MRI Markers of Degenerative Disc Disease in Young Patients With Multiple Sclerosis

Background and Purpose: Evidence has emerged for an association between degenerative disc disease (DDD) and multiple sclerosis (MS). The purpose of the current study is to determine the presence and extent of cervical DDD in young patients (age <35) with MS, an age cohort that is less well studied for these changes. Methods: Retrospective chart review of consecutive patients aged <35 referred from the local MS clinic who were MRI scanned between May 2005 and November 2014. 80 patients (51 female and 29 male) with MS of any type ranging between 16 and 32 years of age (average 26) were included. Images were reviewed by 3 raters and assessed for presence and extent of DDD, as well as cord signal abnormalities. Interrater agreement was assessed using Kendall's W and Fleiss' Kappa statistics. Results: Substantial to very good interrater agreement was observed using our novel DDD grading scale. At least some degree of DDD was found in over 91% of patients. The majority scored mild (grade 1, 30-49%) to moderate (grade 2, 39-51%) degenerative changes. Cord signal abnormality was seen in 56-63%. Cord signal abnormality, when present, occurred exclusively at degenerative disc levels in only 10-15%, significantly lower than other distributions (P < .001 for all pairwise comparisons). Conclusions: MS patients demonstrate unexpected cervical DDD even at a young age. Future study is warranted to investigate the underlying etiology, such as altered biomechanics. Furthermore, cord lesions were found to occur independently of DDD.

MRI texture analysis of acetabular cancellous bone can discriminate between normal, cam positive, and cam-FAI hips

OBJECTIVES

To compare the MRI texture profile of acetabular subchondral bone in normal, asymptomatic cam positive, and symptomatic cam-FAI hips and determine the accuracy of a machine learning model for discriminating between the three hip classes.

METHODS

A case-control, retrospective study was performed including 68 subjects (19 normal, 26 asymptomatic cam, 23 symptomatic cam-FAI). Acetabular subchondral bone of unilateral hip was contoured on 1.5 T MR images. Nine first-order 3D histogram and 16 s-order texture features were evaluated using specialized texture analysis software. Between-group differences were assessed using Kruskal-Wallis and Mann-Whitney U tests, and differences in proportions compared using chi-square and Fisher's exact tests. Gradient-boosted ensemble methods of decision trees were created and trained to discriminate between the three groups of hips, with percent accuracy calculated.

RESULTS

Sixty-eight subjects (median age 32 (28-40), 60 male) were evaluated. Significant differences among all three groups were identified with first-order (4 features, all p ≤ 0.002) and second-order (11 features, all p ≤ 0.002) texture analyses. First-order texture analysis could differentiate between control and cam positive hip groups (4 features, all p ≤ 0.002). Second-order texture analysis could additionally differentiate between asymptomatic cam and symptomatic cam-FAI groups (10 features, all p ≤ 0.02). Machine learning models demonstrated high classification accuracy of 79% (SD 16) for discriminating among all three groups.

CONCLUSION

Normal, asymptomatic cam positive, and cam-FAI hips can be discriminated based on their MRI texture profile of subchondral bone using descriptive statistics and machine learning algorithms.

CLINICAL RELEVANCE STATEMENT

Texture analysis can be performed on routine MR images of the hip and used to identify early changes in bone architecture, differentiating morphologically abnormal from normal hips, prior to onset of symptoms.

KEY POINTS

• MRI texture analysis is a technique for extracting quantitative data from routine MRI images. • MRI texture analysis demonstrates that there are different bone profiles between normal hips and those with femoroacetabular impingement. • Machine learning models can be used in conjunction with MRI texture analysis to accurately differentiate between normal hips and those with femoroacetabular impingement.

Diagnostic Accuracy of Centrally Restricted Diffusion Sign in Cerebral Metastatic Disease: Differentiating Radiation Necrosis from Tumor Recurrence

Purpose: The centrally restricted diffusion sign of diffusion-weighted imaging (DWI) is associated with radiation necrosis (RN) in treated gliomas. Our goal was to evaluate its diagnostic accuracy to distinguish RN from tumor recurrence (TR) in treated brain metastases. Methods: Retrospective study of consecutive patients with brain metastases who developed a newly centrally necrotic lesion after radiotherapy (RT). One reader placed regions of interest (ROI) in the enhancing solid lesion and the non-enhancing central necrosis on the apparent diffusion coefficient (ADC) map. Two readers qualitatively assessed the presence of the centrally restricted diffusion sign. The final diagnosis was made by histopathology (n = 39) or imaging follow-up (n = 2). Differences between groups were assessed by Fisher's exact or Mann-Whitney U tests. Diagnostic accuracy and inter-reader agreement were evaluated using receiver operating characteristic (ROC) curve analysis and kappa scores. Results: Forty-one lesions (32 predominant RN; 9 predominant TR) were analyzed. An ADC value ≤ 1220 × 10-6 mm2/s (sensitivity 74%, specificity 89%, area under the curve [AUC] .85 [95% confidence interval {CI}, .70-.94] P < .0001) from the necrosis and an ADC necrosis/enhancement ratio ≤1.37 (sensitivity 74%, specificity 89%, AUC .82 [95% CI, .67-.93] P < .0001) provided the highest performance for RN diagnosis. The qualitative centrally restricted diffusion sign had a sensitivity of 69% (95% CI, .50-.83), specificity of 77% (95% CI, .40-.96), and a moderate (k = .49) inter-reader agreement for RN diagnosis. Conclusions: Radiation necrosis is associated with lower ADC values in the central necrosis than TR. A moderate interobserver agreement might limit the qualitative assessment of the centrally restricted diffusion sign.

Rule-based natural language processing for automation of stroke data extraction: a validation study

PURPOSE

Data extraction from radiology free-text reports is time consuming when performed manually. Recently, more automated extraction methods using natural language processing (NLP) are proposed. A previously developed rule-based NLP algorithm showed promise in its ability to extract stroke-related data from radiology reports. We aimed to externally validate the accuracy of CHARTextract, a rule-based NLP algorithm, to extract stroke-related data from free-text radiology reports.

METHODS

Free-text reports of CT angiography (CTA) and perfusion (CTP) studies of consecutive patients with acute ischemic stroke admitted to a regional stroke center for endovascular thrombectomy were analyzed from January 2015 to 2021. Stroke-related variables were manually extracted as reference standard from clinical reports, including proximal and distal anterior circulation occlusion, posterior circulation occlusion, presence of ischemia or hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status. These variables were simultaneously extracted using a rule-based NLP algorithm. The NLP algorithm's accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were assessed.

RESULTS

The NLP algorithm's accuracy was > 90% for identifying distal anterior occlusion, posterior circulation occlusion, hemorrhage, and ASPECTS. Accuracy was 85%, 74%, and 79% for proximal anterior circulation occlusion, presence of ischemia, and collateral status respectively. The algorithm confirmed the absence of variables from radiology reports with an 87-100% accuracy.

CONCLUSIONS

Rule-based NLP has a moderate to good performance for stroke-related data extraction from free-text imaging reports. The algorithm's accuracy was affected by inconsistent report styles and lexicon among reporting radiologists.

Assessment of left atrial fibrosis progression in canines following rapid ventricular pacing using 3D late gadolinium enhanced CMR images

Background

Atrial fibrillation (AF) is associated with extracellular matrix (ECM) remodelling and often coexists with myocardial fibrosis (MF); however, the causality of these conditions is not well established.

Objective

We aim to corroborate AF to MF causality by quantifying left atrial (LA) fibrosis in cardiac magnetic resonance (CMR) images after persistent rapid ventricular pacing and subsequent AF using a canine model and histopathological validation.

Methods

Twelve canines (9 experimental, 3 control) underwent baseline 3D LGE-CMR imaging at 3T followed by insertion of a pacing device and 5 weeks of rapid ventricular pacing to induce AF (experimental) or no pacing (control). Following the 5 weeks, pacing devices were removed to permit CMR imaging followed by excision of the hearts and histopathological imaging. LA myocardial segmentation was performed manually at baseline and post-pacing to permit volumetric %MF quantification using the image intensity ratio (IIR) technique, wherein fibrosis was defined as pixels > mean LA myocardium intensity + 2SD.

Results

Volumetric %MF increased by an average of 2.11 ± 0.88% post-pacing in 7 of 9 experimental dogs. While there was a significant difference between paired %MF measurements from baseline to post-pacing in experimental dogs (P = 0.019), there was no significant change in control dogs (P = 0.019 and P = 0.5, Wilcoxon signed rank tests). The median %MF for paced animals was significantly greater than that of non-paced dogs at the 5-week post-insertion time point (P = 0.009, Mann Whitney U test). Histopathological imaging yielded an average %MF of 19.42 ± 4.80% (mean ± SD) for paced dogs compared to 1.85% in one control dog.

Conclusion

Persistent rapid ventricular pacing and subsequent AF leads to an increase in LA fibrosis volumes measured by the IIR technique; however, quantification is limited by inherent image acquisition parameters and observer variability.

Preoperative Determination of Isocitrate Dehydrogenase Mutation in Gliomas Using Spectral Editing MRS: A Prospective Study

BACKGROUND

The edited magnetic resonance spectroscopy (MRS) technique has not yet been formally evaluated for the in vivo detection of 2-hydroxyglutarate (2-HG) in patients with gliomas of various grades.

PURPOSE

To evaluate the diagnostic accuracy of edited MRS in the preoperative identification of the isocitrate dehydrogenase (IDH) mutation status in patients with gliomas.

STUDY TYPE

Prospective.

POPULATION

Fifty-eight subjects (31 glioblastomas, 27 grade II and III gliomas).

FIELD STRENGTH/SEQUENCE

Mescher-Garwood (MEGA)-PRESS and routine clinical brain tumor MR sequences were used at 3T.

ASSESSMENT

Data were analyzed using an advanced method for accurate, robust, and efficient spectral fitting (AMARES) from jMRUI software. The amplitudes of the 2-HG, N-acetyl-aspartate (NAA), choline (Cho), and creatine/phosphocreatine (Cr) resonances were calculated with their associated Cramer-Rao lower bound (CRLB). The IDH1 R132H mutation status was assessed by immunohistochemistry for all patients. Patients with grades II and III gliomas with negative immunohistochemistry underwent DNA sequencing to further interrogate IDH mutation status.

STATISTICAL TEST

The differences in 2-HG amplitudes, 2-HG/NAA, 2-HG/Cho, and 2-HG/Cr between IDH-mutant and IDH-wildtype gliomas were assessed using Mann-Whitney U-tests. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic accuracy of each parameter.

RESULTS

The 2-HG amplitudes, 2-HG/NAA, and 2-HG/Cho were higher for IDH-mutant gliomas than IDH-wildtype gliomas (P < 0.007). Using a CRLB threshold <30%, a 2-HG cutoff greater than 0 had a sensitivity of 80% (95% confidence interval [CI]: 52-96%) and a specificity of 81% (95% CI: 54-96%) in identifying IDH-mutant gliomas. In the subset of patients with grades II and III gliomas, the sensitivity was 80% (95% CI: 52-96%) and specificity was 100% (95% CI: 40-100%). Among 2-HG ratios, the highest AUC for the identification of IDH mutant status was achieved using the 2-HG/NAA (AUC = 0.8, 95% CI 0.67-.89).

DATA CONCLUSION

Preoperative edited MRS appears to be able to help identify IDH-mutant gliomas with high specificity. Level of Evidence 1 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:416-426.

Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images

OBJECTIVE

To compare texture analysis (TA) features of solid renal masses on renal protocol (non-contrast enhanced [NECT], corticomedullary [CM], nephrographic [NG]) CT.

MATERIALS AND METHODS

A total of 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe, and 61 benign masses; 49 oncocytomas, 12 fat-poor angiomyolipomas) with three-phase CT between 2012 and 2017 were studied. Two blinded radiologists independently assessed tumor heterogeneity (5-point Likert scale) and segmented tumors. TA features (N = 25) were compared between groups and between phases. Accuracy (area under the curve [AUC]) for RCC versus benign and cc-RCC versus other masses was compared.

RESULTS

Subjectively, tumor heterogeneity differed between phases (p < 0.01) and between tumors within the same phase (p = 0.03 [NECT] and p < 0.01 [CM, NG]). Inter-observer agreement was moderate to substantial (intraclass correlation coefficient = 0.55-0.73). TA differed in 92.0% (23/25) features between phases (p < 0.05) except for GLNU and f6. More TA features differed significantly on CM (80.0% [20/25]) compared with NG (40.0% [10/25]) and NECT (16.0% [4/25]) (p < 0.01). For RCC versus benign, AUCs of texture features did not differ comparing CM and NG (p > 0.05), but were higher for 20% (5/25) and 28% (7/25) of features comparing CM and NG with NECT (p < 0.05). For cc-RCC versus other, 36% (9/25) and 40% (10/25) features on CM had higher AUCs compared with NECT and NG images (p < 0.05).

CONCLUSION

Texture analysis of renal masses differs, when evaluated subjectively and quantitatively, by phase of CT enhancement. The corticomedullary phase had the highest discriminatory value when comparing masses and for differentiating cc-RCC from other masses.

KEY POINTS

• Subjectively evaluated renal tumor heterogeneity on CT differs by phase of enhancement. • Quantitative CT texture analysis features in renal tumors differ by phases of enhancement with the corticomedullary phase showing the highest number and most significant differences compared with non-contrast-enhanced and nephrographic phase images. • For diagnosis of clear cell RCC, corticomedullary phase texture analysis features had improved accuracy of classification in approximately 40% of features studied compared with non-contrast-enhanced and nephrographic phase images.

Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT

OBJECTIVE

To compare machine learning (ML) of texture analysis (TA) features for classification of solid renal masses on non-contrast-enhanced CT (NCCT), corticomedullary (CM) and nephrographic (NG) phase contrast-enhanced (CE) CT.

MATERIALS AND METHODS

With IRB approval, we retrospectively identified 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe and 61 benign tumors; 49 oncocytomas and 12 fat-poor angiomyolipomas) with renal protocol CT between 2012 and 2017. Tumors were independently segmented by two blinded radiologists. Twenty-five 2-dimensional TA features were extracted from each phase. Diagnostic accuracy for 1) RCC versus benign tumor and 2) cc-RCC versus other tumor was assessed using XGBoost.

RESULTS

ML of texture analysis features on different phases achieved mean area under the ROC curve (AUC [SD]), sensitivity/specificity for 1) RCC vs benign = 0.70(0.19), 96%/32% on CM-CECT and 0.71(0.14), 83%/58% on NG-CECT and; 2) cc-RCC vs other = 0.77(0.12), 49%/90% on CM-CECT and 0.71(0.16), 22%/94% on NG-CECT. There was no difference in AUC comparing CECT to NCCT (p = 0.058-0.54) and no improvement when combining data across all three phases compared single-phase assessment (p = 0.39-0.68) for either outcome. AUCs decreased when ML models were trained with one phase and tested on a different phase for both outcomes (RCC;p = 0.045-0.106, cc-RCC; < 0.001).

CONCLUSION

Accuracy of machine learning classification of renal masses using texture analysis features did not depend on phase; however, models trained using one phase performed worse when tested on another phase particularly when associating NCCT and CECT. These findings have implications for large registries which use varying CT protocols to study renal masses.

Prospective comparative diagnostic accuracy evaluation of dynamic contrast-enhanced (DCE) vs. dynamic susceptibility contrast (DSC) MR perfusion in differentiating tumor recurrence from radiation necrosis in treated high-grade gliomas

BACKGROUND

The appearance of a new enhancing lesion after surgery and chemoradiation for high-grade glioma (HGG) presents a common diagnostic dilemma. Histopathological analysis remains the reference standard in this situation.

PURPOSE

To prospectively compare the diagnostic accuracy of dynamic contrast-enhanced (DCE) vs. dynamic susceptibility contrast (DSC) in differentiating tumor recurrence (TR) from radiation necrosis (RN).

STUDY TYPE

Prospective diagnostic accuracy study.

POPULATION

In all, 98 consecutive treated HGG patients with new enhancing lesion. We excluded 32 patients due to inadequate follow-up or technical limitation.

FIELD STRENGTH/SEQUENCE

3 T DCE and DSC MR.

ASSESSMENT

Histogram and hot-spot analysis of cerebral blood volume (CBV), corrected CBV, K^trans , area under the curve (AUC), and plasma volume (Vp). The reference standard of TR and/or RN was determined by histopathology in 43 surgically resected lesions or by clinical/imaging follow-up in the rest.

STATISTICAL TESTS

Mann-Whitney U-tests, receiver operating characteristic (ROC) curve, and logistic regression analysis.

RESULTS

A total of 68 lesions were included. There were 37 TR, 28 RN, and three lesions with equal proportions of TR and RN. TR had significantly higher CBV, corrected CBV, CBV ratio, corrected CBV ratio, AUC ratio, and Vp ratio (P < 0.05) than RN on hot-spot analysis. CBV had the highest diagnostic accuracy (AUROC 0.71). On histogram analysis, TR had higher CBV and corrected CBV maximal value compared with RN (P = 0.006, AUROC = 0.70). Only CBV on hot-spot analysis remained significant after correction for multiple comparison, with no significant improvement in diagnostic accuracy when using a combination of parameters (AUROC 0.71 vs. 0.76, P = 0.24).

DATA CONCLUSION

DSC-derived CBV is the most accurate perfusion parameter in differentiating TR and RN. DSC and DCE-derived parameters reflecting the blood volume in an enhancing lesion are more accurate than the DCE-derived parameter K^trans . Clinical practice may be best guided by blood volume measurements, rather than permeability assessment for differentiation of TR from RN.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2019;50:573-582.

Bi-atrial fibrosis detected using three-dimensional late gadolinium enhancement magnetic resonance imaging in a patient with cardiac sarcoidosis

Presented is the case of a 62-year old male with a history of sarcoidosis and sinus node dysfunction, who underwent late gadolinium enhancement magnetic resonance imaging, which demonstrated left ventricular hyperenhancement and bi-atrial fibrosis.

Can CT and MR Shape and Textural Features Differentiate Benign Versus Malignant Pleural Lesions?

RATIONALE AND OBJECTIVES

The study aimed to identify a radiomic approach based on CT and or magnetic resonance (MR) features (shape and texture) that may help differentiate benign versus malignant pleural lesions, and to assess if the radiomic model may improve confidence and accuracy of radiologists with different subspecialty backgrounds.

MATERIALS AND METHODS

Twenty-nine patients with pleural lesions studied on both contrast-enhanced CT and MR imaging were reviewed retrospectively. Three texture and three shape features were extracted. Combinations of features were used to generate logistic regression models using histopathology as outcome. Two thoracic and two abdominal radiologists evaluated their degree of confidence in malignancy. Diagnostic accuracy of radiologists was determined using contingency tables. Cohen's kappa coefficient was used to assess inter-reader agreement. Using optimal threshold criteria, sensitivity, specificity, and accuracy of each feature and combination of features were obtained and compared to the accuracy and confidence of radiologists.

RESULTS

The CT model that best discriminated malignant from benign lesions revealed an AUC_CT = 0.92 ± 0.05 (P < 0.0001). The most discriminative MR model showed an AUC_MR = 0.87 ± 0.09 (P < 0.0001). The CT model was compared to the diagnostic confidence of all radiologists and the model outperformed both abdominal radiologists (P < 0.002), whereas the top discriminative MR model outperformed one of the abdominal radiologists (P = 0.02). The most discriminative MR model was more accurate than one abdominal (P = 0.04) and one thoracic radiologist (P = 0.02).

CONCLUSION

Quantitative textural and shape analysis may help distinguish malignant from benign lesions. A radiomics-based approach may increase diagnostic confidence of abdominal radiologists on CT and MR and may potentially improve radiologists' accuracy in the assessment of pleural lesions characterized by MR.

Correlation of Tumor Immunohistochemistry with Dynamic Contrast-Enhanced and DSC-MRI Parameters in Patients with Gliomas

BACKGROUND AND PURPOSE

Tumor CBV is a prognostic and predictive marker for patients with gliomas. Tumor CBV can be measured noninvasively with different MR imaging techniques; however, it is not clear which of these techniques most closely reflects histologically-measured tumor CBV. Our aim was to investigate the correlations between dynamic contrast-enhanced and DSC-MR imaging parameters and immunohistochemistry in patients with gliomas.

MATERIALS AND METHODS

Forty-three patients with a new diagnosis of glioma underwent a preoperative MR imaging examination with dynamic contrast-enhanced and DSC sequences. Unnormalized and normalized cerebral blood volume was obtained from DSC MR imaging. Two sets of plasma volume and volume transfer constant maps were obtained from dynamic contrast-enhanced MR imaging. Plasma volume obtained from the phase-derived vascular input function and bookend T1 mapping (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function and bookend T1 mapping (K^trans_Φ) were determined. Plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K^trans_SI) were acquired, without T1 mapping. Using CD34 staining, we measured microvessel density and microvessel area within 3 representative areas of the resected tumor specimen. The Mann-Whitney U test was used to test for differences according to grade and degree of enhancement. The Spearman correlation was performed to determine the relationship between dynamic contrast-enhanced and DSC parameters and histopathologic measurements.

RESULTS

Microvessel area, microvessel density, dynamic contrast-enhanced, and DSC-MR imaging parameters varied according to the grade and degree of enhancement (P < .05). A strong correlation was found between microvessel area and Vp_Φ and between microvessel area and unnormalized blood volume (r_s ≥ 0.61). A moderate correlation was found between microvessel area and normalized blood volume, microvessel area and Vp_SI, microvessel area and K^trans_Φ, microvessel area and K^trans_SI, microvessel density and Vp_Φ, microvessel density and unnormalized blood volume, and microvessel density and normalized blood volume (0.44 ≤ r_s ≤ 0.57). A weaker correlation was found between microvessel density and K^trans_Φ and between microvessel density and K^trans_SI (r_s ≤ 0.41).

CONCLUSIONS

With dynamic contrast-enhanced MR imaging, use of a phase-derived vascular input function and bookend T1 mapping improves the correlation between immunohistochemistry and plasma volume, but not between immunohistochemistry and the volume transfer constant. With DSC-MR imaging, normalization of tumor CBV could decrease the correlation with microvessel area.

Comparison of the Diagnostic Accuracy of DSC- and Dynamic Contrast-Enhanced MRI in the Preoperative Grading of Astrocytomas

BACKGROUND AND PURPOSE

Dynamic contrast-enhanced MR imaging parameters can be biased by poor measurement of the vascular input function. We have compared the diagnostic accuracy of dynamic contrast-enhanced MR imaging by using a phase-derived vascular input function and "bookend" T1 measurements with DSC MR imaging for preoperative grading of astrocytomas.

MATERIALS AND METHODS

This prospective study included 48 patients with a new pathologic diagnosis of an astrocytoma. Preoperative MR imaging was performed at 3T, which included 2 injections of 5-mL gadobutrol for dynamic contrast-enhanced and DSC MR imaging. During dynamic contrast-enhanced MR imaging, both magnitude and phase images were acquired to estimate plasma volume obtained from phase-derived vascular input function (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function (K(trans)_Φ) as well as plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K(trans)_SI). From DSC MR imaging, corrected relative CBV was computed. Four ROIs were placed over the solid part of the tumor, and the highest value among the ROIs was recorded. A Mann-Whitney U test was used to test for difference between grades. Diagnostic accuracy was assessed by using receiver operating characteristic analysis.

RESULTS

Vp_ Φ and K(trans)_Φ values were lower for grade II compared with grade III astrocytomas (P < .05). Vp_SI and K(trans)_SI were not significantly different between grade II and grade III astrocytomas (P = .08-0.15). Relative CBV and dynamic contrast-enhanced MR imaging parameters except for K(trans)_SI were lower for grade III compared with grade IV (P ≤ .05). In differentiating low- and high-grade astrocytomas, we found no statistically significant difference in diagnostic accuracy between relative CBV and dynamic contrast-enhanced MR imaging parameters.

CONCLUSIONS

In the preoperative grading of astrocytomas, the diagnostic accuracy of dynamic contrast-enhanced MR imaging parameters is similar to that of relative CBV.

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

PURPOSE

To determine the accuracy of texture analysis to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC) on unenhanced computed tomography (CT) images.

MATERIALS AND METHODS

In this institutional review board-approved retrospective case-control study, patients with AML and RCC were identified from the pathology database: there were 16 patients with fp-AML (no visible fat at unenhanced CT) and 84 patients with RCC. Axial unenhanced CT images were contoured manually by two independent analysts. Texture analysis was performed for each lesion, and reproducibility was assessed. Texture features related to the gray-level histogram, gray-level co-occurrence, and run-length matrix statistics were evaluated. The most discriminative features were used to generate support vector machine (SVM) classifiers. Diagnostic accuracy of textural features was assessed and 10-fold cross validation was performed. Unenhanced CT images for each patient were independently reviewed by two blinded radiologists who subjectively graded lesion heterogeneity on a five-point scale. Differences in area under the receiver operating characteristic curve (AUC) between subjective heterogeneity ratings and textural features were evaluated by using the DeLong method.

RESULTS

There was lower lesion homogeneity and higher lesion entropy in RCCs (P ≤ .01). A model incorporating several texture features resulted in an AUC of 0.89 ± 0.04. The average SVM accuracy of textural features ranged from 83% to 91% (after 10-fold cross validation). An optimal subjective heterogeneity rating of 2 or higher was identified as a predictor of RCC for both readers, with no significant difference in AUC between readers (P = .06). Each of the three textural-based classifiers was more accurate than either radiologists' subjective heterogeneity ratings for the models incorporating a subset of the top three textural features (difference in AUC between textural features and subjective visual heterogeneity, 0.25; 95% confidence interval: 0.02, 0.47; P = .03).

CONCLUSION

CT texture analysis can be used to accurately differentiate fp-AML from RCC on unenhanced CT images.

Vulnerable Carotid Artery Plaque Evaluation: Detection Agreement between Advanced Ultrasound, Computed Tomography, and Magnetic Resonance Imaging: A Phantom Study

PURPOSE

Imaging plaque morphology, in addition to luminal grading, may improve stroke risk-management by identifying structural atherosclerotic plaques alterations responsible for cerebrovascular events. The purpose of this study was to evaluate the agreement between our enhanced ultrasound (US) imaging method and high-resolution cross-sectional imaging modalities, such as multidetector-row computed tomography (CT) and magnetic resonance imaging (MRI), in the characterization of vulnerable plaques.

METHODS

Sixty tissue-like phantoms were created to simulate various types of diseased plaque segments. We prospectively assessed each sample with US, CT, and MRI. Plaque characteristics considered included surface irregularity, ulceration, fissure, and presence of internal fluid core(s). We evaluated the agreement between and among the three modalities, as well as the accuracy of each compared with the true pathology.

RESULTS

There was moderate to substantial agreement among the three modalities in the detection of morphologic characteristics. There was no significant difference in accuracy between US and CT in the presence of ulceration(s) (P = .23), lucency (P = .23), or fissures (P = .07); however, US was significantly more accurate than MRI for each of these characteristics (P = .0001, P = .0001, P = .02, respectively). None of the three modalities did display any significant difference in accuracy in the identification of irregular surface. There was substantial agreement among the three radiologists (intraclass correlation coefficient, 0.61; 95% confidence interval, 0.46-0.74) in their assessment of plaque subtype, ranging from 80%-85% accuracy in identifying the plaque subtypes for each classification.

CONCLUSIONS

Enhanced plaque imaging can identify potentially significant plaque characteristics and provide insight into early causative conditions of carotid atherosclerosis. Our results suggest that the types of plaque pathologies derived from our US method showed good agreement with CT and surpass information gathered on MRI. This imaging protocol could potentially shift the paradigm in early carotid plaque imaging likely to predict the onset of vulnerable plaques, thus improving preventative management of atherosclerosis.

Preoperative prognostic value of dynamic contrast-enhanced MRI-derived contrast transfer coefficient and plasma volume in patients with cerebral gliomas

BACKGROUND AND PURPOSE

The prognostic value of dynamic contrast-enhanced MR imaging-derived plasma volume obtained in tumor and the contrast transfer coefficient has not been well-established in patients with gliomas. We determined whether plasma volume and contrast transfer coefficient in tumor correlated with survival in patients with gliomas in addition to other factors such as age, type of surgery, preoperative Karnofsky score, contrast enhancement, and histopathologic grade.

MATERIALS AND METHODS

This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. The contrast transfer coefficient and plasma volume obtained in tumor maps were calculated directly from the signal-intensity curve without T1 measurements, and values were obtained from multiple small ROIs placed within tumors. Survival curve analysis was performed by dichotomizing patients into groups of high and low contrast transfer coefficient and plasma volume. Univariate analysis was performed by using dynamic contrast-enhanced parameters and clinical factors. Factors that were significant on univariate analysis were entered into multivariate analysis.

RESULTS

For all patients with gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). In subgroups of high- and low-grade gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). Univariate analysis showed that factors associated with lower survival were age older than 50 years, low Karnofsky score, biopsy-only versus resection, marked contrast enhancement versus no/mild enhancement, high contrast transfer coefficient, and high plasma volume obtained in tumor (P < .05). In multivariate analysis, a low Karnofsky score, biopsy versus resection in combination with marked contrast enhancement, and a high contrast transfer coefficient were associated with lower survival rates (P < .05).

CONCLUSIONS

In patients with glioma, those with a high contrast transfer coefficient have lower survival than those with low parameters.

Differentiation of lipoma from liposarcoma on MRI using texture and shape analysis

RATIONALE AND OBJECTIVES

To determine if differentiation of lipoma from liposarcoma on magnetic resonance imaging can be improved using computer-assisted diagnosis (CAD).

MATERIALS AND METHODS

Forty-four histologically proven lipomatous tumors (24 lipomas and 20 liposarcomas) were studied retrospectively. Studies were performed at 1.5T and included T1-weighted, T2-weighted, T2-fat-suppressed, short inversion time inversion recovery, and contrast-enhanced sequences. Two experienced musculoskeletal radiologists blindly and independently noted their degree of confidence in malignancy using all available images/sequences for each patient. For CAD, tumors were segmented in three dimensions using T1-weighted images. Gray-level co-occurrence and run-length matrix textural features, as well as morphological features, were extracted from each tumor volume. Combinations of shape and textural features were used to train multiple, linear discriminant analysis classifiers. We assessed sensitivity, specificity, and accuracy of each classifier for delineating lipoma from liposarcoma using 10-fold cross-validation. Diagnostic accuracy of the two radiologists was determined using contingency tables. Interreader agreement was evaluated by Cohen kappa.

RESULTS

Using optimum-threshold criteria, CAD produced superior values (sensitivity, specificity, and accuracy are 85%, 96%, and 91%, respectively) compared to radiologist A (75%, 83%, and 80%) and radiologist B (80%, 75%, and 77%). Interreader agreement between radiologists was substantial (kappa [95% confidence interval]=0.69 [0.48-0.90]).

CONCLUSIONS

CAD may help radiologists distinguish lipoma from liposarcoma.

Can shape analysis differentiate free-floating internal carotid artery thrombus from atherosclerotic plaque in patients evaluated with CTA for stroke or transient ischemic attack?

RATIONALE AND OBJECTIVES

Patients presenting with transient ischemic attack or stroke may have symptom-related lesions on acute computed tomography angiography (CTA) such as free-floating intraluminal thrombus (FFT). It is difficult to distinguish FFT from carotid plaque, but the distinction is critical as management differs. By contouring the shape of these vascular lesions ("virtual endarterectomy"), advanced morphometric analysis can be performed. The objective of our study is to determine whether quantitative shape analysis can accurately differentiate FFT from atherosclerotic plaque.

MATERIALS AND METHODS

We collected 23 consecutive cases of suspected carotid FFT seen on CTA (13 men, 65 ± 10 years; 10 women, 65.5 ± 8.8 years). True-positive FFT cases (FFT+) were defined as filling defects resolving with anticoagulant therapy versus false-positives (FFT-), which remained unchanged. Lesion volumes were extracted from CTA images and quantitative shape descriptors were computed. The five most discriminative features were used to construct receiver operator characteristic (ROC) curves and to generate three machine-learning classifiers. Average classification accuracy was determined by cross-validation.

RESULTS

Follow-up imaging confirmed sixteen FFT+ and seven FFT- cases. Five shape descriptors delineated FFT+ from FFT- cases. The logistic regression model produced from combining all five shape features demonstrated a sensitivity of 87.5% and a specificity of 71.4% with an area under the ROC curve = 0.85 ± 0.09. Average accuracy for each classifier ranged from 65.2%-76.4%.

CONCLUSIONS

We identified five quantitative shape descriptors of carotid FFT. This shape "signature" shows potential for supplementing conventional lesion characterization in cases of suspected FFT.

Diagnostic accuracy of dynamic contrast-enhanced MR imaging using a phase-derived vascular input function in the preoperative grading of gliomas

BACKGROUND AND PURPOSE

The accuracy of tumor plasma volume and K(trans) estimates obtained with DCE MR imaging may have inaccuracies introduced by a poor estimation of the VIF. In this study, we evaluated the diagnostic accuracy of a novel technique by using a phase-derived VIF and "bookend" T1 measurements in the preoperative grading of patients with suspected gliomas.

MATERIALS AND METHODS

This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. Both magnitude and phase images were acquired during DCE MR imaging for estimates of K(trans)_φ and V(p_)φ (calculated from a phase-derived VIF and bookend T1 measurements) as well as K(trans)_SI and V(p_)SI (calculated from a magnitude-derived VIF without T1 measurements).

RESULTS

Median K(trans)_φ values were 0.0041 minutes(-1) (95 CI, 0.00062-0.033), 0.031 minutes(-1) (0.011-0.150), and 0.088 minutes(-1) (0.069-0.110) for grade II, III, and IV gliomas, respectively (P ≤ .05 for each). Median V(p_)φ values were 0.64 mL/100 g (0.06-1.40), 0.98 mL/100 g (0.34-2.20), and 2.16 mL/100 g (1.8-3.1) with P = .15 between grade II and III gliomas and P = .015 between grade III and IV gliomas. In differentiating low-grade from high-grade gliomas, AUCs for K(trans)_φ, V(p_φ), K(trans)_SI, and V(p_)SI were 0.87 (0.73-1), 0.84 (0.69-0.98), 0.81 (0.59-1), and 0.84 (0.66-0.91). The differences between the AUCs were not statistically significant.

CONCLUSIONS

K(trans)_φ and V(p_)φ are parameters that can help in differentiating low-grade from high-grade gliomas.

Assessment of tumor angiogenesis: dynamic contrast-enhanced MRI with paramagnetic nanoparticles compared with Gd-DTPA in a rabbit Vx-2 tumor model

The purpose of this study was to evaluate the suitability of a macromolecular MRI contrast agent (paramagnetic nanoparticles, PNs) for the characterization of tumor angiogenesis. Our aim was to estimate the permeability of PNs in developing tumor vasculature and compare it with that of a low molecular weight contrast agent (Gd-DTPA) using dynamic contrast-enhanced MRI (DCE). Male New Zealand white rabbits (n = 5) underwent DCE MRI 12-14 days after Vx-2 tumor fragments were implanted into the left hind limb. Each contrast agent (PNs followed by Gd-DTPA) was evaluated using a DCE protocol and transendothelial transfer coefficient (K(i)) maps were calculated using a two-compartment model. Two regions of interest (ROIs) were located within the tumor core and hindlimb muscle and five ROIs were placed within the tumor rim. Comparisons were performed using repeated measures analysis of variance (ANOVA). The K(i) values estimated using PNs were significantly lower than those obtained for Gd-DTPA (p = 0.018). When PNs and Gd-DTPA data were analyzed separately, significant differences were identified among tumor rim ROIs for PNs (p < 0.0001), but not for Gd-DTPA data (p = 0.34). The mean K(i) for the tumor rim was significantly greater than that of either the core or the hindlimb muscle for both contrast agents (p < 0.05 for each comparison). In summary, the extravasation of Gd-DTPA was far greater than that of PNs, suggesting that PNs can reveal regional differences in tumor vascular permeability that are not otherwise apparent with clinical contrast agents such as Gd-DTPA. These results suggest that PNs show potential for the noninvasive delineation of tumor angiogenesis.

A randomized trial of coenzyme Q10 in mitochondrial disorders

Case reports and open-label studies suggest that coenzyme Q(10) (CoQ(10)) treatment may have beneficial effects in mitochondrial disease patients; however, controlled trials are warranted to clinically prove its effectiveness. Thirty patients with mitochondrial cytopathy received 1200 mg/day CoQ(10) for 60 days in a randomized, double-blind, cross-over trial. Blood lactate, urinary markers of oxidative stress, body composition, activities of daily living, quality of life, forearm handgrip strength and oxygen desaturation, cycle exercise cardiorespiratory variables, and brain metabolites were measured. CoQ(10) treatment attenuated the rise in lactate after cycle ergometry, increased (∽1.93 ml) VO(2)/kg lean mass after 5 minutes of cycling (P < 0.005), and decreased gray matter choline-containing compounds (P < 0.05). Sixty days of moderate- to high-dose CoQ(10) treatment had minor effects on cycle exercise aerobic capacity and post-exercise lactate but did not affect other clinically relevant variables such as strength or resting lactate.

Texture analysis: a review of neurologic MR imaging applications

Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.

Contrast-enhanced MR imaging in acute ischemic stroke: T2* measures of blood-brain barrier permeability and their relationship to T1 estimates and hemorrhagic transformation

BACKGROUND AND PURPOSE

rtPA is an effective treatment for AIS, yet it is substantially underused due to the increased risk of HT. Recent work suggests that permeability-related information can be extracted from routine T2*-based perfusion images by measuring the rR of the contrast agent. Given that other T2*-based measures have recently been proposed, the purpose of this study was to evaluate 4 such permeability measures in identifying patients with AIS who will proceed to HT.

MATERIALS AND METHODS

Eighteen patients with AIS were examined within a mean of 3.3 +/- 1.4 hours postonset. Dynamic T2*-weighted imaging consisted of a single-shot EPI following a bolus of gadodiamide. HT was determined on follow-up CT or MR imaging at 24-72 hours. Mean values of rR, Peak Height, Recovery, as well as Slope were calculated and analyzed on the basis of follow-up HT status.

RESULTS

Eight patients proceeded to HT. The mean rR for patients with HT was significantly greater than that for patients without HT (0.22 +/- 0.06 versus 0.14 +/- 0.06, P = .006), while there was a trend toward decreased %Recovery in patients with HT (76 +/- 6 versus 82 +/- 11%, P = .092). There was a significant negative correlation between %Recovery and rR (r = -0.88, P < .001). No significant differences or trends were detected with respect to Peak Height or Slope.

CONCLUSIONS

Both rR and %Recovery can be readily extracted from a routine perfusion MR imaging dataset and show potential for identifying HT during the acute phase poststroke.

Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1-weighted MR images

PURPOSE

To test the hypothesis that texture analysis of postcontrast T1-weighted MR images will predict hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) with better accuracy than visual evidence of contrast-enhancement (VE).

MATERIALS AND METHODS

Thirty-four AIS patients were examined within 3.5 +/- 1.5 h after stroke. T1-weighted MR images were acquired 19 +/- 7 min postcontrast injection. HT was determined by follow-up imaging at 24-72 h. Postcontrast images were evaluated for VE. Four second-order textural features were extracted (f1, f2, f3, and f9) for each patient. Receiver operating characteristic (ROC) curves were constructed for VE and for textural features, with HT as the outcome measure.

RESULTS

The f2 for HT patients (n = 12) was significantly lower than in non-HT patients (1058 +/- 356 versus 1568 +/- 527; P = 0.005); the converse was true for f3 (0.67 +/- 0.12 versus 0.54 +/- 0.13; P = 0.007). ROC analysis indicated that the f2 and f3 textural features were the only two significant predictors of HT (P = 0.0018 and P = 0.0042). The addition of VE to either f2 or f3 did not result in a significant improvement in accuracy.

CONCLUSION

Texture analysis of postcontrast T1-weighted images may be superior to visual evidence of enhancement for the prediction of HT.

Quantitative permeability magnetic resonance imaging in acute ischemic stroke: how long do we need to scan?

Blood-brain barrier (BBB) permeability estimation with dynamic contrast-enhanced MRI (DCE-MRI) has shown significant potential for predicting hemorrhagic transformation (HT) in patients presenting with acute ischemic stroke (AIS). In this work, the effects of scan duration on quantitative BBB permeability estimates (KPS) were investigated. Data from eight patients (three with HT) aged 37-93 years old were retrospectively studied by directly calculating the standard deviation of KPS as a function of scan time. The uncertainty in KPS was reduced only slightly for a scan time of 3 min and 30 s (4% reduction in P value from .047 to .045). When more than 3 min and 30 s of data were used, quantitative permeability MRI was able to separate those patients who proceeded to HT from those who did not (P value <.05). Our findings indicate that reducing permeability acquisition times is feasible in keeping with the need to maintain time-efficient MR protocols in the setting of AIS.

Feasibility of the single-bolus strategy for measuring the partition coefficient of Gd-DTPA in patients with myocardial infarction: independence of image delay time and maturity of scar

The partition coefficient of Gd-DTPA (lambda) is elevated in infarcted relative to normal myocardium. Although MRI following an infusion of Gd-DTPA allows for the quantification of lambda, infarct imaging is more routinely performed using a bolus. In this study we sought to determine how image delay time and time postinfarction influence the estimation of lambda by the bolus strategy. Both infusion and bolus imaging were performed twice in the same group of patients (N = 9): once at 3-4 weeks and again 6 months after reperfusion therapy for myocardial infarction (MI). Bolus estimates of lambda were compared with those calculated after 60 min infusion, and comparisons were repeated at 6 months. The lambda of infarcted myocardium was significantly greater than that of normal tissue, irrespective of either the technique used or the time postinfarction (P < 0.0001, for each). The concordance (Rc) between bolus and infusion estimates of lambda was >0.83 for all image delays >4 min postinjection, and Rc at 2 min (0.78 +/- 0.04) was significantly less than Rc determined for longer image delay times (P = 0.009). Rc did not change with time postinfarction (P = 0.604). Thus, the bolus strategy can be used to provide estimates of lambda that are stable from 1-6 months postinfarction and independent of image delay time.

Determining the extent to which delayed-enhancement images reflect the partition-coefficient of Gd-DTPA in canine studies of reperfused and unreperfused myocardial infarction

MRI after a constant infusion (CI) of Gd-DTPA has been used to identify the extent of myocardial infarction (MI). However, Gd-DTPA-enhanced "viability" imaging is more commonly performed with a bolus (for "delayed-enhancement" (DE) imaging). This study sought to determine how image delay time and time postinfarction influence the assessment of necrosis by DE. Both infusion and DE imaging was performed in dogs with reperfused (N = 6) or unreperfused (N = 4) MI. Estimates of the partition-coefficient of Gd-DTPA (lambda) with DE were compared with those calculated after 60 min of infusion, and the comparisons were repeated until 4 (reperfused) or 8 (unreperfused) weeks postinfarction. In reperfused animals, the concordance (Rc) between DE and infusion estimates of lambda was > 0.90 for most image delays > 8 min postinjection, for day 0 through week 3, with Rc at day 0 greater than at week 4 (P = 0.022). In unreperfused animals, there was an interaction between image delay time and time postinfarction (P < 0.001): Rc > 0.90 corresponded to longer image delays at week 1 than at weeks 4-8. Therefore, when image delays are selected appropriately, DE images can strongly reflect lambda and identify irreversibly injured myocardium.

The assessment of myocardial viability: a review of current diagnostic imaging approaches

The management of patients with coronary artery disease, both in the post-infarction setting, and in patients with chronic advanced left ventricular (LV) dysfunction, is complicated by the presence of both reversibly damaged and infarcted myocardium. Although acute revascularization with thrombolytic therapy and percutaneous angioplasty have served to reduce the overall mortality from myocardial infarction, the ability to predict whether or not dysfunctional myocardium will recoverfollowing revascularization presents the clinician with a serious challenge. The success of revascularization, both on improvement of LV function, and short and long-term prognosis, depends on both the existence and extent of viable but dysfunctional myocardium present, as there is little to be gained from revascularizing a territory consisting exclusively of scar. There is a clear demand for procedures that can identify reversible asynergy prospectively and thus deliver the information that is needed for clinical decision-making. The objective of this review is to summarize the diagnostic tools that are currently availablefor the identification of reversible injury (ie., stunned or hibernating myocardium). The relative merits of echocardiography, nuclear medicine imaging, and magnetic resonance imaging are discussed in detail. Within the discussion of each modality, special attention is paid to the more recent innovations that have arisen to enhance the diagnostic and prognostic value of older approaches. Cost, availability, and local expertise will always affect the clinical popularity of a given diagnostic approach. However, the overriding conclusion that emerges from this review is that the future "techniques of choice" will be those that can reliably predict and quantify the extent of potential functional recovery.

Myocardial viability imaging using Gd-DTPA: physiological modeling of infarcted myocardium, and impact on injection strategy and imaging time

Results of simulations are shown which illustrate how the concentration-time curves of an extravascular extracellular (EVEC) contrast agent, such as Gd-DTPA, vary in myocardial tissue. The simulations show that the variable permeability of dead myocytes within a recent myocardial infarction will significantly alter delayed enhancement patterns following a bolus injection, invariably reducing the sensitivity of this technique for the detection of permanently damaged tissue. It is further predicted that if the bolus injection is followed by a suitably selected constant infusion, the infarct size and infarct volume of distribution may be more accurately determined, even though the degree of enhancement of an infarcted region (with normal flow) above normal tissue is slightly higher for the bolus technique within the first 30 min following the injection. The degree of enhancement of an infarcted region (with normal flow) above normal tissue was comparable between the two techniques at the point in the constant infusion at which the volume of contrast injected was the same as in the bolus case, i.e., at approximately 30 min after the bolus injection. The constant infusion approach became superior thereafter as overall tissue concentrations became greater in both normal and infarcted tissue, and these concentrations remained more stable with the constant infusion approach. Preliminary experimental results in a canine model of infarction/reperfusion illustrated a delayed wash-in of contrast agent in infarcted tissue, which may be explained by a physiological model in which dead myocytes in infarcted myocardium have non-infinite permeability.

Examining a canine model of stunned myocardium with Gd-DTPA-enhanced MRI

It has previously been shown that the distribution volume of Gd-DTPA (lambda) in infarcted, canine myocardium is higher than that of normal tissue. The purpose of this study was to determine whether stunned myocardium exhibits increased lambda. Stunning was produced in beagles by means of 30 min LAD occlusion followed by 3 weeks (n = 4) reperfusion. Gd-DTPA was infused at each imaging session and lambda determined in vivo using a saturation recovery turboFLASH sequence; cine imaging was used to assess ventricular wall thickening (%WT). (201)Tl uptake was used as an independent assessment of viability. %WT data confirmed that the brief insult caused prolonged, yet reversible, regional contractile dysfunction in each animal. %WT was not significantly different from baseline values by 3 weeks post-reflow. Normal (201)Tl uptake confirmed the absence of infarction. The lambda of stunned tissue (lambda = 0.381 +/- 0.030 ml/g) was not elevated above that of normal tissue (lambda = 0.398 +/- 0.027 ml/g, P = NS), at any time point studied, in vivo. These data suggest that an increase in lambda is a specific indicator of irreversible damage.

T(1) fast acquisition relaxation mapping (T(1)-FARM): optimized data acquisition

A theoretical procedure for estimating the precision of the T(1) Fast Acquisition Relaxation Mapping sequence as a function of a number of acquisition parameters has been validated by both simulations and experimental results. These results have clarified the selection of sequence parameters to give optimal accuracy and precision in the R(1)* measurements. There is excellent agreement between theory, simulation, and experiment except for flip angles greater than 9 degrees, at which point slice profile imperfections significantly degrade the precision of the technique. The experimental results indicate that over a range of T(1)s that would be seen in a bolus tracking experiment (25-1200 ms), T(1) Fast Acquisition Relaxation Mapping can be used to obtain 64 x 128 R(1)* maps at a rate of 1 map/s, with a precision of 10% or better.