Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling. Transl Oncol. 2014;7:65-71. [PMID: 24772209 DOI: 10.1593/tlo.13811]

Phantoms for Quantitative Body MRI: a review and discussion of the phantom value

In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.

Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group

BACKGROUND AND PURPOSE

The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it.

MATERIALS AND METHODS

Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group.

RESULTS

The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types.

CONCLUSION

Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.

Perfusion and Ultrasonication Produce a Decellularized Porcine Whole-Ovary Scaffold with a Preserved Microarchitecture

The application of decellularized scaffolds for artificial tissue reconstruction has been an approach with great therapeutic potential in regenerative medicine. Recently, biomimetic ovarian tissue reconstruction was proposed to reestablish ovarian endocrine functions. Despite many decellularization methods proposed, there is no established protocol for whole ovaries by detergent perfusion that is able to preserve tissue macro and microstructure with higher efficiency. This generated biomaterial may have the potential to be applied for other purposes beyond reproduction and be translated to other areas in the tissue engineering field. Therefore, this study aimed to establish and standardize a protocol for porcine ovaries' decellularization based on detergent perfusion and ultrasonication to obtain functional whole-ovary scaffolds. For that, porcine ovaries (n = 5) were perfused with detergents (0.5% SDS and 1% Triton X-100) and submitted to an ultrasonication bath to produce acellular scaffolds. The decellularization efficiency was evaluated by DAPI staining and total genomic DNA quantification. ECM morphological evaluation was performed by histological, immunohistochemistry, and ultrastructural analyses. ECM physico-chemical composition was evaluated using FTIR and Raman spectroscopy. A cytocompatibility and cell adhesion assay using murine fibroblasts was performed. Results showed that the proposed method was able to remove cellular components efficiently. There was no significant ECM component loss in relation to native tissue, and the scaffolds were cytocompatible and allowed cell attachment. In conclusion, the proposed decellularization protocol produced whole-ovaries scaffolds with preserved ECM composition and great potential for application in tissue engineering.

Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom

Relevant to co-clinical trials, the goal of this work was to assess repeatability, reproducibility, and bias of the apparent diffusion coefficient (ADC) for preclinical MRIs using standardized procedures for comparison to performance of clinical MRIs. A temperature-controlled phantom provided an absolute reference standard to measure spatial uniformity of these performance metrics. Seven institutions participated in the study, wherein diffusion-weighted imaging (DWI) data were acquired over multiple days on 10 preclinical scanners, from 3 vendors, at 6 field strengths. Centralized versus site-based analysis was compared to illustrate incremental variance due to processing workflow. At magnet isocenter, short-term (intra-exam) and long-term (multiday) repeatability were excellent at within-system coefficient of variance, wCV [±CI] = 0.73% [0.54%, 1.12%] and 1.26% [0.94%, 1.89%], respectively. The cross-system reproducibility coefficient, RDC [±CI] = 0.188 [0.129, 0.343] µm2/ms, corresponded to 17% [12%, 31%] relative to the reference standard. Absolute bias at isocenter was low (within 4%) for 8 of 10 systems, whereas two high-bias (>10%) scanners were primary contributors to the relatively high RDC. Significant additional variance (>2%) due to site-specific analysis was observed for 2 of 10 systems. Base-level technical bias, repeatability, reproducibility, and spatial uniformity patterns were consistent with human MRIs (scaled for bore size). Well-calibrated preclinical MRI systems are capable of highly repeatable and reproducible ADC measurements.

Validating the accuracy of multispectral metal artifact suppressed diffusion-weighted imaging

BACKGROUND

Diffusion-weighted imaging (DWI) provides quantitative measurement of random water displacement in tissue as calculated by the apparent diffusion coefficient (ADC). While heavily utilized in stroke and oncology applications, DWI is a promising tool to map microstructural changes in musculoskeletal applications including evaluation of synovial reactions resulting from total hip arthroplasty (THA). One major challenge facing the application of DWI in THA is the significant artifacts related to the conventional echo-planar imaging (EPI) readout used. Multispectral imaging (MSI) techniques, including the multiacquisition with variable resonance image combination (MAVRIC), have been shown to effectively reduce metallic susceptibility artifacts around total joint replacements to render clinically useful images. Recently, a 2D periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) FSE acquisition that incorporates a diffusion preparation pulse with 2D-MAVRIC has been developed to mitigate both distortion and dropout artifacts. While there have been some preliminary assessments of DWI-MAVRIC, the repeatability of DWI-MAVRIC and the effects of key parameters, such as the number of spectral bins, are unknown.

PURPOSE

To evaluate the quantitative accuracy of DWI-MAVRIC as compared to conventional diffusion sequences.

METHODS

A diffusion phantom with different reference diffusivities (ADC = 113-1123 μm2 /s) was used. Scans were performed on two 1.5T MRI scanners. DWI-EPI and DWI-MAVRIC were acquired in both the axial and coronal planes. Three spatial offsets (0 cm, 10 cm left, and 10 cm right off iso-center) were used to evaluate effects of off-isocenter positioning. To assess intraday and interday repeatability, DWI-EPI and DWI-MAVRIC acquisitions were repeated on one scanner at same-day and 9-month intervals. To assess inter-scanner repeatability, DWI-EPI and DWI-MAVRIC acquisitions were compared between two scanners. ADC maps were generated with and without gradient nonlinearity correction (GNC). Linear regression, correlation, and error statistics were determined between calculated and reference ADC values. Bland-Altman plots were generated to evaluate intraday, interday, and interscanner repeatability.

RESULTS

DWI-MAVRIC had excellent correlation to reference values but at reduced linearity (r = 1.00, slope = 0.91-0.94) as compared to DWI-EPI (r = 1.00, slope = 0.99-1.01). A greater than 5% ADC bias was observed at the lowest ADC values, predominantly in the DWI-MAVRIC scans. ADC values did not vary with DWI-MAVRIC parameters. DWI-EPI acquisitions had intraday, interday, and interscanner repeatability of 3.18 μm2 /s, 19.2 μm2 /s, and 20.2 μm2 /s, respectively. DWI-MAVRIC acquisitions had inferior intraday, interday, and interscanner repeatability of 13.3 μm2 /s, 44.7 μm2 /s, 110 μm2 /s, respectively. Lower ADC errors were found at isocenter, as compared to the left and right positions. GNC reduced the absolute error by 0.31% ± 0.89%, 3.6% ± 1.4%, 0.65% ± 2.4% for the center, left, and right positions, respectively.

CONCLUSIONS

DWI-MAVRIC provides good linearity with respect to reference values and good intra- and interday repeatability.

Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths

Radiomic features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As medical centers transition from 1.5 T to 3.0 T magnetic resonance (MR) imaging, it is beneficial to identify potentially robust radiomic features across field strengths because images acquired at different field strengths could be used in machine learning models. Dynamic contrast-enhanced MR images of benign breast lesions and hormone receptor positive/HER2-negative (HR+/HER2-) breast cancers were acquired retrospectively, yielding 612 unique cases: 150 and 99 benign lesions imaged at 1.5 T and 3.0 T, and 223 and 140 HR+/HER2- cancerous lesions imaged at 1.5 T and 3.0 T, respectively. In addition, an independent set of seven lesions imaged at both field strengths, three benign lesions and four HR+/HER2- cancers, was analyzed separately. Lesions were automatically segmented using a 4D fuzzy c-means method; thirty-eight radiomic features were extracted. Feature value distributions were compared by cancer status and imaging field strength using the Kolmogorov-Smirnov test. Features that did not demonstrate a statistically significant difference were considered to be potentially robust. The area under the receiver operating characteristic curve (AUC), for the task of classifying lesions as benign or HR+/HER2- cancer, was determined for each feature at each field strength. Three features were found to be both potentially robust across field strength and of high classification performance, i.e., AUCs statistically greater than 0.5 in the classification task: one shape feature (irregularity), one texture feature (sum average) and one enhancement variance kinetics features (enhancement variance increasing rate). In the demonstration set of lesions imaged at both field strengths, two of the three potentially robust features showed qualitative agreement across field strength. These findings may contribute to the development of computer-aided diagnosis models that are robust across field strength for this classification task.

Radiofrequency Bias Correction of Magnetization Prepared Rapid Gradient Echo MRI at 7.0 Tesla Using an External Reference in a Sequential Protocol

At field strengths of 7 T and above, T₁-weighted imaging of human brain suffers increasingly from radiofrequency (RF) B₁ inhomogeneities. The well-known MP2RAGE (magnetization prepared two rapid acquisition gradient echoes) sequence provides a solution but may not be readily available for all MR systems. Here, we describe the implementation and evaluation of a sequential protocol to obtain normalized magnetization prepared rapid gradient echo (MPRAGE) images at 0.7, 0.8, or 0.9-mm isotropic spatial resolution. Optimization focused on the reference gradient-recalled echo (GRE) that was used for normalization of the MPRAGE. A good compromise between white-gray matter contrast and the signal-to-noise ratio (SNR) was reached at a flip angle of 3° and total scan time was reduced by increasing the reference voxel size by a factor of 8 relative to the MPRAGE resolution. The average intra-subject coefficient-of-variation (CV) in segmented white matter (WM) was 7.9 ± 3.3% after normalization, compared to 20 ± 8.4% before. The corresponding inter-subject average CV in WM was 7.6 ± 7.6% and 13 ± 7.8%. Maps of T₁ derived from forward signal modelling showed no obvious bias after correction by a separately acquired flip angle map. To conclude, a non-interleaved acquisition for normalization of MPRAGE offers a simple alternative to MP2RAGE to obtain semi-quantitative purely T₁-weighted images. These images can be converted to T₁ maps, analogously to the established MP2RAGE approach. Scan time can be reduced by increasing the reference voxel size which has only a miniscule effect on image quality.

Accuracy and repeatability of QRAPMASTER and MRF-vFA

Our purpose is to evaluate bias and repeatability of the quantitative MRI sequences QRAPMASTER, based on steady-state imaging, and variable Flip Angle MRF (MRF-VFA), based on the transient response. Both techniques are assessed with a standardized phantom and five volunteers on 1.5 T and 3 T clinical scanners. All scans were repeated eight times in consecutive weeks. In the phantom, the mean bias±95% confidence interval for T1 values with QRAPMASTER was 10 ± 10% on 1.5 T and 4 ± 13% on 3.0 T. The mean bias for T1 values with MRF-vFA was 21 ± 17% on 1.5 T and 9 ± 9% on 3.0 T. For T2 values the mean bias with QRAPMASTER was 12 ± 3% on 1.5 T and 23 ± 1% on 3.0 T. For T2 values the mean bias with MRF-vFA was 17 ± 1% on 1.5 T and 19 ± 2% on 3.0 T. QRAPMASTER estimated lower T1 and T2 values than MRF-vFA. Repeatability was good with low coefficients of variation (CoV). Mean CoV ± 95% confidence interval for T1 were 3.2 ± 0.4% on 1.5 T and 4.5 ± 0.8% on 3.0 T with QRAPMASTER and 2.7% ± 0.2% on 1.5 T and 2.5 ± 0.2% on 3.0 T with MRF-vFA. For T2 were 3.3 ± 1.9% on 1.5 T and 3.2 ± 0.6% on 3.0 T with QRAPMASTER and 2.0 ± 0.4% on 1.5 T and 5.7 ± 1.0% on 3.0 T with MRF-vFA. The in-vivo T1 and T2 are in the range of values previously reported by other authors. The in-vivo mean CoV ± 95% confidence interval in gray matter were for T1 1.7 ± 0.2% using QRAPMASTER and 0.7 ± 0.5% using MRF-vFA and for T2 were 0.9 ± 0.4% using QRAPMASTER and 2.4 ± 0.5% using MRF-vFA. In white matter were for T1 0.9 ± 0.3% using QRAPMASTER and 1.3 ± 1.1% using MRF-vFA and for T2 were 0.7 ± 0.4% using QRAPMASTER and 2.4 ± 0.4% using MRF-vFA. A GLM analysis showed that the variations in T1 and T2 mainly depend on the field strength and the subject, but not on the follow-up repetition in different days. This confirms the high repeatability of QRAPMASTER and MRF-vFA. In summary, QRAPMASTER and MRF-vFA on both systems were highly repeatable with moderate accuracy, providing results comparable to standard references. While repeatability was similar for both methods, QRAPMASTER was more accurate. QRAPMASTER is a tested commercial product but MRF-vFA is 4.77 times faster, which would ease the inclusion of quantitative relaxometry.

Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom

Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T₁. The purpose of this study was to determine the bias and reproducibility of T₁ measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T₁ measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T₁ measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T₁ measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T₁ measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T₁ values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T₁ values than the inversion recovery measurements. At 3 T, the measured variable flip angle T₁ for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T₁. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community.

Histopathological Validation of Dark-Blood Late Gadolinium Enhancement MRI Without Additional Magnetization Preparation

Background

Conventional bright‐blood late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (MRI) often suffers from poor scar‐to‐blood contrast due to the bright blood pool adjacent to the enhanced scar tissue. Recently, a dark‐blood LGE method was developed which increases scar‐to‐blood contrast without using additional magnetization preparation.

Purpose

We aim to histopathologically validate this dark‐blood LGE method in a porcine animal model with induced myocardial infarction (MI).

Study Type

Prospective.

Animal Model

Thirteen female Yorkshire pigs.

Field Strength/Sequence

1.5 T, two‐dimensional phase‐sensitive inversion‐recovery radiofrequency‐spoiled turbo field‐echo.

Assessment

MI was experimentally induced by transient coronary artery occlusion. At 1‐week and 7‐week post‐infarction, in‐vivo cardiac MRI was performed including conventional bright‐blood and novel dark‐blood LGE. Following the second MRI examination, the animals were sacrificed, and histopathology was obtained. Matching LGE slices and histopathology samples were selected based on anatomical landmarks. Independent observers, while blinded to other data, manually delineated the endocardial, epicardial, and infarct borders on either LGE images or histopathology samples. The percentage of infarcted left‐ventricular myocardium was calculated for both LGE methods on a per‐slice basis, and compared with histopathology as reference standard. Contrast‐to‐noise ratios were calculated for both LGE methods at 1‐week and 7‐week post‐infarction.

Statistical Tests

Pearson's correlation coefficient and paired‐sample t‐tests were used. Significance was set at P < 0.05.

Results

A combined total of 24 matched LGE and histopathology slices were available for histopathological validation. Dark‐blood LGE demonstrated a high level of agreement compared to histopathology with no significant bias (−0.03%, P = 0.75). In contrast, bright‐blood LGE showed a significant bias of −1.57% (P = 0.03) with larger 95% limits of agreement than dark‐blood LGE. Image analysis demonstrated significantly higher scar‐to‐blood contrast for dark‐blood LGE compared to bright‐blood LGE, at both 1‐week and 7‐weeks post‐infarction.

Data Conclusion

Dark‐blood LGE without additional magnetization preparation provides superior visualization and quantification of ischemic scar compared to the current in vivo reference standard.

Level of Evidence

1

Technical Efficacy Stage

2

Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features

Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.

In vivo gradients of thalamic damage in paediatric multiple sclerosis: a window into pathology

The thalamus represents one of the first structures affected by neurodegenerative processes in multiple sclerosis. A greater thalamic volume reduction over time, on its CSF side, has been described in paediatric multiple sclerosis patients. However, its determinants and the underlying pathological changes, likely occurring before this phenomenon becomes measurable, have never been explored. Using a multiparametric magnetic resonance approach, we quantified, in vivo, the different processes that can involve the thalamus in terms of focal lesions, microstructural damage and atrophy in paediatric multiple sclerosis patients and their distribution according to the distance from CSF/thalamus interface and thalamus/white matter interface. In 70 paediatric multiple sclerosis patients and 26 age- and sex-matched healthy controls, we tested for differences in thalamic volume and quantitative MRI metrics-including fractional anisotropy, mean diffusivity and T1/T2-weighted ratio-in the whole thalamus and in thalamic white matter, globally and within concentric bands originating from CSF/thalamus interface. In paediatric multiple sclerosis patients, the relationship of thalamic abnormalities with cortical thickness and white matter lesions was also investigated. Compared to healthy controls, patients had significantly increased fractional anisotropy in whole thalamus (f2 = 0.145; P = 0.03), reduced fractional anisotropy (f2 = 0.219; P = 0.006) and increased mean diffusivity (f2 = 0.178; P = 0.009) in thalamic white matter and a trend towards a reduced thalamic volume (f2 = 0.027; P = 0.058). By segmenting the whole thalamus and thalamic white matter into concentric bands, in paediatric multiple sclerosis we detected significant fractional anisotropy abnormalities in bands nearest to CSF (f2 = 0.208; P = 0.002) and in those closest to white matter (f2 range = 0.183-0.369; P range = 0.010-0.046), while we found significant mean diffusivity (f2 range = 0.101-0.369; P range = 0.018-0.042) and T1/T2-weighted ratio (f2 = 0.773; P = 0.001) abnormalities in thalamic bands closest to CSF. The increase in fractional anisotropy and decrease in mean diffusivity detected at the CSF/thalamus interface correlated with cortical thickness reduction (r range = -0.27-0.34; P range = 0.004-0.028), whereas the increase in fractional anisotropy detected at the thalamus/white matter interface correlated with white matter lesion volumes (r range = 0.24-0.27; P range = 0.006-0.050). Globally, our results support the hypothesis of heterogeneous pathological processes, including retrograde degeneration from white matter lesions and CSF-mediated damage, leading to thalamic microstructural abnormalities, likely preceding macroscopic tissue loss. Assessing thalamic microstructural changes using a multiparametric magnetic resonance approach may represent a target to monitor the efficacy of neuroprotective strategies early in the disease course.

Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN..

Repeatability and reproducibility of ADC measurements: a prospective multicenter whole-body-MRI study

OBJECTIVES

Multicenter oncology trials increasingly include MRI examinations with apparent diffusion coefficient (ADC) quantification for lesion characterization and follow-up. However, the repeatability and reproducibility (R&R) limits above which a true change in ADC can be considered relevant are poorly defined. This study assessed these limits in a standardized whole-body (WB)-MRI protocol.

METHODS

A prospective, multicenter study was performed at three centers equipped with the same 3.0-T scanners to test a WB-MRI protocol including diffusion-weighted imaging (DWI). Eight healthy volunteers per center were enrolled to undergo test and retest examinations in the same center and a third examination in another center. ADC variability was assessed in multiple organs by two readers using two-way mixed ANOVA, Bland-Altman plots, coefficient of variation (CoV), and the upper limit of the 95% CI on repeatability (RC) and reproducibility (RDC) coefficients.

RESULTS

CoV of ADC was not influenced by other factors (center, reader) than the organ. Based on the upper limit of the 95% CI on RC and RDC (from both readers), a change in ADC in an individual patient must be superior to 12% (cerebrum white matter), 16% (paraspinal muscle), 22% (renal cortex), 26% (central and peripheral zones of the prostate), 29% (renal medulla), 35% (liver), 45% (spleen), 50% (posterior iliac crest), 66% (L5 vertebra), 68% (femur), and 94% (acetabulum) to be significant.

CONCLUSIONS

This study proposes R&R limits above which ADC changes can be considered as a reliable quantitative endpoint to assess disease or treatment-related changes in the tissue microstructure in the setting of multicenter WB-MRI trials.

KEY POINTS

• The present study showed the range of R&R of ADC in WB-MRI that may be achieved in a multicenter framework when a standardized protocol is deployed. • R&R was not influenced by the site of acquisition of DW images. • Clinically significant changes in ADC measured in a multicenter WB-MRI protocol performed with the same type of MRI scanner must be superior to 12% (cerebrum white matter), 16% (paraspinal muscle), 22% (renal cortex), 26% (central zone and peripheral zone of prostate), 29% (renal medulla), 35% (liver), 45% (spleen), 50% (posterior iliac crest), 66% (L5 vertebra), 68% (femur), and 94% (acetabulum) to be detected with a 95% confidence level.

Prospective Imaging Trial Assessing Gadoteridol Retention in the Deep Brain Nuclei of Women Undergoing Breast MRI

RATIONALE AND OBJECTIVES

To assess for indirect evidence of gadoteridol retention in the deep brain nuclei of women undergoing serial screening breast MRI.

METHODS

This HIPAA-compliant prospective observational noninferiority imaging trial was approved by the IRB. From December 2016 to March 2018, 12 consented subjects previously exposed to 0-1 doses of gadoteridol (group 1) and 7 consented subjects previously exposed to ≥4 doses of gadoteridol (group 2) prospectively underwent research-specific unenhanced brain MRI including T1w spin echo imaging and T1 mapping. Inclusion criteria were: (1) planned breast MRI with gadoteridol, (2) no gadolinium exposure other than gadoteridol, (3) able to undergo MRI, (4) no neurological illness, (5) no metastatic disease, (6) no chemotherapy. Regions of interest were manually drawn in the globus pallidus, thalamus, dentate nucleus, and pons. Globus pallidus/thalamus and dentate nucleus/pons signal intensities and T1-time ratios were calculated using established methods and correlated with cumulative gadoteridol dose (mL).

RESULTS

All subjects were female (mean age: 50 ± 12 years) and previously had received an average of 0.5 ± 0.5 (group 1) and 5.9 ± 2.1 (group 2) doses of gadoteridol (cumulative dose: 8 ± 8 and 82 ± 31 mL, respectively), with the last dose an average of 492 ± 299 days prior to scanning. There was no significant correlation between cumulative gadoteridol dose (mL) and deep brain nuclei signal intensity at T1w spin echo imaging (p = 0.365-0.512) or T1 mapping (p = 0.197-0.965).

CONCLUSION

We observed no indirect evidence of gadolinium retention in the deep brain nuclei of women undergoing screening breast MRI with gadoteridol.

Multiparameter mapping of relaxation (R1, R2*), proton density and magnetization transfer saturation at 3 T: A multicenter dual-vendor reproducibility and repeatability study

Multicenter clinical and quantitative magnetic resonance imaging (qMRI) studies require a high degree of reproducibility across different sites and scanner manufacturers, as well as time points. We therefore implemented a multiparameter mapping (MPM) protocol based on vendor's product sequences and demonstrate its repeatability and reproducibility for whole‐brain coverage. Within ~20 min, four MPM metrics (magnetization transfer saturation [MT], proton density [PD], longitudinal [R1], and effective transverse [R2*] relaxation rates) were measured using an optimized 1 mm isotropic resolution protocol on six 3 T MRI scanners from two different vendors. The same five healthy participants underwent two scanning sessions, on the same scanner, at each site. MPM metrics were calculated using the hMRI‐toolbox. To account for different MT pulses used by each vendor, we linearly scaled the MT values to harmonize them across vendors. To determine longitudinal repeatability and inter‐site comparability, the intra‐site (i.e., scan‐rescan experiment) coefficient of variation (CoV), inter‐site CoV, and bias across sites were estimated. For MT, R1, and PD, the intra‐ and inter‐site CoV was between 4 and 10% across sites and scan time points for intracranial gray and white matter. A higher intra‐site CoV (16%) was observed in R2* maps. The inter‐site bias was below 5% for all parameters. In conclusion, the MPM protocol yielded reliable quantitative maps at high resolution with a short acquisition time. The high reproducibility of MPM metrics across sites and scan time points combined with its tissue microstructure sensitivity facilitates longitudinal multicenter imaging studies targeting microstructural changes, for example, as a quantitative MRI biomarker for interventional clinical trials.

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for different k. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of different modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coefficients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information.

QIN Benchmarks for Clinical Translation of Quantitative Imaging Tools

The Quantitative Imaging Network of the National Cancer Institute is in its 10th year of operation, and research teams within the network are developing and validating clinical decision support software tools to measure or predict the response of cancers to various therapies. As projects progress from development activities to validation of quantitative imaging tools and methods, it is important to evaluate the performance and clinical readiness of the tools before committing them to prospective clinical trials. A variety of tests, including special challenges and tool benchmarking, have been instituted within the network to prepare the quantitative imaging tools for service in clinical trials. This article highlights the benchmarking process and provides a current evaluation of several tools in their transition from development to validation.

[Method for Quantitative Evaluation of the Substantia Nigra Using Phase-sensitive Inversion Recovery in 1.5 T Magnetic Resonance Imaging]

PURPOSE

Parkinson's disease is a neurodegenerative disease with movement disorders caused by degeneration of the substantia nigra (SN) in the midbrain. Magnetic resonance imaging (MRI) is used to exclude similar diseases. Recently, neuro-melanin imaging (NMI) has been proposed as an imaging method for evaluating the SN. However, the evaluation of the image using this is qualitative, and normal SN cannot be visualized at 1.5 T. This study aimed to investigate whether the SN can be quantitatively evaluated by using phase-sensitive inversion recovery (PSIR) at 1.5 T.

METHOD

The phantom was imaged by PSIR and the accuracy of T₁ value was verified. Next, the healthy volunteers were imaged by PSIR, and the T₁ value and area of the SN were measured.

RESULT

As a result of the phantom study, the difference of the T₁value between PSIR and inversion recovery spin-echo in the range of the T₁ value the SN and the cerebral peduncle (500 to 1000 ms) was lesser than ±10%. The difference between the average of T₁ values of the SN measured by PSIR and the previously reported T₁ values of the SN was about 1.1%. Furthermore, the average of the area of the SN measured by PSIR was measured independently of imaging parameters by using the T₁ value as a reference.

CONCLUSION

These results indicate the possibility of a quantitative evaluation of the SN by measuring its area and T₁ value by using PSIR at 1.5 T.

Phantom validation of quantitative susceptibility and dynamic contrast-enhanced permeability MR sequences across instruments and sites

BACKGROUND

Quantitative susceptibility mapping (QSM) and dynamic contrast-enhanced quantitative permeability (DCEQP) on magnetic resonance (MR) have been shown to correlate with neurovascular disease progression as markers of vascular leakage and hemosiderin deposition. Applying these techniques as monitoring biomarkers in clinical trials will be necessary; however, their validation across multiple MR platforms and institutions has not been rigorously verified.

PURPOSE

To validate quantitative measurement of MR biomarkers on multiple instruments at different institutions.

STUDY TYPE

Phantom validation between platforms and institutions.

PHANTOM MODEL

T₁ /susceptibility phantom, two-compartment dynamic flow phantom.

FIELD STRENGTH/SEQUENCE

3T/QSM, T₁ mapping, dynamic 2D SPGR.

ASSESSMENT

Philips Ingenia, Siemens Prisma, and Siemens Skyra at three different institutions were assessed. A QSM phantom with concentrations of gadolinium, corresponding to magnetic susceptibilities of 0, 0.1, 0.2, 0.4, and 0.8 ppm was assayed. DCEQP was assessed by measuring a MultiHance bolus as the consistency of the width ratio of the curves at the input and outputs over a range of flow ratios between outputs.

STATISTICAL TESTS

Each biomarker was assessed by measures of accuracy (Pearson correlation), precision (paired t-test between repeated measurements), and reproducibility (analysis of covariance [ANCOVA] between instruments).

RESULTS

QSM accuracy of r²  > 0.997 on all three platforms was measured. Precision (P = 0.66 Achieva, P = 0.76 Prisma, P = 0.69 Skyra) and reproducibility (P = 0.89) were good. T₁ mapping of accuracy was r²  > 0.98. No significant difference between width ratio regression slopes at site 2 (P = 0.669) or site 3 (P = 0.305), and no significant difference between width ratio regression slopes between sites was detected by ANCOVA (P = 0.48).

DATA CONCLUSION

The phantom performed as expected and determined that MR measures of QSM and DCEQP are accurate and consistent across repeated measurements and between platforms.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1192-1199.

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.

Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data

Multiparametric imaging is a critical tool in the noninvasive study and assessment of cancer. Imaging methods have evolved over the past several decades to provide quantitative measures of tumor and healthy tissue characteristics related to, for example, cell number, blood volume fraction, blood flow, hypoxia, and metabolism. Mechanistic models of tumor growth also have matured to a point where the incorporation of patient-specific measures could provide clinically relevant predictions of tumor growth and response. In this review, we identify and discuss approaches that use multiparametric imaging data, including diffusion-weighted magnetic resonance imaging, dynamic contrast-enhanced magnetic resonance imaging, diffusion tensor imaging, contrast-enhanced computed tomography, [18F]fluorodeoxyglucose positron emission tomography, and [18F]fluoromisonidazole positron emission tomography to initialize and calibrate mechanistic models of tumor growth and response. We focus the discussion on brain and breast cancers; however, we also identify three emerging areas of application in kidney, pancreatic, and lung cancers. We conclude with a discussion of the future directions for incorporating multiparametric imaging data and mechanistic modeling into clinical decision making for patients with cancer.

Comparison of contrast-enhanced and diffusion-weighted MRI in assessment of the terminal ileum in Crohn's disease patients

PURPOSE

The purpose of the study was to compare the performance of contrast-enhanced (CE)-MRI and diffusion-weighted imaging (DW)-MRI in grading Crohn's disease activity of the terminal ileum.

METHODS

Three readers evaluated CE-MRI, DW-MRI, and their combinations (CE/DW-MRI and DW/CE-MRI, depending on which protocol was used at the start of evaluation). Disease severity grading scores were correlated to the Crohn's Disease Endoscopic Index of Severity (CDEIS). Diagnostic accuracy, severity grading, and levels of confidence were compared between imaging protocols and interobserver agreement was calculated.

RESULTS

Sixty-one patients were included (30 female, median age 36). Diagnostic accuracy for active disease for CE-MRI, DW-MRI, CE/DW-MRI, and DW/CE-MRI ranged between 0.82 and 0.85, 0.75 and 0.83, 0.79 and 0.84, and 0.74 and 0.82, respectively. Severity grading correlation to CDEIS ranged between 0.70 and 0.74, 0.66 and 0.70, 0.69 and 0.75, and 0.67 and 0.74, respectively. For each reader, CE-MRI values were consistently higher than DW-MRI, albeit not significantly. Confidence levels for all readers were significantly higher for CE-MRI compared to DW-MRI (P < 0.001). Further increased confidence was seen when using combined imaging protocols.

CONCLUSIONS

There was no significant difference of CE-MRI and DW-MRI in determining disease activity, but the higher confidence levels may favor CE-MRI. DW-MRI is a good alternative in cases with relative contraindications for the use of intravenous contrast medium.

In vitro pharmacokinetic phantom for two-compartment modeling in DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an established minimally-invasive method for assessment of extravascular leakage, hemodynamics, and tissue viability. However, differences in acquisition protocols, variety of pharmacokinetic models, and uncertainty on physical sources of MR signal hamper the reliability and widespread use of DCE-MRI in clinical practice. Measurements performed in a controlled in vitro setup could be used as a basis for standardization of the acquisition procedure, as well as objective evaluation and comparison of pharmacokinetic models. In this paper, we present a novel flow phantom that mimics a two-compartmental (blood plasma and extravascular extracellular space/EES) vascular bed, enabling systemic validation of acquisition protocols. The phantom consisted of a hemodialysis filter with two compartments, separated by hollow fiber membranes. The aim of this phantom was to vary the extravasation rate by adjusting the flow in the two compartments. Contrast agent transport kinetics within the phantom was interpreted using two-compartmental pharmacokinetic models. Boluses of gadolinium-based contrast-agent were injected in a tube network connected to the hollow fiber phantom; time-intensity curves (TICs) were obtained from image series, acquired using a T1-weighted DCE-MRI sequence. Under the assumption of a linear dilution system, the TICs obtained from the input and output of the system were then analyzed by a system identification approach to estimate the trans-membrane extravasation rates in different flow conditions. To this end, model-based deconvolution was employed to determine (identify) the impulse response of the investigated dilution system. The flow rates in the EES compartment significantly and consistently influenced the estimated extravasation rates, in line with the expected trends based on simulation results. The proposed phantom can therefore be used to model a two-compartmental vascular bed and can be employed to test and optimize DCE-MRI acquisition sequences in order to determine a standardized acquisition procedure leading to consistent quantification results.

Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study

PURPOSE

To determine the in vitro accuracy, test-retest repeatability, and interplatform reproducibility of T₁ quantification protocols used for dynamic contrast-enhanced MRI at 1.5 and 3 T.

METHODS

A T₁ phantom with 14 samples was imaged at eight centers with a common inversion-recovery spin-echo (IR-SE) protocol and a variable flip angle (VFA) protocol using seven flip angles, as well as site-specific protocols (VFA with different flip angles, variable repetition time, proton density, and Look-Locker inversion recovery). Factors influencing the accuracy (deviation from reference NMR T₁ measurements) and repeatability were assessed using general linear mixed models. Interplatform reproducibility was assessed using coefficients of variation.

RESULTS

For the common IR-SE protocol, accuracy (median error across platforms = 1.4-5.5%) was influenced predominantly by T₁ sample (P < 10^-6 ), whereas test-retest repeatability (median error = 0.2-8.3%) was influenced by the scanner (P < 10^-6 ). For the common VFA protocol, accuracy (median error = 5.7-32.2%) was influenced by field strength (P = 0.006), whereas repeatability (median error = 0.7-25.8%) was influenced by the scanner (P < 0.0001). Interplatform reproducibility with the common VFA was lower at 3 T than 1.5 T (P = 0.004), and lower than that of the common IR-SE protocol (coefficient of variation 1.5T: VFA/IR-SE = 11.13%/8.21%, P = 0.028; 3 T: VFA/IR-SE = 22.87%/5.46%, P = 0.001). Among the site-specific protocols, Look-Locker inversion recovery and VFA (2-3 flip angles) protocols showed the best accuracy and repeatability (errors < 15%).

CONCLUSIONS

The VFA protocols with 2 to 3 flip angles optimized for different applications achieved acceptable balance of extensive spatial coverage, accuracy, and repeatability in T₁ quantification (errors < 15%). Further optimization in terms of flip-angle choice for each tissue application, and the use of B₁ correction, are needed to improve the robustness of VFA protocols for T₁ mapping. Magn Reson Med 79:2564-2575, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Mesenteric lymph nodes in MR enterography: are they reliable followers of bowel in active Crohn's disease?

OBJECTIVES

To demonstrate magnetic resonance enterography (MRE) features of mesenteric lymph nodes (LN) in patients with Crohn's disease (CD) and investigate whether they follow enhancement or apparent diffusion coefficient (ADC) parameters of bowel.

METHODS

This study was approved by the institutional review board. A total of 788 MREs from patients with CD were retrospectively reviewed. Eighty-eight patients, aged 16-66 years, including 59 active cases, were enrolled based on inclusion criteria. In each MRE, two segments (normal and abnormal) and two LNs (regional and non-regional) were independently suggested, consensually chosen, and analyzed by two radiologists. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated to assess signal intensities (SI) at 30, 60 and 180 s after contrast administration, as well as slope of enhancement (SOE). Enhancement parameters and ADC values were compared.

RESULTS

Regional LNs showed significantly higher SI₃₀, SI₆₀ and SI₁₈₀ (CNR&SNR) and lower ADC values in active vs. inactive groups (all p<0.05) without significant difference in number or size. Strong correlations were demonstrated between abnormal segments and regional LNs in active group in terms of SI₃₀, SI₆₀, SI₁₈₀, SOE_0-30 and ADC values (r = 0.679 to 0.774, all p<0.001). SI₁₈₀, SOE_60-180 and ADC values were moderately correlated between abnormal segments and regional LNs in inactive group (r = 0.448 to 0.595, all p<0.05). In logistic regression analyses, SOE_0-30 and ADC value of regional LNs independently predicted active CD.

CONCLUSION

Mesenteric LNs follow quantitative enhancement and diffusion parameters of bowel in active CD. SOE_0-30 and ADC value of LN could predict disease activity.

KEY POINTS

• Mesenteric LNs may strongly follow enhancement pattern of bowel in active CD. • DWI parameters of LNs and bowel were strongly correlated in active CD. • SI₁₈₀ was moderately correlated between bowel and LNs in inactive CD. • DWI parameters were moderately correlated between LNs and bowel in inactive CD. • SOE_0-30 and ADC value of mesenteric LN could predict disease activity.

Sequential Apparent Diffusion Coefficient for Assessment of Tumor Progression in Patients with Low-Grade Glioma

BACKGROUND AND PURPOSE

Early and accurate identification of tumor progression in patients with low-grade gliomas is challenging. We aimed to assess the role of quantitative ADC analysis in the sequential follow-up of patients with low-grade gliomas as a potential imaging marker of tumor stability or progression.

MATERIALS AND METHODS

In this retrospective study, patients with a diagnosis of low-grade glioma with at least 12 months of imaging follow-up were retrospectively reviewed. Two neuroradiologists independently reviewed sequential MR imaging in each patient to determine tumor progression using the Response Assessment in Neuro-Oncology criteria. Normalized mean ADC (ADC_mean) and 10th percentile ADC (ADC₁₀) values from FLAIR hyperintense tumor volume were calculated for each MR image and compared between patients with stable disease versus tumor progression using univariate analysis. The interval change of ADC values between sequential scans was used to differentiate stable disease from progression using the Fisher exact test.

RESULTS

Twenty-eight of 69 patients who were evaluated met our inclusion criteria. Fifteen patients were classified as stable versus 13 patients as having progression based on consensus reads of MRIs and the Response Assessment in Neuro-Oncology criteria. The interval change of ADC values showed greater concordance with ultimate lesion disposition than quantitative ADC values at a single time point. The interval change in ADC₁₀ matched the expected pattern in 12/13 patients with tumor progression (overall diagnostic accuracy of 86%, P <.001). On average, the ADC₁₀ interval change predicted progression 8 months before conventional MR imaging.

CONCLUSIONS

The interval change of ADC₁₀ values can be used to identify progression versus stability of low-grade gliomas with a diagnostic accuracy of 86% and before apparent radiologic progression on conventional MR imaging.

Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies

This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation.

Special Section Guest Editorial: Quantitative Imaging and the Pioneering Efforts of Laurence P. Clarke

This guest editorial introduces the Special Section honoring Dr. Laurence P. Clarke.

Dark-blood late gadolinium enhancement without additional magnetization preparation

BACKGROUND

This study evaluates a novel dark-blood late gadolinium enhancement (LGE) cardiovascular magnetic resonance imaging (CMR) method, without using additional magnetization preparation, and compares it to conventional bright-blood LGE, for the detection of ischaemic myocardial scar. LGE is able to clearly depict myocardial infarction and macroscopic scarring from viable myocardium. However, due to the bright signal of adjacent left ventricular blood, the apparent volume of scar tissue can be significantly reduced, or even completely obscured. In addition, blood pool signal can mimic scar tissue and lead to false positive observations. Simply nulling the blood magnetization by choosing shorter inversion times, leads to a negative viable myocardium signal that appears equally as bright as scar due to the magnitude image reconstruction. However, by combining blood magnetization nulling with the extended grayscale range of phase-sensitive inversion-recovery (PSIR), a darker blood signal can be achieved whilst a dark myocardium and bright scar signal is preserved.

METHODS

LGE was performed in nine male patients (63 ± 11y) using a PSIR pulse sequence, with both conventional viable myocardium nulling and left ventricular blood nulling, in a randomized order. Regions of interest were drawn in the left ventricular blood, viable myocardium, and scar tissue, to assess contrast-to-noise ratios. Maximum scar transmurality, scar size, circumferential scar angle, and a confidence score for scar detection and maximum transmurality were also assessed. Bloch simulations were performed to simulate the magnetization levels of the left ventricular blood, viable myocardium, and scar tissue.

RESULTS

Average scar-to-blood contrast was significantly (p < 0.001) increased by 99% when nulling left ventricular blood instead of viable myocardium, while scar-to-myocardium contrast was maintained. Nulling left ventricular blood also led to significantly (p = 0.038) higher expert confidence in scar detection and maximum transmurality. No significant changes were found in scar transmurality (p = 0.317), normalized scar size (p = 0.054), and circumferential scar angle (p = 0.117).

CONCLUSIONS

Nulling left ventricular blood magnetization for PSIR LGE leads to improved scar-to-blood contrast and increased expert confidence in scar detection and scar transmurality. As no additional magnetization preparation is used, clinical application on current MR systems is readily available without the need for extensive optimizations, software modifications, and/or additional training.

Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma

OBJECTIVE

To develop and validate a volume-based, quantitative imaging marker by integrating multi-parametric MR images for predicting glioblastoma survival, and to investigate its relationship and synergy with molecular characteristics.

METHODS

We retrospectively analysed 108 patients with primary glioblastoma. The discovery cohort consisted of 62 patients from the cancer genome atlas (TCGA). Another 46 patients comprising 30 from TCGA and 16 internally were used for independent validation. Based on integrated analyses of T1-weighted contrast-enhanced (T1-c) and diffusion-weighted MR images, we identified an intratumoral subregion with both high T1-c and low ADC, and accordingly defined a high-risk volume (HRV). We evaluated its prognostic value and biological significance with genomic data.

RESULTS

On both discovery and validation cohorts, HRV predicted overall survival (OS) (concordance index: 0.642 and 0.653, P < 0.001 and P = 0.038, respectively). HRV stratified patients within the proneural molecular subtype (log-rank P = 0.040, hazard ratio = 2.787). We observed different OS among patients depending on their MGMT methylation status and HRV (log-rank P = 0.011). Patients with unmethylated MGMT and high HRV had significantly shorter survival (median survival: 9.3 vs. 18.4 months, log-rank P = 0.002).

CONCLUSION

Volume of the high-risk intratumoral subregion identified on multi-parametric MRI predicts glioblastoma survival, and may provide complementary value to genomic information.

KEY POINTS

• High-risk volume (HRV) defined on multi-parametric MRI predicted GBM survival. • The proneural molecular subtype tended to harbour smaller HRV than other subtypes. • Patients with unmethylated MGMT and high HRV had significantly shorter survival. • HRV complements genomic information in predicting GBM survival.

Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network

The Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI) conducts research in development and validation of imaging tools and methods for predicting and evaluating clinical response to cancer therapy. Members of the network are involved in examining various imaging and image assessment parameters through network-wide cooperative projects. To more effectively use the cooperative power of the network in conducting computational challenges in benchmarking of tools and methods and collaborative projects in analytical assessment of imaging technologies, the QIN Challenge Task Force has developed policies and procedures to enhance the value of these activities by developing guidelines and leveraging NCI resources to help their administration and manage dissemination of results. Challenges and Collaborative Projects (CCPs) are further divided into technical and clinical CCPs. As the first NCI network to engage in CCPs, we anticipate a variety of CCPs to be conducted by QIN teams in the coming years. These will be aimed to benchmark advanced software tools for clinical decision support, explore new imaging biomarkers for therapeutic assessment, and establish consensus on a range of methods and protocols in support of the use of quantitative imaging to predict and assess response to cancer therapy.

Impact of uncertainty in longitudinal T1 measurements on quantification of dynamic contrast-enhanced MRI

The objective of this study was to assess the uncertainty in T1 measurement, by estimating the repeatability coefficient (RC) from two repeated scans, in normal appearing brain tissues employing two different T1 mapping methods. All brain MRI scans were performed on a 3 T MR scanner in 10 patients who had low grade/benign tumors and partial brain radiation therapy (RT) without chemotherapy, at pre-RT, 3 weeks into RT, end RT (6 weeks) and 11, 33, and 85 weeks after RT. T1-weighted images were acquired using (1) a spoiled gradient echo sequence with two flip angles (2FA: 5° and 15°) and (2) a progressive saturation recovery sequence (pSR) with five different TR values (100-2000 ms). Manually drawn volumes of interest (VOIs) included left and right normal putamen and thalamus in gray matter, and frontal and parietal white matter, which were distant from tumors and received a total of accumulated radiation doses less than 5 Gy at 3 weeks. No significant changes or even trends in mean T1 from pre-RT to 3 weeks into RT in these VOIs (p ≥ 0.11, Wilcoxon sign test) allowed us to calculate the repeatability statistics of between-subject means of squares, within-subject means of squares, F-score, and RC. The 2FA method produced RCs in the range of (9.7-11.7)% in gray matter and (12.2-14.5)% in white matter; while the pSR method led to RCs ranging from 10.9 to 17.9% in gray matter and 7.5 to 10.3% in white matter. The overall mean (±SD) RCs produced by the two methods, 12.0 (±1.6)% for 2FA and 12.0 (±3.8)% for pSR, were not significantly different (p = 0.97). A similar repeatability in T1 measurement produced by the time efficient 2FA method compared with the time consuming pSR method demonstrates that the 2FA method is desirable to integrate into dynamic contrast-enhanced MRI for rapid acquisition.

Technical Note: Independent component analysis for quality assurance in functional MRI

PURPOSE

Independent component analysis (ICA) is an established method of analyzing human functional MRI (fMRI) data. Here, an ICA-based fMRI quality control (QC) tool was developed and used.

METHODS

ICA-based fMRI QC tool to be used with a commercial phantom was developed. In an attempt to assess the performance of the tool relative to preexisting alternative tools, it was used seven weeks before and eight weeks after repair of a faulty gradient amplifier of a non-state-of-the-art MRI unit. More specifically, its performance was compared with the AAPM 100 acceptance testing and quality assurance protocol and two fMRI QC protocols, proposed by Freidman et al. ["Report on a multicenter fMRI quality assurance protocol," J. Magn. Reson. Imaging 23, 827-839 (2006)] and Stocker et al. ["Automated quality assurance routines for fMRI data applied to a multicenter study," Hum. Brain Mapp. 25, 237-246 (2005)], respectively.

RESULTS

The easily developed and applied ICA-based QC protocol provided fMRI QC indices and maps equally sensitive to fMRI instabilities with the indices and maps of other established protocols. The ICA fMRI QC indices were highly correlated with indices of other fMRI QC protocols and in some cases theoretically related to them. Three or four independent components with slow varying time series are detected under normal conditions.

CONCLUSIONS

ICA applied on phantom measurements is an easy and efficient tool for fMRI QC. Additionally, it can protect against misinterpretations of artifact components as human brain activations. Evaluating fMRI QC indices in the central region of a phantom is not always the optimal choice.

Diffusion-weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility

There is considerable disparity in the published apparent diffusion coefficient (ADC) values across different anatomies. Institutions are increasingly assessing repeatability and reproducibility of the derived ADC to determine its variation, which could potentially be used as an indicator in determining tumour aggressiveness or assessing tumour response. In this manuscript, a review of selected articles published to date in healthy extra-cranial body diffusion-weighted magnetic resonance imaging is presented, detailing reported ADC values and discussing their variation across different studies. In total 115 studies were selected including 28 for liver parenchyma, 15 for kidney (renal parenchyma), 14 for spleen, 13 for pancreatic body, 6 for gallbladder, 13 for prostate, 13 for uterus (endometrium, myometrium, cervix) and 13 for fibroglandular breast tissue. Median ADC values in selected studies were found to be 1.28 × 10(-3) mm(2)/s in liver, 1.94 × 10(-3) mm(2)/s in kidney, 1.60 × 10(-3) mm(2)/s in pancreatic body, 0.85 × 10(-3) mm(2)/s in spleen, 2.73 × 10(-3) mm(2)/s in gallbladder, 1.64 × 10(-3) mm(2)/s and 1.31 × 10(-3) mm(2)/s in prostate peripheral zone and central gland respectively (combined median value of 1.54×10(-3) mm(2)/s), 1.44 × 10(-3) mm(2)/s in endometrium, 1.53 × 10(-3) mm(2)/s in myometrium, 1.71 × 10(-3) mm(2)/s in cervix and 1.92 × 10(-3) mm(2)/s in breast. In addition, six phantom studies and thirteen in vivo studies were summarized to compare repeatability and reproducibility of the measured ADC. All selected phantom studies demonstrated lower intra-scanner and inter-scanner variation compared to in vivo studies. Based on the findings of this manuscript, it is recommended that protocols need to be optimised for the body part studied and that system-induced variability must be established using a standardized phantom in any clinical study. Reproducibility of the measured ADC must also be assessed in a volunteer population, as variations are far more significant in vivo compared with phantom studies.

Multi-site clinical evaluation of DW-MRI as a treatment response metric for breast cancer patients undergoing neoadjuvant chemotherapy

PURPOSE

To evaluate diffusion weighted MRI (DW-MR) as a response metric for assessment of neoadjuvant chemotherapy (NAC) in patients with primary breast cancer using prospective multi-center trials which provided MR scans along with clinical outcome information.

MATERIALS AND METHODS

A total of 39 patients with locally advanced breast cancer accrued from three different prospective clinical trials underwent DW-MR examination prior to and at 3-7 days (Hull University), 8-11 days (University of Michigan) and 35 days (NeoCOMICE) post-treatment initiation. Thirteen patients, 12 of which participated in treatment response study, from UM underwent short interval (<1hr) MRI examinations, referred to as "test-retest" for examination of repeatability. To further evaluate stability in ADC measurements, a thermally controlled diffusion phantom was used to assess repeatability of diffusion measurements. MRI sequences included contrast-enhanced T1-weighted, when appropriate, and DW images acquired at b-values of 0 and 800 s/mm2. Histogram analysis and a voxel-based analytical technique, the Parametric Response Map (PRM), were used to derive diffusion response metrics for assessment of treatment response prediction.

RESULTS

Mean tumor apparent diffusion coefficient (ADC) values generated from patient test-retest examinations were found to be very reproducible (|ΔADC|<0.1x10-3mm2/s). This data was used to calculate the 95% CI from the linear fit of tumor voxel ADC pairs of co-registered examinations (±0.45x10-3mm2/s) for PRM analysis of treatment response. Receiver operating characteristic analysis identified the PRM metric to be predictive of outcome at the 8-11 (AUC = 0.964, p = 0.01) and 35 day (AUC = 0.770, p = 0.05) time points (p<.05) while whole-tumor ADC changes where significant at the later 35 day time interval (AUC = 0.825, p = 0.02).

CONCLUSION

This study demonstrates the feasibility of performing a prospective analysis of DW-MRI as a predictive biomarker of NAC in breast cancer patients. In addition, we provide experimental evidence supporting the use of sensitive analytical tools, such as PRM, for evaluating ADC measurements.