T2‐weighted four dimensional magnetic resonance imaging with result‐driven phase sorting. Issue 8 (2nd July 2015)

• Record Type: Journal Article
• Authors: Liu, Yilin; Yin, Fang‐Fang; Czito, Brian G.; Bashir, Mustafa R.; Cai, Jing
• Is Part Of: Medical physics. Volume 42:Issue 8(2015)Part 1
• Page Start: 4460
• Page End: 4471
• Publication Date: 2015-07-02
• Subjects: biological organs -- biomedical MRI -- image reconstruction -- image sequences -- medical image processing -- phantoms -- pneumodynamics -- tumours; Magnetic resonance imaging -- Reconstruction; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; 4D‐MRI -- T2‐weighted -- phase sorting -- hepatic cancer; Cancer -- Digital image processing -- Medical magnetic resonance imaging -- Medical image reconstruction -- Medical image quality -- Image reconstruction -- Sequence analysis -- Anatomy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4923168
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: T2‐weighted MRI provides excellent tumor‐to‐tissue contrast for target volume delineation in radiation therapy treatment planning. This study aims at developing a novel T2‐weighted retrospective four dimensional magnetic resonance imaging (4D‐MRI) phase sorting technique for imaging organ/tumor respiratory motion. Methods: A 2D fast T2‐weighted half‐Fourier acquisition single‐shot turbo spin‐echo MR sequence was used for image acquisition of 4D‐MRI, with a frame rate of 2–3 frames/s. Respiratory motion was measured using an external breathing monitoring device. A phase sorting method was developed to sort the images by their corresponding respiratory phases. Besides, a result‐driven strategy was applied to effectively utilize redundant images in the case when multiple images were allocated to a bin. This strategy, selecting the image with minimal amplitude error, will generate the most representative 4D‐MRI. Since we are using a different image acquisition mode for 4D imaging (the sequential image acquisition scheme) with the conventionally used cine or helical image acquisition scheme, the 4D dataset sufficient condition was not obviously and directly predictable. An important challenge of the proposed technique was to determine the number of repeated scans ( NR ) required to obtain sufficient phase information at each slice position. To tackle this challenge, the authors first conducted computer simulations using real‐time position management respiratory signals of the 29 cancer patients under an IRB‐approved retrospective study to derive the relationships between NR and the following factors: number of slices ( NS ), number of 4D‐MRI respiratory bins ( NB ), and starting phase at image acquisition ( P 0 ). To validate the authors' technique, 4D‐MRI acquisition and reconstruction were simulated on a 4D digital extended cardiac‐torso (XCAT) human phantom using simulation derived parameters. Twelve healthy volunteers were involved in an IRB‐approved study to investigate the feasibility of this technique. Results: 4D data acquisition completeness ( Cp ) increases as NR increases in an inverse‐exponential fashion ( Cp = 100 − 99 × exp(−0.18 × NR ), when NB = 6, fitted using 29 patients' data). The NR required for 4D‐MRI reconstruction (defined as achieving 95% completeness, Cp = 95%, NR = N R, 95 ) is proportional to NB ( N R, 95 ∼ 2.86 × NB, r = 1.0), but independent of NS and P 0 . Simulated XCAT 4D‐MRI showed a clear pattern of respiratory motion. Tumor motion trajectories measured on 4D‐MRI were comparable to the average input signal, with a mean relative amplitude error of 2.7% ± 2.9%. Reconstructed 4D‐MRI for healthy volunteers illustrated clear respiratory motion on three orthogonal planes, with minimal image artifacts. The artifacts were presumably caused by breathing irregularity and incompleteness of data acquisition (95% acquired only). The mean relative amplitude error between critical structure trajectory and average breathing curve for 12 healthy volunteers is 2.5 ± 0.3 mm in superior–inferior direction. Conclusions: A novel T2‐weighted retrospective phase sorting 4D‐MRI technique has been developed and successfully applied on digital phantom and healthy volunteers.