Autocorrection of MR projection images

Image analysis – Applications – Biomedical applications

Reexamination Certificate

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C382S130000

Reexamination Certificate

active

06647134

ABSTRACT:

BACKGROUND OF THE INVENTION
The field of the invention is nuclear magnetic resonance imaging methods and systems. More particularly, the invention relates to the correction of motion artifacts in MR images.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B
0
), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B
0
) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, M
z
, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment M
t
. A signal is emitted by the excited spins after the excitation signal B
1
is terminated, this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (G
x
G
y
and G
z
) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
Object motion during the acquisition of NMR image data produces both blurring and “ghosts” in the phase-encoded direction. Ghosts are particularly apparent when the motion is periodic, or nearly so. For most physiological motion each view of the NMR signal is acquired in a period short enough that the object may be considered stationary during the acquisition window. In such case the blurring and ghosting is due to the inconsistent appearance of the object from view to view. Motion that changes the appearance between views such as that produced by a patient moving, by the respiration or the cardiac cycle, or by peristalsis, is referred to hereinafter as “view-to-view motion”. Motion may also change the amplitude and phase of the NMR signal as it evolves during the pulse sequence and such motion is referred to hereinafter as “in-view motion”.
Both blurring and ghosting can be reduced if the data acquisition is synchronized with the functional cycle of the object to reduce view-to-view motion. This method is known as gated NMR scanning, and its objective is to acquire NMR data at the same point during successive functional cycles so that the object “looks” the same in each view. The drawback of gating is that NMR data may be acquired only during a small fraction of the object's functional cycle, and even when the shortest acceptable pulse sequence is employed, the gating technique can significantly lengthen the data acquisition.
Another proposed method for eliminating ghost artifacts is disclosed in U.S. Pat. No. 4,567,893, issued on Feb. 4, 1986. This prior patent teaches that the distance in the image between the ghosts and the object being imaged is maximized when the NMR pulse sequence repetition time is an odd multiple of one-fourth of the duration of the periodic signal variation. This can be used to alleviate ghosts due to respiratory motion. While this method, indeed, improves image quality, it does impose a constraint on the NMR pulse sequence repetition time and it often results in a longer total scan time. It also assumes that the motion is periodic.
Yet another method for reducing the undesirable effects due to periodic signal variations is disclosed in U.S. Pat. No. 4,706,026 issued on Nov. 10, 1987 and entitled “A Method For Reducing Image Artifacts Due To Periodic Variations In NMR Imaging.” In one embodiment of this method, an assumption is made about the signal variation period (e.g. due, for example, to patient respiration) and the view order is altered from the usual monotonically increasing phase-encoding gradient to a preselected order. For a given signal variation period, a view order is chosen so as to make the NMR signal variation as a function of the phase-encoding amplitude be at a desired frequency. In one embodiment, the view order is selected such that the variation period appears to be equal to the total NMR scan time (low frequency) so that the ghost artifacts are brought as close to the object being imaged as possible. In another embodiment (high frequency), the view order is chosen to make the variation period appear to be as short as possible so as to push the ghost artifacts as far from the object as possible.
This prior method is effective in reducing artifacts, and is in some respects ideal if the variation is rather regular and at a known frequency. On the other hand, the method is not very robust if the assumption made about the motion temporal period does not hold (e.g., because the patient's breathing pattern changes or is irregular). If this occurs, the method loses some of its effectiveness because the focusing of the ghosts, either as close to the object or as far from the object as possible, becomes blurred. A solution to this problem is disclosed in U.S. Pat. No. 4,663,591 which is entitled “A Method For Reducing Image Artifacts Due To Periodic Signal Variations in NMR Imaging.” In this method, the non-monotonic view order is determined as the scan is executed and is responsive to changes in the period so as to produce a desired relationship (low frequency or high frequency) between the signal variations and the gradient parameter. The effectiveness of this method, of course, depends upon the accuracy of the means used to sense the patient motion, and particularly, any variations in the periodicity of that motion.
Yet another method for reducing motion artifacts in NMR images is referred to in the art as “gradient moment nulling”. This method requires the addition of gradient pulses to the pulse sequence which cancel, or null, the effect on the NMR signal phase caused by spins moving in the gradients employed for position encoding. Such a solution is disclosed, for example, in U.S. Pat. No. 4,731,583 entitled “Method For Reduction of NMR Image Artifacts Due To Flowing Nuclei By Gradient Moment Nulling”.
The most successful method for correcting MR images for motion artifacts employs navigator signals acquired during the scan. As described in U.S. Pat. No. 4,937,526, such navigator signals are acquired periodically during the scan, and the information in these signals may be used to correct the image data for patient. motion. Unfortunately, acquisition of the navigator signals increases the scan time.
An automatic correction method has been proposed by D. Atkinson et al., “Information Processing in Medical Imaging”, P.341-354, 1997 in which the entropy of the reconstructed image is examined as a focus criterion by which to iteratively adjust motion estimate. This prior method, due to the properties of entropy, works mostly by making dark areas as dark as possible (thus removing ghosting), but does not use much information from the bright areas of the image. While this method works well on simple test images, clinical MR images often do not become as sharp as they should be and the processing time may be very long.
As disclosed in co-pending PCT application Ser. No. PCT/US99/08123 filed on Apr. 14, 1999, and entitled “Autocorrection of MR Images for Motion Artifacts” improvements in the autocorrection process have been made which make it a clinically useful method for correcting medical images. This autocorrection method is an iterative process in which phase corrections are made to acquired NMR data, an image is reconstructed from the corrected data, and the quality of the image is evaluated using a chosen metric. This process repeats until the corrections have improved image quality to a preset level.
SUMMARY OF THE INVENTION
The present invention is a method for correcting images derived from image data acquired with an MRI system to form a k-space image data set; reconstructing an image from the k-space image data set; producing a derivative image by processing the reconstructed image; evaluat

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