Diffusion-weighted MRI: influence of intravoxel fat signal and breast density on breast tumor conspicuity and apparent diffusion coefficient measurements

Promising recent investigations have shown that breast malignancies exhibit restricted diffusion on diffusion-weighted imaging (DWI) and may be distinguished from normal tissue and benign lesions in the breast based on differences in apparent diffusion coefficient (ADC) values. In this study, we assessed the influence of intravoxel fat signal on breast diffusion measures by comparing ADC values obtained using a diffusion-weighted single shot fast spin-echo sequence with and without fat suppression. The influence of breast density on ADC measures was also evaluated. ADC values were calculated for both tumor and normal fibroglandular tissue in a group of 21 women with diagnosed breast cancer. There were systematic underestimations of ADC for both tumor and normal breast tissue due to intravoxel contribution from fat signal on non–fat-suppressed DWI. This ADC underestimation was more pronounced for normal tissue values (mean difference=40%) than for tumors (mean difference=27%, P<.001) and was worse in women with low breast tissue density vs. those with extremely dense breasts (P<.05 for both tumor and normal tissue). Tumor conspicuity measured by contrast-to-noise ratio was significantly higher on ADC maps created with fat suppression and was not significantly associated with breast density. In summary, robust fat suppression is important for accurate breast ADC measures and optimal lesion conspicuity on DWI.     

Intravoxel incoherent motion imaging of the kidney: alterations in diffusion and perfusion in patients with renal dysfunction

Purpose

To investigate the relationship between estimated glomerular filtration rate (eGFR) and parameters calculated using intravoxel incoherent motion (IVIM) imaging of the kidneys.

Materials and Methods

We studied 365 patients, divided into 4 groups based on eGFR levels (mL/min/1.73 m²): group 1, eGFR ≥ 80(n = 80); group 2, eGFR 60–80 (n = 156); group 3, eGFR 30–60 (n = 114); and group 4 ,eGFR < 30 (n = 15). IVIM imaging was used to acquire diffusion-weighted images at 12 b values. The diffusion coefficient of pure molecular diffusion (D), the diffusion coefficient of microcirculation or perfusion (D*), and perfusion fraction (f) were compared among the groups using group 1 as control.

Results

In the renal cortex, D* values were significantly lower in groups 2, 3, and 4 than in group 1. The D value of renal cortex was significantly low in only group 3. In the renal medulla, the D* and D values were significantly lower only in groups 2 and 3, respectively.

Conclusion

As renal dysfunction progresses, renal perfusion might be reduced earlier and affected more than molecular diffusion in the renal cortex. These changes are effectively detected by IVIM MR imaging.     

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann–Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.     

Fitting of two-tensor models without ad hoc assumptions to detect crossing fibers using clinical DWI data

Analysis of crossing fibers is a challenging topic in recent diffusion-weighted imaging (DWI). Resolving crossing fibers is expected to bring major changes to present tractography results based on the standard tensor model. Model free approaches, like Q-ball or diffusion spectrum imaging, as well as multi-tensor models are used to unfold the different diffusion directions mixed in a voxel of DWI data. Due to its seeming simplicity, the two-tensor model (TTM) is applied frequently to provide two positive-definite tensors and the relative population fraction modeling two crossing fiber branches. However, problems with uniqueness and noise instability are apparent. To stabilize the fit, several of the 13 physical parameters are fixed ad hoc, before fitting the model to the data. Our analysis of the TTM aims at fitting procedures where ad hoc parameters are avoided. Revealing sources of instability, we show that the model's inherent ambiguity can be reduced to one scalar parameter which only influences the fraction and the eigenvalues of the TTM, whereas the diffusion directions are not affected. Based on this, two fitting strategies are proposed: the parsimonious strategy detects the main diffusion directions without extra parameter fixation, to determine the eigenvalues and the population fraction an empirically motivated condition must be added. The expensive strategy determines all 13 physical parameters of the TTM by a fit to DWIs alone; no additional assumption is necessary. Ill-posedness of the model in case of noisy data is cured by denoising of the data and by L-curve regularization combined with global minimization performing a least-squares fit of the full model. By model simulations and real data applications, we demonstrate the feasibility of our fitting strategies and achieve convincing results. Using clinically affordable diffusion acquisition paradigms (encoding numbers: 21, 2*15, 2*21) and b values (b = 500–1500 s/mm²), this methodology can place the TTM parameters involved in crossing fibers on a more empirical basis than fitting procedures with technical assumptions.     

Diffusion tensor imaging to study anisotropy in a particular porous system: the trabecular bone network

During the last decade, considerable effort has been invested into the development of diffusion tensor imaging (DTI) mainly used to investigate cerebral morphology. The aim of this paper is to review and to discuss our recent results about high magnetic field DTI application to study spongy bone tissue. Due to its peculiar properties, spongy bone represents a particular porous system sample. Strategies to perform DTI on porous systems and issues linked to DTI outcome interpretation are presented on the basis of our results concerning trabecular bone network characterization.     

Functional MRI for radiotherapy dose painting

Modern radiation therapy techniques are exceptionally flexible in the deposition of radiation dose in a target volume. Complex distributions of dose can be delivered reliably, so that the tumor is exposed to a high dose, whereas nearby healthy structures can be avoided. As a result, an increase in curative dose is no longer invariably associated with an increased level of toxicity. This modern technology can be exploited further by modulating the required dose in space so as to match the variation in radiation sensitivity in the tumor. This approach is called dose painting.     

Diffusion-weighted magnetic resonance imaging to evaluate microvascular density after transarterial embolization ablation in a rabbit VX2 liver tumor model

Objective

To evaluate the correlation between findings from diffusion weighted imaging (DWI) and microvascular density (MVD) measurements in VX2 liver tumors after transarterial embolization ablation (TEA).

Materials and Methods

Eighteen New Zealand white rabbits were used in this study. VX2 tumor cells were implanted in livers by percutaneous puncture under computed tomography (CT) guidance. Two weeks later, all rabbits underwent conventional magnetic resonance imaging (MRI) (T1 and T2 imaging), DWI, (b = 100, 600, and 1000 s/mm²) and TEA. MRI was performed again1 week after TEA. Liver tissue was then harvested and processed for hematoxylin and eosin (H&E) staining and immunohistochemical staining for CD31to determine MVD.

Results

VX2 liver tumors were successfully established in all 18 rabbits. Optimal contrast was achieved with a b value of 600 s/mm².The maximum pre-operative apparent diffusion coefficient (ADC)_difference value was 0.28 × 10^− 3 ± 0.10 × 10^− 3 mm²/s, and was significantly different (P < 0.001) from the maximum postoperative ADC_difference value of 0.47 × 10^− 3 ± 0.10 × 10^− 3 mm²/s. However, the mean ADC value for the entire tumor was not significantly correlated with MVD (r = 0.221, P = 0.379), nor was the ADC value for the regions of viable tumor (r = − 0.044, P = 0.862). However, the maximum postoperative ADC_difference value was positively correlated with MVD(r = 0.606, F = 12.247, P = 0.003).

Conclusion

DWI is effective to evaluate the therapeutic efficacy of TEA. The maximum ADC_difference offers a promising new method to noninvasively assess tumor angiogenesis.     

Intermolecular double quantum coherences (iDQc) and diffusion-weighted imaging (DWI) imaging of the human brain at 1.5 T

To study the sensitivity of intermolecular double quantum coherences (iDQc) imaging contrast to brain microstructure and brain anisotropy, we investigated the iDQC contrast between differently structured areas of the brain according to the strength and the direction of the applied correlation gradient. Thus diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) maps have been obtained. This procedure, which consists of analyzing both iDQc and DWI images at different gradient strength and gradient direction, could be a promising tool for clinical brain investigations performed with higher than 1.5 T magnetic fields.     

Neuroprotective effect of agmatine in rats with transient cerebral ischemia using MR imaging and histopathologic evaluation

Purpose

This study aimed to further investigate the effects of agmatine on brain edema in the rats with middle cerebral artery occlusion (MCAO) injury using magnetic resonance imaging (MRI) monitoring and biochemical and histopathologic evaluation.

Materials and methods

Following surgical induction of MCAO for 90 min, agmatine was injected 5 min after beginning of reperfusion and again once daily for the next 3 post-operative days. The events during ischemia and reperfusion were investigated by T2-weighted images (T2WI), serial diffusion-weighted images (DWI), calculated apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted images (CE-T1WI) during 3 h–72 h in a 1.5 T Siemens MAGNETON Avanto Scanner. Lesion volumes were analyzed in a blinded and randomized manner. Triphenyltetrazolium chloride (TTC), Nissl, and Evans Blue stainings were performed at the corresponding sections.

Results

Increased lesion volumes derived from T2WI, DWI, ADC, CE-T1WI, and TTC all were noted at 3 h and peaked at 24 h–48 h after MCAO injury. TTC-derived infarct volumes were not significantly different from the T2WI, DWI-, and CE-T1WI-derived lesion volumes at the last imaging time (72 h) point except for significantly smaller ADC lesions in the MCAO model (P < 0.05). Volumetric calculation based on TTC-derived infarct also correlated significantly stronger to volumetric calculation based on last imaging time point derived on T2WI, DWI or CE-T1WI than ADC (P < 0.05). At the last imaging time point, a significant increase in Evans Blue extravasation and a significant decrease in Nissl-positive cells numbers were noted in the vehicle-treated MCAO injured animals. The lesion volumes derived from T2WI, DWI, CE-T1WI, and Evans blue extravasation as well as the reduced numbers of Nissl-positive cells were all significantly attenuated in the agmatine-treated rats compared with the control ischemia rats (P < 0.05).

Conclusion

Our results suggest that agmatine has neuroprotective effects against brain edema on a reperfusion model after transient cerebral ischemia.     

Comparison of different mathematical models of diffusion-weighted prostate MR imaging

Purpose

To evaluate which mathematical model (monoexponential, biexponential, statistical, kurtosis) fits best to the diffusion-weighted signal in prostate magnetic resonance imaging (MRI).

Materials and Methods

24 prostate 3-T MRI examinations of young volunteers (YV, n= 8), patients with biopsy proven prostate cancer (PC, n= 8) and an aged matched control group (AC, n= 8) were included. Diffusion-weighted imaging was performed using 11 b-values ranging from 0 to 800 s/mm².

Results

Monoexponential apparent diffusion coefficient (ADC) values were significantly (P<.001) lower in the peripheral (PZ) zone (1.18±0.16 mm²/s) and the central (CZ) zone (0.73±0.13 mm²/s) of YV compared to AC (PZ 1.92±0.17 mm²/s; CZ 1.35±0.21 mm²/s). In PC ADC_mono values (0.61±0.06 mm²/s) were significantly (P<.001) lower than in the peripheral of central zone of AC. Using the statistical analysis (Akaike information criteria) in YV most pixels were best described by the biexponential model (82%), the statistical model, respectively kurtosis (93%) each compared to the monoexponential model. In PC the majority of pixels was best described by the monoexponential model (57%) compared to the biexponential model.

Conclusion

Although a more complex model might provide a better fitting when multiple b-values are used, the monoexponential analyses for ADC calculation in prostate MRI is sufficient to discriminate prostate cancer from normal tissue using b-values ranging from 0 to 800 s/mm².