Kawashima, Akira

AbstractPurposeTo study if adaptive image receive (AIR) receiver coil elements can be configured into a 2D array with high (>45% by diameter) element‐to‐element overlap, allowing improved SNR at depth (0.7–1.5× element diameter) versus conventional (20%) overlap.MethodsAn anterior array composed of twenty 10‐cm diameter elements with 45% overlap arranged into a 4 × 5 grid and a similar 3 × 7 twenty‐one‐element posterior array were constructed. SNR and g‐factor were measured in a pelvic phantom using the new high‐density (HD) arrays (41 total elements) and compared to vendor AIR‐based arrays (30 total elements) with conventional overlap. T2‐weighted fast‐spin‐echo (T2SE) images acquired using both arrays were compared in 20 subjects. SNR was estimated in vivo. Results were compared blindly by three uroradiologists using a five‐point scale. Images using the HD arrays were also compared to a set of images acquired over a range of acceleration factors (R = 2.0, 2.5, 3.0) with the conventional arrays.ResultsSNR within the phantom was on average 15% higher for R = 1.0, 1.5, and 2.0 using the HD arrays. Across the 20 subjects SNR within the prostate was 11% higher and assessed radiologically as significantly higher (p < 0.001) for the HD versus conventional arrays. At all acceleration factors the new HD arrays outperformed the conventional arrays (p ≤ 0.01), allowing increased R for similar SNR.ConclusionAIR elements can be configured into 2D arrays with high (45%) element‐to‐element overlap, consistently providing increased SNR at depth versus arrays with conventional (20%) overlap. The SNR improvement allows increased acceleration in T2SE prostate MRI.

The performance of a deep learning model was not different from that of radiologists in the detection of clinically significant prostate cancer at MRI, and gradient-weighted class activation maps localized the tumor.

PurposeThe purpose is to develop a model‐based image‐reconstruction method using wavelet sparsity regularization for maintaining restoration of through‐plane resolution but with improved retention of SNR versus linear reconstruction using Tikhonov (TK) regularization in high through‐plane resolution (1 mm) T2‐weighted spin‐echo (T2SE) images of the prostate.MethodsA wavelet sparsity (WS)–regularized image reconstruction was developed that takes as input a set of ≈80 overlapped 3‐mm‐thick slices acquired using a T2SE multislice scan and typically 30 coil elements. After testing in contrast and resolution phantoms and calibration in 6 subjects, the WS reconstruction was evaluated in 16 consecutive prostate T2SE MRI exams. Results reconstructed with nominal 1‐mm thickness were compared with those from the TK reconstruction with the same raw data. Results were evaluated radiologically. The ratio of magnitude of prostate signal to periprostatic muscle signal was used to assess the presence of noise reduction. Technical performance was also compared with a commercial 3D‐T2SE sequence.ResultsThe new WS reconstruction was assessed as superior statistically to TK for overall SNR, contrast, and multiple evaluation criteria related to sharpness while retaining the high (1 mm) through‐plane resolution. Wavelet sparsity tended to provide improved overall diagnostic quality versus TK, but not significantly so. In all 16 studies, the prostate‐to‐muscle signal ratio increased.ConclusionsModel‐based WS‐regularized reconstruction consistently provides improved SNR in high (1 mm) through‐plane resolution images of prostate T2SE MRI versus linear reconstruction using TK regularization.

PurposeThe purpose is to develop a retrospective correction for subtle slice‐to‐slice positional inconsistencies that can occur when overlapped slices are acquired for super resolution in T2‐weighted spin‐echo multislice imaging.MethodsSpin‐echo acquisition of overlapped slices is typically done using multiple passes. After the passes are assembled into the final slice set, consecutive slices are correlated due to their overlap. Cross correlation was used to measure slice‐to‐slice displacement. After Z‐dependent filtering to preserve true object shape, the displacements were used to correct slice position. The method was tested in a phantom moved slowly (0.16‐0.63 mm/pass) under computer control and in vivo in 16 patients having prostate MRI.ResultsOver the motion range, the correlation method had an accuracy within 0.03 mm/pass and precision ± 0.20 mm (ie, subpixel). Corrected images visually resemble the true object. Over the patient studies, the mean range of motion in the anterior–posterior direction was 1.63 mm. Motion‐corrected axial images and the sagittal reformats were evaluated as significantly superior over those formed without motion correction.ConclusionThe retrospective correlation‐based motion‐correction method provides significant improvement in the slice‐to‐slice registration necessary for effective super resolution using overlapped slices.