Feasibility of diffusional kurtosis tensor imaging in prostate MRI for the assessment of prostate cancer: Preliminary results

Purpose

To assess the feasibility of full diffusional kurtosis tensor imaging (DKI) in prostate MRI in clinical routine. Histopathological correlation was achieved by targeted biopsy.

Materials and Methods

Thirty-one men were prospectively included in the study. Twenty-one were referred to our hospital with increased prostate specific antigen (PSA) values (> 4 ng/ml) and suspicion of prostate cancer. The other 10 men were volunteers without any history of prostate disease. DKI applying diffusion gradients in 20 different spatial directions with four b-values (0, 300, 600, 1000 s/mm²) was performed additionally to standard functional prostate MRI. Region of interest (ROI)-based measurements were performed in all histopathologically verified lesions of every patient, as well as in the peripheral zone, and the central gland of each volunteer.

Results

DKI showed a substantially better fit to the diffusion-weighted signal than the monoexponential apparent diffusion coefficient (ADC). Altogether, 29 lesions were biopsied in 14 different patients with the following results: Gleason score 3 + 3 = 6 (n = 1), 3 + 4 = 7 (n = 7), 4 + 3 = 7 (n = 6), 4 + 4 = 8 (n = 1), and 4 + 5 = 9 (n = 2), and prostatitis (n = 12). Values of axial (K_ax) and mean kurtosis (K_mean) were significantly different in the tumor (K_ax 1.78 ± 0.39, K_mean 1.84 ± 0.43) compared with the normal peripheral zone (K_ax 1.09 ± 0.12, K_mean 1.16 ± 0.13; p < 0.001) or the central gland (K_ax 1.40 ± 0.12, K_mean 1.44 ± 0.17; p = 0.01 respectively). There was a minor correlation between axial kurtosis (r = 0.19) and the Gleason score.

Conclusion

Full DKI is feasible to utilize in a routine clinical setting. Although there is some overlap some DKI parameters can significantly distinguish prostate cancer from the central gland or the normal peripheral zone. Nevertheless, the additional value of DKI compared with conventional monoexponential ADC calculation remains questionable and requires further research.     

Diffusion-weighted imaging of local recurrent prostate cancer after radiation therapy: comparison with 22-core three-dimensional prostate mapping biopsy

Accurate localization of local recurrence within the prostate gland is important to perform focal salvage therapy effectively with minimal complications. The purpose of this study was to evaluate the usefulness of diffusion-weighted imaging (DWI) in the detection and localization of prostate cancer recurrence in patients with biochemical failure after definitive radiation therapy using 22-core three-dimensional prostate mapping biopsy (3D-PMB) as a standard reference. Ten patients who underwent magnetic resonance imaging and 22-core 3D-PMB were retrospectively analyzed. For visual assessment of DWI, the prostate was divided into 22 regions corresponding to 3D-PMB. Two diagnostic radiologists determined the presence of abnormal high signal intensity in each region on DWI, and the results of DWI were compared with those of 3D-PMB. Of the 220 regions, 16 regions in six patients were positive for cancer on 3D-PMB, and 30 regions in six patients were judged as positive on DWI. On a patient-by-patient basis, sensitivity and specificity were 100% (6/6) and 100% (4/4), respectively. On a region-by-region basis, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 69% (11/16), 91% (185/204), 37% (11/30), 97% (185/190) and 89% (196/220), respectively. For discrepant localization between DWI and pathology, DWI-positive and pathology-positive regions tended to be adjacent to each other. In conclusion, DWI is a useful tool for the detection and localization of recurrent prostate cancer in patients with biochemical failure after radiation therapy and may be helpful in the planning of focal salvage therapy.     

Biexponential apparent diffusion coefficients in prostate cancer

Purpose

The purpose of this study was to investigate the need for biexponential signal decay modeling for prostate cancer diffusion signal decays with b-factor over an extended b-factor range.

Materials and Methods

Ten healthy volunteers and 12 patients with a bulky prostate cancer underwent line scan diffusion-weighted MR imaging in which b-factors from 0 to 3000 s/mm² in 16 steps were sampled. The acquired signal decay curves were fit with both monoexponential and biexponential signal decay functions and a statistical comparison between the two fits was performed.

Results

The biexponential model provided a statistically better fit over the monoexponential model on the peripheral zone (PZ), transitional zone (TZ) and prostate cancer. The fast and slow apparent diffusion coefficients (ADCs) in the PZ, TZ and cancer were 2.9±0.2, 0.7±0.2×10⁻³ mm²/ms (PZ); 2.9±0.4, 0.7±0.2×10⁻³ mm²/ms (TZ); and 1.7±0.4, 0.3±0.1×10⁻³ mm²/ms (cancer), respectively. The apparent fractions of the fast diffusion component in the PZ, TZ and cancer were 70±10%, 60±10% and 50±10%, respectively. The fast and slow ADCs of cancer were significantly lower than those of TZ and PZ, and the apparent fraction of the fast diffusion component was significantly smaller in cancer than in PZ.

Conclusions

Biexponential diffusion decay functions are required for prostate cancer diffusion signal decay curves when sampled over an extended b-factor range, providing additional, unique tissue characterization parameters for prostate cancer.     

Two-dimensional spectroscopic imaging for pretreatment evaluation of prostate cancer: comparison with the step-section histology after radical prostatectomy

Purpose

To minimize user and vendor dependence of the spectrum processing of prostate spectra, to measure the ratio of choline (Cho) plus creatine (Cr) to citrate (Cit) in the prostate tissue of normal volunteers and cancer patients, and to compare the results with pathologic findings after radical prostatectomy.

Materials and methods

Four healthy volunteers and 13 patients with prostate cancer were measured. Measurements were performed using two-dimensional magnetic resonance spectroscopic imaging (MRSI) and endorectal coil. A standard vendor's spectrum processing approach has been modified. An original feature of this methodology was the combination of vendor-optimized and user-independent spectrum preprocessing in the scanner and user-independent quantitation in the environment of an MRUI software package. (Cho+Cr)/Cit ratio was used for the classification of prostate tissue. Results were compared with histopathology after radical prostatectomy.

Results

Eight of 13 cancer patients were classified as suspicious or very suspicious for cancer at spectroscopy, three were ambiguous for cancer and two patients were evaluated as false negative. A considerable overlap of metabolite ratios at various Gleason score was found.

Conclusion

The proposed spectrum processing has the potential to improve the accuracy and user independency of the (Cho+Cr)/Cit quantitation. This study confirmed the previous results that a considerable overlap of (Cho+Cr)/Cit ratios exists at various Gleason score levels.     

In vivo quantification of citrate concentration and water T2 relaxation time of the pathologic prostate gland using H MRS and MRI

We have previously reported a striking correlation between water T₂ relaxation time and citrate concentration in the normal prostate (Liney G.P.; Lowry M.; Turnbull L.W.; Manton D.J.; Knowles A.J.; Blackband S.J.; Horsman A. Proton MR T₂ maps correlate with the citrate concentration in the prostate. NMR Biomed. 9:59–64; 1996). In this study we present data from similar studies of the pathologic gland. The findings support the hypothesis that measurement of both citrate concentration and water T₂ relaxation time in vivo may aid the differentiation of prostatic carcinoma from benign disease and normal tissue.     

Repeatability of echo-planar-based diffusion measurements of the human prostate at 3 T

Echo-planar-based diffusion-weighted imaging (DWI) of the prostate is increasingly being suggested as a viable technique, complementing information derived from conventional magnetic resonance imaging methods for use in tissue discrimination. DWI has also been suggested as a potentially useful tool in the assessment of tumor response to treatment. In this study, the repeatability of apparent diffusion coefficient (ADC) values obtained from both DWI and diffusion tensor imaging (DTI) has been assessed as a precursor to determining the magnitude of treatment-induced changes required for reliable detection. The repeatability values of DWI and DTI were found to be similar, with ADC values repeatable to within 35% or less over a short time period of a few minutes and a longer time period of a month. Fractional anisotropy measurements were found to be less repeatable (between 26% and 71%), and any changes duly recorded in longitudinal studies must therefore be treated with a degree of caution.     

Diffusion-weighted MRI in prostate cancer -- comparison between single-shot fast spin echo and echo planar imaging sequences

Diffusion-weighted (DW) MRI at 1.5 T was carried out in two groups of patients. MRI data were correlated with the biopsy and histopathology (where available). The performance of two sequences -- a single-shot FSE (14 patients) and a single-shot EPI (15 patients) -- was compared. Average ADC values from the normal peripheral zone (PZ), central gland (CG) and the tumour [prostate carcinoma (PCa)] were calculated from b values of 0 and 600. Tukey-Kramer test was used for statistical analysis. EPI produced higher values of ADC (10(-3) mm(2)/s) than FSE sequence: 1.992+/-0.208 vs. 1.573+/-0.270 in PZ (P<.001), 1.518+/-0.126 vs. 1.373+/-0.179 in CG and 1.214+/-0.254 vs. 0.993+/-0.158 in PCa (P<.01). In conclusion, both EPI and FSE sequences showed differences in ADC between normal PZ, CG and PCa; however, EPI produced significantly higher ADC values than FSE.     

Non-Gaussian diffusion imaging: a brief practical review

The departure from purely mono-exponential decay of the signal, as observed from brain tissue following a diffusion-sensitized sequence, has prompted the search for alternative models to characterize these unconventional water diffusion dynamics. Several approaches have been proposed in the last few years. While multi-exponential models have been applied to characterize brain tissue, several unresolved controversies about the interpretations of the results have motivated the search for alternative models that do not rely on the Gaussian diffusion hypothesis. In this brief review, diffusional kurtosis imaging (DKI) and anomalous diffusion imaging (ADI) techniques are addressed and compared with diffusion tensor imaging. Theoretical and experimental issues are briefly described to allow readers to understand similarities, differences and limitations of these two non-Gaussian models. However, since the ultimate goal is to improve specificity, sensitivity and spatial localization of diffusion MRI for the detection of brain diseases, special attention will be paid on the clinical feasibility of the proposed techniques as well as on the context of brain pathology investigations.     

Investigation of prostate cancer using diffusion-weighted intravoxel incoherent motion imaging

Purpose

The objective of this work was to evaluate the diagnostic performance of the intravoxel incoherent motion (IVIM) model to differentiate between healthy and malignant prostate tissue.

Materials and Methods

Regions of interest were drawn in healthy and cancerous tissue of 13 patients with histologically proven prostate carcinoma and fitted to a monoexponential model [yielding the apparent diffusion coefficient (ADC)] and the IVIM signal equation (yielding the perfusion fraction f, the diffusion constant D and the pseudodiffusion coefficient of perfusion D?). Parameter maps were calculated for all parameters.

Results

The ADC, D and f were significantly (P<.005) lowered in cancerous tissue (1.01±0.22 μm²/ms, 0.84±0.19 μm²/ms and 14.27±7.10%, respectively) compared to benign tissue (1.49±0.17 μm²/ms, 1.21±0.22 μm²/ms and 21.25±8.32%, respectively). Parameter maps of D and f allowed for a delineation of the tumor, but showed higher variations compared to the ADC map.

Conclusion

Apparent diffusion coefficient maps provide better diagnostic performance than IVIM maps for tumor detection. However, the results suggest that the reduction of the ADC in prostate cancer stems not only from changes in cellularity but also from perfusion effects. IVIM imaging might hold promise for the diagnosis of other prostatic lesions.     

Research and progress in magnetic resonance imaging of triple-negative breast cancer

Triple-negative breast cancer (TNBC), which characterized by distinct biological and clinical pathological features, has a worse prognosis because the lack of effective therapeutic targets. Breast MR is the most accurate imaging modality for diagnosis of breast cancer currently. MR imaging recognition could assist in diagnosis, pretreatment planning and prognosis evaluation of TNBC. MR findings of a larger solitary lesion, mass with smooth mass margin, high signal intensity on T₂-weighted images and rim enhancement are typical MRI features associated with TNBC. Further work is necessary about the clinical application of dynamic contrast-enhanced MR imaging (DCE-MRI), DWI and MRS.     

Keyhole and zero-padding approaches for reduced-encoding diffusion tensor imaging of the mouse brains

Keyhole diffusion tensor imaging (keyhole DTI) was previously proposed in cardiac imaging to reconstruct DTI maps from the reduced phase-encoding images. To evaluate the feasibility of keyhole DTI in brain imaging, keyhole and zero-padding DTI algorithms were employed on in vivo mouse brain. The reduced phase-encoding portion, also termed as the sharing rate, was varied from 50% to 90% of the full k-space. Our data showed that zero-padding DTI resulted in decreased fractional anisotropy (FA) and decreased mean apparent diffusion coefficient (mean ADC) in white matter (WM) regions. Keyhole DTI showed a better edge preservation on mean ADC maps but not on FA maps as compared to the zero-padding DTI. When increasing the sharing rate in keyhole approach, an underestimation of FA and an over- or underestimation of mean ADC were measured in WM depending on the selected reference image. The inconsistency of keyhole DTI may add a challenge for the wide use of this modality. However, with a carefully selected directive diffusion-weighted image to serve as the reference image in the keyhole approach, this study demonstrated that one may obtain DTI indices of reduced-encoding images with high consistency to those derived with full k-space DTI.     

Diffusional kurtosis imaging of cingulate fibers in Parkinson disease: Comparison with conventional diffusion tensor imaging

Objective

The pathological changes in Parkinson disease begin in the brainstem; reach the limbic system and ultimately spread to the cerebral cortex. In Parkinson disease (PD) patients, we evaluated the alteration of cingulate fibers, which comprise part of the limbic system, by using diffusional kurtosis imaging (DKI).

Methods

Seventeen patients with PD and 15 age-matched healthy controls underwent DKI with a 3-T MR imager. Diffusion tensor tractography images of the anterior and posterior cingulum were generated. The mean kurtosis (MK) and conventional diffusion tensor parameters measured along the images in the anterior and posterior cingulum were compared between the groups. Receiver operating characteristic (ROC) analysis was also performed to compare the diagnostic abilities of the MK and conventional diffusion tensor parameters.

Results

The MK and fractional anisotropy (FA) in the anterior cingulum were significantly lower in PD patients than in healthy controls. The area under the ROC curve was 0.912 for MK and 0.747 for FA in the anterior cingulum. MK in the anterior cingulum had the best diagnostic performance (mean cutoff, 0.967; sensitivity, 0.87; specificity, 0.94).

Conclusions

DKI can detect alterations of the anterior cingulum in PD patients more sensitively than can conventional diffusion tensor imaging. Use of DKI can be expected to improve the ability to diagnose PD.     

Earlier detection of tumor treatment response using magnetic resonance diffusion imaging with oscillating gradients

An improved method for detecting early changes in tumors in response to treatment, based on a modification of diffusion-weighted magnetic resonance imaging, has been demonstrated in an animal model. Early detection of therapeutic response in tumors is important both clinically and in pre-clinical assessments of novel treatments. Noninvasive imaging methods that can detect and assess tumor response early in the course of treatment, and before frank changes in tumor morphology are evident, are of considerable interest as potential biomarkers of treatment efficacy. Diffusion-weighted magnetic resonance imaging is sensitive to changes in water diffusion rates in tissues that result from structural variations in the local cellular environment, but conventional methods mainly reflect changes in tissue cellularity and do not convey information specific to microstructural variations at sub-cellular scales. We implemented a modified imaging technique using oscillating gradients of the magnetic field for evaluating water diffusion rates over very short spatial scales that are more specific for detecting changes in intracellular structure that may precede changes in cellularity. Results from a study of orthotopic 9L gliomas in rat brains indicate that this method can detect changes as early as 24 h following treatment with 1,3-bis(2-chloroethyl)-1-nitrosourea, when conventional approaches do not find significant effects. These studies suggest that diffusion imaging using oscillating gradients may be used to obtain an earlier indication of treatment efficacy than previous magnetic resonance imaging methods.     

Diffusion kurtosis imaging of the human kidney: A feasibility study

Purpose

To assess the feasibility and to optimize imaging parameters of diffusion kurtosis imaging (DKI) in human kidneys.

Methods

The kidneys of ten healthy volunteers were examined on a clinical 3 T MR scanner. For DKI, respiratory triggered EPI sequences were acquired in the coronal plane (3 b-values: 0, 300, 600 s/mm², 30 diffusion directions). A goodness of fit analysis was performed and the influence of the signal-to-noise ratio (SNR) on the DKI results was evaluated. Region-of-interest (ROI) measurements were performed to determine apparent diffusion coefficient (ADC), fractional anisotropy (FA) and mean kurtosis (MK) of the cortex and the medulla of the kidneys. Intra-observer and inter-observer reproducibility using Bland-Altman plots as well as subjective image quality of DKI were examined and ADC, FA, and MK parameters were compared.

Results

The DKI model fitted better to the experimental data (r = 0.99) with p < 0.05 than the common mono-exponential ADC model (r = 0.96).Calculation of reliable kurtosis parameters in human kidneys requires a minimum SNR of 8.31 on b = 0 s/mm² images.Corticomedullary differentiation was possible on FA and MK maps. ADC, FA and MK revealed significant differences in medulla (ADC = 2.82 × 10^− 3 mm²/s ± 0.25, FA = 0.42 ± 0. 05, MK = 0.78 ± 0.07) and cortex (ADC = 3.60 × 10^− 3 mm²/s ± 0.28, FA = 0.18 ± 0.04, MK = 0.94 ± 0.07) with p < 0.001.

Conclusion

Our initial results indicate the feasibility of DKI in the human kidney presuming an adequate SNR. Future studies in patients with kidney diseases are required to determine the value of DKI for functional kidney imaging.     

Perfusion and diffusion sensitive C stimulated-echo MRSI for metabolic imaging of cancer

Metabolic imaging with hyperpolarized [1-¹³C]-pyruvate can rapidly probe tissue metabolic profiles in vivo and has been shown to provide cancer imaging biomarkers for tumor detection, progression, and response to therapy. This technique uses a bolus injection followed by imaging within 1–2 minutes. The observed metabolites include vascular components and their generation is also influenced by cellular transport. These factors complicate image interpretation, especially since [1-¹³C]lactate, a metabolic product that is a biomarker of cancer, is also produced by red blood cells. It would be valuable to understand the distribution of metabolites between the vasculature, interstitial space, and intracellular compartments. The purpose of this study was to better understand this compartmentalization by using a perfusion and diffusion-sensitive stimulated-echo acquisition mode (STEAM) MRSI acquisition method tailored to hyperpolarized substrates. Our results in mouse models showed that among metabolites, the injected substrate ¹³C-pyruvate had the largest vascular fraction overall while ¹³C-alanine had the smallest vascular fraction. We observed a larger vascular fraction of pyruvate and lactate in the kidneys and liver when compared to back muscle and prostate tumor tissue. Our data suggests that ¹³C-lactate in prostate tumor tissue voxels was the most abundant labeled metabolite intracellularly. This was shown in STEAM images that highlighted abnormal cancer cell metabolism and suppressed vascular ¹³C metabolite signals.     

Evaluation of diffusion models of fiber tracts using diffusion tensor magnetic resonance imaging

Modeling of water diffusion in white matter is useful for revealing microstructure of the brain tissue and hence diagnosis and evaluation of white matter diseases. Researchers have modeled diffusion in white matter using mathematical and mechanical analysis at the cellular level. However, less work has been devoted to evaluate these models using macroscopic real data such as diffusion tensor magnetic resonance imaging (DTMRI) data. DTMRI is a noninvasive tool for evaluating white matter microstructure by measuring random motion of water molecules referred to as diffusion. It reflects directional information of microscopic structures such as fibers. Thus, it is applicable for evaluation and modification of mathematical models of white matter. Nevertheless, a realistic relation between a fiber model and imaging data does not exist. This work opens a promising avenue for relating DTMRI data to microstructural parameters of white matter. First, we propose a strategy for relating DTMRI and fiber model parameters to evaluate mathematical models in light of real data. The proposed strategy is then applied to evaluate and extend an existing model of white matter based on clinically available DTMRI data. Next, the proposed strategy is used to estimate microstructural characteristics of fiber tracts. We illustrate this approach through its application to approximation of myelin sheath thickness and fraction of volume occupied by fibers. Using sufficiently small imaging voxels, the proposed approach is capable of estimating model parameters with desirable precision.     

Combined prostate diffusion tensor imaging and dynamic contrast enhanced MRI at 3T — quantitative correlation with biopsy

The purpose of this work was to compare diagnostic accuracy of Diffusion Tensor Imaging (DTI), dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) and their combination in diagnosing prostate cancer. Twenty-five patients with clinical suspicion of prostate cancer underwent MRI, prior to transrectal ultrasound-guided biopsies. MRI data were correlated to biopsy results. Logistic regression models were constructed for the DTI parameters, DCE MRI parameters, and their combination. The areas under the receiver operator characteristic curves (AUC) were compared between the models. The nonparametric Wilcoxon signed rank test was used for statistical analysis. The sensitivity and specificity values were respectively 81% (74–87%) and 85% (79–90%) for DTI and 63% (55–70%) and 90% (85–94%) for DCE. The combination “DTI or DCE MRI” had 100% (97–100%) sensitivity and 77% (69–83%) specificity, while “DTI and DCE MRI” had 44% (37–52%) sensitivity and 98% (94–100%) specificity. The AUC for DTI+DCE parameters was significantly higher than that for either DTI (0.96 vs. 0.92, P=.0143) or DCE MRI parameters (0.96 vs. 0.87, P=.00187) alone. In conclusion, the combination of DTI and DCE MRI has significantly better accuracy in prostate cancer diagnosis than either technique alone.     

High resolution diffusion weighted magnetic resonance imaging of the pancreas using reduced field of view single-shot echo-planar imaging at 3 T

Diffusion weighted magnetic resonance imaging (DWI) has been mostly acquired using single-shot echo-planar imaging (ss EPI) to minimize motion induced artifacts. The spatial resolution, however, is inherently limited in ss EPI especially for abdominal imaging, even with the advances in parallel imaging. A novel method of reduced Field of View ss EPI (rFOV ss EPI) has achieved high resolution DWI in human carotid artery, spinal cord with reduced blurring and higher spatial resolution than conventional ss EPI, but it has not been used to pancreas imaging. In the work, comparisons between the full FOV ss-DW EPI and rFOV ss-DW EPI in image qualities and ADC values of pancreatic tumors and normal pancreatic tissues were performed to demonstrate the feasibility of pancreatic high resolution rFOV DWI. There were no significant differences in the mean ADC values between full FOV DWI and rFOV DWI for the 17 subjects using b = 600 s/mm² (P = 0.962). However, subjective scores of image quality was significantly higher at rFOV ss DWI (P = 0.008 and 0.000 for b-value = 0 s/mm² and 600 s/mm² respectively). The spatial resolution of DWI for pancreas was increased by a factor of over 2.0 (from almost 3.0 mm/pixel to 1.25 mm/pixel) using rFOV ss EPI technique. Reduced FOV ss EPI can provide good DW images and is promising to benefit applications for pancreatic diseases.     

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.     

Effects of random subject rotation on optimised diffusion gradient sampling schemes in diffusion tensor MRI

The choice of the number (N) and orientation of diffusion sampling gradients required to measure accurately the water diffusion tensor remains contentious. Monte Carlo studies have suggested that between 20 and 30 uniformly distributed sampling orientations are required to provide robust estimates of water diffusions parameters. These simulations have not, however, taken into account what effect random subject motion, specifically rotation, might have on optimised gradient schemes, a problem which is especially relevant to clinical diffusion tensor MRI (DT-MRI). Here this question is investigated using Monte Carlo simulations of icosahedral sampling schemes and in vivo data. These polyhedra-based schemes, which have the advantage that large N can be created from optimised subsets of smaller N, appear to be ideal for the study of restless subjects since if scanning needs to be prematurely terminated it should be possible to identify a subset of images that have been acquired with a near optimised sampling scheme. The simulations and in vivo data show that as N increases, the rotational variance of fractional anisotropy (FA) estimates becomes progressively less dependent on the magnitude of subject rotation (), while higher FA values are progressively underestimated as increases. These data indicate that for large subject rotations the B-matrix should be recalculated to provide accurate diffusion anisotropy information.