal data and it gives competitive results for patient data. Finally, it is tested on in vivo acquired rat data where accuracy of BAT estimation was also improved upon the state-of-the-art method. The results indicate that the proposed method is suitable for accurate BAT estimation of DCE-MRI data, especially for small animals.

Exercise-stimulated arterial transit time in calf muscles measured by dynamic contrast-enhanced magnetic resonance imaging

The primary goal of this study was to evaluate arterial transit time (ATT) in exercise-stimulated calf muscles as a promising indicator of muscle function. Following plantar flexion, ATT was measured by dynamic contrast-enhanced (DCE) MRI in young and elderly healthy subjects and patients with peripheral artery disease (PAD). In the young healthy subjects, gastrocnemius ATT decreased significantly (P < 0.01) from 4.3 ± 1.5 to 2.4 ± 0.4 sec when exercise load increased from 4 lbs to 16 lbs. For the same load of 4 lbs, gastrocnemius ATT was lower in the elderly healthy subjects (3.2 ± 1.1 sec; P = 0.08) and in the PAD patients (2.4 ± 1.2 sec; P = 0.02) than in the young healthy subjects. While the sensitivity of the exercise-stimulated ATT is diagnostically useful, it poses a challenge for arterial spin labeling (ASL), a noncontrast MRI method for measuring muscle perfusion. As a secondary goal of this study, we assessed the impact of ATT on ASL-measured perfusion with ASL data of multiple post labeling delays (PLDs) acquired from a healthy subject. Perfusion varied substantially with PLD in the activated gastrocnemius, which can be attributed to the ATT variability as verified by a simulation. In conclusion, muscle ATT is sensitive to exercise intensity, and it potentially reflects the functional impact of aging and PAD on calf muscles. For precise measurement of exercise-stimulated muscle perfusion, it is recommended that ATT be considered when quantifying muscle ASL data.

Could perfusion heterogeneity at dynamic contrast-enhanced MRI be used to predict rectal cancer sensitivity to chemoradiotherapy?

AIM

To evaluate whether perfusion heterogeneity of rectal cancer prior to chemoradiotherapy (CRT) using histogram analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) quantitative parameters can predict response to treatment.

MATERIALS AND METHODS

Twenty-one patients with histologically proven rectal adenocarcinoma were enrolled prospectively. All patients underwent 1.5 T DCE-MRI before CRT. Tumour volumes were drawn on Ktrans and Ve maps, using T2-weighted (W) images as reference, and the following first-order texture parameters of Ve and Ktrans values were extracted: 25th, 50th, 75th percentile, mean, standard deviation, skewness, and kurtosis. After CRT, patients underwent surgery and according with Rödel's tumour regression grade (TRG), they were classified as poor responders "non-GR" (TRG 0-2) and good responders "GR" (TRG 3-4). Differences between GR and non-GR in DCE-MRI first-order texture parameters were evaluated using the Mann-Whitney test, and their role in the prediction of response was investigated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Sixteen (76%) patients were classified as GR and five (24%) were non-GR. Skewness and kurtosis of Ve was significantly higher in non-GR (4.886±1.320 and 36.402±24.486, respectively) than in GR patients (1.809±1.280, p=0.003 and 6.268±8.130, p= 0.011). Ve skewness <3.635 was able to predict GR with an area under the ROC curve (AUC) of 0.988, sensitivity 93.8%, specificity 80%, and accuracy 90.5%. Ve kurtosis <21.095 was able to predict response with an AUC of 0.963, sensitivity 93.8%, specificity 80%, and accuracy 90.5%. Other parameters were not different between groups or predictors of response.

CONCLUSION

Ve skewness and kurtosis seem to be promising in the prediction of response to CRT in rectal cancer patients.

Improved detection of a tumorous involvement of the mesorectal fascia and locoregional lymph nodes in locally advanced rectal cancer using DCE-MRI

PURPOSE

The prediction of an infiltration of the mesorectal fascia (MRF) and malignant lymph nodes is essential for treatment planning and prognosis of patients with rectal cancer. The aim of this study was to assess the additional diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the detection of a malignant involvement of the MRF and of mesorectal lymph nodes in patients with locally advanced rectal cancer.

METHODS

In this prospective study, 22 patients with locally advanced rectal cancer were examined with 1.5-T MRI between September 2012 and April 2015. Histopathological assessment of tumor size, tumor infiltration to the MRF, and malignant involvement of locoregional lymph nodes served as standard of reference. Sensitivity and specificity of detecting MRF infiltration and malignant nodes (nodal cut-off size [NCO] ≥ 5 and ≥ 10 mm, respectively) was determined by conventional MRI (cMRI; precontrast and postcontrast T1-weighted, T2-weighted, and diffusion-weighted images) and by additional semi-quantitative DCE-MRI maps (cMRI+DCE-MRI).

RESULTS

Compared to cMRI, additional semi-quantitative DCE-MRI maps significantly increased sensitivity (86 vs. 71% [NCO ≥ 5 mm]/29% [NCO ≥ 10 mm]) and specificity (90 vs. 70% [NCO ≥ 5 mm]) of detecting malignant lymph nodes (p < 0.05). Moreover, DCE-MRI significantly augmented specificity (91 vs. 82%) of discovering a MRF infiltration (p < 0.05), while there was no change in sensitivity (83%; p > 0.05).

CONCLUSION

DCE-MRI considerably increases both sensitivity and specificity for the detection of small mesorectal lymph node metastases (≥ 5 mm but < 10 mm) and sufficiently improves specificity of a suspected MRF infiltration in patients with locally advanced rectal cancer.

Free-breathing dynamic contrast-enhanced MRI for assessment of pulmonary lesions using golden-angle radial sparse parallel imaging

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been shown to be a promising technique for assessing lung lesions. However, DCE-MRI often suffers from motion artifacts and insufficient imaging speed. Therefore, highly accelerated free-breathing DCE-MRI is of clinical interest for lung exams.

PURPOSE

To test the performance of rapid free-breathing DCE-MRI for simultaneous qualitative and quantitative assessment of pulmonary lesions using Golden-angle RAdial Sparse Parallel (GRASP) imaging.

STUDY TYPE

Prospective.

POPULATION

Twenty-six patients (17 males, mean age = 55.1 ± 14.4) with known pulmonary lesions.

FIELD STRENGTH/SEQUENCE

3T MR scanner; a prototype fat-saturated, T₁ -weighted stack-of-stars golden-angle radial sequence for data acquisition and a Cartesian breath-hold volumetric-interpolated examination (BH-VIBE) sequence for comparison.

ASSESSMENT

After a dual-mode GRASP reconstruction, one with 3-second temporal resolution (3s-GRASP) and the other with 15-second temporal resolution (15s-GRASP), all GRASP and BH-VIBE images were pooled together for blind assessment by two experienced radiologists, who independently scored the overall image quality, lesion delineation, overall artifact level, and diagnostic confidence of each case. Perfusion analysis was performed for the 3s-GRASP images using a Tofts model to generate the volume transfer coefficient (K^trans ) and interstitial volume (V_e ).

STATISTICAL TESTS

Nonparametric paired two-tailed Wilcoxon signed-rank test; Cohen's kappa; unpaired Student's t-test.

RESULTS

15s-GRASP achieved comparable image quality with conventional BH-VIBE (P > 0.05), except for the higher overall artifact level in the precontrast phase (P = 0.018). The K^trans and V_e in inflammation were higher than those in malignant lesions (K^trans : 0.78 ± 0.52 min^-1 vs. 0.37 ± 0.22 min^-1 , P = 0.020; V_e : 0.36 ± 0.16 vs. 0.26 ± 0.1, P = 0.177). Also, the K^trans and V_e in malignant lesions were also higher than those in benign lesions (K^trans : 0.37 ± 0.22 min^-1 vs. 0.04 ± 0.04 min^-1 , P = 0.001; V_e : 0.26 ± 0.12 vs. 0.10 ± 0.00, P = 0.063).

DATA CONCLUSION

This feasibility study demonstrated the performance of high spatiotemporal resolution free-breathing DCE-MRI of the lung using GRASP for qualitative and quantitative assessment of pulmonary lesions.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:459-468.

Comparison of perfusion models for quantitative T1 weighted DCE-MRI of rectal cancer

In this work, the two compartment exchange model and two compartment uptake model were applied to obtain quantitative perfusion parameters in rectum carcinoma and the results were compared to those obtained by the deconvolution algorithm. Eighteen patients with newly diagnosed rectal carcinoma underwent 3 T MRI of the pelvis including a T₁ weighted dynamic contrastenhanced (DCE) protocol before treatment. Mean values for Plasma Flow (PF), Plasma Volume (PV) and Mean Transit Time (MTT) were obtained for all three approaches and visualized in parameter cards. For the two compartment models, Akaike Information Criterion (AIC) and [Formula: see text] were calculated. Perfusion parameters determined with the compartment models show results in accordance with previous studies focusing on rectal cancer DCE-CT (PF_2CX = 68 ± 44 ml/100 ml/min, PF_2CU = 55 ± 36 ml/100 ml/min) with similar fit quality (AIC:169 ± 81/179 ± 77, [Formula: see text]:10 ± 12/9 ± 10). Values for PF are overestimated whereas PV and MTT are underestimated compared to results of the deconvolution algorithm. Significant differences were found among all models for perfusion parameters as well as between the AIC and [Formula: see text] values. Quantitative perfusion parameters are dependent on the chosen tracer kinetic model. According to the obtained parameters, all approaches seem capable of providing quantitative perfusion values in DCE-MRI of rectal cancer.

Understanding K trans: a simulation study based on a multiple-pathway model

The transfer constant K ^trans is commonly employed in dynamic contrast-enhanced MRI studies, but the utility and interpretation of K ^trans as a potential biomarker of tumor vasculature remains unclear. In this study, computer simulations based on a comprehensive tracer kinetic model with multiple pathways was used to provide clarification on the interpretation and application of K ^trans. Tissue concentration-time curves pertaining to a wide range of transport conditions were simulated using the multiple-pathway (MP) model and fitted using the generalized kinetic (GK) and extended GK models. Relationships between K ^trans and plasma flow F _p, vessel permeability PS and extraction rate EF _p under various transport conditions were assessed by correlation and regression analysis. Results show that the MP model provides an alternative two-tier interpretation of K ^trans based on the vascular transit time. K ^trans is primarily associated with F _p and EF _p respectively, in the slow and rapid vascular transit states, independent of the magnitude of PS. The relative magnitudes of PS and F _p only serve as secondary constraints for which K ^trans can be further associated with EF _p and PS in the slow and rapid transit states, respectively.

Tissue perfusion modelling in optical coherence tomography

Background

Optical coherence tomography (OCT) is a well established imaging technique with different applications in preclinical research and clinical practice. The main potential for its application lies in the possibility of noninvasively performing “optical biopsy”. Nevertheless, functional OCT imaging is also developing, in which perfusion imaging is an important approach in tissue function study. In spite of its great potential in preclinical research, advanced perfusion imaging using OCT has not been studied. Perfusion analysis is based on administration of a contrast agent (nanoparticles in the case of OCT) into the bloodstream, where during time it specifically changes the image contrast. Through analysing the concentration-intensity curves we are then able to find out further information about the examined tissue.

Methods

We have designed and manufactured a tissue mimicking phantom that provides the possibility of measuring dilution curves in OCT sequence with flow rates 200, 500, 1000 and 2000 μL/min. The methodology comprised of using bolus of 50 μL of gold nanorods as a contrast agent (with flow rate 5000 μL/min) and continuous imaging by an OCT system. After data acquisition, dilution curves were extracted from OCT intensity images and were subjected to a deconvolution method using an input–output system description. The aim of this was to obtain impulse response characteristics for our model phantom within the tissue mimicking environment. Four mathematical tissue models were used and compared: exponential, gamma, lagged and LDRW.

Results

We have shown that every model has a linearly dependent parameter on flow (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}R2 values from 0.4914 to 0.9996). We have also shown that using different models can lead to a better understanding of the examined model or tissue. The lagged model surpassed other models in terms of the minimisation criterion and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}R2 value.

Conclusions

We used a tissue mimicking phantom in our study and showed that OCT can be used for advanced perfusion analysis using mathematical model and deconvolution approach. The lagged model with three parameters is the most appropriate model. Nevertheless, further research have to be performed, particularly with real tissue.

Electronic supplementary material

The online version of this article (doi:10.1186/s12938-017-0320-4) contains supplementary material, which is available to authorized users.