Automated segmentation of epilepsy surgical resection cavities: Comparison of four methods to manual segmentation.

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n = 44, 88 %) and Epic-CHOP (n = 42, 84 %) were able to detect more resection cavities than Resseg (n = 22, 44 %, P < 0.001) and Deep Resection (n = 23, 46 %, P < 0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts. In the temporal cohort, the SPM-based pipelines had higher detection rates, however there was no difference in the accuracy between methods. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

Association of Localizing 18F-FDG-PET Hypometabolism and Outcome Following Epilepsy Surgery: Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVES: Although commonly used in the evaluation of patients for epilepsy surgery, the association between the detection of localizing 18fluorine fluorodeoxyglucose PET (18F-FDG-PET) hypometabolism and epilepsy surgery outcome is uncertain. We conducted a systematic review and meta-analysis to determine whether localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery. METHODS: A systematic literature search was undertaken. Eligible publications included evaluation with 18F-FDG-PET before epilepsy surgery, with ≥10 participants, and those that reported surgical outcome at ≥12 months. Random-effects meta-analysis was used to calculate the odds of achieving a favorable outcome, defined as Engel class I, International League Against Epilepsy class 1-2, or seizure-free, with localizing 18F-FDG-PET hypometabolism, defined as concordant with the epilepsy surgery resection zone. Meta-regression was used to characterize sources of heterogeneity. RESULTS: The database search identified 8,916 studies, of which 98 were included (total patients n = 4,104). Localizing 18F-FDG-PET hypometabolism was associated with favorable outcome after epilepsy surgery for all patients with odds ratio (OR) 2.68 (95% CI 2.08-3.45). Subgroup analysis yielded similar findings for those with (OR 2.64, 95% CI 1.54-4.52) and without epileptogenic lesion detected on MRI (OR 2.49, 95% CI 1.80-3.44). Concordance with EEG (OR 2.34, 95% CI 1.43-3.83), MRI (OR 1.69, 95% CI 1.19-2.40), and triple concordance with both (OR 2.20, 95% CI 1.32-3.64) was associated with higher odds of favorable outcome. By contrast, diffuse 18F-FDG-PET hypometabolism was associated with worse outcomes compared with focal hypometabolism (OR 0.34, 95% CI 0.22-0.54). DISCUSSION: Localizing 18F-FDG-PET hypometabolism is associated with favorable outcome after epilepsy surgery, irrespective of the presence of an epileptogenic lesion on MRI. The extent of 18F-FDG-PET hypometabolism provides additional information, with diffuse hypometabolism associated with worse surgical outcome than focal 18F-FDG-PET hypometabolism. These findings support the incorporation of 18F-FDG-PET into routine noninvasive investigations for patients being evaluated for epilepsy surgery to improve epileptogenic zone localization and to aid patient selection for surgery.

Utility of Acute and Subacute Blood Biomarkers to Assist Diagnosis in CT-Negative Isolated Mild Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Blood biomarkers glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) have recently been Food and Drug Administration approved as predictors of intracranial lesions on CT after mild traumatic brain injury (mTBI). However, most cases with mTBI are CT negative, and no biomarkers are approved to assist diagnosis in these individuals. In this study, we aimed to determine the optimal combination of blood biomarkers to assist mTBI diagnosis in otherwise healthy adults younger than 50 years presenting to an emergency department within 6 hours of injury. To further understand the utility of biomarkers, we assessed how biological sex, presence or absence of loss of consciousness and/or post-traumatic amnesia (LOC/PTA), and delayed presentation affected classification performance. METHODS: Blood samples, symptom questionnaires, and cognitive tests were prospectively conducted for participants with mTBI recruited from The Alfred Hospital Level 1 Emergency & Trauma Center and uninjured controls. Follow-up testing was conducted at 7 days. Simoa quantified plasma GFAP, UCH-L1, tau, neurofilament light chain (NfL), interleukin (IL)-6, and IL-1ß. Area under the receiver operating characteristic (AUC) analysis assessed classification accuracy for diagnosed mTBI, and logistic regression models identified optimal biomarker combinations. RESULTS: Plasma IL-6 (AUC 0.91, 95% CI 0.86-0.96), GFAP (AUC 0.85, 95% CI 0.78-0.93), and UCH-L1 (AUC 0.79, 95% CI 0.70-0.88) best differentiated mTBI (n = 74) from controls (n = 44) acutely (<6 hours), with NfL (AUC 0.81, 95% CI 0.72-0.90) the only marker to have such utility subacutely (7 days). Biomarker performance was similar between sexes and for participants with and without LOC/PTA, with the exception at 7 days, where GFAP and IL-6 retained some utility in female participants (GFAP: AUC 0.71, 95% CI 0.55-0.88; IL-6: AUC 0.71, 95% CI 0.55-0.87) and in those with LOC/PTA (GFAP: AUC 0.73, 95% CI 0.59-0.86; IL-6: AUC 0.71, 95% CI 0.57-0.84). Acute IL-6 (R 2 = 0.50, 95% CI 0.34-0.64) outperformed GFAP and UCH-L1 combined (R 2 = 0.35, 95% CI 0.17-0.50), with the best acute model featuring GFAP and IL-6 (R 2 = 0.54, 95% CI 0.34-0.68). DISCUSSION: These findings indicate that adding IL-6 to a panel of brain-specific proteins such as GFAP and UCH-L1 might assist in the acute diagnosis of mTBI in adults younger than 50 years. Multiple markers had high classification accuracy in participants without LOC/PTA. When compared with the best-performing acute markers, subacute measures of plasma NfL resulted in minimal reduction in classification accuracy. Future studies will investigate the optimal time frame over which plasma IL-6 might assist diagnostic decisions and how extracranial trauma affects utility.

18F-FDG-PET hypometabolism as a predictor of favourable outcome in epilepsy surgery: protocol for a systematic review and meta-analysis.

INTRODUCTION: A substantial proportion of patients who undergo surgery for drug resistant focal epilepsy do not become seizure free. While some factors, such as the detection of hippocampal sclerosis or a resectable lesion on MRI and electroencephalogram-MRI concordance, can predict favourable outcomes in epilepsy surgery, the prognostic value of the detection of focal hypometabolism with 18F-fluorodeoxyglucose positive emission tomography (18F-FDG-PET) hypometabolism is uncertain. We propose a protocol for a systematic review and meta-analysis to examine whether localisation with 18F-FDG-PET hypometabolism predicts favourable outcomes in epilepsy surgery. METHODS AND ANALYSIS: A systematic literature search of Medline, Embase and Web of Science will be undertaken. Publications which include evaluation with 18F-FDG-PET prior to surgery for drug resistant focal epilepsy, and which report ≥12 months of postoperative surgical outcome data will be included. Non-human, non-English language publications, publications with fewer than 10 participants and unpublished data will be excluded. Screening and full-text review of publications for inclusion will be undertaken by two independent investigators, with discrepancies resolved by consensus or a third investigator. Data will be extracted and pooled using random effects meta-analysis, with heterogeneity quantified using the I2 analysis. ETHICS AND DISSEMINATION: Ethics approval is not required. Once complete, the systematic review will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022324823.

Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy.

OBJECTIVES: Around 30% of patients undergoing surgical resection for drug-resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG-PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. METHODS: Eighty two patients with drug resistant MTLE were scanned with FDG-PET pre-surgery and T1-weighted MRI pre- and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. RESULTS: In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug-resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow-up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75-.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59-.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. SIGNIFICANCE: Collectively, these results indicate that "acceptable" to "good" patient-specific prognostication for drug-resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.

Non-radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports.

INTRODUCTION: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on non-radiologist clinicians' opinion on this topic. METHOD: Single-centre online survey targeting non-radiologist medical staff conducted from May to August 2021 at a tertiary referral hospital in Melbourne, Australia. Survey questions revolved around clinicians' level of comfort acting on AI-generated reports with varying levels of radiologist involvement and scan complexity, opinion on medicolegal responsibility for erroneous AI-issued reports and perception of data privacy and security. RESULTS: Eighty-eight responses were collected, including 47.9% of consultants. Non-radiologist clinicians across all seniorities and specialties felt significantly less comfortable acting on AI-issued reports compared with radiologist-issued reports (mean comfort radiologist 6.44/7, mean comfort AI 3.35/7, P < 0.001) but felt equally comfortable with an AI-hybrid model of care (mean comfort hybrid 6.38/7, P = 0.676). Non-radiologist clinicians believed that medicolegal responsibility with errors in AI-issued reports mostly lay with hospitals or health service providers (65.9%) and radiologists (54.5%). Regarding data privacy and security, non-radiologist clinicians felt significantly less comfortable with AI issuing image reports instead of radiologists (P < 0.001). CONCLUSION: A hybrid AI-generated radiologist-confirmed method of image reporting may be the ideal way of integrating AI into clinical practice based on the perception of our referring non-radiologist medical colleagues. Formal guidelines on medicolegal responsibility and data privacy should be established prior to utilising AI in the clinical setting.

Artificial intelligence in medical imaging: implications for patient radiation safety.

Artificial intelligence, including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost every facet of diagnostic imaging, including patient radiation safety. This paper introduces basic concepts in deep learning and provides an overview of its recent history and its application in tomographic reconstruction as well as other applications in medical imaging to reduce patient radiation dose, as well as a brief description of previous tomographic reconstruction techniques. This review also describes the commonly used deep learning techniques as applied to tomographic reconstruction and draws parallels to current reconstruction techniques. Finally, this paper reviews some of the estimated dose reductions in CT and positron emission tomography in the recent literature enabled by deep learning, as well as some of the potential problems that may be encountered such as the obscuration of pathology, and highlights the need for additional clinical reader studies from the imaging community.

Technique, radiation safety and image quality for chest X-ray imaging through glass and in mobile settings during the COVID-19 pandemic.

The COVID-19 pandemic in 2020 has led to preparations within our hospital for an expected surge of patients. This included developing a technique to perform mobile chest X-ray imaging through glass, allowing the X-ray unit to remain outside of the patient's room, effectively reducing the cleaning time associated with disinfecting equipment. The technique also reduced the infection risk of radiographers. We assessed the attenuation of different types of glass in the hospital and the technique parameters required to account for the glass filtration and additional source to image distance (SID). Radiation measurements were undertaken in a simulated set-up to determine the appropriate position for staff inside and outside the room to ensure occupational doses were kept as low as reasonably achievable. Image quality was scored and technical parameter information collated. The alternative to imaging through glass is the standard portable chest X-ray within the room. The radiation safety requirements for this standard technique were also assessed. Image quality was found to be acceptable or borderline in 90% of the images taken through glass and the average patient dose was 0.02 millisieverts (mSv) per image. The majority (67%) of images were acquired at 110 kV, with an average 5.5 mAs and with SID ranging from 180 to 300 cm. With staff positioned at greater than 1 m from the patient and at more than 1 m laterally from the tube head outside the room to minimise scatter exposure, air kerma values did not exceed 0.5 microgray (µGy) per image. This method has been implemented successfully.

Treatment of symptomatic fibroid disease using uterine fibroid embolisation: An Australian perspective.

Uterine leiomyomata (fibroids) are symptomatic in up to 35% of women and treatment can be a costly burden to the individual and society. Options for treatment range from non-hormonal, hormonal, minimally invasive, to surgery. While symptoms from smaller fibroids may respond to simple treatment, those with larger fibroids or with a large volume of disease require a more definitive option. Surgery (hysterectomy or myomectomy) are both well-established treatment modalities with good clinical outcomes. Since the 1990s, uterine fibroid embolisation has emerged as a less invasive option for women than for surgical techniques, while level 1 evidence shows that in the short to mid-term, there is a similar improvement in symptom-related quality of life outcomes to surgery, but with reduced hospital stay and reduced cost. However, in the longer term there may be a need for further treatment or retreatment in some patients compared with surgery. Since its introduction, uptake of this procedure in Australia has been low relative to surgical options. This manuscript reviews the current literature surrounding treatment, along with the trends in uptake of embolisation by Australian women, places this in context of current guidelines from major societies, and encourages gynaecologists and interventional radiologists to be aware of the advantages and limitations of embolisation.

Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction.

PURPOSE: To apply tracer kinetic models as temporal constraints during reconstruction of under-sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). METHODS: A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under-sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in-vivo 3T datasets. The performances of the proposed under-sampled reconstruction scheme and an existing compressed sensing-based temporal finite-difference (tFD) under-sampled reconstruction were compared against the fully sampled inverse Fourier Transform-based reconstruction. RESULTS: Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts-Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO-based experiments showed good fidelity in recovery of kinetic maps from 20-fold under-sampled data. In-vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under-sampled reduction factors >= 20. CONCLUSIONS: Tracer kinetic models can be applied as temporal constraints during brain tumor DCE-MRI reconstruction. The proposed under-sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters.

Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma.

Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps.

Clinical 7 T MRI: Are we there yet? A review about magnetic resonance imaging at ultra-high field.

In recent years, ultra-high field MRI (7 T and above) has received more interest for clinical imaging. Indeed, a number of studies have shown the benefits from the application of this powerful tool not only for research purposes, but also in realms of improved diagnostics and patient management. The increased signal-to-noise ratio and higher spatial resolution compared with conventional and high-field clinical scanners allow imaging of small anatomical detail and subtle pathological findings. Furthermore, greater spectral resolution achieved at ultra-high field allows the resolution of metabolites for MR spectroscopic imaging. All these advantages have a significant impact on many neurological diseases, including multiple sclerosis, cerebrovascular disease, brain tumors, epilepsy and neurodegenerative diseases, in part because the pathology can be subtle and lesions small in these diseases, therefore having higher signal and resolution will help lesion detection. In this review, we discuss the main clinical neurological applications and some technical challenges which remain with ultra-high field MRI.

Value of pituitary gland MRI at 7 T in Cushing's disease and relationship to inferior petrosal sinus sampling: case report.

Cushing's disease is caused by adrenocorticotrophic hormone (ACTH)-secreting pituitary adenomas, which are often difficult to identify on standard 1.5-T or 3-T MRI, including dynamic contrast imaging. Inferior petrosal and cavernous sinus sampling remains the gold standard for MRI-negative Cushing's disease.The authors report on a 27-year-old woman with Cushing's disease in whom the results of standard 1.5-T and 3-T MRI, including 1.5-T dynamic contrast imaging, were negative. Inferior petrosal sinus sampling showed a high central-to-peripheral ACTH ratio (148:1) as well as a right-to-left ACTH gradient (19:1), suggesting a right-sided pituitary microadenoma. The patient underwent 7-T MRI, which showed evidence of a right-sided pituitary lesion with focal hypoenhancement not visualized on 1.5-T or 3-T MRI. The patient underwent an endoscopic endonasal transsphenoidal operation, with resection of a right-sided pituitary mass. Postoperatively, she developed clinical symptoms suggestive of adrenal insufficiency and a nadir cortisol level of 1.6 µg/dl on postoperative day 3, and hydrocortisone therapy was initiated. Permanent histopathology specimens showed Crooke's hyaline change and ACTH-positive cells suggestive of an adenoma.MRI at 7 T may be beneficial in identifying pituitary microadenoma location in cases of standard 1.5-T and 3-T MRI-negative Cushing's disease. In the future, 7-T MRI may preempt inferior petrosal sinus sampling and help in cases of standard and dynamic contrast 1.5-T and 3-T MRI-negative Cushing's disease.

Conventional and Advanced Imaging of Spine Oncologic Disease, Nonoperative Post-treatment Effects, and Unique Spinal Conditions.

In this review, we discuss the imaging features of diseases and conditions ranging from neoplastic to nonoperative post-treatment effects to unique conditions of the spine. Additionally, advanced imaging may increase diagnostic certainty in cases where conventional imaging characteristics of benign lesions and malignant pathology are variable.

Dysregulation of PINCH signaling in mesial temporal epilepsy.

Mounting evidence suggests that inflammation is important in epileptogenesis. Particularly Interesting New Cysteine Histidine-rich (PINCH) protein is a highly conserved, LIM-domain protein known to interact with hyperphosphorylated Tau. We assessed PINCH expression in resected epileptogenic human hippocampi and further explored the relationships among PINCH, hpTau and associated kinases. Resected hippocampal tissue from 7 patients with mesial temporal lobe epilepsy (MTLE) was assessed by Western analyses to measure levels of PINCH and hyperphosphorylated Tau, as well as changes in phosphorylation levels of associated kinases AKT and GSK3ß in comparison to normal control tissue. Immunolabeling was also conducted to evaluate PINCH and hpTau patterns of expression, co-localization and cell-type specific expression. Hippocampal PINCH was increased by 2.6 fold in the epilepsy cases over controls and hpTau was increased 10 fold over control. Decreased phospho-AKT and phospho-GSK3ß in epilepsy tissue suggested involvement of this pathway in MTLE. PINCH and hpTau co-localized in some neurons in MTLE tissue. While PINCH was expressed by both neurons and astrocytes in MTLE tissue, hpTau was extracellular or associated with neurons. PINCH was absent from the serum of control subjects but readily detectable from the serum of patients with chronic epilepsy. Our study describes the expression of PINCH and points to AKT/GSK3ß signaling dysregulation as a possible pathway in hpTau formation in MTLE. In view of the interactions between hpTau and PINCH, understanding the role of PINCH in MTLE may provide increased understanding of mechanisms leading to inflammation and MTLE epileptogenesis and a potential biomarker for drug-resistant epilepsy.

Brain cortical structural differences between non-central nervous system cancer patients treated with and without chemotherapy compared to non-cancer controls: a cross-sectional pilot MRI study using clinically-indicated scans.

BACKGROUND: Cognitive deficit associated with cancer and its treatment is called cancer-related cognitive impairment (CRCI). Increases in cancer survival have made understanding the basis of CRCI more important. CRCI neuroimaging studies have traditionally used dedicated research brain MRIs in breast cancer survivors after chemotherapy with small sample sizes; little is known about other non-central nervous system (CNS) cancers after chemotherapy as well as those not exposed to chemotherapy. However, there may be a wealth of unused data from clinically-indicated MRIs that could be used to study CRCI. OBJECTIVE: Evaluate brain cortical structural differences in those with various non-CNS cancers using clinically-indicated MRIs. DESIGN: Cross-sectional. PATIENTS: Adult non-CNS cancer and non-cancer control (C) patients who underwent clinically-indicated MRIs. METHODS: Brain cortical surface area and thickness were measured using 3D T1-weighted images. An age-adjusted linear regression model was used and the Benjamini and Hochberg false discovery rate (FDR) corrected for multiple comparisons. Group comparisons were: cancer cases with chemotherapy (Ch+), cancer cases without chemotherapy (Ch-) and subgroup of lung cancer cases with and without chemotherapy vs C. RESULTS: Sixty-four subjects were analyzed: 22 Ch+, 23 Ch- and 19 C patients. Subgroup analysis of 16 lung cancer (LCa) patients was also performed. Statistically significant decreases in either cortical surface area or thickness were found in multiple regions of interest (ROIs) primarily within the frontal and temporal lobes for all comparisons. Effect sizes were variable with the greatest seen in the left middle temporal surface area ROI (Cohen's d -0.690) in the Ch- vs C group comparison. LIMITATIONS: Several limitations were apparent including a small sample size that precluded adjustment for other covariates. CONCLUSIONS: Our preliminary results suggest that, in addition to breast cancer, other types of non-CNS cancers treated with chemotherapy may result in brain structural abnormalities. Similar findings also appear to occur in those not exposed to chemotherapy. These results also suggest that there is potentially a wealth of untapped clinical MRIs that could be used for future CRCI studies.

Atypical central neurocytoma with metastatic craniospinal dissemination: a case report.

Central neurocytomas comprise nearly half of adult intraventricular neoplasms. The median age of onset is 34 years. It is typically a low-grade neoplasm (World Health Organization grade II), although some cases of malignant neurocytomas have been described. We present a rare case of an atypical central neurocytoma with craniospinal dissemination, including both imaging and pathologic findings.

High-resolution whole-brain DCE-MRI using constrained reconstruction: Prospective clinical evaluation in brain tumor patients.

PURPOSE: To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. METHODS: Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm(3), FOV 22 × 22 × 4.2 cm(3), and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm(3), and broader coverage 22 × 22 × 19 cm(3). Temporal resolution was 5 s for both protocols. Time-resolved images and blood-brain barrier permeability maps were qualitatively evaluated by two radiologists. RESULTS: The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. CONCLUSIONS: The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.

Predicting Meningioma Consistency on Preoperative Neuroimaging Studies.

This article provides an overview of the neuroimaging literature focused on preoperative prediction of meningioma consistency. A validated, noninvasive neuroimaging method to predict tumor consistency can provide valuable information regarding neurosurgical planning and patient counseling. Most of the neuroimaging literature indicates conventional MRI using T2-weighted imaging may be helpful to predict meningioma consistency; however, further rigorous validation is necessary. Much less is known about advanced MRI techniques, such as diffusion MRI, MR elastography (MRE), and MR spectroscopy. Of these methods, MRE and diffusion tensor imaging appear particularly promising.

GOCART: GOlden-angle CArtesian randomized time-resolved 3D MRI.

PURPOSE: To develop and evaluate a novel 3D Cartesian sampling scheme which is well suited for time-resolved 3D MRI using parallel imaging and compressed sensing. METHODS: The proposed sampling scheme, termed GOlden-angle CArtesian Randomized Time-resolved (GOCART) 3D MRI, is based on golden angle (GA) Cartesian sampling, with random sampling of the ky-kz phase encode locations along each Cartesian radial spoke. This method was evaluated in conjunction with constrained reconstruction of retrospectively and prospectively undersampled in-vivo dynamic contrast enhanced (DCE) MRI data and simulated phantom data. RESULTS: In in-vivo retrospective studies and phantom simulations, images reconstructed from phase encodes defined by GOCART were equal to or superior to those with Poisson disc or GA sampling schemes. Typical GOCART sampling tables were generated in <100ms. GOCART has also been successfully utilized prospectively to produce clinically valuable whole-brain DCE-MRI images. CONCLUSION: GOCART is a practical and efficient sampling scheme for time-resolved 3D MRI. It shows great potential for highly accelerated DCE-MRI and is well suited to modern reconstruction methods such as parallel imaging and compressed sensing.