Scanning Conditions in Functional Connectivity Magnetic Resonance Imaging: How to Standardise Resting-State for Optimal Data Acquisition and Visualisation?

Functional connectivity magnetic resonance imaging (fcMRI), performed during resting wakefulness without tasks or stimulation, is a non-invasive technique to assess and visualise functional brain networks in vivo. Acquisition of resting-state imaging data has become increasingly common in longitudinal studies to investigate brain health and disease. However, the scanning protocols vary considerably across different institutions creating challenges for comparability especially for the interpretation of findings in patient cohorts and establishment of diagnostic or prognostic imaging biomarkers. The aim of this chapter is to discuss the effect of two experimental conditions (i.e. a low cognitive demand paradigm and a pure resting-state fcMRI) on the reproducibility of brain networks between a baseline and a follow-up session, 30 (±5) days later, acquired from 12 right-handed volunteers (29 ± 5 yrs). A novel method was developed and used for a direct statistical comparison of the test-retest reliability using 28 well-established functional brain networks. Overall, both scanning conditions produced good levels of test-retest reliability. While the pure resting-state condition showed higher test-retest reliability for 18 of the 28 analysed networks, the low cognitive demand paradigm produced higher test-retest reliability for 8 of the 28 brain networks (i.e. visual, sensorimotor and frontal areas); in 2 of the 28 brain networks no significant changes could be detected. These results are relevant to planning of longitudinal studies, as higher test-retest reliability generally increases statistical power. This work also makes an important contribution to neuroimaging where optimising fcMRI experimental scanning conditions, and hence data visualisation of brain function, remains an on-going topic of interest. In this chapter, we provide a full methodological explanation of the two paradigms and our analysis so that readers can apply them to their own scanning protocols.

Quantitative 3T multiparametric MRI of benign and malignant prostatic tissue in patients with and without local recurrent prostate cancer after external-beam radiation therapy.

BACKGROUND: Post-radiotherapy locally recurrent prostate cancer (PCa) patients are candidates for focal salvage treatment. Multiparametric MRI (mp-MRI) is attractive for tumor localization. However, radiotherapy-induced tissue changes complicate image interpretation. To develop focal salvage strategies, accurate tumor localization and distinction from benign tissue is necessary. PURPOSE: To quantitatively characterize radio-recurrent tumor and benign radiation-induced changes using mp-MRI, and investigate which sequences optimize the distinction between tumor and benign surroundings. STUDY TYPE: Prospective case-control. SUBJECTS: Thirty-three patients with biochemical failure after external-beam radiotherapy (cases), 35 patients without post-radiotherapy recurrent disease (controls), and 13 patients with primary PCa (untreated). FIELD STRENGTH/SEQUENCES: 3T; quantitative mp-MRI: T2 -mapping, ADC, and Ktrans and kep maps. ASSESSMENT: Quantitative image-analysis of prostatic regions, within and between cases, controls, and untreated patients. STATISTICAL TESTS: Within-groups: nonparametric Friedman analysis of variance with post-hoc Wilcoxon signed-rank tests; between-groups: Mann-Whitney tests. All with Bonferroni corrections. Generalized linear mixed modeling to ascertain the contribution of each map and location to tumor likelihood. RESULTS: Benign imaging values were comparable between cases and controls (P = 0.15 for ADC in the central gland up to 0.91 for kep in the peripheral zone), both with similarly high peri-urethral Ktrans and kep values (min-1 ) (median [range]: Ktrans = 0.22 [0.14-0.43] and 0.22 [0.14-0.36], P = 0.60, kep = 0.43 [0.24-0.57] and 0.48 [0.32-0.67], P = 0.05). After radiotherapy, benign central gland values were significantly decreased for all maps (P ≤ 0.001) as well as T2 , Ktrans , and kep of benign peripheral zone (all with P ≤ 0.002). All imaging maps distinguished recurrent tumor from benign peripheral zone, but only ADC, Ktrans , and kep were able to distinguish it from benign central gland. Recurrent tumor and peri-urethral Ktrans values were not significantly different (P = 0.81), but kep values were (P < 0.001). Combining all quantitative maps and voxel location resulted in an optimal distinction between tumor and benign voxels. DATA CONCLUSION: Mp-MRI can distinguish recurrent tumor from benign tissue. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:269-278.

Quantitative 3-T multi-parametric MRI and step-section pathology of recurrent prostate cancer patients after radiation therapy.

OBJECTIVES: Diagnosis of radio-recurrent prostate cancer using multi-parametric MRI (mp-MRI) can be challenging due to the presence of radiation effects. We aim to characterize imaging of prostate tissue after radiation therapy (RT), using histopathology as ground truth, and to investigate the visibility of tumor lesions on mp-MRI. METHODS: Tumor delineated histopathology slides from salvage radical prostatectomy patients, primarily treated with RT, were registered to MRI. Median T2-weighted, ADC, Ktrans, and kep values in tumor and other regions were calculated. Two radiologists independently performed mp-MRI-based tumor delineations which were compared with the true pathological extent. General linear mixed-effect modeling was used to establish the contribution of each imaging modality and combinations thereof in distinguishing tumor and benign voxels. RESULTS: Nineteen of the 21 included patients had tumor in the available histopathology slides. Recurrence was predominantly multifocal with large tumor foci seen after external beam radiotherapy, whereas these were small and sparse after low-dose-rate brachytherapy. MRI-based delineations missed small foci and slightly underestimated tumor extent. The combination of T2-weighted, ADC, Ktrans, and kep had the best performance in distinguishing tumor and benign voxels. CONCLUSIONS: Using high-resolution histopathology delineations, the real tumor extent and size were found to be underestimated on MRI. mp-MRI obtained the best performance in identifying tumor voxels. Appropriate margins around the visible tumor-suspected region should be included when designing focal salvage strategies. Recurrent tumor delineation guidelines are warranted. KEY POINTS: • Compared to the use of individual sequences, multi-parametric MRI obtained the best performance in distinguishing recurrent tumor from benign voxels. • Delineations based on mp-MRI miss smaller foci and slightly underestimate tumor volume of local recurrent prostate cancer. • Focal salvage strategies should include appropriate margins around the visible tumor.

Quantitative assessment of carotid ultrasound diameter measurements in the operating room: a comparable analysis of long-axis versus rotated and tilted orientation.

Objective. Carotid ultrasound (US) has been studied as a non-invasive alternative for hemodynamic monitoring. A long-axis (LA) view is traditionally employed but is difficult to maintain and operator experience may impact the diameter estimates, making it unsuitable for monitoring. Preliminary results show that a new, i.e. rotated and tilted (RT) view is more robust to motion and less operator-dependent. This study aimed to quantitatively assess common carotid diameter estimates obtained in a clinical setting from an RT view and compare those to corresponding estimates obtained using other views.Approach. Carotid US measurements were performed in 30 adult cardiac-surgery patients (26 males, 4 females) with short-axis (SA), LA, and RT probe orientations, the first being used as a reference for measuring the true vessel diameter. Per 30 s acquisition, the median and spread in diameter values were computed, the latter representing a measure of robustness, and were statistically compared between views.Main results. The median (IQR) over all the patients of the median diameter per 30 s acquisition was 7.15 (1.15) mm for the SA view, 7.03 (1.51) mm for the LA view, and 6.99 (1.72) mm for the RT view. The median spread in diameter values was 0.18 mm for the SA view, 0.16 mm for the LA view, and 0.18 mm for the RT view. There were no statistically significant differences between views in the median diameter values (p= 0.088) or spread (p= 0.122).Significance. The RT view results in comparable and equally robust median carotid diameter values compared to the reference. These findings open the path for future studies investigating the use of the RT view in new applications, such as in wearable ultrasound devices.

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer.

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

Multiparametric MRI Tumor Probability Model for the Detection of Locally Recurrent Prostate Cancer After Radiation Therapy: Pathologic Validation and Comparison With Manual Tumor Delineations.

PURPOSE: Focal salvage treatments of recurrent prostate cancer (PCa) after radiation therapy require accurate delineation of the target volume. Magnetic resonance imaging (MRI) is used for this purpose; however, radiation therapy-induced changes complicate image interpretation, and guidelines are lacking on the assessment and delineation of recurrent PCa. A tumor probability (TP) model was trained and independently tested using multiparametric magnetic resonance imaging (mp-MRI) of patients with radio-recurrent PCa. The resulting probability maps were used to derive target regions for radiation therapy treatment planning. METHODS AND MATERIALS: Two cohorts of patients with radio-recurrent PCa were used in this study. All patients underwent mp-MRI (T2 weighted, diffusion-weighted imaging, and dynamic contrast enhanced). A logistic regression model was trained using imaging features from 21 patients with biopsy-proven recurrence who qualified for salvage treatment. The test cohort consisted of 17 patients treated with salvage prostatectomy. The model was tested against histopathology-derived tumor delineations. The voxel-wise TP maps were clustered using k-means to generate a gross tumor volume (GTV) contour for voxel-level comparisons with manual tumor delineations performed by 2 radiologists and with histopathology-validated contours. Later, k-means was used with 3 clusters to define a clinical target volume (CTV), high-risk CTV, and GTV, with increasing tumor risk. RESULTS: In the test cohort, the model obtained a median (range) area under the curve of 0.77 (0.41-0.99) for the whole prostate. The GTV delineation resulted in a median sensitivity of 0.31 (0-0.87) and specificity of 0.97 (0.84-1.0) with no significant differences between model and manual delineations. The 3-level clustering GTV and high-risk CTV delineations had median sensitivities of 0.17 (0-0.59) and 0.49 (0-0.97) and specificities of 0.98 (0.84-1.00) and 0.94 (0.84-0.99), respectively. CONCLUSIONS: The TP model had a good performance in predicting voxel-wise presence of recurrent tumor. Model-derived tumor risk levels achieved sensitivity and specificity similar to manual delineations in localizing recurrent tumor. Voxel-wise TP derived from mp-MRI can in this way be incorporated for target definition in focal salvage of radio-recurrent PCa.

Biochemical recurrence prediction after radiotherapy for prostate cancer with T2w magnetic resonance imaging radiomic features.

BACKGROUND AND PURPOSE: High-risk prostate cancer patients are frequently treated with external-beam radiotherapy (EBRT). Of all patients receiving EBRT, 15-35% will experience biochemical recurrence (BCR) within five years. Magnetic resonance imaging (MRI) is commonly acquired as part of the diagnostic procedure and imaging-derived features have shown promise in tumour characterisation and biochemical recurrence prediction. We investigated the value of imaging features extracted from pre-treatment T2w anatomical MRI to predict five year biochemical recurrence in high-risk patients treated with EBRT. MATERIALS AND METHODS: In a cohort of 120 high-risk patients, imaging features were extracted from the whole-prostate and a margin surrounding it. Intensity, shape and textural features were extracted from the original and filtered T2w-MRI scans. The minimum-redundancy maximum-relevance algorithm was used for feature selection. Random forest and logistic regression classifiers were used in our experiments. The performance of a logistic regression model using the patient's clinical features was also investigated. To assess the prediction accuracy we used stratified 10-fold cross validation and receiver operating characteristic analysis, quantified by the area under the curve (AUC). RESULTS: A logistic regression model built using whole-prostate imaging features obtained an AUC of 0.63 in the prediction of BCR, outperforming a model solely based on clinical variables (AUC = 0.51). Combining imaging and clinical features did not outperform the accuracy of imaging alone. CONCLUSIONS: These results illustrate the potential of imaging features alone to distinguish patients with an increased risk of recurrence, even in a clinically homogeneous cohort.