On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: A large multicenter and multiparametric phantom study with unsupervised clustering analysis.

PURPOSE: The purpose of this multicenter phantom study was to exploit an innovative approach, based on an extensive acquisition protocol and unsupervised clustering analysis, in order to assess any potential bias in apparent diffusion coefficient (ADC) estimation due to different scanner characteristics. Moreover, we aimed at assessing, for the first time, any effect of acquisition plan/phase encoding direction on ADC estimation. METHODS: Water phantom acquisitions were carried out on 39 scanners. DWI acquisitions (b-value = 0-200-400-600-800-1000 s/mm2) with different acquisition plans (axial, coronal, sagittal) and phase encoding directions (anterior/posterior and right/left, for the axial acquisition plan), for 3 orthogonal diffusion weighting gradient directions, were performed. For each acquisition setup, ADC values were measured in-center and off-center (6 different positions), resulting in an entire dataset of 84 × 39 = 3276 ADC values. Spatial uniformity of ADC maps was assessed by means of the percentage difference between off-center and in-center ADC values (Δ). RESULTS: No significant dependence of in-center ADC values on acquisition plan/phase encoding direction was found. Ward unsupervised clustering analysis showed 3 distinct clusters of scanners and an association between Δ-values and manufacturer/model, whereas no association between Δ-values and maximum gradient strength, slew rate or static magnetic field strength was revealed. Several acquisition setups showed significant differences among groups, indicating the introduction of different biases in ADC estimation. CONCLUSIONS: Unsupervised clustering analysis of DWI data, obtained from several scanners using an extensive acquisition protocol, allows to reveal an association between measured ADC values and manufacturer/model of scanner, as well as to identify suboptimal DWI acquisition setups for accurate ADC estimation.

SBRT planning for spinal metastasis: indications from a large multicentric study.

BACKGROUND: The dosimetric variability in spine stereotactic body radiation therapy (SBRT) planning was investigated in a large number of centres to identify crowd knowledge-based solutions. METHODS: Two spinal cases were planned by 48 planners (38 centres). The required prescription dose (PD) was 3â€¯× 10 Gy and the planning target volume (PTV) coverage request was: VPD > 90% (minimum request: VPD > 80%). The dose constraints were: planning risk volume (PRV) spinal cord: V18Gy < 0.35 cm3, V21.9 Gy < 0.03 cm3; oesophagus: V17.7 Gy < 5 cm3, V25.2 Gy < 0.03 cm3. Planners who did not fulfil the protocol requirements were asked to re-optimize the plans, using the results of planners with the same technology. Statistical analysis was performed to assess correlations between dosimetric results and planning parameters. A quality index (QI) was defined for scoring plans. RESULTS: In all, 12.5% of plans did not meet the protocol requirements. After re-optimization, 98% of plans fulfilled the constraints, showing the positive impact of knowledge sharing. Statistical analysis showed a significant correlation (p < 0.05) between the homogeneity index (HI) and PTV coverage for both cases, while the correlation between HI and spinal cord sparing was significant only for the single dorsal PTV case. Moreover, the multileaf collimator leaf thickness correlated with the spinal cord sparing. Planners using comparable delivery/planning system techniques produced different QI, highlighting the impact of the planner's skills in the optimization process. CONCLUSION: Both the technology and the planner's skills are fundamentally important in spine SBRT planning optimization. Knowledge sharing helped to follow the plan objectives.

Quality assurance multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging.

PURPOSE: To propose a magnetic resonance imaging (MRI) quality assurance procedure that can be used for multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging (DWI). MATERIALS AND METHODS: Twenty-six centers (35 MR scanners with field strengths: 1T, 1.5T, and 3T) were enrolled in the study. Two different DWI acquisition series (b-value ranges 0-1000 and 0-3000 s/mm(2) , respectively) were performed for each MR scanner. All DWI acquisitions were performed by using a cylindrical doped water phantom. Mean apparent diffusion coefficient (ADC) values as well as ADC values along each of the three main orthogonal directions of the diffusion gradients (x, y, and z) were calculated. Short-term repeatability of ADC measurement was evaluated for 26 MR scanners. RESULTS: A good agreement was found between the nominal and measured mean ADC over all the centers. More than 80% of mean ADC measurements were within 5% from the nominal value, and the highest deviation and overall standard deviation were 9.3% and 3.5%, respectively. Short-term repeatability of ADC measurement was found <2.5% for all MR scanners. CONCLUSION: A specific and widely accepted protocol for quality controls in DWI is still lacking. The DWI quality assurance protocol proposed in this study can be applied in order to assess the reliability of DWI-derived indices before tackling single- as well as multicenter studies.

Differences in dynamic susceptibility contrast MR perfusion maps generated by different methods implemented in commercial software.

PURPOSE: There are several potential sources of difference that can influence the reproducibility of magnetic resonance (MR) perfusion values. We aimed to investigate the reproducibility and variability of dynamic susceptibility contrast (DSC) MR imaging (MRI) parameters obtained from identical source data by using 2 commercially available software applications with different postprocessing algorithms. METHODS AND MATERIALS: We retrospectively evaluated DSC-MRI data sets of 24 consecutive patients with glioblastoma multiforme. Perfusion data were postprocessed with 2 commercial software packages, NordicICE (NordicNeuroLab, Bergen, Norway) and GE Brainstat (GE Healthcare, Milwaukee, Wis), each of which offers the possibility of different algorithms. We focused the comparison on their main analysis issues, that is, the gamma-variate fitting function (GVF) and the arterial input function (AIF). Two regions of interest were placed on maps of perfusion parameters (cerebral blood volume [CBV], cerebral blood flow [CBF], mean transit time [MTT]): one around tumor hot spot and one in the contralateral normal brain. A one-way repeated-measures analysis of variance was conducted to determine whether there was a significant difference in the calculated MTT, CBV, and CBF values. RESULTS: As regards NordicICE software application, the use of AIF is significant (P = 0.048) but not the use of GVF (P = 0.803) for CBV values. Additionally, in GE, the calculation method discloses a statistical effect on data. Comparing similar GE-NordicICE algorithms, both method (P = 0.005) and software (P < 0.0001) have a statistical effect in the difference. Leakage-corrected and uncorrected normalized CBV (nCBV) values are statistically equal. No statistical differences have been found in nMTT values when directly calculated. Values of nCBF are affected by the use of GVF. CONCLUSION: The use of a different software application determines different results, even if the algorithms seem to be the same. The introduction of AIF in the data postprocessing determines a higher estimates variability that can make interhospital and intrahospital examinations not completely comparable. A simpler approach based on raw curve analysis produces more stable results.

Lipid and macromolecules quantitation in differentiating glioblastoma from solitary metastasis: a short-echo time single-voxel magnetic resonance spectroscopy study at 3 T.

OBJECTIVE: The differentiation between solitary metastasis (MET) and glioblastoma (GBM) is difficult using only magnetic resonance imaging techniques. Magnetic resonance spectroscopy (MRS) lipid signal indicates cellular necrosis both in GBMs and METs. The purpose of this prospective study was to determine whether a class of lipids and/or macromolecules (MMs), able to efficiently discriminate between these two types of lesions, exists. METHODS: Forty-one patients with solitary brain tumor (23 GBMs and 18 METs) underwent magnetic resonance imaging and single-voxel MRS. Short-echo time point resolved spectroscopy sequence acquisition with water suppression technique was used. Spectra were analyzed using LCModel. Absolute quantification was performed with "water-scaling" procedure. The analysis was focused on sums of lipid and macromolecular (LM) components at 0.9 and 1.3 ppm. RESULTS: The LM13 absolute concentration was statistically different (P < 0.0001) between GBMs and METs. With a cutoff of 81 mM in LM13 absolute concentration, METs and GBMs can be distinguished with a 78% of specificity and an 81% of sensitivity. The presence of the MM12 peak, related to the fucose II complex, in tumors harboring a K-ras gene mutation has been investigated. CONCLUSIONS: We exploited the performance of a clinically easily implementable method, such as short-echo time single-voxel MRS, for the differentiation between brain metastasis and primary brain tumors. The study showed that MRS absolute lipid and macromolecular signals could be helpful in differentiating GBM from metastasis. LM13 class was found to be a discriminant parameter with an accuracy of 85%. Detection of the MM12-fucose peak may also have a role in understanding molecular biology of brain metastasis and should be further investigated to address specific metabolic phenotypes.