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Objective To develop a phantom system which can be integrated with an automated injection system, eliminating the experimental variability that arises with manual injection; for the purposes of pulse sequence testing and metric derivation in hyperpolarised 13C-MR. Methods The custom dynamic phantom was machined from Ultem and filled with a nicotinamide adenine dinucleotide and lactate dehydrogenase mixture dissolved in phosphate buffered saline. Hyperpolarised [1-13C]-pyruvate was then injected into the phantom (n = 8) via an automated syringe pump and the conversion of pyruvate to lactate monitored through a 13C imaging sequence. Results The phantom showed low coefficient of variation for the lactate to pyruvate peak signal heights (11.6%) and dynamic area-under curve ratios (11.0%). The variance for the lactate dehydrogenase enzyme rate constant (kP) was also seen to be low at 15.6%. Conclusion The dynamic phantom demonstrates high reproducibility for quantification of 13C-hyperpolarised MR-derived metrics. Establishing such a phantom is needed to facilitate development of hyperpolarsed 13C-MR pulse sequenced; and moreover, to enable multisite hyperpolarised 13C-MR clinical trials where assessment of metric variability across sites is critical. Advances in knowledge The dynamic phantom developed during the course of this study will be a useful tool in testing new pulse sequences and standardisation in future hyperpolarised work.

Abnormal contrast enhancement on MRI is advocated as a biomarker for inflammation in colitis, although the enhancement kinetics of normal colon are poorly described. Our purpose was to quantitatively assess mural enhancement in normal colon and test for intersegmental differences.Eight patients without prior history of inflammatory bowel disease underwent standard MRI colonography followed by normal same-day colonoscopy. Acquired sequences included a volumetric interpolated breath-hold examination (VIBE) to encompass the whole colonic volume, performed at 5°, 10° and 35° flip angles for T(1) quantitation and then at a fixed 35° flip angle three times prior to and every 30 s following intravenous gadoterate meglumine for 220 s. Ascending colon, descending colon and rectal R(1) (1/T(1)) was plotted against time. Mean pre-contrast R(1), initial change of R(1) (ΔR(1)), early and late "plateau phase" enhancement and the area under the R(1)-time (AUC-R(1)) curve were compared between segments using the Student's paired t-test.There was no significant difference of pre-contrast R(1) between segments (p=0.49 to 0.62). ΔR(1) was higher for ascending colon compared with descending colon (0.0023±0.0012 ms(-1) vs 0.0010±0.0011 ms(-1), p=0.03). There was no significant difference for early or late plateau phase R(1) between colonic segments (p=0.08 to 1.00). AUC-R(1) was greater for ascending than descending colon (0.54±0.19 vs 0.30±0.14, p=0.03).Intersegmental differences in colonic enhancement are present and should be considered when interpreting differential segmental enhancement.

This retrospective study compares dynamic contrast-enhanced (DCE) MRI with the serial prostate-specific antigen (PSA) measurement for detection of residual disease following whole-gland high-intensity focused ultrasound (HIFU) therapy of prostate cancer.Patients in whom post-HIFU DCE-MRI was followed within 3 months by ultrasound-guided transrectal biopsy were selected from a local database. 26 patients met the study inclusion criteria. Serial PSA levels following HIFU and post-HIFU follow-up MRI were retrieved for each patient. Three radiologists unaware of other investigative results independently assessed post-HIFU MRI studies for the presence of cancer, scoring on a four-point scale (1, no disease; 2, probably no disease; 3, probably residual disease; and 4, residual disease). Sensitivity, specificity and receiver operating characteristic (ROC) analysis were performed for each reader, post-HIFU PSA nadir and pre-biopsy PSA level thresholds of0.2 and0.5 ng ml(-1).The sensitivity of DCE-MRI for detection of residual disease for the three readers ranged between 73% and 87%, and the specificity between 73% and 82%. There was good agreement between readers (κ = 0.69-0.77). The sensitivity and specificity of PSA thresholds was 60-87% and 73-100%, respectively. The area under the ROC curve was greatest for pre-biopsy PSA (0.95).DCE-MRI performed following whole-gland HIFU has similar sensitivity and specificity and ROC performance to serial PSA measurements for detection of residual or recurrent disease.

Computer-aided detection (CAD) for CT colonography (CTC) has been developed to detect benign polyps in asymptomatic patients. We aimed to determine whether such a CAD system can also detect cancer in symptomatic patients.CTC data from 137 symptomatic patients subsequently proven to have colorectal cancer were analysed by a CAD system at 4 different sphericity settings: 0, 50, 75 and 100. CAD prompts were classified by an observer as either true-positive if overlapping a cancer or false-positive if elsewhere. Colonoscopic data were used to aid matching.Of 137 cancers, CAD identified 124 (90.5%), 122 (89.1%), 119 (86.9%) and 102 (74.5%) at a sphericity of 0, 50, 75 and 100, respectively. A substantial proportion of cancers were detected on either the prone or supine acquisition alone. Of 125 patients with prone and supine acquisitions, 39.3%, 38.3%, 43.2% and 50.5% of cancers were detected on a single acquisition at a sphericity of 0, 50, 75 and 100, respectively. CAD detected three cancers missed by radiologists at the original clinical interpretation. False-positive prompts decreased with increasing sphericity value (median 65, 57, 45, 24 per patient at values of 0, 50, 75, 100, respectively) but many patients were poorly prepared.CAD can detect symptomatic colorectal cancer but must be applied to both prone and supine acquisitions for best performance.

Adipose cells have traditionally been viewed as a simple, passive energy storage depot for triglycerides. However, in recent years it has become clear that adipose cells are highly physiologically active and have a multitude of endocrine, metabolic, haematological and immune functions. Changes in the number or size of adipose cells may be directly implicated in disease (e.g. in the metabolic syndrome), but may also be linked to other pathological processes such as inflammation, malignant infiltration or infarction. MRI is ideally suited to the quantification of fat, since most of the acquired signal comes from water and fat protons. Fat fraction (FF, the proportion of the acquired signal derived from fat protons) has, therefore, emerged as an objective, image-based biomarker of disease. Methods for FF quantification are becoming increasingly available in both research and clinical settings, but these methods vary depending on the scanner, manufacturer, imaging sequence and reconstruction software being used. Careful selection of the imaging method—and correct interpretation—can improve the accuracy of FF measurements, minimize potential confounding factors and maximize clinical utility. Here, we review methods for fat quantification and their strengths and weaknesses, before considering how they can be tailored to specific applications, particularly in the gastrointestinal and musculoskeletal systems. FF quantification is becoming established as a clinical and research tool, and understanding the underlying principles will be helpful to both imaging scientists and clinicians.

Tumour heterogeneity has, in recent times, come to play a vital role in how we understand and treat cancers; however, the clinical translation of this has lagged behind advances in research. Although significant advancements in oncological management have been made, personalized care remains an elusive goal. Inter- and intratumour heterogeneity, particularly in the clinical setting, has been difficult to quantify and therefore to treat. The histological quantification of heterogeneity of tumours can be a logistical and clinical challenge. The ability to examine not just the whole tumour but also all the molecular variations of metastatic disease in a patient is obviously difficult with current histological techniques. Advances in imaging techniques and novel applications, alongside our understanding of tumour heterogeneity, have opened up a plethora of non-invasive biomarker potential to examine tumours, their heterogeneity and the clinical translation. This review will focus on how various imaging methods that allow for quantification of metastatic tumour heterogeneity, along with the potential of developing imaging, integrated with other in vitro diagnostic approaches such as genomics and exosome analyses, have the potential role as a non-invasive biomarker for guiding the treatment algorithm.