Background and Purpose. Although several methods have been developed to predict the outcome of patients with prostate cancer, early diagnosis of individual patient remains challenging. The aim of the present study was to correlate tumor perfusion parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical prognostic factors and further to explore the diagnostic value of DCE-MRI parameters in early stage prostate cancer. Patients and Methods. Sixty-two newly diagnosed patients with histologically proven prostate adenocarcinoma were enrolled in our prospective study. Transrectal ultrasound-guided biopsy (12 cores, 6 on each lobe) was performed in each patient. Pathology was reviewed and graded according to the Gleason system. DCE-MRI was performed and analyzed using a two-compartmental model; quantitative parameters including volume transfer constant (Ktrans), reflux constant (Kep), and initial area under curve (iAUC) were calculated from the tumors and correlated with prostate-specific antigen (PSA), Gleason score, and clinical stage. Results. Ktrans (0.11 ± 0.02 min−1 versus 0.16 ± 0.06 min−1; p<0.05), Kep (0.38 ± 0.08 min−1 versus 0.60 ± 0.23 min−1; p<0.01), and iAUC (14.33 ± 2.66 mmoL/L/min versus 17.40 ± 5.97 mmoL/L/min; p<0.05) were all lower in the clinical stage T1c tumors (tumor number, n=11) than that of tumors in clinical stage T2 (n=58). Serum PSA correlated with both tumor Ktrans (r=0.304, p<0.05) and iAUC (r=0.258, p<0.05). Conclusions. Our study has confirmed that DCE-MRI is a promising biomarker that reflects the microcirculation of prostate cancer. DCE-MRI in combination with clinical prognostic factors may provide an effective new tool for the basis of early diagnosis and treatment decisions.

BACKGROUND:Diffusion-weighted imaging (DWI) with measurement of apparent diffusion coefficient (ADC) allows for assessment of tumor aggressiveness. The objective of this study was to evaluate the changes of ADC value in prostate cancer after volumetric-modulated arc radiotherapy (VMAT) and to identify magnetic resonance imaging (MRI) biomarkers for monitoring tissue changes after radiotherapy.PATIENTS AND METHODS:Thirty-seven patients with biopsy-proven prostate cancer treated with VMAT underwent serial MRI examinations including DWI before radiotherapy, and at 3 and 12 months after radiotherapy. ADC values of the tumor and healthy prostate tissue were measured and compared at these three time points.RESULTS:The tumor ADC value increased significantly 3 months after radiotherapy (p<0.0001). There was a further increase of tumor ADC from 3 to 12 months after radiotherapy (p<0.01). The ADC of healthy prostate tissue did not show any significant changes.CONCLUSION:The ADC value is a useful biomarker for evaluating the efficacy of radiotherapy in prostate cancer.

Background: This paper addresses two subtypes of multiple sclerosis (MS), primary progressive multiple sclerosis (PPMS) and relapsing-remitting multiple sclerosis (RRMS). The separation of PPMS and RRMS is challenging in certain cases.Purpose: To quantitatively determine MS subtypes using texture analysis (TA) and diffusion tensor imaging (DTI).Material and Methods: T1-weighted (T1W) magnetic resonance imaging (MRI) and DTI of the left and right brain hemispheres of 17 patients with PPMS and 19 patients with RRMS were studied. Areas of the caudate nucleus and thalamus were investigated as normal appearing gray matter (NAGM), and areas of the cerebral peduncle and centrum semiovale were investigated as normal appearing white matter (NAWM). The described locations were symmetrical and were accurately marked. TA was performed on the T1W images, and the fractional anisotropy and apparent diffusion coefficient were determined from the DTI data.Results: Hemispherical differences were found with both TA and DTI. Several texture and diffusion tensor parameter values calculated for the left and right hemispheres of the patients showed statistically significant differences. The patients with RRMS had greater significant differences (P<0.01) in the thalamus between the hemispheres than did the patients with PPMS. The TA classification accuracy of the PPMS and RRMS subtypes was above 80%.Conclusion: TA can be helpful when distinguishing between PPMS and RRMS, while DTI appears to reveal the hemispherical asymmetry of RRMS patients.

Texture analysis provides quantitative information describing the properties of a digital image. The value of texture analysis has been tested in various medical applications, using mostly magnetic resonance images because of the amount of information the method is capable to provide. However, there exists no certain practice to define the region of interest (ROI) within the texture parameters are calculated. Many parameters seem to be dependent on the ROI size. We studied the effect of the ROI size with magnetic resonance head images from 64 healthy adults and artificial noise images. According to our results, ROI size has a significant effect on the computed value of several second-order texture features. We conclude that comparisons of different size ROIs will therefore lead to falsely optimistic classification between analyzed tissues.

Background Few of the structural changes caused by Parkinson’s disease (PD) are visible in magnetic resonance imaging (MRI) with visual inspection but there is a need for a method capable of observing the changes beyond the human eye. Texture analysis offers a technique that enables the quantification of the image gray-level patterns. Purpose To investigate the value of quantitative image texture analysis method in diagnosis and follow-up of PD patients. Material and Methods Twenty-six PD patients underwent MRI at baseline and after 2 years of follow-up. Four co-occurrence matrix-based texture parameters, describing the image homogeneity and complexity, were calculated within clinically interesting areas of the brain. In addition, correlations with clinical characteristics (Unified Parkinson’s Disease Ranking Scales I–III and Mini-Mental State Examination score) along with a comparison to healthy controls were evaluated. Results Patients at baseline and healthy volunteers differed in their brain MR image textures mostly in the areas of substantia nigra pars compacta, dentate nucleus, and basilar pons. During the 2-year follow-up of the patients, textural differences appeared mainly in thalamus and corona radiata. Texture parameters in all the above mentioned areas were also found to be significantly related to clinical scores describing the severity of PD. Conclusion Texture analysis offers a quantitative method for detecting structural changes in brain MR images. However, the protocol and repeatability of the method must be enhanced before possible clinical use.

Texture analysis provides quantitative information describing the properties of a digital image. The value of texture analysis has been tested in various medical applications, using mostly magnetic resonance images because of the amount of information the method is capable to provide. However, there exists no certain practice to define the region of interest (ROI) within the texture parameters are calculated. Many parameters seem to be dependent on the ROI size. We studied the effect of the ROI size with magnetic resonance head images from 64 healthy adults and artificial noise images. According to our results, ROI size has a significant effect on the computed value of several second-order texture features. We conclude that comparisons of different size ROIs will therefore lead to falsely optimistic classification between analyzed tissues.

This study was designed to (i) evaluate the influence of age on diffusion tensor imaging measures of white matter assessed using tract-based spatial statistics; (ii) determine if mild traumatic brain injury is associated with microstructural changes in white matter, in the acute phase following injury, in a large homogenous sample that was carefully screened for pre-injury medical, psychiatric, or neurological problems; and (iii) examine if injury severity is related to white matter changes. Participants were 75 patients with acute mild traumatic brain injury (age = 37.2 ± 12.0 years, 45 males and 30 females) and 40 controls (age = 40.6 ± 12.2 yrs, 20 males and 20 females). Age effects were analysed by comparing control subgroups aged 31-40, 41-50, and 51-60 years against a group of 18-30-year-old control subjects. Widespread statistically significant areas of abnormal diffusion tensor measures were observed in older groups. Patients and controls were compared using age and gender as covariates and in age- and gender-matched subgroups. Subgroups of patients with more severe injuries were compared to age-and gender-matched controls. No significant differences were detected in patient-control or severity analyses (all P-value > 0.01). In this large, carefully screened sample, acute mild traumatic brain injury was not associated with diffusion tensor imaging abnormalities detectable with tract-based spatial statistics.

BACKGROUND:Patients with acute ST-elevation myocardial infarction (STEMI) benefit substantially from emergent coronary reperfusion. The principal mechanism is to open the occluded coronary artery to minimize myocardial injury. Thus the size of the area at risk is a critical determinant of the patient outcome, although other factors, such as reperfusion injury, have major impact on the final infarct size. Acute coronary occlusion almost immediately induces metabolic changes within the myocardium, which can be assessed with both the electrocardiogram (ECG) and cardiac magnetic resonance (CMR) imaging.METHODS:The 12-lead ECG is the principal diagnostic method to detect and risk-stratify acute STEMI. However, to achieve a correct diagnosis, it is paramount to compare different ECG parameters with golden standards in imaging, such as CMR. In this review, we discuss aspects of ECG and CMR in the assessment of acute regional ischemic changes in the myocardium using the 17 segment model of the left ventricle presented by American Heart Association (AHA), and their relation to coronary artery anatomy.RESULTS:Using the 17 segment model of AHA, the segments 12 and 16 remain controversial. There is an important overlap in myocardial blood supply at the antero-lateral region between LAD and LCx territories concerning these two segments.CONCLUSION:No all-encompassing correlation can be found between ECG and CMR findings in acute ischemia with respect to coronary anatomy.

BACKGROUND AND PURPOSEDiffusion tensor imaging (DTI) is shown to reveal changes caused by cerebral infarction. The aim of this study is to reveal those changes also in the conventional magnetic resonance (MR) images using a quantitative image analysis method, texture analysis (TA).METHODSThirty patients who had suffered their first ever infarction located on the right hemisphere underwent DTI and conventional MRI studies in the chronic phase. DTI parameters fractional anisotropy and mean diffusivity, as well as four second-order texture parameters were calculated. Interhemispheric differences and correlations between DTI and TA parameters were evaluated.RESULTSOur DTI findings supported earlier studies as fractional anisotropy values were lowered and mean diffusivity values elevated in the lesion site, and ipsilateral cerebral peduncle, thalamus, and centrum semiovale compared to the unaffected side. Textural homogeneity parameters showed lower and complexity parameters higher values in the lesion site and ipsilateral centrum semiovale compared to the contralateral hemisphere. Correlation between the two methods was found in ipsilateral mesencephalon.CONCLUSIONSIn addition to DTI method, TA could assist in revealing the changes caused by infarction, also outside the lesion site. Damaged areas were found more heterogeneous and random in texture compared to unaffected sites.

BACKGROUND:Compared to diffusion tensor imaging (DTI) of the brain, there is a paucity of reports addressing the applicability of DTI in the evaluation of the spinal cord. Most normative data of cervical spinal cord DTI consist of relatively small and arbitrarily collected populations. Comprehensive normative data are necessary for clinical decision-making.PURPOSE:To establish normal values for cervical spinal cord DTI metrics with region of interest (ROI)- and fiber tractography (FT)-based measurements and to assess the reproducibility of both measurement methods.MATERIAL AND METHODS:Forty healthy adults underwent cervical spinal cord 3T MRI. Sagittal and axial conventional T2 sequences and DTI in the axial plane were performed. Whole cord fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were determined at different cervical levels from C2 to C7 using the ROI method. DTI metrics (FA, axial, and radial diffusivities based on eigenvalues λ1, λ2, and λ3, and ADC) of the lateral and posterior funicles were measured at C3 level. FA and ADC of the whole cord and the lateral and posterior funicles were also measured using quantitative tractography. Intra- and inter-observer variation of the measurement methods were assessed.RESULTS:Whole cord FA values decreased and ADC values increased in the rostral to caudal direction from C2 to C7. Between the individual white matter funicles no statistically significant difference for FA or ADC values was found. Both axial diffusivity and radial diffusivity of both lateral funicles differed significantly from those of the posterior funicle. Neither gender nor age correlated with any of the DTI metrics. Intra-observer variation of the measurements for whole cord FA and ADC showed almost perfect agreement with both ROI and tractography-based measurements. There was more variation in measurements of individual columns. Inter-observer agreement varied from moderate to strong for whole cord FA and ADC.CONCLUSION:Both ROI- and FT-based measurements are applicable methods yielding reproducible results for cervical spinal cord DTI metrics. Normative values for both measurement methods are presented.

Adaptation to exercise training can affect bone marrow adiposity; muscle-fat distribution; and muscle volume, strength and architecture. The objective of this study was to identify exercise-load-associated differences in magnetic resonance image textures of thigh soft tissues between various athlete groups and non-athletes. Ninety female athletes representing five differently loading sport types (high impact, odd impact, high magnitude, repetitive low impact and repetitive non-impact), and 20 non-athletic clinically healthy female controls underwent magnetic resonance imaging. Five thigh muscles, subcutaneous fat and femoral bone marrow were analysed with co-occurrence matrix-based quantitative texture analysis at two anatomical levels of the dominant leg. Compared with the controls thigh muscle textures differed especially in high-impact and odd-impact exercise-loading groups. However, all sports appeared to modulate muscle textures to some extent. Fat tissue was found different among the low-impact group, and bone marrow was different in the high-impact group when compared to the controls. Exercise loading was associated with textural variation in magnetic resonance images of thigh soft tissues. Texture analysis proved a potential method for detecting apparent structural differences in the muscle, fat and bone marrow.

The purpose of this study was to analyze the lesion brightness (image contrast) in multiple MRI sequences in patients with relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS), and clinically isolated syndrome (CIS); and to correlate the lesion contrast with lesion volumes and neurological disability. MRI ex- amination at 1.5 T was performed on 80 patients with RRMS, SPMS, PPMS, or CIS. The protocol included T1- and T2-weighted spin echo (SE), fluid attenuated inversion recovery (FLAIR), T1-weighted SE with magnetization transfer preparation, and diffusion weighted imaging (DWI). Contrast was measured between MS lesions and normal appearing white matter. Lesion volume was calculated in T1-weighted- and FLAIR-images. All patients were examined neurologically including evaluation of expanded disability status scale (EDSS) score. Lesion contrast correlated with total brain lesion volume (p = 0.000 - 0.040). In patients with low EDSS, three sequences were able to differentiate between CIS and RRMS. SPMS and PPMS were separated by DWI. Lesion contrast correlated with EDSS score on T1-weighted imaging, with or without magnetization transfer preparation. Patient age correlated with lesion contrasts. Contrast measurements seem limited in radiological and clinical diagnosis of MS in reference to disease course, its activity and progression. The differentiation between MS subgroups might improve at 3 T and could help in leading to earlier treatment of the disease.

To compare different MRI sequences for the detection of lesions and the evaluation of response to chemotherapy in patients with diffuse large B‐cell lymphoma (DLBCL), 18 patients with histology‐confirmed DLBCL underwent 3‐T MRI scanning prior to and 1 week after chemotherapy. The MRI sequences included T 1 ‐weighted pre‐ and post‐contrast, T 2 ‐weighted with and without fat suppression, and a single‐shot echo‐planar diffusion‐weighted imaging (DWI) with two b values (0 and 800 s/mm 2 ). Conventional MRI sequence comparisons were performed using the contrast ratio between tumor and normal vertebral body instead of signal intensity. The apparent diffusion coefficient (ADC) of the tumor was measured directly on the parametric ADC map. The tumor volume was used as a reference for the evaluation of chemotherapy response. The mean tumor volume was 374 mL at baseline, and decreased by 65% 1 week after chemotherapy ( p < 0.01). The T 2 ‐weighted image with fat suppression showed a significantly higher contrast ratio compared with images from all other conventional MRI sequences, both before and after treatment ( p < 0.01, respectively). The contrast ratio of the T 2 ‐weighted image with fat suppression decreased significantly ( p < 0.01), and that of the T 1 ‐weighted pre‐contrast image increased significantly ( p < 0.01), after treatment. However, there was no correlation between the change in contrast ratio and tumor volume. The mean ADC value was 0.68 × 10 –3 mm 2 /s at baseline; it increased by 89% after chemotherapy ( p < 0.001), and the change in ADC value correlated with the change in tumor volume ( r = 0.66, p < 0.01). The baseline ADC value also correlated inversely with the percentage change in ADC after treatment ( r = −0.62, p < 0.01). In conclusion, this study indicates that T 2 ‐weighted imaging with fat suppression is the best conventional sequence for the detection of lesions and evaluation of the efficacy of chemotherapy in DLBCL. DWI with ADC mapping is an imaging modality with both diagnostic and prognostic value that could complement conventional MRI. Copyright © 2013 John Wiley & Sons, Ltd.

BACKGROUND:Diffusion tensor imaging (DTI) is increasingly used in various diseases as a clinical tool for assessing the integrity of the brain's white matter. Reduced fractional anisotropy (FA) and an increased apparent diffusion coefficient (ADC) are nonspecific findings in most pathological processes affecting the brain's parenchyma. At present, there is no gold standard for validating diffusion measures, which are dependent on the scanning protocols, methods of the softwares and observers. Therefore, the normal variation and repeatability effects on commonly-derived measures should be carefully examined.METHODS:Thirty healthy volunteers (mean age 37.8 years, SD 11.4) underwent DTI of the brain with 3T MRI. Region-of-interest (ROI) -based measurements were calculated at eleven anatomical locations in the pyramidal tracts, corpus callosum and frontobasal area. Two ROI-based methods, the circular method (CM) and the freehand method (FM), were compared. Both methods were also compared by performing measurements on a DTI phantom. The intra- and inter-observer variability (coefficient of variation, or CV%) and repeatability (intra-class correlation coefficient, or ICC) were assessed for FA and ADC values obtained using both ROI methods.RESULTS:The mean FA values for all of the regions were 0.663 with the CM and 0.621 with the FM. For both methods, the FA was highest in the splenium of the corpus callosum. The mean ADC value was 0.727 ×10-3 mm2/s with the CM and 0.747 ×10-3 mm2/s with the FM, and both methods found the ADC to be lowest in the corona radiata. The CV percentages of the derived measures were < 13% with the CM and < 10% with the FM. In most of the regions, the ICCs were excellent or moderate for both methods. With the CM, the highest ICC for FA was in the posterior limb of the internal capsule (0.90), and with the FM, it was in the corona radiata (0.86). For ADC, the highest ICC was found in the genu of the corpus callosum (0.93) with the CM and in the uncinate fasciculus (0.92) with FM.CONCLUSIONS:With both ROI-based methods variability was low and repeatability was moderate. The circular method gave higher repeatability, but variation was slightly lower using the freehand method. The circular method can be recommended for the posterior limb of the internal capsule and splenium of the corpus callosum, and the freehand method for the corona radiata.

Early-stage diagnosis of Parkinson's disease (PD) is essential in making decisions related to treatment and prognosis. However, there is no specific diagnostic test for the diagnosis of PD. The aim of this study was to evaluate the role of texture analysis (TA) of magnetic resonance images in detecting subtle changes between the hemispheres in various brain structures in patients with early symptoms of parkinsonism. In addition, functional TA parameters for detecting textural changes are presented.Fifty-one patients with symptoms of PD and 20 healthy controls were imaged using a 3-T magnetic resonance device. Co-occurrence matrix-based TA was applied to detect changes in textures between the hemispheres in the following clinically interesting areas: dentate nucleus, basilar pons, substantia nigra, globus pallidus, thalamus, putamen, caudate nucleus, corona radiata, and centrum semiovale. The TA results were statistically evaluated using the Mann-Whitney U test.The results showed interhemispheric textural differences among the patients, especially in the area of basilar pons and midbrain. Concentrating on this clinically interesting area, the four most discriminant parameters were defined: co-occurrence matrix correlation, contrast, difference variance, and sum variance. With these parameters, differences were also detected in the dentate nucleus, globus pallidus, and corona radiata.On the basis of this study, interhemispheric differences in the magnetic resonance images of patients with PD can be identified by the means of co-occurrence matrix-based TA. The detected areas correlate with the current pathophysiologic and neuroanatomic knowledge of PD.

RATIONALE AND OBJECTIVES:Early-stage diagnosis of Parkinson's disease (PD) is essential in making decisions related to treatment and prognosis. However, there is no specific diagnostic test for the diagnosis of PD. The aim of this study was to evaluate the role of texture analysis (TA) of magnetic resonance images in detecting subtle changes between the hemispheres in various brain structures in patients with early symptoms of parkinsonism. In addition, functional TA parameters for detecting textural changes are presented.MATERIALS AND METHODS:Fifty-one patients with symptoms of PD and 20 healthy controls were imaged using a 3-T magnetic resonance device. Co-occurrence matrix-based TA was applied to detect changes in textures between the hemispheres in the following clinically interesting areas: dentate nucleus, basilar pons, substantia nigra, globus pallidus, thalamus, putamen, caudate nucleus, corona radiata, and centrum semiovale. The TA results were statistically evaluated using the Mann-Whitney U test.RESULTS:The results showed interhemispheric textural differences among the patients, especially in the area of basilar pons and midbrain. Concentrating on this clinically interesting area, the four most discriminant parameters were defined: co-occurrence matrix correlation, contrast, difference variance, and sum variance. With these parameters, differences were also detected in the dentate nucleus, globus pallidus, and corona radiata.CONCLUSIONS:On the basis of this study, interhemispheric differences in the magnetic resonance images of patients with PD can be identified by the means of co-occurrence matrix-based TA. The detected areas correlate with the current pathophysiologic and neuroanatomic knowledge of PD.

In this study, we have studied the effect of background noise on the texture analysis of muscle, bone marrow and fat tissues in 1.5 T magnetic resonance (MR) images using different statistical methods. Variable levels of noise were first added on 3-mm thick T2 weighted image slices of voluntary subjects to simulate several signal-to-noise ratio (SNR) levels. For each original and simulated image, the values for 264 texture parameters were calculated using MaZda, a texture analysis toolkit. We also determined Fisher coefficients based on the texture parameter values in order to enable high discrimination between different tissues. Linear discriminant analysis (LDA) and two different nearest neighbour (NN) methods were then applied for the texture parameters with the highest Fisher coefficient values. Several training and test sets were used to approximate the variation in the classification results. All the above-mentioned methods had the same classification accuracy, which in turn depended on the image SNR. We conclude that these tissues can be detected by texture analysis methods with a sufficient accuracy (90%) especially if SNR is at least 30-40 dB, even though the separation of different muscles remains a very challenging task.

PURPOSE:To assess the ability of co-occurrence matrix-based texture parameters to detect exercise load-associated differences in MRI texture at the femoral neck cross-section.MATERIALS AND METHODS:A total of 91 top-level female athletes representing five differently loading sports and 20 referents participated in this cross-sectional study. Axial T1-weighted FLASH and T2*-weighted MEDIC sequence images of the proximal femur were obtained with a 1.5T MRI. The femoral neck trabecular bone at the level of the insertion of articular capsule was divided manually into regions of interest representing four anatomical sectors (anterior, posterior, superior, and inferior). Selected co-occurrence matrix-based texture parameters were used to evaluate differences in apparent trabecular structure between the exercise loading groups and anatomical sectors of the femoral neck.RESULTS:Significant differences in the trabecular bone texture, particularly at the superior femoral neck, were observed between athletes representing odd-impact (soccer and squash) and high-magnitude exercise loading (power-lifting) groups and the nonathletic reference group.CONCLUSION:MRI texture analysis provides a quantitative method for detecting and classifying apparent structural differences in trabecular bone that are associated with specific exercise loading.

Objectives: To compare quantitatively T-2- and T-2*-based magnetic resonance imaging sequences in patients with symptoms of Parkinson disease and to evaluate the information content of those sequences regarding brain iron concentration.Materials and Methods: We imaged 51 patients with symptoms of Parkinson disease on 3-T magnetic resonance imaging with T-2-weighted sampling perfection with application optimized contrasts using different flip-angle evolution (SPACE), fluid attenuation inversion recovery (FLAIR)-SPACE, susceptibility-weighted imaging (SWI), and parametric T-2* sequence (MapIt). Signal analysis was performed in 22 regions of interest in the brain.Results: Correlations (r(2) = 0.82...0.96) with brain iron concentration were excellent. Contrast and tissue separability ratios were best in the T-2* maps and FLAIR-SPACE, respectively. Good correlations of contrast were reached between SWI and both T-2-weighted SPACE and FLAIR-SPACE. Their relation to quantitative T-2* values was reminiscent of a quadratic curve shape. However, separation into gray and white matter revealed a linear positive and negative correlation, respectively.Conclusions: SWI showed potential in differentiating illnesses characterized by brain iron deposition. Closely similar information was given by T 2 weighted SPACE and FLAIR-SPACE, whereas other sequence comparisons revealed dispersion from intersequence agreement.