Biexponential characterization of prostate tissue water diffusion decay curves over an extended b-factor range

Improved diagnostic accuracy of readout-segmented echo-planar imaging for peripheral zone clinically significant prostate cancer: a retrospective 3T MRI study

This study compares the readout-segmented echo-planar imaging (rsEPI) from the conventional single-shot EPI (ssEPI) diffusion-weighted imaging (DWI) for the discrimination of patients with clinically significant prostate cancer (csPCa) within the peripheral zone (PZ) using apparent diffusion coefficient (ADC) maps and pathology report from magnetic resonance imaging (MRI)-targeted biopsy. We queried a retrospective monocentric database of patients with targeted biopsy. csPCa patients were defined as an International Society of Urological Pathology grade group ≥ 2. Group-level analyses and diagnostic accuracy of mean ADC values (ADCmean) within the tumor volume were assessed from Kruskal-Wallis tests and receiving operating characteristic curves, respectively. Areas under the curve (AUC) and optimal cut-off values were calculated. 159 patients (105 rsEPI, 54 ssEPI; mean age ± standard deviation: 65 ± 8 years) with 3T DWI, PZ lesions and targeted biopsy were selected. Both DWI sequences showed significantly lower ADCmean values for patients with csPCa. The rsEPI sequence better discriminates patients with csPCa (AUCrsEPI = 0.84, AUCssEPI = 0.68, p < 0.05) with an optimal cut-off value of 1232 μm2/s associated with a sensitivity-specificity of 97%-63%. Our study showed that the rsEPI DWI sequence enhances the discrimination of patients with csPCa.

Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment

Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.

Diagnostic Usefulness of Diffusion-Weighted MRI for Axillary Lymph Node Evaluation in Patients with Breast Cancer

This study aimed to determine whether apparent diffusion coefficient (ADC) and morphological features on diffusion-weighted MRI (DW-MRI) can discriminate metastatic axillary lymph nodes (ALNs) from benign in patients with breast cancer. Two radiologists measured ADC, long and short diameters, long-to-short diameter ratio, and cortical thickness and assessed eccentric cortical thickening, loss of fatty hilum, irregular margin, asymmetry in shape or number, and rim sign of ALNs on DW-MRI and categorized them into benign or suspicious ALNs. Pathologic reports were used as a reference standard. Statistical analysis was performed using the Mann-Whitney U test and chi-square test. Overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of DW-MRI were calculated. The ADC of metastatic ALNs was 0.905 × 10^-3 mm²/s, and that of benign ALNs was 0.991 × 10^-3 mm²/s (p = 0.243). All morphologic features showed significant difference between the two groups. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the final categorization on DW-MRI were 77.1%, 93.3%, 79.4%, 92.5%, and 86.2%, respectively. Our results suggest that morphologic evaluation of ALNs on DWI can discriminate metastatic ALNs from benign. The ADC value of metastatic ALNs was lower than that of benign nodes, but the difference was not statistically significant.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

Prostate Cancer Diffusion-Weighted Magnetic Resonance Imaging: Does the Choice of Diffusion-Weighting Level Matter?

BACKGROUND

Diffusion-weighted magnetic resonance imaging plays an important role in multiparametric assessment of prostate lesions. The derived apparent diffusion coefficient (ADC) could be a useful quantitative biomarker for malignant growth, but lacks acceptance because of low reproducibility.

PURPOSE

To investigate the impact of the choice of diffusion-weighting levels (b-values) on contrast-to-noise ratio and quantitative measures in prostate diffusion-weighted MRI.

STUDY TYPE

Retrospective and simulation based on published data.

SUBJECTS

Patient cohort (21 men with Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score ≥3) from a single-center study.

FIELD STRENGTH/SEQUENCE

3 T/diffusion-weighted imaging with single-shot echo-planar imaging.

ASSESSMENT

Both clinical data and simulations based on previously acquired data were used to quantify the influence of b-value choice in normal peripheral zone (PZ) and PZ tumor lesions. For clinical data, ADC was determined for different combinations of b-values. Contrast-to-noise ratio and quantitative diffusion measures were simulated for a wide range of b-values.

STATISTICAL TESTS

Tissue ADC and the lesion-to-normal tissue ADC ratios of different b-value combinations were compared with paired two-tailed Student's t-tests. A P-value <0.05 was considered statistically significant.

RESULTS

Findings about b-value dependence derived from clinical data and from simulations agreed with each other. Provided measurement was limited to two b-values, simulation-derived optimal b-value choices coincided with PI-RADSv2 recommendations. For two-point measurements, ADC decreased by 15% when the maximum b-value increased from 1000 to 1500 seconds/mm² , but corresponding lesion-to-normal tissue ADC ratio showed no significant change (P = 0.86 for acquired data). Simulations with three or more measurement points produced ADCs that declined by only 8% over this range of maximum b-value. Corresponding ADC ratios declined between 2.6% (three points) and 3.8% (21 points). Simulations also revealed an ADC reduction of about 19% with the shorter echo and diffusion time evaluated.

DATA CONCLUSION

The comprehensive assessment of b-value dependence permits better formulation of protocol and analysis recommendations for obtaining reproducible results in prostate cancer diffusion-weighted MRI.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Optimized bias and signal inference in diffusion-weighted image analysis (OBSIDIAN)

PURPOSE

Correction of Rician signal bias in magnitude MR images.

METHODS

A model-based, iterative fitting procedure is used to simultaneously estimate true signal and underlying Gaussian noise with standard deviation σ g on a pixel-by-pixel basis in magnitude MR images. A precomputed function that relates absolute residuals between measured signals and model fit to σ g is used to iteratively estimate σ g . The feasibility of the method is evaluated and compared to maximum likelihood estimation (MLE) for diffusion signal decay simulations and diffusion-weighted images of the prostate considering 21 linearly spaced b-values from 0 to 3000 s/mm² . A multidirectional analysis was performed with publically available brain data.

RESULTS

Model simulations show that the Rician bias correction algorithm is fast, with an accuracy and precision that is on par to model-based MLE and direct fitting in the case of pure Gaussian noise. Increased accuracy in parameter prediction in a low signal-to-noise ratio (SNR) scenario is ideally achieved by using a composite of multiple signal decays from neighboring voxels as input for the algorithm. For patient data, good agreement with high SNR reference data of diffusion in prostate is achieved.

CONCLUSIONS

OBSIDIAN is a novel, alternative, simple to implement approach for rapid Rician bias correction applicable in any case where differences between true signal decay and underlying model function can be considered negligible in comparison to noise. The proposed composite fitting approach permits accurate parameter estimation even in typical clinical scenarios with low SNR, which significantly simplifies comparison of complex diffusion parameters among studies.

Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate

Background

Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment.

Purpose

To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI).

Material and methods

62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm²) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SF_slow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models.

Results

All three models showed a significant difference (p<0.0001) between peripheral zone (PZ) tumor and normal tissue ROIs, but not between non-PZ tumor and BPH ROIs. The area under the receiver operating characteristics curve distinguishing tumor from prostate voxels was 0.956, 0.949 and 0.949 for the two-component, bi-exponential and mono-exponential models, respectively. The corresponding Spearman correlation coefficients between tumor values and Gleason Grade Group were fair (0.370, 0.499 and -0.490), but not significant.

Conclusion

Signal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models.

Detecting microstructural white matter abnormalities of frontal pathways in children with ADHD using advanced diffusion models

Studies using diffusion tensor imaging (DTI) have documented alterations in the attention and executive system in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). While abnormalities in the frontal lobe have also been reported, the associated white matter fiber bundles have not been investigated comprehensively due to the complexity in tracing them through fiber crossings. Furthermore, most studies have used a non-specific DTI model to understand white matter abnormalities. We present results from a first study that uses a multi-shell diffusion MRI (dMRI) data set coupled with an advanced multi-fiber tractography algorithm to probe microstructural measures related to axonal/cellular density and volume of fronto-striato-thalamic pathways in children with ADHD (N = 30) and healthy controls (N = 28). Head motion was firstly examined as a priority in order to assure that no group difference existed. We investigated 45 different white matter fiber bundles in the brain. After correcting for multiple comparisons, we found lower axonal/cellular packing density and volume in ADHD children in 8 of the 45 fiber bundles, primarily in the right hemisphere as follows: 1) Superior longitudinal fasciculus-II (SLF-II) (right), 2) Thalamus to precentral gyrus (right), 3) Thalamus to superior-frontal gyrus (right), 4) Caudate to medial orbitofrontal gyrus (right), 5) Caudate to precentral gyrus (right), 6) Thalamus to paracentral gyrus (left), 7) Caudate to caudal middlefrontal gyrus (left), and 8) Cingulum (bilateral). Our results demonstrate reduced axonal/cellular density and volume in certain frontal lobe white matter fiber tracts, which sub-serve the attention function and executive control systems. Further, our work shows specific microstructural abnormalities in the striato-thalamo-cortical connections, which have not been previously reported in children with ADHD.

Accelerated Segmented Diffusion-Weighted Prostate Imaging for Higher Resolution, Higher Geometric Fidelity, and Multi-b Perfusion Estimation

PURPOSE

The aim of this study was to improve the geometric fidelity and spatial resolution of multi-b diffusion-weighted magnetic resonance imaging of the prostate.

MATERIALS AND METHODS

An accelerated segmented diffusion imaging sequence was developed and evaluated in 25 patients undergoing multiparametric magnetic resonance imaging examinations of the prostate. A reduced field of view was acquired using an endorectal coil. The number of sampled diffusion weightings, or b-factors, was increased to allow estimation of tissue perfusion based on the intravoxel incoherent motion (IVIM) model. Apparent diffusion coefficients measured with the proposed segmented method were compared with those obtained with conventional single-shot echo-planar imaging (EPI).

RESULTS

Compared with single-shot EPI, the segmented method resulted in faster acquisition with 2-fold improvement in spatial resolution and a greater than 3-fold improvement in geometric fidelity. Apparent diffusion coefficient values measured with the novel sequence demonstrated excellent agreement with those obtained from the conventional scan (R = 0.91 for bmax = 500 s/mm and R = 0.89 for bmax = 1400 s/mm). The IVIM perfusion fraction was 4.0% ± 2.7% for normal peripheral zone, 6.6% ± 3.6% for normal transition zone, and 4.4% ± 2.9% for suspected tumor lesions.

CONCLUSIONS

The proposed accelerated segmented prostate diffusion imaging sequence achieved improvements in both spatial resolution and geometric fidelity, along with concurrent quantification of IVIM perfusion.

Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I² statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization

Purpose

To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T₂-weighted imaging (T₂w), diffusion weighted imaging (DWI) acquired using high b values, and T₂-mapping (T₂).

Methods

T₂w, DWI (12 b values, 0–2000 s/mm²), and T₂ data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T₂w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS.

Results

In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T₂w, ADC_m and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T₂ mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments.

Conclusion

Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T₂w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.

Relationship between kurtosis and bi-exponential characterization of high b-value diffusion-weighted imaging: application to prostate cancer

BACKGROUND

High b-value diffusion-weighted imaging has application in the detection of cancerous tissue across multiple body sites. Diffusional kurtosis and bi-exponential modeling are two popular model-based techniques, whose performance in relation to each other has yet to be fully explored.

PURPOSE

To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions.

MATERIAL AND METHODS

This retrospective study analyzed patients with normal prostate tissue (n = 12) or suspicious lesions (n = 13, one lesion per patient), as determined by a radiologist whose clinical care included a high b-value diffusion series. The observed signal intensity was modeled using a bi-exponential decay, from which the signal fraction of the slow-moving component was derived ( SFs). In addition, the excess kurtosis was calculated using the signal fractions and ADCs of the two exponentials ( KCOMP). As a comparison, the kurtosis was also calculated using the cumulant expansion for the diffusion signal ( KCE).

RESULTS

Both K and KCE were found to increase with SFs within the range of SFs commonly found within the prostate. Voxel-wise receiver operating characteristic performance of SFs, KCE, and KCOMP in discriminating between suspicious lesions and normal prostate tissue was 0.86 (95% confidence interval [CI] = 0.85 - 0.87), 0.69 (95% CI = 0.68-0.70), and 0.86 (95% CI = 0.86-0.87), respectively.

CONCLUSION

In a two-component diffusion environment, KCOMP is a scaled value of SFs and is thus able to discriminate suspicious lesions with equal precision . KCE provides a computationally inexpensive approximation of kurtosis but does not provide the same discriminatory abilities as SFs and KCOMP.

Diffusion-Weighted Magnetic Resonance Imaging in Liver Graft Rejection

OBJECTIVE

The purpose of this study is to evaluate the use of diffusion-weighted magnetic resonance imaging (DWMRI) in the assessment of graft rejection after liver transplantation (LT).

METHODS

From June 2017 to January 2018, 32 patients were included in the study with a mean age of 52.3 years. All patients underwent LT. The DWMRI was performed using the apparent diffusion coefficient map and measuring the different b-values (b-400, b-600, b-800, and b-1000). These measurements were compared with the histopathology results. Statistical analysis included t test, analysis of variance, and area under the curve for receiver operating characteristic (ROC).

RESULTS

There were 17 patients without rejection and 15 patients with liver graft rejection diagnosed by histopathology. The mean (SD) results between the nonrejection and rejection groups were as follows: b-400 = 1.568 (0.265) vs 1.519 (0.119) (P = .089), b-600 = 1.380 (0.181) vs 1.284 (0.106) (P = .039), b-800 = 1.262 (0.170) vs 1.170 (0.086) (P = .035), b-1000 = 1.109 (0.129) vs 1.098 (0.078) (P = .095); B-values × 10^-3 mm²/s. Only b-600 (P = .04) and b-800 (P = .04) values have significant differences between the 2 groups. B-600 showed 90.48% sensitivity and 83.33% specificity (ROC area under the curve = 0.784; P < .001), and b-800 showed 90.38% sensitivity and 83.03% specificity (ROC area under the curve = 0.816; P < .001). The values obtained with the apparent diffusion coefficient in b-800 were clearly differentiated between the mild, moderate, and severe degrees of rejection (P < .001).

CONCLUSION

Measurement of b-600 and b-800 values using DWMRI may be used for the diagnosis of graft rejection after LT.

Diffusion kurtosis imaging in the assessment of liver function: Its potential as an effective predictor of liver function

OBJECTIVES:

We aimed to determine whether diffusion kurtosis imaging (DKI) analysis with the breath-hold technique can replace liver function results obtained from laboratory tests.

METHODS:

Patients (n = 79) suspected of having a hepatobiliary disease, and control group without liver diseases (n = 15) were examined with non-Gaussian diffusion-weighted imaging using a 3.0 T magnetic resonance imaging unit. Based on the findings of DKI, various blood serum parameters, including the indocyanine green (ICG) retention rate 15 min after an intravenous injection of ICG (ICG-R15) and mean kurtosis values and Child-Pugh and albumin-bilirubin (ALBI) scores, were calculated. In total, 17 patients were tested using ICG-R15. For evaluating liver function, correlations between the mean kurtosis value and the Child-Pugh score, ALBI score, and ICG-R15 value as indicators of liver function obtained from blood data were assessed using Spearman's rank correlation. In apparent diffusion coefficient as well, we assessed correlations with these indicators.

RESULTS:

The mean kurtosis value correlated with the Child-Pugh score (Spearman's rank-correlation coefficient, ρ = 0.3992; p < 0.0001). Moreover, the mean kurtosis value revealed a correlation with the ICG-R15 value (Spearman's rank-correlation coefficient, ρ = 0.5972; p = 0.00114). The correlation between the mean kurtosis value and the ALBI score was the poorest among these (Spearman's rank-correlation coefficient, ρ = 0.3395; p = 0.0008).

CONCLUSION:

Liver function correlating with the Child-Pugh score and ICG-R15 value can be quantitatively estimated using the mean kurtosis value obtained from DKI analysis. DKI analysis with the breath-hold technique can be used to determine liver function instead of performing laboratory tests.

ADVANCES IN KNOWLEDGE:

Previous studies have not evaluated liver function in vivo using DKI.

Comparison of quantitative apparent diffusion coefficient parameters with prostate imaging reporting and data system V2 assessment for detection of clinically significant peripheral zone prostate cancer

PURPOSE

To compare diagnostic performance of PI-RADSv2 with ADC parameters to identify clinically significant prostate cancer (csPC) and to determine the impact of csPC definitions on diagnostic performance of ADC and PI-RADSv2.

METHODS

We retrospectively identified treatment-naïve pathology-proven peripheral zone PC patients who underwent 3T prostate MRI, using high b-value diffusion-weighted imaging from 2011 to 2015. Using 3D slicer, areas of suspected tumor (T) and normal tissue (N) on ADC (b = 0, 1400) were outlined volumetrically. Mean ADC_T, mean ADC_N, ADC_ratio (ADC_T/ADC_N) were calculated. PI-RADSv2 was assigned. Three csPC definitions were used: (A) Gleason score (GS) ≥ 4 + 3; (B) GS ≥ 3 + 4; (C) MRI-based tumor volume >0.5 cc. Performances of ADC parameters and PI-RADSv2 in identifying csPC were measured using nonparametric comparison of receiver operating characteristic curves using the area under the curve (AUC).

RESULTS

Eighty five cases met eligibility requirements. Diagnostic performances (AUC) in identifying csPC using three definitions were: (A) ADC_T (0.83) was higher than PI-RADSv2 (0.65, p = 0.006); (B) ADC_T (0.86) was higher than ADC_ratio (0.68, p < 0.001), and PI-RADSv2 (0.70, p = 0.04); (C) PI-RADSv2 (0.73) performed better than ADC_ratio (0.56, p = 0.02). ADC_T performance was higher when csPC was defined by A or B versus C (p = 0.038 and p = 0.01, respectively). ADC_ratio performed better when csPC was defined by A versus C (p = 0.01). PI-RADSv2 performance was not affected by csPC definition.

CONCLUSIONS

When csPC was defined by GS, ADC parameters provided better csPC discrimination than PI-RADSv2, with ADC_T providing best result. When csPC was defined by MRI-calculated volume, PI-RADSv2 provided better discrimination than ADC_ratio. csPC definition did not affect PI-RADSv2 diagnostic performance.

Computed diffusion weighted imaging (cDWI) and voxelwise-computed diffusion weighted imaging (vcDWI) for oncologic liver imaging: A pilot study

Objective

Aim of the study was to evaluate the influence of the selection of measured b-values on the precision of cDWI in the upper abdomen as well as on the lesion contrast of PET-positive liver metastases in cDWI and vcDWI.

Methods

We performed a retrospective analysis of 10 patients (4 m, 63.5 ± 12.9 y/o) with PET-positive liver metastases examined in 3 T-PET/MRI with b = 100,600,800,1000 and 1500s/mm². cDWI (cb1000/cb1500) and vcDWI were computed based on following combinations: i) b = 100/600 s/mm², ii) b = 100/800 s/mm², iii) b = 100/1000s/mm², iv) b = 100/600/1000s/mm² v) all measured b-values. Mean signal intensity (SI) and standard deviation (SD) in the liver, spleen, kidney, bone marrow and in liver lesions were acquired. The coefficient of variation (CV = SD/SI), the differences of SI between measured and calculated high b-value images and the lesion contrast (SI lesion/liver) were computed.

Results

With increasing upper measured b-values, the CV in cDWI and vcDWI decreased (CV in the liver in cb1500: 0.42 with b100/600 s/mm² and 0.28 with b100/b1000s/mm²) while the differences of measured and calculated b-value images decreased (in the liver in cb1500: 30.7% with b = 100/600 s/mm², 19.7% with b100/b1000s/mm²). In diffusion-restricted lesions, lesion contrast was at least 1.6 in cb1000 and 1.4 in cb1500, respectively, with an upper measured b-value of b = 800 s/mm² and 2.1 for vcDWI with an upper measured b-value of b = 1000s/mm². Overall, the lesion contrast was superior in cb1500 and vcDWI compared to cb1000 (15% and 11%, respectively).

Conclusion

Measuring higher upper b-values seems to lead to more precise computed high b-value images and a decrease of CV. vcDWI provides a comparable lesion contrast to b = 1500s/mm² and offers additionally the reduction of T2 shine-through effects. For vcDWI, measuring b = 1000s/mm² as upper b-value seems to be necessary to guarantee good lesion visibility in the liver based on our preliminary results.

Diffusion kurtosis imaging with the breath-hold technique for staging hepatic fibrosis: A preliminary study

PURPOSE

To evaluate the potential of diffusion kurtosis imaging (DKI) analysis with the breath-hold technique to assess the stage or classify hepatic fibrosis.

MATERIALS AND METHODS

Patients (n=67) suspected of having a disease of the hepatobiliary system examined by diffusion-weighted imaging (DWI) using a 3.0-T magnetic resonance imaging unit were enrolled in this study. To evaluate hepatic fibrosis, mean kurtosis, Mean apparent diffusion (MD) and apparent diffusion coefficient (ADC) values were compared between groups with varying fibrosis; F0-F1, F2-F3, and F4. The Steel-Dwass test was used for overall comparisons. Correlations between the fibrosis stage and mean kurtosis, MD or ADC values were assessed using Spearman's rank correlation. Discriminative capacities of DKI were evaluated using receiver operating characteristic (ROC) analysis.

RESULTS

There were significant differences in ADC, MD and mean kurtosis values between non-cirrhosis and cirrhosis groups. Moreover, the mean kurtosis value was statistically different between the F0-F1 and F2-F3, F0-F1 and F4, and F2-F3 and F4 groups (all P<0.05). MD value was statistically different between the F0-F1 and F4 groups, and F2-F3 and F4 groups (all P<0.05). However, there was no significant difference in ADC values for all groups (all P>0.05). In addition, mean kurtosis and MD values significantly correlated with the extent of hepatic fibrosis staging (Spearman's rank correlation coefficient, ρ=0.851 and -0.672; P<0.0001). However, ADC values did not reveal a correlation with the extent of hepatic fibrosis staging (ρ=-0.227; P=0.078). According to the ROC analysis for the assessment of no fibrosis (F0), fibrosis (≥F1), and advanced fibrosis (≥F2) and liver cirrhosis, the DKI cut-off values were 0.923, 0.955, and 1.11, respectively.

CONCLUSION

Using the DKI method with the breath-hold technique in the liver, the stage of hepatic fibrosis can be classified into normal and early hepatic fibrosis, substantial stages, and advanced hepatic fibrosis.

Model selection for high b-value diffusion-weighted MRI of the prostate

PURPOSE

To assess the abilities of the standard mono-exponential (ME), bi-exponential (BE), diffusion kurtosis (DK) and stretched exponential (SE) models to characterize diffusion signal in malignant and prostatic tissues and determine which of the four models best characterizes these tissues on a per-voxel basis.

MATERIALS AND METHODS

This institutional-review-board-approved, HIPAA-compliant, retrospective study included 55 patients (median age, 61years; range, 42-77years) with untreated, biopsy-proven PCa who underwent endorectal coil MRI at 3-Tesla, diffusion-weighted MRI acquired at eight b-values from 0 to 2000s/mm². Estimated parameters were apparent diffusion coefficent (ME model); diffusion coefficients for the fast (D_fast) and slow (D_slow) components and fraction of fast component, f_fast (BE model); diffusion coefficient D, and kurtosis K (DK model); distributed diffusion coefficient DDC and α for (SE model). For one region-of-interest (ROI) in PZ and another in PCa in each patient, the corrected Akaike information criterion (AICc) and the Akaike weight (w) were calculated for each voxel.

RESULTS

Based on AICc and w, all non-monoexponential models outperformed the ME model in PZ and PCa. The DK model in PZ and SE model in PCa ROIs best fit the greatest average percentages of voxels (39% and 43%, respectively) and had the highest mean w (35±16×10^-2 and 41±22×10^-2, respectively).

CONCLUSION

DK and SE models best fit DWI data in PZ and PCa, and non-ME models consistently outperformed the ME model. Voxel-wise mapping of the preferential model demonstrated that the vast majority of voxels in either tissue type were best fit with one of the non-monoexponential models. At the given SNR levels, the maximum b-value of 2000s/mm² is not sufficiently high to identify the preferred non-monoexponential model.

Initial experience of correlating diffusion spectral parameters with histopathologic indexes in murine colorectal tumor homografts

Purpose

To determine the correlation between continuously distributed diffusion-weighted image (DWI)-derived parameters and histopathologic indexes.

Methods

Fifty-four mice bearing HCT-116 colorectal tumors were included for analysis; 12 mice were used for continuous observation, and the other 42 mice were used for break-point observation. All mice were randomly divided into radiotherapy and non-radiotherapy groups. Optical imaging and MRI were performed at different time points according to radiotherapy regimen (baseline, 24 h, 48 h, 72 h, 7 d, 14 d, and 28 d). Continuous observation data were analyzed to show the difference of dynamic changing trends of optical and MR-DWI–derived parameters between radiotherapy and non-radiotherapy groups (photon numbers, D_max, full width half maximum [FWHM], and apparent diffusion coefficient [ADC] value). Break-point observation data were used to analyze the correlation between histopathologic indices and DWI-derived parameters.

Results

There was a significant difference in the changing trends of photon numbers, D_max, FWHM, and ADC value between radiotherapy and non-radiotherapy groups, especially at early time points. There was moderate negative correlation between Ki67 and percentage changes of D_max, FWHM, and ADC values (the correlation coefficients were 0.632, 0.449, and 0.586, P<0.001, P=0.008, and P<0.001, respectively). There was moderate negative correlation between survivin and percentage changes of D_max and ADC values (correlation coefficients were 0.496 and 0.473, P=0.004 and P=0.006, respectively).

Conclusion

The continuously distributed DWI-derived parameters could reflect histological behavior to some extent and, thus, are potential markers for early noninvasive monitoring of tumor cell apoptosis and proliferation.

Evaluation of fitting models for prostate tissue characterization using extended-range b-factor diffusion-weighted imaging

PURPOSE

To compare the fitting and tissue discrimination performance of biexponential, kurtosis, stretched exponential, and gamma distribution models for high b-factor diffusion-weighted images in prostate cancer.

METHODS

Diffusion-weighted images with 15 b-factors ranging from b = 0 to 3500 s/mm² were obtained in 62 prostate cancer patients. Pixel-wise signal decay fits for each model were evaluated with the Akaike Information Criterion (AIC). Parameter values for each model were determined within normal prostate and the index lesion. Their potential to differentiate normal from cancerous tissue was investigated through receiver operating characteristic analysis and comparison with Gleason score.

RESULTS

The biexponential slow diffusion fraction f_slow , the apparent kurtosis diffusion coefficient ADC_K , and the excess kurtosis factor K differ significantly among normal peripheral zone (PZ), normal transition zone (TZ), tumor PZ, and tumor TZ. Biexponential and gamma distribution models result in the lowest AIC, indicating a superior fit. Maximum areas under the curve (AUCs) of all models ranged from 0.93 to 0.96 for the PZ and from 0.95 to 0.97 for the TZ. Similar AUCs also result from the apparent diffusion coefficient (ADC) of a monoexponential fit to a b-factor sub-range up to 1250 s/mm² . For kurtosis and stretched exponential models, single parameters yield the highest AUCs, whereas for the biexponential and gamma distribution models, linear combinations of parameters produce the highest AUCs. Parameters with high AUC show a trend in differentiating low from high Gleason score, whereas parameters with low AUC show no such ability.

CONCLUSION

All models, including a monoexponential fit to a lower-b sub-range, achieve similar AUCs for discrimination of normal and cancer tissue. The biexponential model, which is favored statistically, also appears to provide insight into disease-related microstructural changes. Magn Reson Med 79:2346-2358, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Ultrahigh b-values MRI in normal human prostate: Initial research on reproducibility and age-related differences

PURPOSE

To investigate the reproducibility of diffusion-weighted imaging (DWI) with ultrahigh b-values, and analyze the age-related differences in normal prostates.

MATERIALS AND METHODS

In all, 67 healthy participants were divided into three age groups (group A, 15-30 years; group B, 31-50 years; group C, ≥51 years), and underwent DWI scanning twice with 15 b-factors from 0 to 3000 at 3.0T. Triexponential fits were applied to calculate the molecular diffusion coefficient (D), the pseudo-diffusion coefficient (D*), the ultrahigh apparent diffusion coefficient (ADC_uh ), and perfusion fraction (f). The interobserver and short-term interscan reproducibility were evaluated, and the change in these parameters with age were assessed.

RESULTS

The D, ADC_uh , and f values presented good to excellent reproducibility. With increasing age, a trend of increasing D values was observed, with significant difference in both peripheral zone (PZ, P = 0.01) and central gland (CG, P = 0.01) of normal prostate tissue. The f value increased in the CG beginning at 50 years of age while the ADC_uh value decreased in the PZ after 50 years of age; all of them showed significant differences between groups A and C and groups B and C (P = 0.01/0.01).

CONCLUSION

The D, ADC_uh , and f values have good to excellent reproducibility in the normal prostate, and these values change with age. The ultrahigh b-values magnetic resonance imaging (MRI) can provide additional information (ADC_uh ), which is different from the IVIM (intravoxel incoherent motion)-derived parameters.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:801-812.

Intravoxel Incoherent Motion (IVIM) Diffusion Weighted Imaging (DWI) in the Periferic Prostate Cancer Detection and Stratification

The aim of this study was to compare the Intravoxel Incoherent Motion (IVIM) parameters between healthy Peripheral Zone (PZ), Benign Prostatic Hyperplasia (BPH) and Prostate Cancer (PCa) and compare them to assess whether there was correlation with Gleason Score (GS) grading system. Thirty-one patients with suspect of PCa underwent 1.5T Multi-Parametric Magnetic Resonance Imaging (MP-MRI) with endorectal coil with a protocol including T2WI, DWI using 10 b values (0, 10, 20, 30, 50, 80, 100, 200, 400, 1000 s/mm²) and DCE. Monoexponential and IVIM model fits were used to calculate both apparent diffusion coefficient (ADC) and the following IVIM parameters: molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*) and perfusion fraction (f). The ADC and D values were significantly lower in the PCa (0.70 ± 0.16 × 10^-3 mm²/s and 0.88 ± 0.31 × 10^-3 mm²/s) compared to those found in the PZ (1.22 ± 0.20 × 10^-3 mm²/s and 1.78 ± 0.34 × 10^-3 mm²/s) and in the BPH (1.53 ± 0.23 × 10^-3 mm²/s and 1.11 ± 0.28 × 10^-3 mm²/s). The D* parameter was significantly increased in the PCa (5.35 ± 5.12 × 10^-3 mm²/s) compare to the healthy PZ (3.02 ± 2.86 × 10^-3 mm²/s), instead there was not significantly difference in the PCa compare to the BPH (5.61 ± 6.77 × 10^-3 mm²/s). The f was statistically lower in the PCa (9.01 ± 5.20%) compared to PZ (10.57 ± 9.30%), but not significantly different between PCa and BPH (9.29 ± 7.29%). The specificity, sensitivity and accuracy of T2WI associated with DWI and IVIM were higher (100, 98 and 99%, respectively) than for T2WI/DWI and IVIM alone (89, 92 and 90%, respectively). Only for ADC was found a statistical difference between low- and intermediate-/high-grade tumors. Adding IVIM to the MP-MRI could increase the diagnostic performance to detect clinically relevant PCa. ADC values have been found to have a rule to discriminate PCa reliably from normal areas and differed significantly in low- and intermediate-/high-grade PCa. In contrast, IVIM parameters were unable to distinguish between the different GS.

Precise Inference and Characterization of Structural Organization (PICASO) of tissue from molecular diffusion

Inferring the microstructure of complex media from the diffusive motion of molecules is a challenging problem in diffusion physics. In this paper, we introduce a novel representation of diffusion MRI (dMRI) signal from tissue with spatially-varying diffusivity using a diffusion disturbance function. This disturbance function contains information about the (intra-voxel) spatial fluctuations in diffusivity due to restrictions, hindrances and tissue heterogeneity of the underlying tissue substrate. We derive the short- and long-range disturbance coefficients from this disturbance function to characterize the tissue structure and organization. Moreover, we provide an exact relation between the disturbance coefficients and the time-varying moments of the diffusion propagator, as well as their relation to specific tissue microstructural information such as the intra-axonal volume fraction and the apparent axon radius. The proposed approach is quite general and can model dMRI signal for any type of gradient sequence (rectangular, oscillating, etc.) without using the Gaussian phase approximation. The relevance of the proposed PICASO model is explored using Monte-Carlo simulations and in-vivo dMRI data. The results show that the estimated disturbance coefficients can distinguish different types of microstructural organization of axons.

A model describing diffusion in prostate cancer

PURPOSE

Quantitative diffusion MRI has frequently been studied as a means of grading prostate cancer. Interpretation of results is complicated by the nature of prostate tissue, which consists of four distinct compartments: vascular, ductal lumen, epithelium, and stroma. Current diffusion measurements are an ill-defined weighted average of these compartments. In this study, prostate diffusion is analyzed in terms of a model that takes explicit account of tissue compartmentalization, exchange effects, and the non-Gaussian behavior of tissue diffusion.

METHOD

The model assumes that exchange between the cellular (ie, stromal plus epithelial) and the vascular and ductal compartments is slow. Ductal and cellular diffusion characteristics are estimated by Monte Carlo simulation and a two-compartment exchange model, respectively. Vascular pseudodiffusion is represented by an additional signal at b = 0. Most model parameters are obtained either from published data or by comparing model predictions with the published results from 41 studies. Model prediction error is estimated using 10-fold cross-validation.

RESULTS

Agreement between model predictions and published results is good. The model satisfactorily explains the variability of ADC estimates found in the literature.

CONCLUSION

A reliable model that predicts the diffusion behavior of benign and cancerous prostate tissue of different Gleason scores has been developed. Magn Reson Med 78:316-326, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

3T multiparametric MRI of the prostate: Does intravoxel incoherent motion diffusion imaging have a role in the detection and stratification of prostate cancer in the peripheral zone?

PURPOSE

To evaluate the potential added value of the intravoxel incoherent motion model to conventional multiparametric magnetic resonance protocol in order to differentiate between healthy and neoplastic prostate tissue in the peripheral zone.

MATERIAL AND METHODS

Mono-exponential and bi-exponential fits were used to calculate ADC and IVIM parameters in 53 patients with peripheral zone biopsy proved tumor. Inferential statistics analysis was performed on T2, ADC and IVIM parameters (D, D*, f) comparing healthy and neoplastic tissues. Linear discriminant analysis was performed for the conventional parameters (T2 and ADC), the IVIM parameters (molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (f) and the combined T2-weighted imaging/DWI and IVIM parameters (T2, ADC, D, D* and f). A correlation with Gleason scores was achieved.

RESULTS

The values of T2, ADC and D were significantly lower in cancerous tissues (2749.82 ± 1324.67 ms, 0.76 ± 0.27 × 10(-3)mm(2)/s and 0.99 ± 0.38 × 10(-3)mm(2)/s respectively) compared to those found in the healthy tissues (3750.70 ± 1735.37 ms, 1.39 ± 0.48 × 10(-3)mm(2)/s and 1.77 ± 0.36 × 10(-3)mm(2)/s respectively); D* parameter was significantly increased in neoplastic compared to healthy tissue (15.56 ± 12.91 × 10(-3)mm(2)/s and 10.25 ± 10.52 × 10(-3)mm(2)/s respectively). The specificity, sensitivity and accuracy of the T2-weighted imaging/DWI and IVIM parameters were 100, 96 and 98%, respectively, compare to 88, 92 and 90% and 96, 92 and 94 for T2-weighted imaging/ADC and IVIM alone.

CONCLUSIONS

IVIM parameters increase the specificity and sensitivity in the evaluation of peripheral zone prostate cancer. A statistical difference between low grade tumors and high grade tumors has been demostrated in that ADC, D and D* dataset; in particular, D has been found to have the highest significativity.

Diffusion anisotropy in fresh and fixed prostate tissue ex vivo

PURPOSE

To investigate diffusion anisotropy in whole human prostate specimens

METHODS

Seven whole radical prostatectomy specimens were obtained with informed patient consent and institutional ethics approval. Diffusion tensor imaging was performed at 9.4 Tesla. Diffusion tensors were calculated from the native acquired data and after progressive downsampling

RESULTS

Fractional anisotropy (FA) decreased as voxel volume increased, and differed widely between prostates. Fixation decreased mean FA by ∼0.05-0.08 at all voxel volumes but did not alter principle eigenvector orientation. In unfixed tissue high FA (> 0.6) was found only in voxels of volume <0.5 mm(3) , and then only in a small fraction of all voxels. At typical clinical voxel volumes (4-16 mm(3) ) less than 50% of voxels had FA > 0.25. FA decreased at longer diffusion times (Δ = 60 or 80 ms compared with 20 ms), but only by ∼0.02 at typical clinical voxel volume. Peripheral zone FA was significantly lower than transition zone FA in five of the seven prostates

CONCLUSION

FA varies widely between prostates. The very small proportion of clinical size voxels with high FA suggests that in clinical DWI studies ADC based on three-direction measurements will be minimally affected by anisotropy. Magn Reson Med 76:626-634, 2016. © 2015 Wiley Periodicals, Inc.

Triexponential function analysis of diffusion-weighted MRI for diagnosing prostate cancer

BACKGROUND

To evaluate more detailed information noninvasively through on diffusion and perfusion in prostate cancer (PCa) using triexponential analysis of diffusion-weighted imaging (DWI).

METHODS

Sixty-three prostate cancer patients underwent preoperative 3.0 Tesla MRI including eight b-values DWI. Triexponential analysis was performed to obtain three diffusion coefficients (Dp , Df , Ds ), as well as fractions (Fp , Ff , Fs ). Each diffusion parameter for cancerous lesions and normal tissues was compared and the relationship between diffusion parameters and Gleason score (GS) was assessed. K(trans) , Ve , and the ratios of intracellular components measured in histopathological specimens were compared with diffusion parameters.

RESULTS

Dp was significantly greater for cancerous lesions than normal peripheral zone (PZ) (P < 0.001), whereas Dp in transition zone (TZ) showed no significant difference (P = 0.74, 95% confidence interval (CI) = -4.69-6.48). Ds was significantly smaller for each cancerous lesions in PZ and TZ (P < 0.001, respectively). There was no significant difference in Df between cancerous lesions and normal tissues in PZ and TZ (P = 0.07, 95% CI = -0.29-0.12 and P = 0.53, 95% CI = -3.51-2.29, respectively). D obtained with biexponential analysis were significantly smaller in cancerous lesions than in normal tissue in PZ and TZ (P < 0.001 for both), while D* in PZ and TZ showed no significant difference (P = 0.14, 95% CI = -1.60-0.24 and P = 0.31, 95% CI = -3.43-1.16, respectively). Dp in PZ and TZ showed significant correlation with K(trans) (R = 0.85, P < 0.001; R = 0.81, P < 0.001, respectively), while D(*) in PZ obtained with biexponential analysis showed no such correlation (P = 0.08, 95% CI = -0.14-0.30). Fs was significantly correlated with intracellular space fraction evaluated in histopathological specimens in PZ and TZ cancer (R = 0.41, P < 0.05; R = 0.59, P < 0.001, respectively). Ff and Fs correlated significantly with GS in PZ and TZ cancer (PZ: R = -0.44, P < 0.05; R = 0.37, P < 0.05, TZ: R = -0.59, P < 0.05; R = 0.57, P < 0.05, respectively).

CONCLUSION

Triexponential analysis is a noninvasive approach that can provide more detailed information regarding diffusion and perfusion of PCa than biexponential analysis.

Apparent Ultra-High b-Value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields

A promising, recently explored, alternative to ultra-high b-value diffusion weighted imaging (UHB-DWI) is apparent ultra-high b-value diffusion-weighted image reconstruction (AUHB-DWR), where a computational model is used to assist in the reconstruction of apparent DW images at ultra-high b -values. Firstly, we present a novel approach to AUHB-DWR that aims to improve image quality. We formulate the reconstruction of an apparent DW image as a hidden conditional random field (HCRF) in which tissue model diffusion parameters act as hidden states in this random field. The second contribution of this paper is a new generation of fully connected conditional random fields, called the hidden stochastically fully connected conditional random fields (HSFCRF) that allows for efficient inference with significantly reduced computational complexity via stochastic clique structures. The proposed AUHB-DWR algorithms, HCRF and HSFCRF, are evaluated quantitatively in nine different patient cases using Fisher's criteria, probability of error, and coefficient of variation metrics to validate its effectiveness for the purpose of improving intensity delineation between expert identified suspected cancerous and healthy tissue within the prostate gland. The proposed methods are also examined using a prostate phantom, where the apparent ultra-high b-value DW images reconstructed using the tested AUHB-DWR methods are compared with real captured UHB-DWI. The results illustrate that the proposed AUHB-DWR methods has improved reconstruction quality and improved intensity delineation compared with existing AUHB-DWR approaches.

Comparison of stretched-Exponential and monoexponential model diffusion-Weighted imaging in prostate cancer and normal tissues

BACKGROUND

To compare stretched-exponential and monoexponential model diffusion-weighted imaging (DWI) in prostate cancer and normal tissues.

METHODS

Twenty-seven patients with prostate cancer underwent DWI exam using b-values of 0, 500, 1000, and 2000 s/mm(2) . The distributed diffusion coefficients (DDC) and α values of prostate cancer and normal tissues were obtained with stretched-exponential model and apparent diffusion coefficient (ADC) values using monoexponential model. The ADC, DDC (both in 10(-3) mm(2)/s), and α values (range, 0-1) were compared among different prostate tissues. The ADC and DDC were also compared and correlated in each tissue, and the standardized differences between DDC and ADC were compared among different tissues.

RESULTS

Data were obtained for 31 cancers, 36 normal peripheral zone (PZ) and 26 normal central gland (CG) tissues. The ADC (0.71 ± 0.12), DDC (0.60 ± 0.18), and α value (0.64 ± 0.05) of tumor were all significantly lower than those of the normal PZ (1.41 ± 0.22, 1.47 ± 0.20, and 0.85 ± 0.09) and CG (1.25 ± 0.14, 1.32 ± 0.13, and 0.82 ± 0.06) (all P < 0.05). ADC was significantly higher than DDC in cancer, but lower than DDC in the PZ and CG (all P < 0.05). The ADC and DDC were strongly correlated (R(2)  = 0.99, 0.98, 0.99, respectively, all P < 0.05) in all the tissue, and standardized difference between ADC and DDC of cancer was slight but significantly higher than that in normal tissue.

CONCLUSION

The stretched-exponential model DWI provides more parameters for distinguishing prostate cancer and normal tissue and reveals slight differences between DDC and ADC values.

Intravoxel incoherent motion diffusion imaging of the liver: optimal b-value subsampling and impact on parameter precision and reproducibility

PURPOSE

To increase diffusion sampling efficiency in intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) of the liver by reducing the number of diffusion weightings (b-values).

MATERIALS AND METHODS

In this IRB approved HIPAA compliant prospective study, 53 subjects (M/F 38/15, mean age 52 ± 13 y) underwent IVIM DWI at 1.5T using 16 b-values (0-800s/mm(2)), with 14 subjects having repeat exams to assess IVIM parameter reproducibility. A biexponential diffusion model was used to quantify IVIM hepatic parameters (PF: perfusion fraction, D: true diffusion and D*: pseudo diffusion). All possible subsets of the 16 b-values were probed, with number of b values ranging from 4 to 15, and corresponding parameters were quantified for each subset. For each b-value subset, global parameter estimation error was computed against the parameters obtained with all 16 b-values and the subsets providing the lowest error were selected. Interscan estimation error was also evaluated between repeat exams to assess reproducibility of the IVIM technique in the liver. The optimal b-values distribution was selected such that the number of b-values was minimal while keeping parameter estimation error below interscan reproducibility error.

RESULTS

As the number of b-values decreased, the estimation error increased for all parameters, reflecting decreased precision of IVIM metrics. Using an optimal set of 4 b-values (0, 15, 150 and 800s/mm(2)), the errors were 6.5, 22.8 and 66.1% for D, PF and D* respectively. These values lie within the range of test-retest reproducibility for the corresponding parameters, with errors of 12.0, 32.3 and 193.8% for D, PF and D* respectively.

CONCLUSION

A set of 4 optimized b-values can be used to estimate IVIM parameters in the liver with significantly shorter acquisition time (up to 75%), without substantial degradation of IVIM parameter precision and reproducibility compared to the 16 b-value acquisition used as the reference.

Evaluation of different mathematical models for diffusion-weighted imaging of normal prostate and prostate cancer using high b-values: a repeatability study

PURPOSE

To evaluate monoexponential, stretched exponential, kurtosis, and biexponential models for diffusion-weighted imaging (DWI) of normal prostate and prostate cancer (PCa), using b-values up to 2000 s/mm(2) , in terms of fitting quality and repeatability.

METHODS

Eight healthy volunteers and 16 PCa patients underwent a total of four repeated 3T DWI examinations using 16 and 12 b-values, respectively. The highest b-value was 2000 s/mm(2) . The normalized mean signal intensities of regions of interest, placed in normal tissue and PCa using anatomical images and prostatectomy sections, were fitted using the four models. The fitting quality was evaluated using Akaike information criteria and F-ratio. Repeatability of the fitted parameters was evaluated using intraclass correlation coefficient ICC(3,1).

RESULTS

The biexponential model provided the best fit to normal prostate and PCa DWI data. The parameters of the monoexponential, kurtosis, and stretched exponential (with the exception of the α parameter) models had higher ICC(3,1) values compared with the biexponential model. The kurtosis model provided a better fit to DWI data of normal prostate and PCa than the monoexponential model, whereas these models had comparable reliability and repeatability based on ICC(3,1) values.

CONCLUSION

Considering the model fit and repeatability, the kurtosis model seems to be the preferred model for characterization of normal prostate and PCa DWI using b-values up to 2000 s/mm(2) .

Optimization of b-value distribution for four mathematical models of prostate cancer diffusion-weighted imaging using b values up to 2000 s/mm(2): simulation and repeatability study

PURPOSE

To find optimal b-value distributions for monoexponential, stretched exponential, kurtosis, and biexponential models of prostate cancer (PCa) diffusion-weighted imaging (DWI) using simulations and repeated DWI examinations.

METHODS

Simulations aiming to minimize estimation accuracy error were performed. Ten PCa patients underwent in total four repeated 3-tesla DWI examinations using 12 equally spaced b values (0-2000 s/mm(2) ). Normalized mean signal intensities of regions-of-interest placed in normal tissue and PCa were fitted. In total, 210 different b-value combinations consisting of six b values, 0 and 100 s/mm(2) included in every b-value distribution, were evaluated in terms of accuracy and repeatability.

RESULTS

The simulations and in vivo DWI data suggest the optimal b-value distribution for the monoexponential model consists of four to five equally distributed b values in the range of 0 to 1200 s/mm(2) . The parameters of the stretched exponential and kurtosis models are best estimated using five to seven b values in the ranges of 300 to 700 and close to 2000 s/mm(2) , in addition to low b value. B-value distribution consisting of eight to 10 b values in the ranges of 0 to 100, 800 to 1200, and 1800 to 2000 s/mm(2) is the preferred method for estimation of the biexponential model parameters of PCa DWI.

CONCLUSION

The optimized b-value distributions demonstrated improved estimation accuracy and repeatability of DWI signal decay-derived parameters.

Characterizing non-gaussian, high b-value diffusion in liver fibrosis: Stretched exponential and diffusional kurtosis modeling

PURPOSE

To employ the stretched exponential and diffusional kurtosis models to study the non-Gaussian behavior of diffusion-related signal decay of the liver in an animal model of hepatic fibrosis.

MATERIALS AND METHODS

High b-value diffusion imaging data (up to 3500 s/mm(2) ) of ex vivo murine liver specimens was acquired using a 9.4 T MRI scanner. A simple monoexponential model as well as the stretched exponential and diffusional kurtosis models were employed to analyze the diffusion data, the results of which were correlated with liver histopathology.

RESULTS

Strong correlations between histopathological assessments of hepatic fibrosis and parameters derived from the stretched exponential and diffusional kurtosis models were found. Using Akaike's Information Criterion (AIC) analyses, the kurtosis model was found to result in an improved fit of the high b-value diffusion data when compared to both the monoexponential and stretched exponential models.

CONCLUSION

The use of diffusional kurtosis or stretched exponential models, applied to the characterization of the non-Gaussian behavior of the molecular diffusion of liver exhibited over an extended b-factor range, affords the potential for an increased capability of magnetic resonance imaging (MRI) in the characterization of chronic liver disease.

Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer

Magnetic resonance (MR) examinations of men with prostate cancer are most commonly performed for detecting, characterizing, and staging the extent of disease to best determine diagnostic or treatment strategies, which range from biopsy guidance to active surveillance to radical prostatectomy. Given both the exam's importance to individual treatment plans and the time constraints present for its operation at most institutions, it is essential to perform the study effectively and efficiently. This article reviews the most commonly employed modern techniques for prostate cancer MR examinations, exploring the relevant signal characteristics from the different methods discussed and relating them to intrinsic prostate tissue properties. Also, a review of recent articles using these methods to enhance clinical interpretation and assess clinical performance is provided. J. Magn. Reson. Imaging 2013;37:1035-1054. © 2013 Wiley Periodicals, Inc.

Diffusion kurtosis imaging study of prostate cancer: preliminary findings

PURPOSE

To evaluate the differences in parameters of diffusion kurtosis imaging (DKI) between prostate cancer, benign prostatic hyperplasia (BPH), and benign peripheral zone (PZ).

MATERIALS AND METHODS

Twenty-four foci of prostate cancer, 41 BPH nodules (14 stromal and 27 nonstromal hyperplasia), and 20 benign PZ from 20 patients who underwent radical prostatectomy were investigated. Diffusion-weighted imaging (DWI) was performed using 11 b-values (0-1500 s/mm(2) ). DKI model relates DWI signal decay to parameters that reflect non-Gaussian diffusion coefficient (D) and deviations from normal distribution (K). A mixed model analysis of variance and receiver operating characteristic (ROC) analyses were performed to assess the statistical significance of the metrics of DKI and apparent diffusion coefficient (ADC).

RESULTS

K was significantly higher in prostate cancer and stromal BPH than in benign PZ (1.19 ± 0.24 and 0.99 ± 0.28 versus 0.63 ± 0.23, P < 0.001 and P < 0.001, respectively). K showed a trend toward higher levels in prostate cancer than in stromal BPH (1.19 ± 0.24 versus 0.99 ± 0.28, P = 0.051). On the ROC analyses, a significant difference in area under the curve was not observed between K and ADC, however, K showed the highest sensitivity among three parameters.

CONCLUSION

DKI may contribute to the imaging diagnosis of prostate cancer, especially in the differential diagnosis of prostate cancer and BPH.