Improved hepatic arterial fraction estimation using cardiac output correction of arterial input functions for liver DCE MRI

Liver dynamic contrast enhanced (DCE) MRI pharmacokinetic modelling could be useful in the assessment of diffuse liver disease and focal liver lesions, but is compromised by errors in arterial input function (AIF) sampling. In this study, we apply cardiac output correction to arterial input functions (AIFs) for liver dynamic contrast enhanced (DCE) MRI and investigate the effect on dual-input single compartment hepatic perfusion parameter estimation and reproducibility. Thirteen healthy volunteers (28.7±1.94 years, seven males) underwent liver DCE MRI and cardiac output measurement using aortic root phase contrast MRI (PCMRI), with reproducibility (n=9) measured at seven days. Cardiac output AIF correction was undertaken by constraining the first pass AIF enhancement curve using the indicator-dilution principle. Hepatic perfusion parameters with and without cardiac output AIF correction were compared and seven-day reproducibility assessed. Differences between cardiac output corrected and uncorrected liver DCE MRI portal venous (PV) perfusion (p=0.066), total liver blood flow (TLBF)(p=0.101), hepatic arterial (HA) fraction (p=0.895), mean transit time (MTT)(p=0.646), distribution volume (DV)(p=0.890) were not significantly different. Seven-day corrected HA fraction reproducibility was improved (mean difference 0.3%, Bland-Altman 95% Limits-of-Agreement (BA95%LoA) ±27.9%, Coefficient of Variation (CoV) 61.4% vs 9.3%, ±35.5%, 81.7% respectively without correction). Seven-day uncorrected PV perfusion was also improved (mean difference 9.3 ml/min/100g, BA95%LoA ±506.1 ml/min/100g, CoV 64.1% vs 0.9 ml/min/100g, ±562.8 ml/min/100g, 65.1% respectively with correction) as was uncorrected TLBF(mean difference 43.8 ml/min/100g, BA95%LoA ±586.7 ml/min/100g, CoV 58.3% vs 13.3 ml/min/100g, ±661.5 ml/min/100g, 60.9% respectively with correction). Reproducibility of uncorrected MTT was similar (uncorrected mean difference 2.4s, BA95%LoA ±26.7s, CoV 60.8% uncorrected vs 3.7s, ±27.8s, 62.0% respectively with correction), as was and DV (uncorrected mean difference 14.1%, BA95%LoA ±48.2%, CoV 24.7% vs 10.3%, ±46.0%, 23.9% respectively with correction). Cardiac output AIF correction does not significantly affect the estimation of hepatic perfusion parameters but demonstrates improvements in normal volunteer seven-day HA fraction reproducibility, but deterioration in PV perfusion and TLBF reproducibility. Improved HA fraction reproducibility maybe important as arterialisation of liver perfusion is increased in chronic liver disease and within malignant liver lesions

Sequential prostate MRI reporting in men on active surveillance: initial experience of a dedicated PRECISE software program

BACKGROUND AND OBJECTIVES: There is interest in using sequential multiparametric magnetic resonance imaging (mpMRI) to assess men on active surveillance (AS) for prostate cancer. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations propose standardised reporting mpMRI data for these men. This includes accurate size measurements of lesions over time, but such approach is time consuming for the radiologist and there is a strong need of dedicated tools to report serial scans in a systematic manner. We present the results from an initial validation cohort using dedicated PRECISE reporting software to allow automated comparison between sequential scans on AS. MATERIALS AND METHODS: We retrospectively analysed baseline and follow-up scans of 20 men randomised to 6 months of daily dutasteride (n = 10) or placebo (n = 10) from the MAPPED trial. Men underwent 3T mpMRI at baseline and after 6 months, and a dedicated radiologist reported the scans using both a widespread commercially-available platform (Osirix®) and a semi-automated dedicated PRECISE reporting tool (MIM®). Tumour volume by planimetry in all sequences and conspicuity on diffusion-weighted imaging were assessed. Reporting time was recorded, and we used the Wilcoxon test for statistical analysis. RESULTS: Median tumour volumes and conspicuity were similar using both approaches. The reporting time of the follow-up scan was quicker using the PRECISE reporting workflow both in the whole population (12'33″ vs 10'52″; p = 0.005) and in the dutasteride arm (15'50″ vs 12'59″; p = 0.01). A structured report including clinical and imaging data was generated according to the PRECISE recommendations and a comparison table between lesion characteristics at baseline and follow-up scans was also included. CONCLUSION: We conclude that a dedicated PRECISE reporting tool for sequential scans in men on AS results in a significant reduction in the reporting time and allows the radiologist to easily compare scans over time. This tool will help with our understanding of the natural history of mpMRI changes during AS

"Ghol-Dara" fishery off Bedi port in the Gulf of Kutch

Trawling done along the Saurashtra coast has shown that fishing grounds oft Dwarka near the mouth of the Gulf of Kutch compare favourably with some of the richest ones in the world (Jayaraman et al. 1959). There is a steady fishery for most of the trawl fishes in the fishing grounds throughout the year. It is significant that three of the most commercially important fishes, namely 'Dora', Polydactylus indicus (Shaw), 'Ghol, Pseudosciaena diacanthus (Lacepede) and 'Koth', Otolithoides brunneus (Day), caught along this coast support good inshore fisheries, though for a very short duration, in the Gulf of Kutch and are collectively referred to as the "Ghol-Dara fishery". The fishery lasts for about six weeks from March to May and is peculiar in that it is made up exclusively of adult fishes, which grow to large sizes (1.000 to 1,500 mm.) and are being caught by a highly selective, large-meshed gill net operated at the botto

Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers

Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b = 300 s/m2) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann–Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p = 0.0114) and ‘peaked’ (6.82 for patients, 4.20 for volunteers; p = 0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC = 0.88) and the 25th percentile α value (ICC = 0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease

Standardisation of prostate multiparametric MRI across a hospital network: a London experience.

OBJECTIVES: National guidelines recommend prostate multiparametric (mp) MRI in men with suspected prostate cancer before biopsy. In this study, we explore prostate mpMRI protocols across 14 London hospitals and determine whether standardisation improves diagnostic quality. METHODS: An MRI physicist facilitated mpMRI set-up across several regional hospitals, working together with experienced uroradiologists who judged diagnostic quality. Radiologists from the 14 hospitals participated in the assessment and optimisation of prostate mpMRI image quality, assessed according to both PiRADSv2 recommendations and on the ability to "rule in" and/or "rule out" prostate cancer. Image quality and sequence parameters of representative mpMRI scans were evaluated across 23 MR scanners. Optimisation visits were performed to improve image quality, and 2 radiologists scored the image quality pre- and post-optimisation. RESULTS: 20/23 mpMRI protocols, consisting of 111 sequences, were optimised by modifying their sequence parameters. Pre-optimisation, only 15% of T2W images were non-diagnostic, whereas 40% of ADC maps, 50% of high b-value DWI and 41% of DCE-MRI were considered non-diagnostic. Post-optimisation, the scores were increased with 80% of ADC maps, 74% of high b-value DWI and 88% of DCE-MRI to be partially or fully diagnostic. T2W sequences were not optimised, due to their higher baseline quality scores. CONCLUSIONS: Targeted intervention at a regional level can improve the diagnostic quality of prostate mpMRI protocols, with implications for improving prostate cancer detection rates and targeted biopsies

Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI.

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement

SARDU-Net: a new method for model-free, data-driven experiment design in quantitative MRI

This work introduces the “Select and retrieve via direct up-sampling” network (SARDU-Net), a new method for model-free, data-driven quantitative MRI (qMRI) experiment design. SARDU-Net identifies informative measurements within lengthy acquisitions and reconstructs fully-sampled signals from a sub-protocol, without prior information on the MRI contrast. It combines two deep networks: a selector, which selects a signal sub-sample, and a predictor, which retrieves input signals. SARDU-Net can be run with standard computational resources and can increase the clinical appeal of qMRI. Here we demonstrate its potential on qMRI of prostate and spinal cord, two areas where fast acquisitions are key

Evolution of multi-parametric MRI quantitative parameters following transrectal ultrasound-guided biopsy of the prostate

To determine the evolution of prostatic multi-parametric magnetic resonance imaging (mp-MRI) signal following transrectal ultrasound (TRUS)-guided biopsy