Data driven MRI inhomogeneity correction

Alakuijala, Jyrki; Oikarinen, Jarkko; Louhisalmi, Y.; Sallinen, S.; Helminen, H.; Koivukangas, John

doi:10.1109/iembs.1993.978486

Cited by 1 publication

(2 citation statements)

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“…Therefore, just the methods and outcomes are discussed. The compensation methods for rf nonuniformity can be classified into two general categories relating to the gain field characterization: ͑1͒ internal methods, derived from the individual image data; 14,[16][17][18]31,32,34,35,[49][50][51][52][53][54][55][56][57] and ͑2͒ external methods, including magnetic field calculations 33,58 and phantom based techniques. 14,15,27,28,36,59 With the exceptions of the methods implemented by Rajapakse et al, 56 Meyer et al, 35 Wells et al, 55 and Guillemaud et al, 57 the data driven methods can be further divided into two subcategories: ͑1͒ filtering methods; and ͑2͒ surface fitting techniques.…”

Section: Correction Techniques: Current Applicationsmentioning

confidence: 99%

“…Although the total magnitude of the error may be appreciable, it has been shown that the noise and rf inhomogeneities are stable over time. 29 In addition to reviewing compensation methods related to head images, methods related to surface coils [30][31][32][33][34][35] and breast coils 35,36 are referenced due to similar methodologies. The noise in a magnitude MR image is often misunderstood.…”

Section: A Introductionmentioning

confidence: 99%

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Review and evaluation of MRI nonuniformity corrections for brain tumor response measurements

et al. 1998

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Current MRI nonuniformity correction techniques are reviewed and investigated. Many approaches are used to remedy this artifact, but it is not clear which method is the most appropriate in a given situation, as the applications have been with different MRI coils and different clinical applications. In this work four widely used nonuniformity correction techniques are investigated in order to assess the effect on tumor response measurements (change in tumor volume over time): a phantom correction method, an image smoothing technique, homomorphic filtering, and surface fitting approach. Six brain tumor cases with baseline and follow-up MRIs after treatment with varying degrees of difficulty of segmentation were analyzed without and with each of the nonuniformity corrections. Different methods give significantly different correction images, indicating that rf nonuniformity correction is not yet well understood. No improvement in tumor segmentation or in tumor growth/shrinkage assessment was achieved using any of the evaluated corrections.

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