Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T 1 -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrastenhanced MRI data.

Perfusion magnetic resonance imaging (MRI) studies quantify the microcirculatory status of liver parenchyma and liver lesions, and can be used for the detection of liver metastases, assessing the effectiveness of anti-angiogenic therapy, evaluating tumor viability after anti-cancer therapy or ablation, and diagnosis of liver cirrhosis and its severity. In this review, we discuss the basic concepts of perfusion MRI using tracer kinetic modeling, the common kinetic models applied for analyses, the MR scanning techniques, methods of data processing, and evidence that supports its use from published clinical and research studies. Technical standardization and further studies will help to establish and validate perfusion MRI as a clinical imaging modality.

Neuroendocrine hepatic metastases exhibit various contrast uptake enhancement patterns in dynamic contrast-enhanced MRI. Using a dual-input two-compartment distributed parameter model, we analyzed the dynamic contrast-enhanced MRI datasets of seven patient study cases with the aim to relate the tumor contrast uptake patterns to parameters of tumor microvasculature. Simulation studies were also performed to provide further insights into the effects of individual microcirculatory parameter on the tumor concentration-time curves. Although the tumor contrast uptake patterns can be influenced by many parameters, initial results indicate that hepatic blood flow and the ratio of fractional vascular volume to fractional interstitial volume may potentially distinguish between the patterns of neuroendocrine hepatic metastases. Key words: neuroendocrine hepatic metastases; DCE MRI; tracer kinetics modeling Neuroendocrine tumors are a rare and heterogeneous group of hormone-secreting neoplasms that arise from neoplastic proliferation of enterochromaffin or Kulchitsky cells of the neuroendocrine system (1,2). Primary tumors can occur in various organs, predominantly in the gastrointestinal tract, pancreas, and lung. Despite their relatively low incidence, neuroendocrine tumors pose a significant clinical challenge due to their varied presentations and the primary tumor is often revealed through detection of metastases (2).Although neuroendocrine hepatic metastases have been described as predominantly hypervascular (1), in practice various contrast uptake enhancement patterns have been observed in dynamic contrast-enhanced (DCE) MRI, possibly due to their heterogeneous histology from diverse locations of origin. Apart from the usual contrast uptake pattern of rapid increase followed by rapid washout commonly associated with hypervascular hepatic lesions, neuroendocrine hepatic metastases can exhibit a spectrum of other contrast uptake behavior ranging from a progressively increasing pattern (within a few minutes following bolus administration of contrast medium) to patterns with an initial moderate increase followed by either a plateau, gradual increase or decrease.Although there are considerable interests in studying the various shapes of tumor contrast uptake patterns, the physiological basis of these patterns has remained unclear. Tracer kinetics analysis of these tumor contrast uptake patterns may provide insights into the differences in tumor microvasculature that result in these patterns. We aim to study the various contrast uptake patterns of neuroendocrine hepatic metastases and relate these patterns to tumor microcirculatory parameters derived from tracer kinetics modeling. MATERIALS AND METHODS Tracer Kinetics ModelingTracer kinetics modeling of the liver is uniquely challenging in two ways: (i) The liver has a dual blood supply derived from the hepatic artery and portal vein. (ii) Normal liver sinusoids are fenestrated, which allow free access of low-molecular weight compounds (including conventional gadolinium-bas...

Purpose: To assess the utility of SEMAC MRI metal artifact reduction technique for radiotherapy (RT) planning using CT/MRI fusion in patients with spine stabilisation devices. Methods: Conventional MRI spin‐echo (SE) sequences used for spinal cord delineation and radiotherapy planning in the spine were compared with a prototype implementation of Slice Encoding for Metal Artifact Correction (SEMAC) (WARP works‐in‐progress software package, Siemens Healthcare, Erlangen, Germany). Sequences were first optimised at 1.5T (Siemens MAGNETOM Aera) using a phantom constructed of a spine stabilisation device suspended in gelatin and then applied in patient studies (n=2). Both patient and phantom MRI series were co‐registered with spine CT data to assess artifact extent and geometrical image distortion. Registered data sets were assessed in terms of artifact size, distortions, image quality, visualisation of the spinal cord, spinal fluid, disease site and soft tissues adjacent to metal implants. Results: The optimal parameters were found as a compromise between artifact reduction and MRI acquisition time, while maintaining image quality comparable to conventional sequences: TR/TE 560/15ms, res: 0.8×0.8×4mm, GRAPPA=3, BW=587Hz/Px, 30 slices, TA=3:57min, SEMAC parameters: Slice encoding steps=4, Excitation & Refocusing RF duration=1500us, slice correction factor=1.2. The metal artifact volumes (signal voids and pile‐ups) were reduced using 2D SEMAC sequence on average by 43±16 % from 15.6±2.5 cm3 to 8.9±1.4 cm3. The difference in spinal cord, fluid and lesion volumes outlined using conventional and SEMAC sequences were 7±0.15, 2±0.42 and 35±3.11% respectively in the 6cm long spine sections affected by metallic artifacts. Conclusion: The work shows the importance of MRI metal artifact reduction techniques for reliable RT planning in the vicinity of metallic implants. The use of SEMAC sequence improves geometrical accuracy of outlined volumes in the presence of spine stabilization devices which degrade geometrical information from both CT and conventional SE MRI.

IntroductionWhole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods.Methods and analysisThis phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment (‘reference standard’). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response.Ethics and disseminationMALIMAR has ethical approval from South Central—Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination.Trial registration numberNCT03574454.

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Objective To establish optimised diffusion weightings (‘b-values’) for acquisition of whole-body diffusion-weighted MRI (WB-DWI) for estimation of the apparent diffusion coefficient (ADC) in patients with metastatic melanoma (MM). Existing recommendations for WB-DWI have not been optimised for the tumour properties in MM; therefore, evaluation of acquisition parameters is essential before embarking on larger studies. Methods Retrospective clinical data and phantom experiments were used. Clinical data comprised 125 lesions from 14 examinations in 11 patients with multifocal MM, imaged before and/or after treatment with immunotherapy at a single institution. ADC estimates from these data were applied to a model to estimate the optimum b-value. A large non-diffusing phantom was used to assess eddy current–induced geometric distortion. Results Considering all tumour sites from pre- and post-treatment examinations together, metastases exhibited a large range of mean ADC values, [0.67–1.49] × 10−3 mm2/s, and the optimum high b-value (bhigh) for ADC estimation was 1100 (10th–90th percentile: 740–1790) s/mm2. At higher b-values, geometric distortion increased, and longer echo times were required, leading to reduced signal. Conclusions Theoretical optimisation gave an optimum bhigh of 1100 (10th–90th percentile: 740–1790) s/mm2 for ADC estimation in MM, with the large range of optimum b-values reflecting the wide range of ADC values in these tumours. Geometric distortion and minimum echo time increase at higher b-values and are not included in the theoretical optimisation; bhigh in the range 750–1100 s/mm2 should be adopted to maintain acceptable image quality but performance should be evaluated for a specific scanner. Key Points • Theoretical optimisation gave an optimum high b-value of 1100 (10th–90th percentile: 740–1790) s/mm2for ADC estimation in metastatic melanoma. • Considering geometric distortion and minimum echo time (TE), a b-value in the range 750–1100 s/mm2is recommended. • Sites should evaluate the performance of specific scanners to assess the effect of geometric distortion and minimum TE.

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