Soft-Tissue Augmentation Around Dental Implants by a New Laser-Aided Pouch Roll Technique: Volumetric and Clinical Evaluation After 1 Year.

The ultimate goal in implantology is to restore the whole tooth-gingival complex in a fashion that cannot be distinguished from the rest of the natural dentition. This study assesses the volumetric and clinical changes in vestibular gingival soft tissues, crucial for satisfactory engraftment and esthetic results, upon treatment with laser-aided pouch roll augmentation in second-stage surgery for dental implant uncovering. Twelve patients with mild ridge deficiencies in 16 edentulous sites, including distal elements, were enrolled and reevaluated for up to 1 year. Digital impressions, taken with an intraoral laser scanner and software, were analyzed before (day 0) and after (month 12) treatment. The digital STL files were superimposed to assess volumetric and linear dimensional variations in selected peri-implant regions of interest by 3D analysis software. Clinical periodontal parameters (probing depth [PD], bleeding on probing [BoP], plaque index [PI]) and subjective patient-reported outcomes were also evaluated. In all patients, the applied technique induced a substantial increase in the volume of the vestibular peri-implant gingiva at 12-month follow-up (range, 24%-69%, mean 40.4%) with respect to day 0. The gingival mucosa appeared normal at both inspection and evaluation of the periodontal parameters (PD 2.7 ± 1 mm; BoP 0.11 ± 0.2 seconds; PI 0.19 ± 0). Patients' satisfaction with perceived pain/discomfort and esthetic outcome was high. These findings indicate that laser-aided pouch roll flap is a safe, patient-liked procedure whose long-term effectiveness has been objectively demonstrated by volumetric and clinical assessment.

Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.

Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping.

Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing-in terms of voxel size resampling, discretization, and filtering-on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson's or Spearman's correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson's and Spearman's dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features' stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps.

Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy.

Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases.

Multi-photonic Adjunctive Therapy for the Management of Periodontitis: Recent Advances and New Treatment Approach.

The efficacy of photonic therapy adjunctive to conventional root cleansing procedures for the treatment of chronic periodontitis is matter of controversy. The meta-analyses of the clinical data available in the literature have failed to reach univocal conclusions because of broad variability among the applied photonic treatments, different in terms of light-emitting devices (laser or LED), wavelengths, irradiation power and modes, clinical indications, disease grading, follow-up times, and results assessment. Hovever, this complexity can also favour a different interpretation, which assigns a specific role to each photonic treatments in order to improve the outcome of the conventional treatments, in terms of reduction of periodontopathogenic bacteria and local inflammation, and increased regeneration of alveolar bone, periodontal ligament and gingiva. In this context, distinction should be made between high- and low-energy photonic therapies: the former can be used to achieve photoablation of the infected dental/periodontal tissues, while the latter can be used for anti-bacterial, anti-inflammatory and tissue biostimulation purposes. Recently, we and others have applied a multi-photonic protocol which combines laser photoablation of the infected epithelium, standard mechanical root cleansing and low-energy antiseptic phototherapy with a λ 405 nm LED in a first surgical session. Then, antisepsis is maintained by weekly sessions of photodynamic therapy with a solution of methylene blue photoactivated with a λ 635 nm low-energy laser to release bactericidal reactive oxygen species. The satisfactory objective results and patients' liking support the view that such multi-photonic treatments are a correct approach to supportive periodontal therapy.

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy.

BACKGROUND: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. METHODS: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. RESULTS: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57-0.60; sensitivity = 0.56, 95% CI 0.54-0.58; specificity = 0.61, 95% CI 0.59-0.63; accuracy = 0.58, 95% CI 0.57-0.59). CONCLUSIONS: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications.

Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging.

The purpose of this study was to investigate the effect of image preprocessing on radiomic features estimation from computed tomography (CT) imaging of locally advanced rectal cancer (LARC). CT images of 20 patients with LARC were used to estimate 105 radiomic features of 7 classes (shape, first-order, GLCM, GLDM, GLRLM, GLSZM, and NGTDM). Radiomic features were estimated for 6 different isotropic resampling voxel sizes, using 10 interpolation algorithms (at fixed bin width) and 6 different bin widths (at fixed interpolation algorithm). The intraclass correlation coefficient (ICC) and the coefficient of variation (CV) were calculated to assess the variability in radiomic features estimation due to preprocessing. A repeated measures correlation analysis was performed to assess any linear correlation between radiomic feature estimate and resampling voxel size or bin width. Reproducibility of radiomic feature estimate, when assessed through ICC analysis, was nominally excellent (ICC > 0.9) for shape features, good (0.75 < ICC ≤ 0.9) or moderate (0.5 < ICC ≤ 0.75) for first-order features, and moderate or poor (0 ≤ ICC ≤ 0.5) for textural features. A number of radiomic features characterized by good or excellent reproducibility in terms of ICC showed however median CV values greater than 15%. For most textural features, a significant (p < 0.05) correlation between their estimate and resampling voxel size or bin width was found. In CT imaging of patients with LARC, the estimate of textural features, as well as of first-order features to a lesser extent, is appreciably biased by preprocessing. Accordingly, this should be taken into account when planning clinical or research studies, as well as when comparing results from different studies and performing multicenter studies.

Effect of data leakage in brain MRI classification using 2D convolutional neural networks.

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer's disease (AD) and Parkinson's disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer's Disease Neuroimaging Initiative (ADNI), 48% on Parkinson's Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy.

PURPOSE: To perform a systematic review on the research on the application of artificial intelligence (AI) to imaging published in Italy and identify its fields of application, methods and results. MATERIALS AND METHODS: A Pubmed search was conducted using terms Artificial Intelligence, Machine Learning, Deep learning, imaging, and Italy as affiliation, excluding reviews and papers outside time interval 2015-2020. In a second phase, participants of the working group AI4MP on Artificial Intelligence of the Italian Association of Physics in Medicine (AIFM) searched for papers on AI in imaging. RESULTS: The Pubmed search produced 794 results. 168 studies were selected, of which 122 were from Pubmed search and 46 from the working group. The most used imaging modality was MRI (44%) followed by CT(12%) ad radiography/mammography (11%). The most common clinical indication were neurological diseases (29%) and diagnosis of cancer (25%). Classification was the most common task for AI (57%) followed by segmentation (16%). 65% of studies used machine learning and 35% used deep learning. We observed a rapid increase of research in Italy on artificial intelligence in the last 5 years, peaking at 155% from 2018 to 2019. CONCLUSIONS: We are witnessing an unprecedented interest in AI applied to imaging in Italy, in a diversity of fields and imaging techniques. Further initiatives are needed to build common frameworks and databases, collaborations among different types of institutions, and guidelines for research on AI.

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.

A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre-A phantom study.

Nowadays, given the technological advance in CT imaging and increasing heterogeneity in characteristics of CT scanners, a number of CT scanners with different manufacturers/technologies are often installed in a hospital centre and used by various departments. In this phantom study, a comprehensive assessment of image quality of 5 scanners (from 3 manufacturers and with different models) for head CT imaging, as clinically used at a single hospital centre, was hence carried out. Helical and/or sequential acquisitions of the Catphan-504 phantom were performed, using the scanning protocols (CTDIvol range: 54.7-57.5 mGy) employed by the staff of various Radiology/Neuroradiology departments of our institution for routine head examinations. CT image quality for each scanner/acquisition protocol was assessed through noise level, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD) and non-uniformity index analyses. Noise values ranged from 3.5 HU to 5.7 HU across scanners/acquisition protocols. NPS curves differed in terms of peak position (range: 0.21-0.30 mm-1). A substantial variation of CNR values with scanner/acquisition protocol was observed for different contrast inserts. The coefficient of variation (standard deviation divided by mean value) of CNR values across scanners/acquisition protocols was 18.3%, 31.4%, 34.2%, 30.4% and 30% for teflon, delrin, LDPE, polystyrene and acrylic insert, respectively. An appreciable difference in MTF curves across scanners/acquisition protocols was revealed, with a coefficient of variation of f50%/f10% of MTF curves across scanners/acquisition protocols of 10.1%/7.4%. A relevant difference in LCD performance of different scanners/acquisition protocols was found. The range of contrast threshold for a typical object size of 3 mm was 3.7-5.8 HU. Moreover, appreciable differences in terms of NUI values (range: 4.1%-8.3%) were found. The analysis of several quality indices showed a non-negligible variability in head CT imaging capabilities across different scanners/acquisition protocols. This highlights the importance of a physical in-depth characterization of image quality for each CT scanner as clinically used, in order to optimize CT imaging procedures.

Altered Regional Brain Homogeneity of BOLD Signal in CADASIL: A Resting State fMRI Study.

BACKGROUND AND PURPOSE: The cognitive decline in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is assumed to be due to a cortical-subcortical disconnection secondary to damage to the cerebral white matter (WM). Using resting state functional MRI (rsfMRI) and analysis of the regional homogeneity (ReHo), we examined a group of CADASIL patients and a group of healthy subjects in order to: (1) explore possible differences between the two groups; and (2) to assess, in CADASIL patients, whether any ReHo abnormalities correlate with individual burdens of WM T2 -weighted hyperintensity and diffusion tensor imaging (DTI)-derived index of mean diffusivity (MD) of the cerebral WM, an index reflecting microstructural damage in CADASIL. METHODS: Twenty-three paucisymptomatic CADASIL patients (13 females; age mean ± standard deviation = 43.6 ± 11.1 years; three symptomatic and 20 with no or few symptoms) and 16 healthy controls (nine females; age 46.6 ± 11.0 years) were examined with T1 -weighted, T2 -weighted fluid attenuated inversion recovery images, DTI, and rsfMRI. RESULTS: When compared to controls, CADASIL patients showed four clusters of significantly lower ReHo values in cortical areas belonging to networks involved in inhibition and attention, including the right insula, the left superior frontal gyrus, and the bilateral anterior cingulated cortex. ReHo changes did not correlate with an individual patient's lesion burden or MD. CONCLUSIONS: This study reveals decreased ReHo of rsfMRI signals in cortical areas involved in inhibition and attention processes, suggesting a potential role for these functional cortical changes in CADASIL.

Toward a more reliable characterization of fractal properties of the cerebral cortex of healthy subjects during the lifespan.

The cerebral cortex manifests an inherent structural complexity of folding. The fractal geometry describes the complexity of structures which show self-similarity in a proper interval of spatial scales. In this study, we aimed at evaluating in-vivo the effect of different criteria for selecting the interval of spatial scales in the estimation of the fractal dimension (FD) of the cerebral cortex in T1-weighted magnetic resonance imaging (MRI). We compared four different strategies, including two a priori selections of the interval of spatial scales, an automated selection of the spatial scales within which the cerebral cortex manifests the highest statistical self-similarity, and an improved approach, based on the search of the interval of spatial scales which presents the highest rounded R2adj coefficient and, in case of equal rounded R2adj coefficient, preferring the widest interval in the log-log plot. We employed two public and international datasets of in-vivo MRI scans for a total of 159 healthy subjects (age range 6-85 years). The improved approach showed strong associations of FD with age and yielded the most accurate machine learning models for individual age prediction in both datasets. Our results indicate that the selection of the interval of spatial scales of the cerebral cortex is thus critical in the estimation of FD.

Corrigendum to "Handedness Side and Magnetization Transfer Ratio in the Primary Sensorimotor Cortex Central Sulcus".

[This corrects the article DOI: 10.1155/2019/5610849.].

Photonic Therapy in Periodontal Diseases an Overview with Appraisal of the Literature and Reasoned Treatment Recommendations.

Recent reviews and meta-analyses of the literature over the past quarter-century have failed to provide enough evidence to prove or disprove the actual utility of photonic therapy in periodontitis, alone or adjunctive to conventional approaches. This apparent paradox has been explained by the many physical, molecular, biological, anatomical, and technical variables of photonic treatments, which can differ in light-emitting devices (laser or LED), wavelengths, irradiation power and modes, clinical objectives, follow-up times, disease grading, and assessment methods. This multi-faceted, controversial scenario has led practitioners to underestimate the actual potential of photonic therapy in periodontal diseases. In this critical appraisal of the literature, we have briefly summarized the main photonic therapies and instruments used in Periodontology, highlighting their main characteristics and limitations. Then, we have tried to identify and discuss the key methodological issues which can have an impact on the outcome of photonic therapies. Our main goal was to identify the best parameters, settings, and methodologies to perform effective periodontal photonic treatments and to extrapolate some recommendations for clinical use. Should these recommendations find a consensus among periodontologists and be adopted in future clinical studies, they will hopefully contribute to dissipate the present confusion and uncertainty on this complex matter.

Handedness Side and Magnetization Transfer Ratio in the Primary Sensorimotor Cortex Central Sulcus.

Left-handers show lower asymmetry in manual ability when compared to right-handers. Unlike right-handers, left-handers do not show larger deactivation of the ipsilateral primary sensorimotor (SM1) cortex on functional magnetic resonance imaging when moving their dominant than their nondominant hand. However, it should be noted that morphometric MRI studies have reported no differences between right-handers and left-handers in volume, thickness, or surface area of the SM1 cortex. In this regard, magnetization transfer (MT) imaging is a technique with the potential to provide information on microstructural organization and macromolecular content of tissue. In particular, MT ratio index of the brain gray matter is assumed to reflect the variable content of afferent or efferent myelinated fibers, with higher MT ratio values being associated with increased fibers number or degree of myelination. The aim of this study was hence to assess, for the first time, through quantitative MT ratio measurements, potential differences in microstructural organization/characteristics of SM1 cortex between left- and right-handers, which could underlay handedness side. Nine left-handed and 9 right-handed healthy subjects, as determined by the Edinburgh handedness inventory, were examined with T1-weighted and MT-weighted imaging on a 3 T scanner. The hands of subjects were kept still during all acquisitions. Using FreeSurfer suite and the automatic anatomical labeling parcellations defined by the Destrieux atlas, we measured MT ratio, as well as cortical thickness, in three regions of interests corresponding to the precentral gyrus, the central sulcus, and the postcentral gyrus in the bilateral SM1 cortex. No significant difference between left- and right-handers was revealed in the thickness of the three partitions of the SM1 cortex. However, left-handers showed a significantly (p = 0.007) lower MT ratio (31.92% ± 0.96%) in the right SM1 central sulcus (i.e., the hand representation area for left-handers) as compared to right-handers (33.28% ± 0.94%). The results of this preliminary study indicate that quantitative MT imaging, unlike conventional morphometric MRI measurements, can be a useful tool to reveal, in SM1 cortex, potential microstructural correlates of handedness side.

DTI-derived indexes of brain WM correlate with cognitive performance in vascular MCI and small-vessel disease. A TBSS study.

Indexes derived from diffusion tensor imaging (DTI) are sensitive to changes of both T2-hyperintense and normal-appearing brain white matter (WM) in elderly subjects with variable cognitive status. We investigated correlations between global cognitive performance and DTI-derived indexes along the WM tracts in the brain of patients with vascular mild cognitive impairment (MCI) and small vessel disease (SVD). Seventy-six patients with vascular MCI and SVD were assessed through Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE) test and underwent DTI examination on a 1.5 T MR scanner. We used Tract Based Spatial Statistics (TBSS) to assess voxel-wise in the entire brain the spatial distribution of the correlation between values of fractional anisotropy, mean, axial/radial diffusivity and global cognitive performance as assessed with MoCA and MMSE tests. All correlations were statistically tested with a significant p-value <0.05 using a family-wise error correction for multiple comparisons. The MoCA score significantly correlated with fractional anisotropy (positive correlation) and mean, axial and radial diffusivity (negative correlations) in WM tracts of cerebral hemispheres and corpus callosum, as well as in the intra-thalamic WM tracts and the superior cerebellar peduncle decussation in the midbrain. No significant correlations were observed for MMSE score. Global cognitive performance, as measured by the MoCA score, in patients with vascular MCI and SVD is associated with microstructural changes in WM tracts underlying intra- and inter-hemispheric cerebral, thalamo-cortical and cerebello-thalamic connections.

A straightforward multiparametric quality control protocol for proton magnetic resonance spectroscopy: Validation and comparison of various 1.5 T and 3 T clinical scanner systems.

PURPOSE: The aim of this study was to propose and validate across various clinical scanner systems a straightforward multiparametric quality assurance procedure for proton magnetic resonance spectroscopy (MRS). METHODS: Eighteen clinical 1.5 T and 3 T scanner systems for MRS, from 16 centres and 3 different manufacturers, were enrolled in the study. A standard spherical water phantom was employed by all centres. The acquisition protocol included 3 sets of single (isotropic) voxel (size 20 mm) PRESS acquisitions with unsuppressed water signal and acquisition voxel position at isocenter as well as off-center, repeated 4/5 times within approximately 2 months. Water peak linewidth (LW) and area under the water peak (AP) were estimated. RESULTS: LW values [mean (standard deviation)] were 1.4 (1.0) Hz and 0.8 (0.3) Hz for 3 T and 1.5 T scanners, respectively. The mean (standard deviation) (across all scanners) coefficient of variation of LW and AP for different spatial positions of acquisition voxel were 43% (20%) and 11% (11%), respectively. The mean (standard deviation) phantom T2values were 1145 (50) ms and 1010 (95) ms for 1.5 T and 3 T scanners, respectively. The mean (standard deviation) (across all scanners) coefficients of variation for repeated measurements of LW, AP and T2 were 25% (20%), 10% (14%) and 5% (2%), respectively. CONCLUSIONS: We proposed a straightforward multiparametric and not time consuming quality control protocol for MRS, which can be included in routine and periodic quality assurance procedures. The protocol has been validated and proven to be feasible in a multicentre comparison study of a fairly large number of clinical 1.5 T and 3 T scanner systems.

Diffusional kurtosis imaging in head and neck cancer: On the use of trace-weighted images to estimate indices of non-Gaussian water diffusion.

PURPOSE: While previous studies have demonstrated the feasibility and potential usefulness of quantitative non-Gaussian diffusional kurtosis imaging (DKI) of the brain, more recent research has focused on oncological application of DKI in various body regions such as prostate, breast, and head and neck (HN). Given the need to minimize scan time during most routine magnetic resonance imaging (MRI) acquisitions of body regions, diffusion-weighted imaging (DWI) with only three orthogonal diffusion weighting directions (x, y, z) is usually performed. Moreover, as water diffusion within malignant tumors is generically thought to be almost isotropic, DWI with only three diffusion weighting directions is considered sufficient for oncological application and it represents the de facto standard in body DKI. In this context, since the kurtosis tensor and diffusion tensor cannot be obtained, the averages of the three directional (Kx , Ky , Kz ) and (Dx , Dy , Dz ) - namely K and D, respectively - represent the best-possible surrogates of directionless DKI-derived indices of kurtosis and diffusivity, respectively. This would require fitting the DKI model to the diffusion-weighted images acquired along each direction (x, y, z) prior to averaging. However, there is a growing tendency to perform only a single fit of the DKI model to the geometric means of the images acquired with diffusion-sensitizing gradient along (x, y, z), referred to as trace-weighted (TW) images. To the best of our knowledge, no in vivo studies have evaluated how TW images affect estimates of DKI-derived indices of K and D. Thus, the aim of this study was to assess the potential bias and error introduced in estimated K and D by fitting the DKI model to the TW images in HN cancer patients. METHODS: Eighteen patients with histologically proven malignant tumors of the HN were enrolled in the study. They underwent pretreatment 3 T MRI, including DWI (b-values: 0, 500, 1000, 1500, 2000 s/mm2 ). Some patients had multiple lesions, and thus a total of 34 lesions were analyzed. DKI-derived indices were estimated, voxel-by-voxel, using single diffusion-weighted images along (x, y, z) as well as TW images. A comparison between the two estimation methods was performed by calculating the percentage error in D (Derr ) and K (Kerr ). Also, diffusivity anisotropy (Danis ) and diffusional kurtosis anisotropy (Kanis ) were estimated. Agreements between the two estimation methods were assessed by Bland-Altman plots. The Spearman rank correlation test was used to study the correlations between Kerr /Derr and Danis /Kanis. RESULTS: The median (95% confidence interval) Kerr and Derr were 5.1% (0.8%, 32.6%) and 1.7% (-2.5%, 5.3%), respectively. A significant relationship was observed between Kerr  and Danis (correlation coefficient R = 0.694, P < 0.0001), as well as between Kerr and Kanis (R = 0.848, P < 0.0001). CONCLUSIONS: In HN cancer, the fit of the DKI model to TW images can introduce bias and error in the estimation of K and D, which may be non-negligible for single lesions, and should hence be adopted with caution.

Dependence of apparent diffusion coefficient measurement on diffusion gradient direction and spatial position - A quality assurance intercomparison study of forty-four scanners for quantitative diffusion-weighted imaging.

PURPOSE: To propose an MRI quality assurance procedure that can be used for routine controls and multi-centre comparison of different MR-scanners for quantitative diffusion-weighted imaging (DWI). MATERIALS AND METHODS: 44 MR-scanners with different field strengths (1 T, 1.5 T and 3 T) were included in the study. DWI acquisitions (b-value range 0-1000 s/mm2), with three different orthogonal diffusion gradient directions, were performed for each MR-scanner. All DWI acquisitions were performed by using a standard spherical plastic doped water phantom. Phantom solution ADC value and its dependence with temperature was measured using a DOSY sequence on a 600 MHz NMR spectrometer. Apparent diffusion coefficient (ADC) along each diffusion gradient direction and mean ADC were estimated, both at magnet isocentre and in six different position 50 mm away from isocentre, along positive and negative AP, RL and HF directions. RESULTS: A good agreement was found between the nominal and measured mean ADC at isocentre: more than 90% of mean ADC measurements were within 5% from the nominal value, and the highest deviation was 11.3%. Away from isocentre, the effect of the diffusion gradient direction on ADC estimation was larger than 5% in 47% of included scanners and a spatial non uniformity larger than 5% was reported in 13% of centres. CONCLUSION: ADC accuracy and spatial uniformity can vary appreciably depending on MR scanner model, sequence implementation (i.e. gradient diffusion direction) and hardware characteristics. The DWI quality assurance protocol proposed in this study can be employed in order to assess the accuracy and spatial uniformity of estimated ADC values, in single- as well as multi-centre studies.