To develop a magnetic resonance (MR) "fingerprinting" technique for quantitative abdominal imaging.
This HIPAA-compliant study had institutional review board approval, and informed consent was ...obtained from all subjects. To achieve accurate quantification in the presence of marked B0 and B1 field inhomogeneities, the MR fingerprinting framework was extended by using a two-dimensional fast imaging with steady-state free precession, or FISP, acquisition and a Bloch-Siegert B1 mapping method. The accuracy of the proposed technique was validated by using agarose phantoms. Quantitative measurements were performed in eight asymptomatic subjects and in six patients with 20 focal liver lesions. A two-tailed Student t test was used to compare the T1 and T2 results in metastatic adenocarcinoma with those in surrounding liver parenchyma and healthy subjects.
Phantom experiments showed good agreement with standard methods in T1 and T2 after B1 correction. In vivo studies demonstrated that quantitative T1, T2, and B1 maps can be acquired within a breath hold of approximately 19 seconds. T1 and T2 measurements were compatible with those in the literature. Representative values included the following: liver, 745 msec ± 65 (standard deviation) and 31 msec ± 6; renal medulla, 1702 msec ± 205 and 60 msec ± 21; renal cortex, 1314 msec ± 77 and 47 msec ± 10; spleen, 1232 msec ± 92 and 60 msec ± 19; skeletal muscle, 1100 msec ± 59 and 44 msec ± 9; and fat, 253 msec ± 42 and 77 msec ± 16, respectively. T1 and T2 in metastatic adenocarcinoma were 1673 msec ± 331 and 43 msec ± 13, respectively, significantly different from surrounding liver parenchyma relaxation times of 840 msec ± 113 and 28 msec ± 3 (P < .0001 and P < .01) and those in hepatic parenchyma in healthy volunteers (745 msec ± 65 and 31 msec ± 6, P < .0001 and P = .021, respectively).
A rapid technique for quantitative abdominal imaging was developed that allows simultaneous quantification of multiple tissue properties within one 19-second breath hold, with measurements comparable to those in published literature.
Purpose To develop and evaluate an examination consisting of magnetic resonance (MR) fingerprinting-based T1, T2, and standard apparent diffusion coefficient (ADC) mapping for multiparametric ...characterization of prostate disease. Materials and Methods This institutional review board-approved, HIPAA-compliant retrospective study of prospectively collected data included 140 patients suspected of having prostate cancer. T1 and T2 mapping was performed with fast imaging with steady-state precession-based MR fingerprinting with ADC mapping. Regions of interest were drawn by two independent readers in peripheral zone lesions and normal-appearing peripheral zone (NPZ) tissue identified on clinical images. T1, T2, and ADC were recorded for each region. Histopathologic correlation was based on systematic transrectal biopsy or cognitively targeted biopsy results, if available. Generalized estimating equations logistic regression was used to assess T1, T2, and ADC in the differentiation of (a) cancer versus NPZ, (b) cancer versus prostatitis, (c) prostatitis versus NPZ, and (d) high- or intermediate-grade tumors versus low-grade tumors. Analysis was performed for all lesions and repeated in a targeted biopsy subset. Discriminating ability was evaluated by using the area under the receiver operating characteristic curve (AUC). Results In this study, 109 lesions were analyzed, including 39 with cognitively targeted sampling. T1, T2, and ADC from cancer (mean, 1628 msec ± 344, 73 msec ± 27, and 0.773 × 10
mm
/sec ± 0.331, respectively) were significantly lower than those from NPZ (mean, 2247 msec ± 450, 169 msec ± 61, and 1.711 × 10
mm
/sec ± 0.269) (P < .0001 for each) and together produced the best separation between these groups (AUC = 0.99). ADC and T2 together produced the highest AUC of 0.83 for separating high- or intermediate-grade tumors from low-grade cancers. T1, T2, and ADC in prostatitis (mean, 1707 msec ± 377, 79 msec ± 37, and 0.911 × 10
mm
/sec ± 0.239) were significantly lower than those in NPZ (P < .0005 for each). Interreader agreement was excellent, with an intraclass correlation coefficient greater than 0.75 for both T1 and T2 measurements. Conclusion This study describes the development of a rapid MR fingerprinting- and diffusion-based examination for quantitative characterization of prostatic tissue.
RSNA, 2017 Online supplemental material is available for this article.
Mechanical thrombectomy is effective in acute ischemic stroke secondary to emergent large-vessel occlusion, but optimal efficacy is contingent on fast and complete recanalization. First-pass ...recanalization does not occur in the majority of patients. The authors undertook this study to determine if anatomical parameters of the intracranial vessels impact the likelihood of first-pass complete recanalization.
The authors retrospectively evaluated data obtained in 230 patients who underwent mechanical thrombectomy for acute ischemic stroke secondary to large-vessel occlusion at their institution from 2016 to 2018. Eighty-six patients were identified as having pure M1 occlusions, and 76 were included in the final analysis. The authors recorded and measured clinical and anatomical parameters and evaluated their relationships to the first-pass effect.
The first-pass effect was achieved in 46% of the patients. When a single device was employed, aspiration thrombectomy was more effective than stent retriever thrombectomy. A larger M1 diameter (p = 0.001), decreased vessel diameter tapering between the petrous segment of the internal carotid artery (ICA) and M1 (p < 0.001), and distal collateral grading (p = 0.044) were associated with first-pass recanalization. LASSO (least absolute shrinkage and selection operator) was used to generate a predictive model for recanalization using anatomical variables.
The authors demonstrated that a larger M1 vessel diameter, low rate of vessel diameter tapering along the course of the intracranial ICA, and distal collateral status are associated with first-pass recanalization for patients with M1 occlusions.
Background
Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining ...the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS).
Purpose
To evaluate the performance of MRI‐based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients.
Study Type
Retrospective.
Subjects Model
MRI/TRUS (transperineal grid ultrasound) fusion‐guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy.
Field Strength/Sequence
3T, T2‐weighted (T2w) and diffusion‐weighted (DW) MRI.
Assessment
A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T2w‐MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI‐negative–biopsy‐negative (Group 1, N = 15), MRI‐positive–biopsy‐positive (Group 2, N = 16), MRI‐negative–biopsy‐positive (Group 3, N = 10), and MRI‐positive–biopsy‐negative (Group 4, N = 15). In all, 308 radiomic features (First‐order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T2w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum‐redundancy–maximum‐relevance and used to construct three machine‐learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease.
Statistical Tests
Wilcoxon rank‐sum tests with P < 0.05 considered statistically significant.
Results
Seven T2w‐based (First‐order Statistics, Haralick, Laws, and Gabor) and three ADC‐based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences (P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone.
Data Conclusion
Radiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI‐TRUS prostate biopsies.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;48:818–828.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Purpose
To evaluate in a multi‐institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi‐parametric MRI (mpMRI) in the transition zone (TZ) ...differ from those in the peripheral zone (PZ).
Materials and Methods
3T mpMRI, including T2‐weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast‐enhanced MRI (DCE‐MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First‐order statistical, co‐occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE‐MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver‐operating characteristic curve (AUC). Classifier performance was compared with a zone‐ignorant classifier.
Results
Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per‐voxel basis, a PZ‐specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61–0.71) than a zone‐ignorant classifier trained to detect cancer throughout the entire prostate (P < 0.05). When classifiers were evaluated on MRI data from multiple institutions, statistically similar AUC values (P > 0.14) were obtained for all institutions.
Conclusion
A zone‐aware classifier significantly improves the accuracy of cancer detection in the PZ.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. MAGN. RESON. IMAGING 2017;46:184–193
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Objectives
To explore the associations between T1 and T2 magnetic resonance fingerprinting (MRF) measurements and corresponding tissue compartment ratios (TCRs) on whole mount histopathology of ...prostate cancer (PCa) and prostatitis.
Materials and methods
A retrospective, IRB-approved, HIPAA-compliant cohort consisting of 14 PCa patients who underwent 3 T multiparametric MRI along with T1 and T2 MRF maps prior to radical prostatectomy was used. Correspondences between whole mount specimens and MRI and MRF were manually established. Prostatitis, PCa, and normal peripheral zone (PZ) regions of interest (ROIs) on pathology were segmented for TCRs of epithelium, lumen, and stroma using two U-net deep learning models. Corresponding ROIs were mapped to T2-weighted MRI (T2w), apparent diffusion coefficient (ADC), and T1 and T2 MRF maps. Their correlations with TCRs were computed using Pearson’s correlation coefficient (
R
). Statistically significant differences in means were assessed using one-way ANOVA.
Results
Statistically significant differences (
p
< 0.01) in means of TCRs and T1 and T2 MRF were observed between PCa, prostatitis, and normal PZ. A negative correlation was observed between T1 and T2 MRF and epithelium (
R
= − 0.38, − 0.44,
p
< 0.05) of PCa. T1 MRF was correlated in opposite directions with stroma of PCa and prostatitis (
R
= 0.35, − 0.44,
p
< 0.05). T2 MRF was positively correlated with lumen of PCa and prostatitis (
R
= 0.57, 0.46,
p
< 0.01). Mean T2 MRF showed significant differences (
p
< 0.01) between PCa and prostatitis across both transition zone (TZ) and PZ, while mean T1 MRF was significant (
p
= 0.02) in TZ.
Conclusion
Significant associations between MRF (T1 in the TZ and T2 in the PZ) and tissue compartments on corresponding histopathology were observed.
Key Points
•
Mean T2 MRF measurements and ADC within cancerous regions of interest dropped with increasing ISUP prognostic groups (IPG).
•
Mean T1 and T2 MRF measurements were significantly different (p < 0.001) across IPGs, prostatitis, and normal peripheral zone (NPZ).
•
T2 MRF showed stronger correlations in the peripheral zone, while T1 MRF showed stronger correlations in the transition zone with histopathology for prostate cancer.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Multi-modal image co-registration via optimizing mutual information (MI) is based on the assumption that intensity distributions of multi-modal images follow a consistent relationship. However, ...images with a substantial difference in appearance violate this assumption, thus MI directly based on image intensity alone may be inadequate to drive similarity based co-registration. To address this issue, we introduce a novel approach for multi-modal co-registration called Multi-scale Spectral Embedding Registration (MSERg). MSERg involves the construction of multi-scale spectral embedding (SE) representations from multimodal images via texture feature extraction, scale selection, independent component analysis (ICA) and SE to create orthogonal representations that decrease the dissimilarity between the fixed and moving images to facilitate better co-registration. To validate the MSERg method, we aligned 45 pairs of in vivo prostate MRI and corresponding ex vivo histopathology images. The dataset was split into a learning set and a testing set. In the learning set, length scales of 5 × 5, 7 × 7 and 17 × 17 were selected. In the independent testing set, we compared MSERg with intensity-based registration, multi-attribute combined mutual information (MACMI) registration and scale-invariant feature transform (SIFT) flow registration. Our results suggest that multi-scale SE representations generated by MSERg are found to be more appropriate for radiology-pathology co-registration.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or ...the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification.
: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D'Amico Risk Classification System.
: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (
= 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1,
116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2,
115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores.
: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3-6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set.
: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Perfusion properties can be estimated from pharmacokinetic models applied to DCE-MRI data using curve fitting algorithms; however, these suffer from drawbacks including the local minimum problem and ...substantial computational time. Here, a dictionary matching approach is proposed as an alternative. Curve fitting and dictionary matching were applied to simulated data using the dual-input single-compartment model with known perfusion property values and 5 in vivo DCE-MRI datasets. In simulation at SNR 60 dB, the dictionary estimate had a mean percent error of 0.4-1.0% for arterial fraction, 0.5-1.4% for distribution volume, and 0.0% for mean transit time. The curve fitting estimate had a mean percent error of 1.1-2.1% for arterial fraction, 0.5-1.3% for distribution volume, and 0.2-1.8% for mean transit time. In vivo, dictionary matching and curve fitting showed no statistically significant differences in any of the perfusion property measurements in any of the 10 ROIs between the methods. In vivo, the dictionary method performed over 140-fold faster than curve fitting, obtaining whole volume perfusion maps in just over 10 s. This study establishes the feasibility of using a dictionary matching approach as a new and faster way of estimating perfusion properties from pharmacokinetic models in DCE-MRI.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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