Prostate Microstructure in Prostate Cancer Using 3-T MRI with                    Diffusion-Relaxation Correlation Spectrum Imaging: Validation with Whole-Mount                    Digital Histopathology

Zhang, Zhaohuan; Wu, Holden H.; Priester, Alan; Magyar, Clara E.; Mirak, Sohrab Afshari; Shakeri, Sepideh; Bajgiran, Amirhossein Mohammadian; Hosseiny, Melina; Azadikhah, Afshin; Sung, Kyunghyun; Reiter, Robert E.; Sisk, Anthony; Raman, Steven S.; Enzmann, Dieter R.

doi:10.1148/radiol.2020192330

Cited by 46 publications

(75 citation statements)

References 26 publications

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“…Fifth, we only evaluated ENCODE DWI with MONO/BIPOLAR using the clinically employed monoexponential ADC model, which is inaccurate, as multiple microscopic tissue compartments exist in prostate tissue. The effects of ENCODE waveforms and potential advantages of shortened TE in modeling prostate microstructure using DWI need to be evaluated in a separate study that uses more advanced multicomponent signal models …”

Section: Discussionmentioning

confidence: 99%

Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding

Zhang

Moulin

Aliotta

et al. 2019

Magnetic Resonance Imaging

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Background: Prostate diffusion-weighted imaging (DWI) using monopolar encoding is sensitive to eddy-current-induced distortion artifacts. Twice-refocused bipolar encoding suppresses eddy current artifacts, but increases echo time (TE), leading to lower signal-to-noise ratio (SNR). Optimization of the diffusion encoding might improve prostate DWI. Purpose: To evaluate eddy current nulled convex optimized diffusion encoding (ENCODE) for prostate DWI with minimal TE. Study Type: Prospective cohort study. Population: A diffusion phantom, an ex vivo prostate specimen, 10 healthy male subjects (27 AE 3 years old), and five prostate cancer patients (62 AE 7 years old). Field Strength/Sequence: 3T; single-shot spin-echo echoplanar DWI. Assessment: Eddy-current artifacts, TE, SNR, apparent diffusion coefficient (ADC), and image quality scores from three independent readers were compared between monopolar, bipolar, and ENCODE prostate DWI for standard-resolution (1.6 × 1.6 mm 2 , partial Fourier factor [pF] = 6/8) and higher-resolution protocols (1.6 × 1.6 mm 2 , pF = off; 1.0 × 1.0 mm 2 , pF = 6/8). Statistical Testing: SNR and ADC differences between techniques were tested with Kruskal-Wallis and Wilcoxon signedrank tests (P < 0.05 considered significant). Results: Eddy current suppression with ENCODE was comparable to bipolar encoding (mean coefficient of variation across three diffusion directions of 9.4% and 9%). For a standard-resolution protocol, ENCODE achieved similar TE as monopolar and reduced TE by 14 msec compared to bipolar, resulting in 27% and 29% higher mean SNR in prostate transition zone (TZ) and peripheral zone (PZ) (P < 0.05) compared to bipolar, respectively. For higher-resolution protocols, ENCODE achieved the shortest TE (67 msec), with 17-21% and 58-70% higher mean SNR compared to monopolar (TE = 77 msec) and bipolar (TE = 102 msec) in PZ and TZ (P < 0.05). No significant differences were found in mean TZ (P = 0.91) and PZ ADC (P = 0.94) between the three techniques. ENCODE achieved similar or higher image quality scores than bipolar DWI in patients, with mean intraclass correlation coefficient of 0.77 for overall quality between three independent readers. Data Conclusion: ENCODE minimizes TE (improves SNR) and reduces eddy-current distortion for prostate DWI compared to monopolar and bipolar encoding. Level of Evidence: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2020;51:1526-1539. P ROSTATE CANCER (PCa) is the most prevalent noncutaneous cancer diagnosed in men and the second leading cause of cancer-related death in men in the United States. 1 Multiparametric magnetic resonance imaging (mp-MRI) of the prostate, including T 2 -weighted MRI, diffusionweighted MRI (DWI), apparent diffusion coefficient (ADC) View this article online at wileyonlinelibrary.com.

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Section: Discussionmentioning

confidence: 99%

Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding

Zhang

Moulin

Aliotta

et al. 2019

Magnetic Resonance Imaging

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“…For rVERDICT, the same assumptions as classic VERDICT for prostate apply, with the additional assumptions about the MR relaxation tissue properties as reported above and based on currently available experimental evidence. (17-19, 30) (17,18,20,22,23,31,32) Note that, unlike classic VERDICT, for rVERDICT we exploit the TE and TR dependence of the b=0 images for the T2 and T1 relaxation time estimation.…”

Section: Relaxation-verdict the New Relaxation-verdict (Rverdict) Model Parameterises The T2mentioning

confidence: 99%

“…We found good agreement between the rVERDICT estimated distribution of T2 values in the prostate tissue and the T2 estimates from an independent multi-TE acquisition, with average values not statistically different (P>0.05). The estimated compartmental T2 values are also in agreement with current literature, with T2ic~60 ms and T2vasc/ees~250 ms. (17,18,20,22,23,31,32) The Figure S4 in Supplementary Material shows the comparison of rVERDICT T2 estimates and the multi-TE acquisition estimates for the best and worst performing cases. 2).…”

Section: Repeatabilitymentioning

confidence: 99%

“…(17)(18)(19)30) The T2 relaxation estimates T2ic within tumours were similar for benign tissue (mean 61 vs 67 ms, respectively, P>0.05) and T2vasc/ees within tumour lesions were lower than in benign tissue (mean 300 vs 383 ms, respectively, P=0.023), in agreement with literature. (17,18,20,22,23,31,32) For seven patients in our cohort, we performed an independent multi-TE acquisition which allowed for a direct comparison of the estimated T2 values using the two methods. Results showed that the rVERDICT T2 values were not statistically different (P>0.05) from the independent T2 measurements.…”

Section: Gleason Grade Classificationmentioning

confidence: 99%

“…In addition to the potential of diffusion parameters, relaxometry parameters such as T2 relaxation time have also shown capability to discriminate Gleason grades 3 and 4. (12,15,16) Works exploiting joint relaxation-diffusion analysis (20)(21)(22)(23) (also in other organs, e.g. brain) have shown that these two types of parameters often contain complementary information.…”

Section: Introductionmentioning

confidence: 99%

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Joint estimation of relaxation and diffusion tissue parameters for prostate cancer grading with relaxation-VERDICT MRI

Palombo

Valindria

Singh

et al. 2021

Preprint

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ObjectivesThe Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours (VERDICT) is a non-invasive MRI technique that has shown promising results in clinical settings discriminating normal from malignant tissue and Gleason grade 3+3 from 3+4. However, VERDICT currently does not account for the inherent relaxation properties of the tissue, whose quantification could provide additional information and enhance its diagnostic power. The aim of this work is to introduce relaxation-VERDICT (rVERDICT) for prostate, a model for the joint estimation of diffusion-based parameters (e.g. the intracellular volume fraction, fic) and relaxation times (e.g. T2) from a VERDICT MRI acquisition; and to evaluate its repeatability and diagnostic utility for differentiating Gleason grades 3 and 4.Materials and Methods72 men with suspected prostate cancer (PCa) or undergoing active surveillance were recruited. All men underwent multiparametric MRI (mp-MRI) and VERDICT MRI. Deep neural network was used for ultra-fast fitting of the rVERDICT parameters. 44 men underwent targeted biopsy, which enabled assessment of rVERDICT parameters in differentiating Gleason grades measured with accuracy, F1-score and Cohen’s kappa of a convolutional neural network classifier. To assess the repeatability of the new model, five men were imaged twice.ResultsThe rVERDICT intracellular volume fraction fic discriminated between Gleason grades (5-class classification) with accuracy, F1-score and kappa 8, 7 and 3 percentage points higher than classic VERDICT, and 12, 13 and 24 percentage points higher than the Apparent Diffusion Coefficient (ADC) from mp-MRI. Repeatability of rVERDICT parameters was high (R2=0.74–0.99, coefficient of variation=1%–10% and intraclass correlation coefficient=78%-98%). T2 values estimated with rVERDICT were not significantly different from those estimated with an independent multi-TE acquisition (p>0.05). The deep neural network fitting approach provided stable fitting of all the rVERDICT parameters with dramatic reduction of the processing time (∼35 seconds vs ∼15 minutes using classic VERDICT), enabling on-the-fly rVERDICT map generation.ConclusionsThe new rVERDICT allows for robust and ultra-fast microstructural estimation of diffusion and relaxation properties of PCa and enables Gleason scoring.

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In vivo disentanglement of diffusion frequency‐dependence, tensor shape, and relaxation using multidimensional MRI

Johnson,

Irfanoglu,

Manninen

et al. 2024

Human Brain Mapping

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Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD‐MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD‐MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, , in addition to the diffusion tensor, , and relaxation, , , correlations. A clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD‐MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well‐defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD‐MRI encoding design demands a nuanced denoising approach. The MD‐MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their distribution, enabling the creation of microstructure‐specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

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Prostate Microstructure in Prostate Cancer Using 3-T MRI with Diffusion-Relaxation Correlation Spectrum Imaging: Validation with Whole-Mount Digital Histopathology

Cited by 46 publications

References 26 publications

Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding

Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding

Joint estimation of relaxation and diffusion tissue parameters for prostate cancer grading with relaxation-VERDICT MRI

In vivo disentanglement of diffusion frequency‐dependence, tensor shape, and relaxation using multidimensional MRI

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